diff --git a/.github/workflows/build-and-release.yaml b/.github/workflows/build-and-release.yaml
index bfa97decdd..c931ead34d 100644
--- a/.github/workflows/build-and-release.yaml
+++ b/.github/workflows/build-and-release.yaml
@@ -11,15 +11,15 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ubuntu-20.04, windows-2019, macos-12]
+        os: [ubuntu-22.04, windows-2022, macos-14, macos-15]
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: "recursive"
 
       # Used to host cibuildwheel
-      - uses: actions/setup-python@v5
+      - uses: actions/setup-python@v6
         with:
           python-version: "3.9"
 
@@ -42,46 +42,132 @@ jobs:
         shell: cmd
 
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.21.1
+        uses: pypa/cibuildwheel@v3.4.1
         env:
-          # disable repair
+          # Keep repair disabled by default for non-Linux platforms in this job.
           CIBW_REPAIR_WHEEL_COMMAND: ""
+          # Linux needs auditwheel repair so manylinux and musllinux wheels are
+          # published with distinct platform tags instead of generic linux tags.
+          CIBW_REPAIR_WHEEL_COMMAND_LINUX: "LD_LIBRARY_PATH=/project/llama_cpp/lib auditwheel repair -w {dest_dir} {wheel}"
+          # cibuildwheel v3 defaults to manylinux_2_28 images whose current
+          # GCC toolchain emits symbols newer than the policy allows.
+          CIBW_MANYLINUX_X86_64_IMAGE: "manylinux2014"
+          # The release wheel is tagged py3-none, so one build per platform
+          # covers all supported Python versions and avoids duplicate names.
+          CIBW_BUILD_LINUX: "cp38-*"
+          CIBW_BUILD_MACOS: "cp39-*"
+          CIBW_BUILD_WINDOWS: "cp39-*"
+          # Skip cibuildwheel's default i686 sidecar and keep Linux release
+          # wheels on a portable x86_64 CPU baseline.
+          CIBW_ARCHS_LINUX: "auto64"
+          CIBW_ARCHS_WINDOWS: "AMD64"
+          CIBW_ENVIRONMENT_LINUX: CMAKE_ARGS="-DGGML_NATIVE=off"
+          # Keep macOS release wheels on a portable CPU baseline instead of
+          # inheriting the hosted runner's native flags.
+          CIBW_ENVIRONMENT_MACOS: CMAKE_ARGS="-DGGML_NATIVE=off"
         with:
           package-dir: .
           output-dir: wheelhouse
 
-      - uses: actions/upload-artifact@v4
+      - uses: actions/upload-artifact@v7
         with:
           name: wheels-${{ matrix.os }}
           path: ./wheelhouse/*.whl
 
   build_wheels_arm64:
     name: Build arm64 wheels
+    runs-on: ubuntu-24.04-arm
+    steps:
+      - uses: actions/checkout@v6
+        with:
+          submodules: "recursive"
+
+      - name: Build wheels
+        uses: pypa/cibuildwheel@v3.4.1
+        env:
+          CIBW_SKIP: "pp*"
+          CIBW_REPAIR_WHEEL_COMMAND: "LD_LIBRARY_PATH=$PWD/llama_cpp/lib auditwheel repair -w {dest_dir} {wheel}"
+          CIBW_ARCHS: "aarch64"
+          # Keep this consistent with the x86_64 Linux release wheels.
+          CIBW_MANYLINUX_AARCH64_IMAGE: "manylinux2014"
+          # Keep native arm64 builds on a portable CPU baseline instead of
+          # tuning wheels to the hosted runner.
+          CIBW_ENVIRONMENT: CMAKE_ARGS="-DGGML_NATIVE=off"
+          # The release wheel is tagged py3-none, so one build covers all
+          # supported Python versions and avoids duplicate wheel names.
+          CIBW_BUILD: "cp38-*"
+        with:
+          output-dir: wheelhouse
+
+      - name: Upload wheels as artifacts
+        uses: actions/upload-artifact@v7
+        with:
+          name: wheels_arm64
+          path: ./wheelhouse/*.whl
+
+  build_wheels_riscv64:
+    name: Build riscv64 wheel
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: "recursive"
 
       - name: Set up QEMU
-        uses: docker/setup-qemu-action@v3
+        uses: docker/setup-qemu-action@v4
         with:
-          platforms: linux/arm64
+          platforms: linux/riscv64
 
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.21.1
+        uses: pypa/cibuildwheel@v3.4.1
         env:
           CIBW_SKIP: "*musllinux* pp*"
           CIBW_REPAIR_WHEEL_COMMAND: ""
-          CIBW_ARCHS: "aarch64"
-          CIBW_BUILD: "cp38-* cp39-* cp310-* cp311-* cp312-*"
+          CIBW_ARCHS: "riscv64"
+          # Build riscv64 wheels against a conservative baseline instead of
+          # enabling RVV-related extensions from the build container.
+          CIBW_ENVIRONMENT: CMAKE_ARGS="-DGGML_NATIVE=off -DGGML_RVV=off -DGGML_RV_ZFH=off -DGGML_RV_ZVFH=off -DGGML_RV_ZICBOP=off -DGGML_RV_ZIHINTPAUSE=off"
+          # The release wheel is tagged py3-none, so one riscv64 build is
+          # enough and avoids duplicate same-name release artifacts.
+          CIBW_BUILD: "cp310-*"
         with:
           output-dir: wheelhouse
 
       - name: Upload wheels as artifacts
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v7
         with:
-          name: wheels_arm64
+          name: wheels_riscv64
+          path: ./wheelhouse/*.whl
+
+  build_wheels_pyodide:
+    name: Build Pyodide wheel
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v6
+        with:
+          submodules: "recursive"
+
+      - uses: actions/setup-python@v6
+        with:
+          python-version: "3.12"
+
+      - name: Build wheel
+        uses: pypa/cibuildwheel@v4.1.0
+        env:
+          CIBW_PLATFORM: "pyodide"
+          CIBW_BUILD: "cp314-pyodide_wasm32"
+          CIBW_BUILD_VERBOSITY: "1"
+          CIBW_REPAIR_WHEEL_COMMAND: ""
+          CIBW_BEFORE_TEST: "curl -L --fail --retry 3 -o /tmp/stories260K.gguf https://huggingface.co/ggml-org/models/resolve/main/tinyllamas/stories260K.gguf"
+          CIBW_TEST_COMMAND: "python -c \"import llama_cpp.mtmd_cpp as mtmd; from llama_cpp import Llama; print('mtmd marker', mtmd.mtmd_default_marker().decode()); llm = Llama(model_path='/tmp/stories260K.gguf', n_ctx=64, n_batch=8, n_threads=1, verbose=False); print('loaded', llm.n_vocab(), llm.n_ctx()); print('generated', llm('Once upon a', max_tokens=1, temperature=0)['choices'][0]['text'])\""
+          CMAKE_ARGS: "-DLLAMA_WASM_MEM64=OFF -DEMSCRIPTEN_SYSTEM_PROCESSOR=wasm32 -DGGML_NATIVE=OFF -DGGML_OPENMP=OFF -DGGML_METAL=OFF -DGGML_BLAS=OFF -DGGML_CUDA=OFF -DGGML_HIP=OFF -DGGML_VULKAN=OFF -DGGML_OPENCL=OFF -DGGML_RPC=OFF -DLLAMA_CURL=OFF -DLLAMA_BUILD_TESTS=OFF -DLLAMA_BUILD_EXAMPLES=OFF -DLLAMA_BUILD_TOOLS=OFF -DLLAMA_BUILD_SERVER=OFF"
+        with:
+          output-dir: wheelhouse
+
+      - name: Upload wheels as artifacts
+        uses: actions/upload-artifact@v7
+        with:
+          name: wheels_pyodide
           path: ./wheelhouse/*.whl
 
   build_sdist:
@@ -89,11 +175,11 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: "recursive"
 
-      - uses: actions/setup-python@v5
+      - uses: actions/setup-python@v6
         with:
           python-version: "3.9"
 
@@ -121,23 +207,24 @@ jobs:
         run: |
           python -m build --sdist
 
-      - uses: actions/upload-artifact@v4
+      - uses: actions/upload-artifact@v7
         with:
           name: sdist
           path: ./dist/*.tar.gz
 
   release:
     name: Release
-    needs: [build_wheels, build_wheels_arm64, build_sdist]
+    needs: [build_wheels, build_wheels_arm64, build_wheels_riscv64, build_wheels_pyodide, build_sdist]
+    if: startsWith(github.ref, 'refs/tags/')
     runs-on: ubuntu-latest
 
     steps:
-      - uses: actions/download-artifact@v4
+      - uses: actions/download-artifact@v8
         with:
           merge-multiple: true
           path: dist
 
-      - uses: softprops/action-gh-release@v2
+      - uses: softprops/action-gh-release@v3
         with:
           files: dist/*
         env:
diff --git a/.github/workflows/build-docker.yaml b/.github/workflows/build-docker.yaml
index b5c7346db4..d306efde48 100644
--- a/.github/workflows/build-docker.yaml
+++ b/.github/workflows/build-docker.yaml
@@ -9,21 +9,30 @@ permissions:
 jobs:
   docker:
     name: Build and push Docker image
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           submodules: "recursive"
 
+      - name: Set image tag
+        run: |
+          if [[ "${GITHUB_REF_TYPE}" == "tag" ]]; then
+            image_tag="${GITHUB_REF_NAME}"
+          else
+            image_tag="${GITHUB_REF_NAME//\//-}"
+          fi
+          echo "IMAGE_TAG=$image_tag" >> "$GITHUB_ENV"
+
       - name: Set up QEMU
-        uses: docker/setup-qemu-action@v3
+        uses: docker/setup-qemu-action@v4
 
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+        uses: docker/setup-buildx-action@v4
 
       - name: Login to GitHub Container Registry
-        uses: docker/login-action@v3 
+        uses: docker/login-action@v4 
         with:
           registry: ghcr.io
           username: ${{ github.repository_owner }}
@@ -31,7 +40,7 @@ jobs:
 
       - name: Build and push
         id: docker_build
-        uses: docker/build-push-action@v6
+        uses: docker/build-push-action@v7
         with:
           context: .
           file: "docker/simple/Dockerfile"
@@ -40,7 +49,7 @@ jobs:
           platforms: linux/amd64,linux/arm64
           tags: |
             ghcr.io/abetlen/llama-cpp-python:latest
-            ghcr.io/abetlen/llama-cpp-python:${{ github.ref_name }}
+            ghcr.io/abetlen/llama-cpp-python:${{ env.IMAGE_TAG }}
           build-args: |
             BUILDKIT_INLINE_CACHE=1
 
diff --git a/.github/workflows/build-wheels-cuda.yaml b/.github/workflows/build-wheels-cuda.yaml
index 647b171e84..59dc3558bf 100644
--- a/.github/workflows/build-wheels-cuda.yaml
+++ b/.github/workflows/build-wheels-cuda.yaml
@@ -1,6 +1,12 @@
 name: Build Wheels (CUDA)
 
-on: workflow_dispatch
+on:
+  workflow_dispatch:
+    inputs:
+      release_tag:
+        description: Release tag to upload wheel assets to
+        required: false
+        type: string
 
 permissions:
   contents: write
@@ -8,7 +14,7 @@ permissions:
 jobs:
   define_matrix:
     name: Define Build Matrix
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
     outputs:
       matrix: ${{ steps.set-matrix.outputs.matrix }}
     defaults:
@@ -20,10 +26,17 @@ jobs:
         id: set-matrix
         run: |
           $matrix = @{
-              'os' = @('ubuntu-latest', 'windows-2019')
-              'pyver' = @("3.9", "3.10", "3.11", "3.12")
-              'cuda' = @("12.1.1", "12.2.2", "12.3.2", "12.4.1", "12.5.0")
+              'os' = @('ubuntu-22.04', 'windows-2022')
+              # wheel.py-api = "py3" makes the CUDA wheel interpreter-agnostic,
+              # so one builder per toolkit version is sufficient.
+              'pyver' = @("3.9")
+              'cuda' = @("11.8.0", "12.1.1", "12.2.2", "12.3.2", "12.4.1", "12.5.1", "13.0.2", "13.2.1")
               'releasetag' = @("basic")
+              'exclude' = @(
+                @{ 'os' = 'windows-2022'; 'cuda' = '12.1.1' },
+                @{ 'os' = 'windows-2022'; 'cuda' = '12.2.2' },
+                @{ 'os' = 'windows-2022'; 'cuda' = '12.3.2' }
+              )
           }
 
           $matrixOut = ConvertTo-Json $matrix -Compress
@@ -43,96 +56,220 @@ jobs:
       AVXVER: ${{ matrix.releasetag }}
 
     steps:
-      - name: Add MSBuild to PATH
-        if: runner.os == 'Windows'
-        uses: microsoft/setup-msbuild@v2
+      - name: Set up MSVC for CUDA 11.8
+        if: runner.os == 'Windows' && matrix.cuda == '11.8.0'
+        uses: ilammy/msvc-dev-cmd@v1
         with:
-          vs-version: '[16.11,16.12)'
+          arch: x64
+          toolset: 14.29
 
-      - uses: actions/checkout@v4
+      - name: Set up MSVC
+        if: runner.os == 'Windows' && matrix.cuda != '11.8.0'
+        uses: ilammy/msvc-dev-cmd@v1
+        with:
+          arch: x64
+
+      - uses: actions/checkout@v6
         with:
           submodules: "recursive"
 
-      - uses: actions/setup-python@v5
+      - uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.pyver }}
           cache: 'pip'
 
       - name: Setup Mamba
-        uses: conda-incubator/setup-miniconda@v3.0.4
+        uses: conda-incubator/setup-miniconda@v4.0.1
         with:
-          activate-environment: "build"
+          activate-environment: "llamacpp"
           python-version: ${{ matrix.pyver }}
-          miniforge-variant: Mambaforge
           miniforge-version: latest
-          use-mamba: true
           add-pip-as-python-dependency: true
           auto-activate-base: false
 
-      - name: VS Integration Cache
-        id: vs-integration-cache
-        if: runner.os == 'Windows'
-        uses: actions/cache@v4
-        with:
-          path: ./MSBuildExtensions
-          key: cuda-${{ matrix.cuda }}-vs-integration
-
-      - name: Get Visual Studio Integration
-        if: runner.os == 'Windows' && steps.vs-integration-cache.outputs.cache-hit != 'true'
-        run: |
-          if ($env:CUDAVER -eq '12.1.1') {$x = '12.1.0'} else {$x = $env:CUDAVER}
-          $links = (Invoke-RestMethod 'https://raw.githubusercontent.com/Jimver/cuda-toolkit/master/src/links/windows-links.ts').Trim().split().where({$_ -ne ''})
-          for ($i=$q=0;$i -lt $links.count -and $q -lt 2;$i++) {if ($links[$i] -eq "'$x',") {$q++}}
-          Invoke-RestMethod $links[$i].Trim("'") -OutFile 'cudainstaller.zip'
-          & 'C:\Program Files\7-Zip\7z.exe' e cudainstaller.zip -oMSBuildExtensions -r *\MSBuildExtensions\* > $null
-          Remove-Item 'cudainstaller.zip'
-
-      - name: Install Visual Studio Integration
-        if: runner.os == 'Windows'
-        run: |
-          $y = (gi '.\MSBuildExtensions').fullname + '\*'
-          (gi 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\MSBuild\Microsoft\VC\*\BuildCustomizations').fullname.foreach({cp $y $_})
-          $cupath = 'CUDA_PATH_V' + $env:CUDAVER.Remove($env:CUDAVER.LastIndexOf('.')).Replace('.','_')
-          echo "$cupath=$env:CONDA_PREFIX" >> $env:GITHUB_ENV
-
       - name: Install Dependencies
         env:
           MAMBA_DOWNLOAD_FAILFAST: "0"
           MAMBA_NO_LOW_SPEED_LIMIT: "1"
         run: |
           $cudaVersion = $env:CUDAVER
-          mamba install -y 'cuda' -c nvidia/label/cuda-$cudaVersion
-          python -m pip install build wheel
+          $cudaChannel = "nvidia/label/cuda-$cudaVersion"
+          if ($cudaVersion -eq '11.8.0') {
+            if ($IsLinux) {
+              $cudaPackages = @(
+                "${cudaChannel}::cuda-nvcc_linux-64=11.8.0",
+                "${cudaChannel}::cuda-cccl=11.8.89",
+                "${cudaChannel}::cuda-cudart=11.8.89",
+                "${cudaChannel}::cuda-cudart-dev=11.8.89",
+                "${cudaChannel}::cuda-driver-dev=11.8.89",
+                "${cudaChannel}::libcublas=11.11.3.6",
+                "${cudaChannel}::libcublas-dev=11.11.3.6"
+              )
+            } elseif ($IsWindows) {
+              $cudaPackages = @(
+                "${cudaChannel}::cuda-nvcc_win-64=11.8.0",
+                "${cudaChannel}::cuda-cccl=11.8.89",
+                "${cudaChannel}::cuda-cudart=11.8.89",
+                "${cudaChannel}::cuda-cudart-dev=11.8.89",
+                "${cudaChannel}::libcublas=11.11.3.6",
+                "${cudaChannel}::libcublas-dev=11.11.3.6"
+              )
+            } else {
+              throw 'Unsupported CUDA wheel build platform'
+            }
+            mamba install -y --channel-priority flexible --override-channels -c $cudaChannel $cudaPackages
+          } elseif ($IsLinux) {
+            mamba install -y --channel-priority flexible --override-channels -c $cudaChannel "${cudaChannel}::cuda-toolkit=$cudaVersion" "${cudaChannel}::cuda-nvcc_linux-64" "${cudaChannel}::cuda-cccl" "${cudaChannel}::cuda-cudart" "${cudaChannel}::cuda-cudart-dev"
+          } elseif ($IsWindows) {
+            if ($cudaVersion -like '12.5.*' -or [version]$cudaVersion -ge [version]"13.0") {
+              # The Windows 12.5+ toolkit meta-package pulls compiler activation
+              # scripts that overflow cmd.exe after MSVC is already initialized.
+              mamba install -y --channel-priority flexible --override-channels -c $cudaChannel "${cudaChannel}::cuda-nvcc_win-64" "${cudaChannel}::cuda-cccl" "${cudaChannel}::cuda-libraries-dev=$cudaVersion" "${cudaChannel}::cuda-cudart" "${cudaChannel}::cuda-cudart-dev"
+            } else {
+              mamba install -y --channel-priority flexible --override-channels -c $cudaChannel "${cudaChannel}::cuda-toolkit=$cudaVersion" "${cudaChannel}::cuda-nvcc_win-64" "${cudaChannel}::cuda-cccl" "${cudaChannel}::cuda-cudart" "${cudaChannel}::cuda-cudart-dev"
+            }
+          } else {
+            throw 'Unsupported CUDA wheel build platform'
+          }
+          if ($LASTEXITCODE -ne 0) {
+            exit $LASTEXITCODE
+          }
+          if ($IsWindows) {
+            python -m pip install build wheel ninja
+          } else {
+            sudo apt-get update
+            sudo apt-get install -y patchelf
+            python -m pip install auditwheel build wheel
+          }
 
       - name: Build Wheel
         run: |
-          $cudaVersion = $env:CUDAVER.Remove($env:CUDAVER.LastIndexOf('.')).Replace('.','')
-          $env:CUDA_PATH = $env:CONDA_PREFIX
-          $env:CUDA_HOME = $env:CONDA_PREFIX
-          $env:CUDA_TOOLKIT_ROOT_DIR = $env:CONDA_PREFIX
+          $pathSeparator = if ($IsWindows) { ';' } else { ':' }
+          if ($IsWindows) {
+            $cudaRoot = Join-Path $env:CONDA_PREFIX 'Library'
+          } elseif (Test-Path (Join-Path $env:CONDA_PREFIX 'targets/x86_64-linux/include/cuda_runtime.h')) {
+            $cudaRoot = Join-Path $env:CONDA_PREFIX 'targets/x86_64-linux'
+          } else {
+            $cudaRoot = $env:CONDA_PREFIX
+          }
+
+          $env:CUDA_PATH = $cudaRoot
+          $env:CUDA_HOME = $cudaRoot
+          $env:CUDAToolkit_ROOT = $cudaRoot
+          $env:CUDA_TOOLKIT_ROOT_DIR = $cudaRoot
+          $cudaHostCompilerArg = ''
+          $cudaRootCmake = $cudaRoot.Replace('\', '/')
+          $env:CMAKE_ARGS = "-DCUDAToolkit_ROOT=$cudaRootCmake -DCUDA_TOOLKIT_ROOT_DIR=$cudaRootCmake"
           if ($IsLinux) {
-            $env:LD_LIBRARY_PATH = $env:CONDA_PREFIX + '/lib:' + $env:LD_LIBRARY_PATH
+            if ([version]$env:CUDAVER -lt [version]"12.0" -and (Test-Path '/usr/bin/g++-11')) {
+              $env:CC = '/usr/bin/gcc-11'
+              $env:CXX = '/usr/bin/g++-11'
+              $env:CUDAHOSTCXX = '/usr/bin/g++-11'
+              $cudaHostCompilerArg = " -DCMAKE_CUDA_HOST_COMPILER=$env:CUDAHOSTCXX"
+            } elseif (Test-Path '/usr/bin/g++-12') {
+              $env:CC = '/usr/bin/gcc-12'
+              $env:CXX = '/usr/bin/g++-12'
+              $env:CUDAHOSTCXX = '/usr/bin/g++-12'
+              $cudaHostCompilerArg = " -DCMAKE_CUDA_HOST_COMPILER=$env:CUDAHOSTCXX"
+            }
+            $env:CMAKE_ARGS = "-DCUDAToolkit_ROOT=$cudaRoot -DCUDA_TOOLKIT_ROOT_DIR=$cudaRoot$cudaHostCompilerArg"
+            $env:CPATH = "$cudaRoot/include$pathSeparator$env:CPATH"
+            $env:CPLUS_INCLUDE_PATH = "$cudaRoot/include$pathSeparator$env:CPLUS_INCLUDE_PATH"
+            $env:LIBRARY_PATH = "$cudaRoot/lib$pathSeparator$env:CONDA_PREFIX/lib$pathSeparator$env:LIBRARY_PATH"
+            $env:LD_LIBRARY_PATH = "$cudaRoot/lib$pathSeparator$env:CONDA_PREFIX/lib$pathSeparator$env:LD_LIBRARY_PATH"
+            $cudaLibraryPaths = @(
+              (Join-Path $cudaRoot 'lib'),
+              (Join-Path $cudaRoot 'lib64'),
+              (Join-Path $env:CONDA_PREFIX 'lib')
+            ) | Where-Object { Test-Path $_ }
+            Write-Output "CUDA_LIBRARY_PATHS=$($cudaLibraryPaths -join ':')" >> $env:GITHUB_ENV
+          } elseif ($IsWindows) {
+            $ninjaPath = ((Get-Command ninja -ErrorAction Stop).Source).Replace('\', '/')
+            $env:CMAKE_GENERATOR = 'Ninja'
+            $env:CMAKE_MAKE_PROGRAM = $ninjaPath
+            $env:PATH = "$(Join-Path $cudaRoot 'bin')$pathSeparator$env:PATH"
+          }
+
+          if ($IsWindows) {
+            $nvccCandidates = @(
+              (Join-Path $cudaRoot 'bin\nvcc.exe'),
+              (Join-Path $env:CONDA_PREFIX 'Library\bin\nvcc.exe'),
+              (Join-Path $env:CONDA_PREFIX 'bin\nvcc.exe')
+            )
+          } else {
+            $nvccCandidates = @(
+              (Join-Path $env:CONDA_PREFIX 'bin/nvcc'),
+              (Join-Path $env:CONDA_PREFIX 'targets/x86_64-linux/bin/nvcc')
+            )
+          }
+          $nvccPath = $nvccCandidates | Where-Object { Test-Path $_ } | Select-Object -First 1
+          if (-not $nvccPath) {
+            throw 'Failed to find nvcc in the conda environment'
+          }
+          $env:CUDACXX = $nvccPath
+          $env:PATH = "$(Split-Path $nvccPath)$pathSeparator$env:PATH"
+          if ($IsWindows) {
+            $nvccPathCmake = $nvccPath.Replace('\', '/')
+            $env:CUDACXX = $nvccPathCmake
+            $env:CMAKE_ARGS = "-DCMAKE_CUDA_COMPILER=$nvccPathCmake -DCMAKE_CUDA_COMPILER_ARG1=-allow-unsupported-compiler -DCMAKE_MAKE_PROGRAM=$env:CMAKE_MAKE_PROGRAM $env:CMAKE_ARGS"
+          }
+          $nvccVersion = ((& $nvccPath --version) | Select-String 'release ([0-9]+\.[0-9]+)').Matches[0].Groups[1].Value
+          if (-not $nvccVersion) {
+            throw 'Failed to detect the installed CUDA toolkit version'
           }
+          $cudaTagVersion = $nvccVersion.Replace('.','')
           $env:VERBOSE = '1'
-          $env:CMAKE_ARGS = '-DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=all'
-          $env:CMAKE_ARGS = "-DGGML_CUDA_FORCE_MMQ=ON $env:CMAKE_ARGS"
-          # if ($env:AVXVER -eq 'AVX') {
+          $cudaArchs = "60-real;61-real;70-real;75-real;80-real;86-real;89-real;90-real;90-virtual"
+          if ([version]$nvccVersion -lt [version]"12.0") {
+            # CUDA 11.8 cannot compile llama.cpp's Hopper PDL device calls.
+            $cudaArchs = "60-real;61-real;70-real;75-real;80-real;86-real;89-real"
+          } elseif ([version]$nvccVersion -ge [version]"13.0") {
+            # CUDA 13 dropped offline compilation support for pre-Turing targets.
+            $cudaArchs = "75-real;80-real;86-real;89-real;90-real;90-virtual"
+          }
+          # Build real cubins for the supported GPUs and keep
+          # one forward-compatible PTX target instead of embedding PTX for every
+          # SM. This keeps the wheel under GitHub's 2 GiB release-asset limit.
+          $env:CMAKE_ARGS = "-DGGML_CUDA_FORCE_MMQ=ON -DGGML_CUDA=on -DCMAKE_CUDA_ARCHITECTURES=$cudaArchs -DCMAKE_CUDA_FLAGS=-allow-unsupported-compiler -DCMAKE_CUDA_FLAGS_INIT=-allow-unsupported-compiler $env:CMAKE_ARGS"
           $env:CMAKE_ARGS = $env:CMAKE_ARGS + ' -DGGML_AVX2=off -DGGML_FMA=off -DGGML_F16C=off'
-          # }
-          # if ($env:AVXVER -eq 'AVX512') {
-          #  $env:CMAKE_ARGS = $env:CMAKE_ARGS + ' -DGGML_AVX512=on'
-          # }
-          # if ($env:AVXVER -eq 'basic') {
-          #  $env:CMAKE_ARGS = $env:CMAKE_ARGS + ' -DGGML_AVX=off -DGGML_AVX2=off -DGGML_FMA=off -DGGML_F16C=off'
-          # }
+          if ($IsLinux) {
+            $env:CMAKE_ARGS = $env:CMAKE_ARGS + ' -DGGML_OPENMP=OFF'
+          }
           python -m build --wheel
-          # write the build tag to the output
-          Write-Output "CUDA_VERSION=$cudaVersion" >> $env:GITHUB_ENV
+          # Publish tags that reflect the actual installed toolkit version.
+          Write-Output "CUDA_VERSION=$cudaTagVersion" >> $env:GITHUB_ENV
+
+      - name: Repair Linux wheel
+        if: runner.os == 'Linux'
+        shell: bash
+        run: |
+          set -euxo pipefail
+          mkdir -p wheelhouse
+          export LD_LIBRARY_PATH="$PWD/llama_cpp/lib:${CUDA_LIBRARY_PATHS}:${LD_LIBRARY_PATH:-}"
+          auditwheel_bin="${CONDA}/envs/llamacpp/bin/auditwheel"
+          "${auditwheel_bin}" repair \
+            --exclude libcuda.so \
+            --exclude libcuda.so.1 \
+            --exclude libcudart.so.11.0 \
+            --exclude libcudart.so.12 \
+            --exclude libcudart.so.13 \
+            --exclude libcublas.so.11 \
+            --exclude libcublas.so.12 \
+            --exclude libcublas.so.13 \
+            --exclude libcublasLt.so.11 \
+            --exclude libcublasLt.so.12 \
+            --exclude libcublasLt.so.13 \
+            -w wheelhouse \
+            dist/*.whl
+          rm dist/*.whl
+          cp wheelhouse/*.whl dist/
+          "${auditwheel_bin}" show dist/*.whl
 
-      - uses: softprops/action-gh-release@v2
+      - uses: softprops/action-gh-release@v3
+        if: startsWith(github.ref, 'refs/tags/') || (github.event_name == 'workflow_dispatch' && inputs.release_tag != '')
         with:
           files: dist/*
-          # Set tag_name to <tag>-cu<cuda_version>
-          tag_name: ${{ github.ref_name }}-cu${{ env.CUDA_VERSION }}
+          # Set tag_name to <tag>-cu<cuda_version>.
+          tag_name: ${{ github.event_name == 'workflow_dispatch' && inputs.release_tag || github.ref_name }}-cu${{ env.CUDA_VERSION }}
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
diff --git a/.github/workflows/build-wheels-metal.yaml b/.github/workflows/build-wheels-metal.yaml
index 8e6fb5cb78..892787ee1f 100644
--- a/.github/workflows/build-wheels-metal.yaml
+++ b/.github/workflows/build-wheels-metal.yaml
@@ -11,50 +11,39 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [macos-12, macos-13, macos-14]
+        os: [macos-14, macos-15]
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: "recursive"
 
       # Used to host cibuildwheel
-      - uses: actions/setup-python@v5
+      - uses: actions/setup-python@v6
         with:
           python-version: "3.12"
           cache: 'pip'
-          
+
       - name: Install dependencies (Linux/MacOS)
-        if: runner.os != 'Windows'
         run: |
           python -m pip install --upgrade pip
           python -m pip install uv
           RUST_LOG=trace python -m uv pip install -e .[all] --verbose
         shell: bash
 
-      - name: Install dependencies (Windows)
-        if: runner.os == 'Windows'
-        env:
-          RUST_LOG: trace        
-        run: |
-          python -m pip install --upgrade pip
-          python -m pip install uv
-          python -m uv pip install -e .[all] --verbose
-        shell: cmd
-
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.21.1
+        uses: pypa/cibuildwheel@v3.4.1
         env:
           # disable repair
           CIBW_REPAIR_WHEEL_COMMAND: ""
           CIBW_ARCHS: "arm64"
-          CIBW_ENVIRONMENT: CMAKE_ARGS="-DCMAKE_OSX_ARCHITECTURES=arm64 -DCMAKE_APPLE_SILICON_PROCESSOR=arm64 -DGGML_METAL=on"
+          CIBW_ENVIRONMENT: CMAKE_ARGS="-DCMAKE_OSX_ARCHITECTURES=arm64 -DCMAKE_APPLE_SILICON_PROCESSOR=arm64 -DGGML_METAL=on -DCMAKE_CROSSCOMPILING=ON"
           CIBW_BUILD: "cp39-* cp310-* cp311-* cp312-*"
         with:
           package-dir: .
           output-dir: wheelhouse2
 
-      - uses: actions/upload-artifact@v4
+      - uses: actions/upload-artifact@v7
         with:
           name: wheels-mac_${{ matrix.os }}
           path: ./wheelhouse2/*.whl
@@ -65,12 +54,12 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-      - uses: actions/download-artifact@v4
+      - uses: actions/download-artifact@v8
         with:
           merge-multiple: true
           path: dist2
-          
-      - uses: softprops/action-gh-release@v2
+
+      - uses: softprops/action-gh-release@v3
         with:
           files: dist2/*
           # set release name to <tag>-metal
diff --git a/.github/workflows/build-wheels-rocm.yaml b/.github/workflows/build-wheels-rocm.yaml
new file mode 100644
index 0000000000..70fca5edb7
--- /dev/null
+++ b/.github/workflows/build-wheels-rocm.yaml
@@ -0,0 +1,283 @@
+name: Build Wheels (ROCm)
+
+on:
+  workflow_dispatch:
+    inputs:
+      release_tag:
+        description: Release tag to upload wheel assets to
+        required: false
+        type: string
+      amdgpu_targets:
+        description: AMDGPU targets to compile into the Linux ROCm wheel
+        required: false
+        default: gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1150;gfx1151;gfx1200;gfx1201
+      windows_amdgpu_targets:
+        description: AMDGPU targets to compile into the Windows HIP Radeon wheel
+        required: false
+        default: gfx1150;gfx1151;gfx1200;gfx1201;gfx1100;gfx1101;gfx1102;gfx1030;gfx1031;gfx1032
+
+permissions:
+  contents: write
+
+jobs:
+  build_wheels:
+    name: Build Wheel ${{ matrix.os }} ${{ matrix.pyver }} ROCm ${{ matrix.rocm }}
+    runs-on: ${{ matrix.os }}
+    container:
+      image: rocm/dev-ubuntu-22.04:${{ matrix.rocm }}-complete
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - os: ubuntu-22.04
+            pyver: "3.9"
+            rocm: "7.2.4"
+            amdgpu_targets: gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1150;gfx1151;gfx1200;gfx1201
+
+    steps:
+      - name: Install system dependencies
+        run: |
+          apt-get update
+          apt-get install -y --no-install-recommends git cmake lsb-release ninja-build patchelf
+
+      - uses: actions/checkout@v6
+        with:
+          submodules: "recursive"
+
+      - uses: actions/setup-python@v6
+        with:
+          python-version: ${{ matrix.pyver }}
+
+      - name: Install build dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install auditwheel build wheel
+
+      - name: Build ROCm wheel
+        env:
+          MATRIX_AMDGPU_TARGETS: ${{ matrix.amdgpu_targets }}
+          INPUT_AMDGPU_TARGETS: ${{ inputs.amdgpu_targets }}
+        run: |
+          export ROCM_PATH="${ROCM_PATH:-/opt/rocm}"
+          export HIP_PATH="${HIP_PATH:-$ROCM_PATH}"
+          export PATH="$ROCM_PATH/bin:$ROCM_PATH/llvm/bin:$PATH"
+          export LD_LIBRARY_PATH="$ROCM_PATH/lib:$ROCM_PATH/lib64:${LD_LIBRARY_PATH:-}"
+          export CC="$ROCM_PATH/llvm/bin/clang"
+          export CXX="$ROCM_PATH/llvm/bin/clang++"
+          export HIPCXX="$ROCM_PATH/llvm/bin/clang"
+          export CMAKE_GENERATOR=Ninja
+
+          hipconfig --version
+          hipcc --version
+
+          rocm_tag="$(hipconfig --version | sed -E 's/^([0-9]+)\.([0-9]+).*/\1\2/')"
+          echo "ROCM_VERSION=$rocm_tag" >> "$GITHUB_ENV"
+
+          amdgpu_targets="${INPUT_AMDGPU_TARGETS:-$MATRIX_AMDGPU_TARGETS}"
+          export CMAKE_ARGS="-DGGML_HIP=on -DGGML_NATIVE=off -DGGML_OPENMP=OFF -DGGML_AVX=off -DGGML_AVX2=off -DGGML_FMA=off -DGGML_F16C=off -DAMDGPU_TARGETS=$amdgpu_targets -DCMAKE_HIP_ARCHITECTURES=$amdgpu_targets"
+          python -m build --wheel
+
+      - name: Repair Linux wheel
+        run: |
+          export ROCM_PATH="${ROCM_PATH:-/opt/rocm}"
+          export LD_LIBRARY_PATH="$PWD/llama_cpp/lib:$ROCM_PATH/lib:$ROCM_PATH/lib64:${LD_LIBRARY_PATH:-}"
+          mkdir -p wheelhouse
+          python -m auditwheel repair \
+            --exclude libamdhip64.so \
+            --exclude libamdhip64.so.6 \
+            --exclude libamdhip64.so.7 \
+            --exclude libhiprtc.so \
+            --exclude libhiprtc.so.6 \
+            --exclude libhiprtc.so.7 \
+            --exclude libhipblas.so \
+            --exclude libhipblas.so.2 \
+            --exclude libhipblas.so.3 \
+            --exclude libhipblaslt.so \
+            --exclude libhipblaslt.so.0 \
+            --exclude libhipblaslt.so.1 \
+            --exclude librocblas.so \
+            --exclude librocblas.so.4 \
+            --exclude librocblas.so.5 \
+            --exclude libhsa-runtime64.so.1 \
+            --exclude libhsakmt.so.1 \
+            -w wheelhouse \
+            dist/*.whl
+          rm dist/*.whl
+          cp wheelhouse/*.whl dist/
+          python -m auditwheel show dist/*.whl
+
+      - uses: actions/upload-artifact@v7
+        with:
+          name: wheels-rocm${{ env.ROCM_VERSION }}-${{ matrix.os }}
+          path: ./wheelhouse/*.whl
+
+  build_wheels_windows_hip:
+    name: Build Wheel windows-2022 ${{ matrix.pyver }} HIP ${{ matrix.name }}
+    runs-on: windows-2022
+    env:
+      HIPSDK_INSTALLER_VERSION: "26.Q1"
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - name: radeon
+            pyver: "3.9"
+            amdgpu_targets: gfx1150;gfx1151;gfx1200;gfx1201;gfx1100;gfx1101;gfx1102;gfx1030;gfx1031;gfx1032
+
+    steps:
+      - uses: actions/checkout@v6
+        with:
+          submodules: "recursive"
+
+      - uses: actions/setup-python@v6
+        with:
+          python-version: ${{ matrix.pyver }}
+          cache: "pip"
+
+      - name: Set up MSVC
+        uses: ilammy/msvc-dev-cmd@v1
+        with:
+          arch: x64
+
+      - name: Install build dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install build wheel
+
+      - name: Grab rocWMMA package
+        run: |
+          curl -o rocwmma.deb "https://repo.radeon.com/rocm/apt/7.2.1/pool/main/r/rocwmma-dev/rocwmma-dev_2.2.0.70201-81~24.04_amd64.deb"
+          7z x rocwmma.deb
+          7z x data.tar
+
+      - name: Cache ROCm installation
+        id: cache-rocm
+        uses: actions/cache@v5
+        with:
+          path: C:\Program Files\AMD\ROCm
+          key: cache-gha-rocm-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ runner.os }}
+
+      - name: Install ROCm
+        if: steps.cache-rocm.outputs.cache-hit != 'true'
+        run: |
+          $ErrorActionPreference = "Stop"
+          Invoke-WebRequest `
+            -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-${{ env.HIPSDK_INSTALLER_VERSION }}-Win11-For-HIP.exe" `
+            -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
+          $proc = Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -PassThru
+          $completed = $proc.WaitForExit(1800000)
+          if (-not $completed) {
+            $proc.Kill()
+            throw "ROCm installation timed out after 30 minutes"
+          }
+          if ($proc.ExitCode -ne 0) {
+            throw "ROCm installation failed with exit code $($proc.ExitCode)"
+          }
+
+      - name: Verify ROCm
+        run: |
+          $clangPath = Get-ChildItem 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | Select-Object -First 1
+          if (-not $clangPath) {
+            throw "ROCm installation not found"
+          }
+          & $clangPath.FullName --version
+
+      - name: Build HIP wheel
+        env:
+          MATRIX_AMDGPU_TARGETS: ${{ matrix.amdgpu_targets }}
+          INPUT_AMDGPU_TARGETS: ${{ inputs.windows_amdgpu_targets }}
+        run: |
+          $ErrorActionPreference = "Stop"
+          $hipPath = Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | Split-Path | Split-Path
+          $rocwmmaInclude = (Join-Path $PWD 'opt\rocm-7.2.1\include').Replace('\', '/')
+          $amdgpuTargets = if ($env:INPUT_AMDGPU_TARGETS) { $env:INPUT_AMDGPU_TARGETS } else { $env:MATRIX_AMDGPU_TARGETS }
+
+          $env:HIP_PATH = $hipPath
+          $env:ROCM_PATH = $hipPath
+          $env:CMAKE_PREFIX_PATH = $hipPath
+          $env:HIP_PLATFORM = 'amd'
+          $env:PATH = "$hipPath\bin;$env:PATH"
+          $env:CC = "$hipPath\bin\clang.exe"
+          $env:CXX = "$hipPath\bin\clang++.exe"
+          $env:HIPCXX = "$hipPath\bin\clang.exe"
+          $env:CMAKE_GENERATOR = 'Unix Makefiles'
+          $env:CXXFLAGS = "-I$rocwmmaInclude -Wno-ignored-attributes -Wno-nested-anon-types"
+          $env:CMAKE_ARGS = "-DGGML_HIP=ON -DGGML_HIP_ROCWMMA_FATTN=ON -DGGML_NATIVE=OFF -DGGML_AVX=OFF -DGGML_AVX2=OFF -DGGML_FMA=OFF -DGGML_F16C=OFF -DGPU_TARGETS=$amdgpuTargets"
+
+          python -m build --wheel
+
+      - name: Bundle ROCm runtime DLLs
+        run: |
+          $ErrorActionPreference = "Stop"
+          $hipPath = Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | Split-Path | Split-Path
+          $wheel = Get-ChildItem dist\*.whl | Select-Object -First 1
+          python -m wheel unpack $wheel.FullName -d wheel-unpacked
+          $wheelRoot = Get-ChildItem wheel-unpacked -Directory | Select-Object -First 1
+          $libDir = Join-Path $wheelRoot.FullName 'llama_cpp\lib'
+          New-Item -ItemType Directory -Force $libDir | Out-Null
+
+          $dllPatterns = @(
+            'amdhip64.dll',
+            'hiprtc*.dll',
+            'libhipblas.dll',
+            'libhipblaslt.dll',
+            'rocblas.dll'
+          )
+          foreach ($pattern in $dllPatterns) {
+            Copy-Item (Join-Path $hipPath "bin\$pattern") $libDir -ErrorAction SilentlyContinue
+          }
+
+          New-Item -ItemType Directory -Force (Join-Path $libDir 'rocblas\library') | Out-Null
+          New-Item -ItemType Directory -Force (Join-Path $libDir 'hipblaslt\library') | Out-Null
+          Copy-Item "$hipPath\bin\rocblas\library\*" (Join-Path $libDir 'rocblas\library') -Recurse -Force
+          Copy-Item "$hipPath\bin\hipblaslt\library\*" (Join-Path $libDir 'hipblaslt\library') -Recurse -Force
+
+          Remove-Item dist\*.whl
+          python -m wheel pack $wheelRoot.FullName -d dist
+          New-Item -ItemType Directory -Force wheelhouse | Out-Null
+          Copy-Item dist/*.whl wheelhouse/
+
+      - uses: actions/upload-artifact@v7
+        with:
+          name: wheels-hip-${{ matrix.name }}-windows-2022
+          path: ./wheelhouse/*.whl
+
+  release_rocm:
+    name: Release ROCm
+    needs: [build_wheels]
+    if: startsWith(github.ref, 'refs/tags/') || (github.event_name == 'workflow_dispatch' && inputs.release_tag != '')
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/download-artifact@v8
+        with:
+          merge-multiple: true
+          path: dist
+
+      - uses: softprops/action-gh-release@v3
+        with:
+          files: dist/*
+          # Set release name to <tag>-rocm<rocm_version>.
+          tag_name: ${{ github.event_name == 'workflow_dispatch' && inputs.release_tag || github.ref_name }}-rocm72
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+
+  release_hip:
+    name: Release HIP
+    needs: [build_wheels_windows_hip]
+    if: startsWith(github.ref, 'refs/tags/') || (github.event_name == 'workflow_dispatch' && inputs.release_tag != '')
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/download-artifact@v8
+        with:
+          merge-multiple: true
+          path: dist
+
+      - uses: softprops/action-gh-release@v3
+        with:
+          files: dist/*
+          # Set release name to <tag>-hip-radeon.
+          tag_name: ${{ github.event_name == 'workflow_dispatch' && inputs.release_tag || github.ref_name }}-hip-radeon
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
diff --git a/.github/workflows/build-wheels-vulkan.yaml b/.github/workflows/build-wheels-vulkan.yaml
new file mode 100644
index 0000000000..822662c3fb
--- /dev/null
+++ b/.github/workflows/build-wheels-vulkan.yaml
@@ -0,0 +1,130 @@
+name: Build Wheels (Vulkan)
+
+on:
+  workflow_dispatch:
+    inputs:
+      release_tag:
+        description: Release tag to upload wheel assets to
+        required: false
+        type: string
+
+permissions:
+  contents: write
+
+env:
+  VULKAN_SDK_VERSION: "1.4.341.0"
+  VULKAN_SDK_LINUX_SHA256: "ed66477d587a5587dc3601b1c2cdcc1fab5529c505f53a00171876cecd9b4fbe"
+
+jobs:
+  build_wheels:
+    name: Build Vulkan wheel on ${{ matrix.os }}
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - os: ubuntu-22.04
+            pyver: "3.9"
+            artifact: wheels-vulkan-ubuntu-22.04
+          - os: windows-2022
+            pyver: "3.9"
+            artifact: wheels-vulkan-windows-2022
+
+    steps:
+      - name: Set up MSVC
+        if: runner.os == 'Windows'
+        uses: ilammy/msvc-dev-cmd@v1
+        with:
+          arch: x64
+
+      - uses: actions/checkout@v6
+        with:
+          submodules: "recursive"
+
+      - uses: actions/setup-python@v6
+        with:
+          python-version: ${{ matrix.pyver }}
+          cache: "pip"
+
+      - name: Install Vulkan SDK
+        if: runner.os == 'Windows'
+        shell: pwsh
+        run: |
+          choco install vulkan-sdk --version="$env:VULKAN_SDK_VERSION" --no-progress -y
+          $vulkanSdk = Join-Path 'C:\VulkanSDK' $env:VULKAN_SDK_VERSION
+          if (-not (Test-Path $vulkanSdk)) {
+            throw "Failed to find Vulkan SDK at $vulkanSdk"
+          }
+          "VULKAN_SDK=$vulkanSdk" >> $env:GITHUB_ENV
+          "$vulkanSdk\Bin" >> $env:GITHUB_PATH
+          & "$vulkanSdk\Bin\glslc.exe" --version
+
+      - name: Install build dependencies
+        if: runner.os == 'Windows'
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install build wheel
+
+      - name: Install Windows build dependencies
+        if: runner.os == 'Windows'
+        run: python -m pip install ninja
+
+      - name: Build Vulkan wheel
+        if: runner.os == 'Linux'
+        uses: pypa/cibuildwheel@v3.4.1
+        env:
+          CIBW_BUILD: "cp38-manylinux_*"
+          CIBW_ARCHS: "auto64"
+          CIBW_MANYLINUX_X86_64_IMAGE: "manylinux2014"
+          CIBW_BEFORE_ALL_LINUX: >
+            yum install -y xz &&
+            curl -L https://micro.mamba.pm/api/micromamba/linux-64/latest -o /tmp/micromamba.tar.bz2 &&
+            mkdir -p /tmp/micromamba &&
+            tar -xjf /tmp/micromamba.tar.bz2 -C /tmp/micromamba bin/micromamba &&
+            /tmp/micromamba/bin/micromamba create -y -p /opt/vulkan -c conda-forge shaderc libvulkan-loader spirv-headers &&
+            /opt/vulkan/bin/glslc --version &&
+            curl -fL "https://sdk.lunarg.com/sdk/download/${{ env.VULKAN_SDK_VERSION }}/linux/vulkansdk-linux-x86_64-${{ env.VULKAN_SDK_VERSION }}.tar.xz" -o /tmp/vulkan-sdk.tar.xz &&
+            echo "${{ env.VULKAN_SDK_LINUX_SHA256 }}  /tmp/vulkan-sdk.tar.xz" | sha256sum -c - &&
+            mkdir -p /opt/vulkan-sdk &&
+            tar -xf /tmp/vulkan-sdk.tar.xz -C /opt/vulkan-sdk
+          CIBW_ENVIRONMENT_LINUX: >
+            CMAKE_ARGS="-DGGML_NATIVE=off -DGGML_METAL=OFF -DGGML_OPENMP=OFF -DGGML_VULKAN=on -DCMAKE_PREFIX_PATH=/opt/vulkan -DVulkan_INCLUDE_DIR=/opt/vulkan-sdk/${{ env.VULKAN_SDK_VERSION }}/x86_64/include -DVulkan_LIBRARY=/opt/vulkan/lib/libvulkan.so -DVulkan_GLSLC_EXECUTABLE=/opt/vulkan/bin/glslc"
+          CIBW_REPAIR_WHEEL_COMMAND_LINUX: "LD_LIBRARY_PATH=/project/llama_cpp/lib:/opt/vulkan/lib auditwheel repair --exclude libvulkan.so.1 -w {dest_dir} {wheel}"
+        with:
+          package-dir: .
+          output-dir: wheelhouse
+
+      - name: Build Vulkan wheel
+        if: runner.os == 'Windows'
+        shell: pwsh
+        run: |
+          $env:CMAKE_GENERATOR = 'Ninja'
+          $env:CMAKE_ARGS = '-DGGML_NATIVE=off -DGGML_VULKAN=on'
+          python -m build --wheel
+          New-Item -ItemType Directory -Force wheelhouse | Out-Null
+          Copy-Item dist/*.whl wheelhouse/
+
+      - uses: actions/upload-artifact@v7
+        with:
+          name: ${{ matrix.artifact }}
+          path: ./wheelhouse/*.whl
+
+  release:
+    name: Release
+    needs: [build_wheels]
+    if: startsWith(github.ref, 'refs/tags/') || (github.event_name == 'workflow_dispatch' && inputs.release_tag != '')
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/download-artifact@v8
+        with:
+          merge-multiple: true
+          path: dist
+
+      - uses: softprops/action-gh-release@v3
+        with:
+          files: dist/*
+          # Set release name to <tag>-vulkan.
+          tag_name: ${{ github.event_name == 'workflow_dispatch' && inputs.release_tag || github.ref_name }}-vulkan
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
diff --git a/.github/workflows/generate-index-from-release.yaml b/.github/workflows/generate-index-from-release.yaml
index 8d80e7e47f..edf292387d 100644
--- a/.github/workflows/generate-index-from-release.yaml
+++ b/.github/workflows/generate-index-from-release.yaml
@@ -3,7 +3,7 @@ name: Wheels Index
 on:
   # Trigger on new release
   workflow_run:
-    workflows: ["Release", "Build Wheels (CUDA)", "Build Wheels (Metal)"]
+    workflows: ["Release", "Build Wheels (CUDA)", "Build Wheels (Metal)", "Build Wheels (Vulkan)", "Build Wheels (ROCm)"]
     types:
       - completed
 
@@ -31,26 +31,32 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
       - name: Setup Pages
-        uses: actions/configure-pages@v5
+        uses: actions/configure-pages@v6
       - name: Build
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
         run: |
           ./scripts/get-releases.sh
           ./scripts/releases-to-pep-503.sh index/whl/cpu '^[v]?[0-9]+\.[0-9]+\.[0-9]+$'
+          ./scripts/releases-to-pep-503.sh index/whl/cu118 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu118$'
           ./scripts/releases-to-pep-503.sh index/whl/cu121 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu121$'
           ./scripts/releases-to-pep-503.sh index/whl/cu122 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu122$'
           ./scripts/releases-to-pep-503.sh index/whl/cu123 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu123$'
           ./scripts/releases-to-pep-503.sh index/whl/cu124 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu124$'
           ./scripts/releases-to-pep-503.sh index/whl/cu125 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu125$'
+          ./scripts/releases-to-pep-503.sh index/whl/cu130 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu130$'
+          ./scripts/releases-to-pep-503.sh index/whl/cu132 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-cu132$'
+          ./scripts/releases-to-pep-503.sh index/whl/rocm72 '^[v]?[0-9]+\.[0-9]+\.[0-9]+-rocm72$'
+          ./scripts/releases-to-pep-503.sh index/whl/hip-radeon '^[v]?[0-9]+\.[0-9]+\.[0-9]+-hip-radeon$'
+          ./scripts/releases-to-pep-503.sh index/whl/vulkan '^[v]?[0-9]+\.[0-9]+\.[0-9]+-vulkan$'
           ./scripts/releases-to-pep-503.sh index/whl/metal '^[v]?[0-9]+\.[0-9]+\.[0-9]+-metal$'
       - name: Upload artifact
-        uses: actions/upload-pages-artifact@v3
+        uses: actions/upload-pages-artifact@v5
         with:
           # Upload entire repository
           path: 'index'
       - name: Deploy to GitHub Pages
         id: deployment
-        uses: actions/deploy-pages@v4
+        uses: actions/deploy-pages@v5
diff --git a/.github/workflows/lint.yaml b/.github/workflows/lint.yaml
new file mode 100644
index 0000000000..ac39181f90
--- /dev/null
+++ b/.github/workflows/lint.yaml
@@ -0,0 +1,29 @@
+name: Lint
+
+on:
+  pull_request:
+    branches:
+      - main
+  push:
+    branches:
+      - main
+
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v6
+
+      - name: Set up Python
+        uses: actions/setup-python@v6
+        with:
+          python-version: "3.12"
+
+      - name: Install Ruff
+        run: python -m pip install "ruff>=0.15.7"
+
+      - name: Lint with Ruff
+        run: python -m ruff check llama_cpp tests
+
+      - name: Check formatting with Ruff
+        run: python -m ruff format --check llama_cpp tests
diff --git a/.github/workflows/publish-to-test.yaml b/.github/workflows/publish-to-test.yaml
index de3ae42aae..70ae5389af 100644
--- a/.github/workflows/publish-to-test.yaml
+++ b/.github/workflows/publish-to-test.yaml
@@ -16,12 +16,12 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v6
       with:
         submodules: "recursive"
         
     - name: Set up Python
-      uses: actions/setup-python@v5
+      uses: actions/setup-python@v6
       with:
         python-version: "3.11"
         cache: 'pip'
diff --git a/.github/workflows/publish.yaml b/.github/workflows/publish.yaml
index bb76f53941..8908ccdd6f 100644
--- a/.github/workflows/publish.yaml
+++ b/.github/workflows/publish.yaml
@@ -10,12 +10,12 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v6
       with:
         submodules: "recursive"
 
     - name: Set up Python
-      uses: actions/setup-python@v5
+      uses: actions/setup-python@v6
       with:
         python-version: "3.9"
 
diff --git a/.github/workflows/test-pypi.yaml b/.github/workflows/test-pypi.yaml
index 335033bba6..416cd0cddf 100644
--- a/.github/workflows/test-pypi.yaml
+++ b/.github/workflows/test-pypi.yaml
@@ -12,7 +12,7 @@ jobs:
 
     steps:
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'
@@ -48,7 +48,7 @@ jobs:
 
     steps:
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'
@@ -84,7 +84,7 @@ jobs:
 
     steps:
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'   
diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
index 8f619cf1f4..82f7a8a7de 100644
--- a/.github/workflows/test.yaml
+++ b/.github/workflows/test.yaml
@@ -8,26 +8,39 @@ on:
       - main
 
 env:
-  REPO_ID: Qwen/Qwen2-0.5B-Instruct-GGUF
-  MODEL_FILE: qwen2-0_5b-instruct-q8_0.gguf
+  REPO_ID: lmstudio-community/Qwen3.5-0.8B-GGUF
+  MODEL_FILE: Qwen3.5-0.8B-Q8_0.gguf
+  RECURRENT_REPO_ID: QuantFactory/mamba-130m-hf-GGUF
+  RECURRENT_MODEL_FILE: mamba-130m-hf.Q2_K.gguf
+  HYBRID_REPO_ID: tiiuae/Falcon-H1-Tiny-90M-Instruct-GGUF
+  HYBRID_MODEL_FILE: Falcon-H1-Tiny-90M-Instruct-Q2_K.gguf
+  EMBEDDING_REPO_ID: CompendiumLabs/bge-small-en-v1.5-gguf
+  EMBEDDING_MODEL_FILE: bge-small-en-v1.5-q4_k_m.gguf
+  MODEL_CACHE_KEY: qwen35-q8-mamba130m-q2-falconh1tiny-q2-bge-small-q4
 
 jobs:
   download-model:
     runs-on: ubuntu-latest
     steps:
       - name: Set up Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: "3.9"
       - name: Install huggingface-hub
         run: pip install huggingface-hub
-      - name: Download model
-        run: huggingface-cli download ${{ env.REPO_ID }} ${{ env.MODEL_FILE }}
-      - name: Cache model
-        uses: actions/cache@v4
+      - name: Restore model cache
+        uses: actions/cache@v5
         with:
           path: ~/.cache/huggingface/hub
-          key: ${{ runner.os }}-model-${{ env.REPO_ID }}-${{ env.MODEL_FILE }}
+          key: ${{ runner.os }}-model-${{ env.MODEL_CACHE_KEY }}
+          restore-keys: |
+            ${{ runner.os }}-model-qwen35-q8-mamba130m-q2-falconh1tiny-q2
+      - name: Download model
+        run: |
+          hf download ${{ env.REPO_ID }} ${{ env.MODEL_FILE }}
+          hf download ${{ env.RECURRENT_REPO_ID }} ${{ env.RECURRENT_MODEL_FILE }}
+          hf download ${{ env.HYBRID_REPO_ID }} ${{ env.HYBRID_MODEL_FILE }}
+          hf download ${{ env.EMBEDDING_REPO_ID }} ${{ env.EMBEDDING_MODEL_FILE }}
 
   build-linux:
     needs: download-model
@@ -36,34 +49,26 @@ jobs:
       matrix:
         python-version: ["3.9", "3.10", "3.11", "3.12"]
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: "recursive"
           
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'
       - name: Restore model cache
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/.cache/huggingface/hub
-          key: ${{ runner.os }}-model-${{ env.REPO_ID }}-${{ env.MODEL_FILE }}
+          key: ${{ runner.os }}-model-${{ env.MODEL_CACHE_KEY }}
       - name: Install dependencies (Linux/MacOS)
-        if: runner.os != 'Windows'
         run: |
           python -m pip install --upgrade pip
           python -m pip install uv
           python -m uv pip install -e .[all] --verbose
         shell: bash
-      - name: Install dependencies (Windows)
-        if: runner.os == 'Windows'
-        run: |
-          python -m pip install --upgrade pip
-          python -m pip install uv
-          python -m uv pip install -e .[all] --verbose
-        shell: cmd
       - name: Test with pytest
         run: |
           python -m pytest
@@ -75,37 +80,28 @@ jobs:
       matrix:
         python-version: ["3.9", "3.10", "3.11", "3.12"]
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: "recursive"
           
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'
 
       - name: Restore model cache
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/.cache/huggingface/hub
-          key: ${{ runner.os }}-model-${{ env.REPO_ID }}-${{ env.MODEL_FILE }}
-          
-      - name: Install dependencies (Linux/MacOS)
-        if: runner.os != 'Windows'
-        run: |
-          python -m pip install --upgrade pip
-          python -m pip install uv
-          python -m uv pip install -e .[all] --verbose
-        shell: bash
+          key: ${{ runner.os }}-model-${{ env.MODEL_CACHE_KEY }}
 
       - name: Install dependencies (Windows)
-        if: runner.os == 'Windows'
         run: |
           python -m pip install --upgrade pip
           python -m pip install uv
           python -m uv pip install -e .[all] --verbose
-        shell: cmd     
+        shell: cmd
           
       - name: Test with pytest
         run: |
@@ -113,79 +109,75 @@ jobs:
 
   build-macos:
     needs: download-model
-    runs-on: macos-latest
+    runs-on: macos-15
     strategy:
       matrix:
         python-version: ["3.9", "3.10", "3.11", "3.12"]
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: "recursive"
           
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
           cache: 'pip'
 
+      - name: System Info
+        run: |
+          uname -a
+          sysctl -n machdep.cpu.brand_string
+          python3 -c "import platform; print(platform.machine(), platform.architecture())"
+
       - name: Restore model cache
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/.cache/huggingface/hub
-          key: ${{ runner.os }}-model-${{ env.REPO_ID }}-${{ env.MODEL_FILE }}
+          key: ${{ runner.os }}-model-${{ env.MODEL_CACHE_KEY }}
           
       - name: Install dependencies (Linux/MacOS)
-        if: runner.os != 'Windows'
         run: |
-          python -m pip install --upgrade pip
-          python -m pip install uv
-          python -m uv pip install -e .[all] --verbose
+          python3 -m pip install --upgrade pip
+          python3 -m pip install uv
+          CMAKE_ARGS="-DGGML_NATIVE=off" python3 -m uv pip install -e .[all] --verbose
         shell: bash
 
-      - name: Install dependencies (Windows)
-        if: runner.os == 'Windows'
-        run: |
-          python -m pip install --upgrade pip
-          python -m pip install uv
-          python -m uv pip install -e .[all] --verbose
-        shell: cmd
-          
       - name: Test with pytest
         run: |
-          python -m pytest
+          python3 -m pytest
 
-  build-macos-metal:
+  build-macos-intel:
     needs: download-model
-    runs-on: macos-latest
+    runs-on: macos-15-intel
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           submodules: "recursive"
           
       - name: Set up Python 3.9
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: "3.9"
 
+      - name: System Info
+        run: |
+          uname -a
+          sysctl -n machdep.cpu.brand_string
+          python3 -c "import platform; print(platform.machine(), platform.architecture())"
+
       - name: Restore model cache
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/.cache/huggingface/hub
-          key: ${{ runner.os }}-model-${{ env.REPO_ID }}-${{ env.MODEL_FILE }}
+          key: ${{ runner.os }}-model-${{ env.MODEL_CACHE_KEY }}
 
-      - name: Install dependencies (Linux/MacOS)
-        if: runner.os != 'Windows'
+      - name: Install dependencies
         run: |
-          python -m pip install --upgrade pip
-          python -m pip install uv
-          CMAKE_ARGS="-DLLAMA_METAL=on" python -m uv pip install .[all] --verbose
+          python3 -m pip install --upgrade pip
+          python3 -m pip install .[all] --verbose
         shell: bash
 
-      - name: Install dependencies (Windows)
-        if: runner.os == 'Windows'
-        run: |
-          python -m pip install --upgrade pip
-          CMAKE_ARGS="-DGGML_METAL=on" python -m pip install .[all] --verbose
       - name: Test with pytest
         run: |
-          python -m pytest
+          python3 -m pytest
diff --git a/.gitignore b/.gitignore
index 9d68dbcd9d..ff773c6684 100644
--- a/.gitignore
+++ b/.gitignore
@@ -66,6 +66,7 @@ coverage.xml
 *.py,cover
 .hypothesis/
 .pytest_cache/
+.ruff_cache/
 cover/
 
 # Translations
diff --git a/.gitmodules b/.gitmodules
index 7edf0975dc..f56cca32df 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -1,3 +1,3 @@
 [submodule "vendor/llama.cpp"]
 	path = vendor/llama.cpp
-	url = https://github.com/ggerganov/llama.cpp.git
+	url = https://github.com/ggml-org/llama.cpp.git
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 8a194fecef..925e941d88 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -7,6 +7,191 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+- feat: update llama.cpp to ggml-org/llama.cpp@92e854ab8
+- fix: preserve recurrent/hybrid model state when the full prompt is already cached by @allthatido and @abetlen in #2306
+
+## [0.3.31]
+
+- feat: update llama.cpp to ggml-org/llama.cpp@f449e0553
+
+## [0.3.30]
+
+- feat: update llama.cpp to ggml-org/llama.cpp@e3a74b299
+- feat: add Pyodide wheel support by @abetlen in #2309
+
+## [0.3.29]
+
+- feat(example): use MTMD batch encoding by @abetlen in #2301
+- feat(example): support server video inputs and Gemma text tool calls by @abetlen in #2291
+- feat: update llama.cpp to ggml-org/llama.cpp@f05cf4676
+- fix(example): support multi-step Responses tool streaming by @abetlen in #2288
+- fix(ci): Repair Linux accelerator wheels for manylinux publishing
+
+## [0.3.28]
+
+- feat(example): align server MTP support with llama.cpp by @abetlen in #2283
+- feat: update llama.cpp to ggml-org/llama.cpp@9e3b928fd
+- feat(example): add OpenAI-compatible embeddings endpoint by @abetlen in #2281
+
+## [0.3.27]
+
+- feat: update llama.cpp to ggml-org/llama.cpp@465b1f0e7
+- feat(example): Updated server example (batch processing, multi-token prediction, `/v1/responses` api, response parsing) by @abetlen in #2174
+
+## [0.3.26]
+
+- feat: Generic Multimodal Chat Handler by @abetlen in #2256
+- feat: update llama.cpp to ggml-org/llama.cpp@7c158fbb4
+- feat(ci): add ROCm wheel builds by @abetlen in #2252
+- feat(ci): add Vulkan wheel builds by @abetlen in #2251
+- fix: handle additional `from_pretrained` files in subfolders by @TNing in #2085
+
+## [0.3.25]
+
+- feat: Update llama.cpp to ggml-org/llama.cpp@210a6570c by @abetlen in #2242
+- feat: add Gemma 4 multimodal chat support by @abetlen in #2241
+- feat(ci): add CUDA 13.0 and 13.2 wheel builds by @abetlen in #2239
+- feat(ci): add CUDA 11.8 wheel builds by @abetlen in #2238
+- fix(ci): add Pascal compute capability targets to CUDA wheel builds by @abetlen in #2237
+
+## [0.3.24]
+
+- docs: update ROCm install instructions by @agronholm in #1867
+- fix: clear prompt for recurrent / hybrid models when only a partial prefix matches by @avion23 in #2108
+- fix: match Transformers `tojson` in chat template rendering by @CISC in #1486
+- fix: use env var configured multimodal library override paths when loading shared libraries by @navratil-matej in #1782
+- feat: add Jinja2 loop controls to chat templates by @handshape in #2018
+- fix: avoid cleanup errors for partially initialized `LlamaModel` objects by @usernames122 in #2173
+- fix: suppress stdout and stderr in Jupyter notebooks by @Anai-Guo in #2181
+- feat: enable arm64 musl builds by @acon96 in #2221
+- feat: Update llama.cpp to ggml-org/llama.cpp@af6528e6d
+- fix: model fails to load when chat template uses HuggingFace generation tags by @tobocop2 in #2226
+- docs: add contributing guide by @abetlen in #2229
+- chore: Migrate llama.cpp submodule URL to ggml-org/llama.cpp by @shalinib-ibm in #2034
+- fix: Enable unified KV cache for embedding contexts to preserve full per-sequence context in batch embedding calls by @SanjanaB123 in #2217
+
+## [0.3.23]
+
+- feat: Update llama.cpp to ggerganov/llama.cpp@7d442abf
+- fix: Correct batched embedding outputs for multi-sequence `embed()` calls by @Anai-Guo in #2205
+- fix: Configure embedding contexts with enough sequence slots for batched `embed()` calls
+- fix: Mark all embedding input tokens as outputs to avoid llama.cpp override warnings by @Anai-Guo in #2212
+
+## [0.3.22]
+
+- feat: Update llama.cpp to ggerganov/llama.cpp@63d93d173
+- feat(ci): Re-enable Windows CUDA wheels and add CUDA 12.5.1 wheel builds
+
+## [0.3.21]
+
+- feat: Update llama.cpp to ggerganov/llama.cpp@f53577432 and sync Python bindings
+- fix(ci): Build one arm64 release wheel for `py3-none` wheel publishing
+
+## [0.3.20]
+
+- refactor: Replace deprecated llama.cpp references in library, docs, and examples by @abetlen in #2170
+- feat: Update llama.cpp to ggerganov/llama.cpp@f49e9178767d557a522618b16ce8694f9ddac628 by @abetlen in #2169
+- feat(server): Add model-load `chat_template_kwargs` support and document the CLI/config usage by @abetlen in #2168
+- ci: Publish release wheels as `py3-none` by @Bing-su in #2166
+- fix(ci): Publish distinct manylinux and musllinux CPU wheels by @abetlen in #2165
+
+## [0.3.19]
+
+- feat: Update llama.cpp to ggerganov/llama.cpp@c0159f9c1f874da15e94f371d136f5920b4b5335 by @abetlen in #2161
+- fix: Handle embedding models without KV memory and test embeddings with a real GGUF embedding model by @abetlen in #2160
+- fix(ci): Shrink CUDA wheel fatbins so CUDA releases stay under GitHub's asset size limit by @abetlen in #2158
+
+## [0.3.18]
+
+- feat: Expose `attention_type` in `Llama.__init__` for non-causal embedding models by @jamesbiederbeck in #2143
+- fix(ci): Build Docker images from the checked-out source and sanitize branch tags by @abetlen in #2156
+- fix(ci): Fix the CUDA wheel workflow and keep release tags aligned with the built toolkit by @abetlen in #2155
+- fix(ci): Speed up release wheel builds by moving arm64 off QEMU and parallelizing riscv64 by @abetlen in #2154
+
+## [0.3.17]
+
+- feat: Update llama.cpp to ggerganov/llama.cpp@49bfddeca18e62fa3d39114a23e9fcbdf8a22388 and sync Python bindings by @abetlen in #2151
+- fix: Handle Qwen 3.5 hybrid prefix reuse by @codavidgarcia and @r-dh in #2152
+- chore(dev): Add Ruff-based formatting and a safe lint baseline, and run it in CI for pull requests and pushes to `main`
+- fix(ci): Run macOS CI on supported Apple Silicon and Intel runners by @abetlen in #2150
+- fix(ci): Use the `hf` CLI instead of the deprecated `huggingface-cli` name in GitHub Actions and docs by @abetlen in #2149
+- ci: add riscv64 wheel builds to release workflow by @gounthar in #2139
+
+## [0.3.16]
+
+- feat: Update llama.cpp to ggerganov/llama.cpp@4227c9be4268ac844921b90f31595f81236bd317
+
+## [0.3.15]
+
+- feat: Update llama.cpp to ggerganov/llama.cpp@9a96389544a08fd829fccda28142ce2066017fde
+- feat: Add gpt-oss chat format support through strftime_now in chat format by @iamlemec in af637928db7351e030011085f818b034c6efc047
+- fix: rename op_offloat to op_offload in llama.py by @sergey21000 in #2046
+
+## [0.3.14]
+
+- feat: Update llama.cpp to ggerganov/llama.cpp@79e0b68c178656bb0632cb8602d2940b755077f8
+
+## [0.3.13]
+
+- feat: Update llama.cpp to ggerganov/llama.cpp@bdca38376f7e8dd928defe01ce6a16218a64b040
+- fix: Better chat format for Qwen2.5-VL by @alcoftTAO in #2040
+
+## [0.3.12]
+
+- feat: Update llama.cpp to ggerganov/llama.cpp@a0374a67e2924f2e845cdc59dd67d9a44065a89c
+
+## [0.3.11]
+
+- fix: Update reference to `llama_kv_cache_clear` in Llama.embed. Closes #2037 by @abetlen in 9e5a4eaa84156084ed7bbb91e6efcc91dc6217bc
+
+## [0.3.10]
+
+- feat: Update llama.cpp to ggerganov/llama.cpp@8846aace4934ad29651ea61b8c7e3f6b0556e3d2
+- feat: Add support for llama.cpp multimodal, add Qwen2.5-VL chat handler by @abetlen in cd548bd0f14210627798237d5c2ea78acfb88ccb
+
+## [0.3.9]
+
+- feat: Update llama.cpp to ggerganov/llama.cpp@8733e0cf6eefc7c7752297cc22d0836706f4222c
+
+## [0.3.8]
+
+- feat: Update llama.cpp to ggerganov/llama.cpp@7841fc723e059d1fd9640e5c0ef19050fcc7c698
+
+## [0.3.7]
+
+- feat: Update llama.cpp to ggerganov/llama.cpp@794fe23f29fb40104975c91fe19f23798f7c726e
+- fix(ci): Fix the CUDA workflow by @oobabooga in #1894
+- fix: error showing time spent in llama perf context print, adds `no_perf` flag to `Llama` class by @shakalaca in #1898
+
+## [0.3.6]
+
+- feat: Update llama.cpp to ggerganov/llama.cpp@f7cd13301c2a88f97073fd119072b4cc92c08df1
+- fix(server): streaming resource lock by @gjpower in #1879
+
+## [0.3.5]
+
+- feat: Update llama.cpp to ggerganov/llama.cpp@26a8406ba9198eb6fdd8329fa717555b4f77f05f
+- fix(ci): Fix release by updating macos runner image to non-deprecated version by @abetlen in afedfc888462f9a6e809dc9455eb3b663764cc3f
+- fix(server): add missing await statements for async exit_stack handling by @gjpower in #1858
+
+## [0.3.4]
+
+- fix(ci): Build wheels for macos 13-15, cuda 12.1-12.4 by @abetlen in ca808028bd16b8327bd84128d48015a4b1304690
+
+## [0.3.3]
+
+- feat: Update llama.cpp to ggerganov/llama.cpp@ce8784bdb153ff7794dde5a50b0ebfa51baa6171
+- fix: chat API logprobs format by @domdomegg in #1788
+- feat: Add support for CUDA 12.6, fix CUDA 12.5 by @Smartappli in #1775
+- fix: Make content not required in ChatCompletionRequestAssistantMessage by @feloy in #1807
+- fix: Fix pickling of Llama class by setting seed from _seed member by @abetlen in 2523472c3eccb9ab9277117cc4ff705212b6888a
+- fix: Fix logit-bias type hint by @ddh0 in #1802
+- fix(server): Avoid thread starvation on many concurrent requests by making use of asyncio to lock llama_proxy context by @gjpower in #1798
+- fix(server): Added missing exit_stack.close() to /v1/chat/completions by @Ian321 in #1796
+- fix(examples): Refactor Batching notebook to use new sampler chain API by @lukestanley in #1793
+- fix(docs): Update development instructions by @Florents-Tselai in #1833
+- fix(docs): Remove ref to llama_eval in llama_cpp.py docs by @richdougherty in #1819
+
 ## [0.3.2]
 
 - feat: Update llama.cpp to ggerganov/llama.cpp@74d73dc85cc2057446bf63cc37ff649ae7cebd80
diff --git a/CMakeLists.txt b/CMakeLists.txt
index c9f3c2b361..5feaaca5b9 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -10,14 +10,22 @@ function(llama_cpp_python_install_target target)
         return()
     endif()
 
-    install(
-        TARGETS ${target}
-        LIBRARY DESTINATION ${CMAKE_CURRENT_SOURCE_DIR}/llama_cpp/lib
-        RUNTIME DESTINATION ${CMAKE_CURRENT_SOURCE_DIR}/llama_cpp/lib
-        ARCHIVE DESTINATION ${CMAKE_CURRENT_SOURCE_DIR}/llama_cpp/lib
-        FRAMEWORK DESTINATION ${CMAKE_CURRENT_SOURCE_DIR}/llama_cpp/lib
-        RESOURCE DESTINATION ${CMAKE_CURRENT_SOURCE_DIR}/llama_cpp/lib
-    )
+    if(EMSCRIPTEN)
+        set_target_properties(${target} PROPERTIES
+            OUTPUT_NAME "${target}.cpython-00-wasm32-emscripten"
+        )
+    endif()
+
+    if(NOT EMSCRIPTEN)
+        install(
+            TARGETS ${target}
+            LIBRARY DESTINATION ${CMAKE_CURRENT_SOURCE_DIR}/llama_cpp/lib
+            RUNTIME DESTINATION ${CMAKE_CURRENT_SOURCE_DIR}/llama_cpp/lib
+            ARCHIVE DESTINATION ${CMAKE_CURRENT_SOURCE_DIR}/llama_cpp/lib
+            FRAMEWORK DESTINATION ${CMAKE_CURRENT_SOURCE_DIR}/llama_cpp/lib
+            RESOURCE DESTINATION ${CMAKE_CURRENT_SOURCE_DIR}/llama_cpp/lib
+        )
+    endif()
     install(
         TARGETS ${target}
         LIBRARY DESTINATION ${SKBUILD_PLATLIB_DIR}/llama_cpp/lib
@@ -62,21 +70,75 @@ if (LLAMA_BUILD)
     # Enable building of the common library
     set(LLAMA_BUILD_COMMON ON CACHE BOOL "Build llama.cpp common library" FORCE)
 
-    # Building llama
-    if (APPLE AND NOT CMAKE_SYSTEM_PROCESSOR MATCHES "arm64")
-        # Need to disable these llama.cpp flags on Apple x86_64,
-        # otherwise users may encounter invalid instruction errors
-        set(GGML_AVX "Off" CACHE BOOL "ggml: enable AVX" FORCE)
-        set(GGML_AVX2 "Off" CACHE BOOL "ggml: enable AVX2" FORCE)
-        set(GGML_FMA "Off" CACHE BOOL "gml: enable FMA" FORCE)
-        set(GGML_F16C "Off" CACHE BOOL "gml: enable F16C" FORCE)
+    # Disable building curl support
+    set(LLAMA_CURL OFF CACHE BOOL "llama.cpp: enable curl" FORCE)
+
+    if (EMSCRIPTEN)
+        if (DEFINED EMSCRIPTEN_SYSTEM_PROCESSOR)
+            set(CMAKE_SYSTEM_PROCESSOR ${EMSCRIPTEN_SYSTEM_PROCESSOR} CACHE STRING "Target processor" FORCE)
+        else()
+            set(CMAKE_SYSTEM_PROCESSOR wasm32 CACHE STRING "Target processor" FORCE)
+        endif()
+
+        set(LLAMA_WASM_MEM64 OFF CACHE BOOL "llama.cpp: enable wasm64 memory" FORCE)
+        set(GGML_NATIVE OFF CACHE BOOL "ggml: enable -march=native" FORCE)
+        set(GGML_OPENMP OFF CACHE BOOL "ggml: use OpenMP" FORCE)
+        set(GGML_METAL OFF CACHE BOOL "ggml: use Metal" FORCE)
+        set(GGML_BLAS OFF CACHE BOOL "ggml: use BLAS" FORCE)
+        set(GGML_CUDA OFF CACHE BOOL "ggml: use CUDA" FORCE)
+        set(GGML_HIP OFF CACHE BOOL "ggml: use HIP" FORCE)
+        set(GGML_VULKAN OFF CACHE BOOL "ggml: use Vulkan" FORCE)
+        set(GGML_OPENCL OFF CACHE BOOL "ggml: use OpenCL" FORCE)
+        set(GGML_RPC OFF CACHE BOOL "ggml: use RPC" FORCE)
+
+        # Pyodide auto-loads side modules from top-level site-packages/lib
+        # before Python imports run, so keep upstream installs package-local.
+        set(CMAKE_INSTALL_BINDIR llama_cpp/lib CACHE PATH "Install binaries" FORCE)
+        set(CMAKE_INSTALL_INCLUDEDIR llama_cpp/include CACHE PATH "Install headers" FORCE)
+        set(CMAKE_INSTALL_LIBDIR llama_cpp/lib CACHE PATH "Install libraries" FORCE)
+        set(LLAMA_BUILD_COMMON OFF CACHE BOOL "Build llama.cpp common library" FORCE)
     endif()
 
+    # Architecture detection and settings for Apple platforms
     if (APPLE)
-        set(GGML_METAL_EMBED_LIBRARY "On" CACHE BOOL "llama: embed metal library" FORCE)
+        # Get the target architecture
+        execute_process(
+            COMMAND uname -m
+            OUTPUT_VARIABLE HOST_ARCH
+            OUTPUT_STRIP_TRAILING_WHITESPACE
+        )
+
+        # If CMAKE_OSX_ARCHITECTURES is not set, use the host architecture
+        if(NOT CMAKE_OSX_ARCHITECTURES)
+            set(CMAKE_OSX_ARCHITECTURES ${HOST_ARCH} CACHE STRING "Build architecture for macOS" FORCE)
+        endif()
+
+        message(STATUS "Host architecture: ${HOST_ARCH}")
+        message(STATUS "Target architecture: ${CMAKE_OSX_ARCHITECTURES}")
+
+        # Configure based on target architecture
+        if(CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64")
+            # Intel Mac settings
+            set(GGML_AVX "OFF" CACHE BOOL "ggml: enable AVX" FORCE)
+            set(GGML_AVX2 "OFF" CACHE BOOL "ggml: enable AVX2" FORCE)
+            set(GGML_FMA "OFF" CACHE BOOL "ggml: enable FMA" FORCE)
+            set(GGML_F16C "OFF" CACHE BOOL "ggml: enable F16C" FORCE)
+        endif()
+
+        # Metal settings (enable for both architectures)
+        set(GGML_METAL "ON" CACHE BOOL "ggml: enable Metal" FORCE)
+        set(GGML_METAL_EMBED_LIBRARY "ON" CACHE BOOL "ggml: embed metal library" FORCE)
     endif()
 
+
     add_subdirectory(vendor/llama.cpp)
+
+    if (WIN32)
+        if (TARGET llama)
+            set_target_properties(llama PROPERTIES WINDOWS_EXPORT_ALL_SYMBOLS ON)
+        endif()
+    endif()
+
     llama_cpp_python_install_target(llama)
     llama_cpp_python_install_target(ggml)
 
@@ -125,36 +187,64 @@ if (LLAMA_BUILD)
             add_compile_definitions(GGML_USE_METAL)
         endif()
 
+        # Upstream mtmd expects LLAMA_INSTALL_VERSION to be set by llama.cpp's
+        # top-level CMakeLists.txt. When we include tools/mtmd directly from the
+        # Python package build, that directory scope is skipped.
+        if (NOT DEFINED LLAMA_INSTALL_VERSION OR "${LLAMA_INSTALL_VERSION}" STREQUAL "")
+            set(LLAMA_INSTALL_VERSION 0.0.0)
+            find_package(Git QUIET)
+            if (Git_FOUND)
+                execute_process(
+                    COMMAND ${GIT_EXECUTABLE} rev-list --count HEAD
+                    WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp
+                    OUTPUT_VARIABLE LLAMA_MTMD_BUILD_NUMBER
+                    OUTPUT_STRIP_TRAILING_WHITESPACE
+                    RESULT_VARIABLE LLAMA_MTMD_BUILD_NUMBER_RESULT
+                )
+                if (LLAMA_MTMD_BUILD_NUMBER_RESULT EQUAL 0)
+                    set(LLAMA_INSTALL_VERSION 0.0.${LLAMA_MTMD_BUILD_NUMBER})
+                endif()
+            endif()
+        endif()
+
         # Building llava
-        add_subdirectory(vendor/llama.cpp/examples/llava)
-        set_target_properties(llava_shared PROPERTIES OUTPUT_NAME "llava")
-        # Set CUDA_ARCHITECTURES to OFF on Windows
+        add_subdirectory(vendor/llama.cpp/tools/mtmd)
+
+        # The Python package only ships mtmd as a shared library.
+        # Upstream mtmd also defines CLI compatibility wrappers, but those are
+        # not installed here and can fail to link in minimal Docker toolchains.
+        foreach(target llama-llava-cli llama-gemma3-cli llama-minicpmv-cli llama-qwen2vl-cli llama-mtmd-cli llama-mtmd-debug)
+            if (TARGET ${target})
+                set_target_properties(${target} PROPERTIES EXCLUDE_FROM_ALL TRUE)
+            endif()
+        endforeach()
+
         if (WIN32)
-            set_target_properties(llava_shared PROPERTIES CUDA_ARCHITECTURES OFF)
+            set_target_properties(mtmd PROPERTIES CUDA_ARCHITECTURES OFF)
         endif()
-        llama_cpp_python_install_target(llava_shared)
+        llama_cpp_python_install_target(mtmd)
         if (WIN32)
             install(
-                FILES $<TARGET_RUNTIME_DLLS:llava_shared>
+                FILES $<TARGET_RUNTIME_DLLS:mtmd>
                 DESTINATION ${CMAKE_CURRENT_SOURCE_DIR}/llama_cpp/lib
             )
             install(
-                FILES $<TARGET_RUNTIME_DLLS:llava_shared>
+                FILES $<TARGET_RUNTIME_DLLS:mtmd>
                 DESTINATION ${SKBUILD_PLATLIB_DIR}/llama_cpp/lib
             )
         endif()
 
-        # Fix for llava build: Add include directory for llama.h
+        # Fix for mtmd build: Add include directory for llama.h
         # Move these commands after the add_subdirectory call
-        target_include_directories(llava PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp/include)
-        target_include_directories(llava PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp/ggml/include)
+        target_include_directories(mtmd PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp/include)
+        target_include_directories(mtmd PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp/ggml/include)
 
         if (BUILD_SHARED_LIBS)
-            target_include_directories(llava_shared PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp/include)
-            target_include_directories(llava_shared PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp/ggml/include)
+            target_include_directories(mtmd PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp/include)
+            target_include_directories(mtmd PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp/ggml/include)
         endif()
 
-        target_include_directories(llama-llava-cli PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp/include)
-        target_include_directories(llama-minicpmv-cli PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp/include)
+        # target_include_directories(llama-llava-cli PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp/include)
+        # target_include_directories(llama-minicpmv-cli PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/vendor/llama.cpp/include)
     endif()
 endif()
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 0000000000..1d8f9e398d
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,77 @@
+# Contributing
+
+Hello human and AI contributors, this document exists to help you understand the project and set some rules for contributions.
+
+## Contribution Workflow
+
+Before opening a pull request, search existing issues and PRs to avoid duplicate work.
+Keep each PR focused on one feature, bug fix, or vendor update.
+Avoid mixing unrelated Python changes, generated binding updates, documentation edits, and `vendor/llama.cpp` changes unless they are required for the same fix.
+
+Describe what changed, why it changed, and how it was tested.
+Link relevant issues, include any required build flags or hardware assumptions, and add a `CHANGELOG.md` entry for user-visible fixes or features (see `CHANGELOG.md` for examples).
+
+BREAKING CHANGES WILL ALMOST CERTAINLY BE REJECTED OR REFACTORED.
+
+## PR Titles and Changelog Entries
+
+Use PR titles in the form `<tag>: <title>`, with an optional scope when it adds clarity: `feat: add X`, `fix(server): handle Y`, `fix(ci): repair Z`, or `chore: bump version to N`.
+Prefer tags already used in the project history, such as `feat`, `fix`, `chore`, `ci`, `docs`, and `refactor`.
+
+Add changelog entries under `## [Unreleased]` using the PR title followed by `by @contributor in #1234`.
+
+```md
+- feat(server): add support for X by @contributor in #1234
+- fix(ci): repair Y wheel builds by @contributor in #1234
+```
+
+## Local Development
+
+Prerequisites: Python 3.8+, CMake 3.21+, a C/C++ compiler, and Git submodules.
+From a fresh checkout of the repository, initialize submodules and create a virtual environment:
+
+```bash
+git submodule update --init --recursive
+python3 -m venv .venv
+source .venv/bin/activate
+python -m pip install --upgrade pip
+make deps
+make build
+```
+
+Run tests and lint checks before submitting changes:
+
+```bash
+make test
+make lint
+```
+
+Use backend-specific build targets when validating native acceleration or backend-specific fixes, for example `make build.openblas`, `make build.cuda`, `make build.metal`, or `make build.vulkan`.
+
+## Testing Expectations
+
+Add or update tests for behavior changes or fixing regressions.
+The test suite uses pytest and lives under `tests/`; name files `test_*.py` and test functions `test_*`.
+
+For changes involving native backends, model behavior, performance, or platform compatibility, document the environment used for validation in the PR.
+If a change cannot be covered by automated tests, include a short manual validation recipe instead.
+
+## Code Style
+
+Python code is formatted with Ruff using an 88-character line length.
+Run `make format` to apply automatic fixes and `make lint` to check formatting and lint rules.
+
+Use 4-space indentation, `snake_case` for functions and variables, `PascalCase` for classes, and `UPPER_CASE` for constants.
+Follow existing patterns when touching ctypes bindings or server APIs, and avoid adding dependencies unless they are necessary for the feature or fix.
+
+## Documentation Style
+
+Write Markdown with one sentence or core idea per physical line to keep diffs focused and easier to review.
+Do not manually wrap lines at a fixed column width.
+Keep `README.md` focused on user-facing setup and usage; link to this guide for contribution workflow details rather than duplicating them.
+
+## Project Layout
+
+The Python package lives in `llama_cpp/`, with tests in `tests/` and examples in `examples/`.
+Documentation lives in `docs/` and is built with `mkdocs.yml`.
+The `vendor/llama.cpp/` directory is a Git submodule containing the upstream llama.cpp sources used by the bindings.
diff --git a/Makefile b/Makefile
index be9b55a3e7..db45246c79 100644
--- a/Makefile
+++ b/Makefile
@@ -21,6 +21,15 @@ build.debug:
 		--config-settings=cmake.args="-DCMAKE_BUILD_TYPE=Debug;-DCMAKE_C_FLAGS='-ggdb -O0';-DCMAKE_CXX_FLAGS='-ggdb -O0'" \
 		--editable .
 
+build.debug.extra:
+	python3 -m pip install \
+		--verbose \
+		--config-settings=cmake.verbose=true \
+		--config-settings=logging.level=INFO \
+		--config-settings=install.strip=false  \
+		--config-settings=cmake.args="-DCMAKE_BUILD_TYPE=Debug;-DCMAKE_C_FLAGS='-fsanitize=address -ggdb -O0';-DCMAKE_CXX_FLAGS='-fsanitize=address -ggdb -O0'" \
+		--editable .
+
 build.cuda:
 	CMAKE_ARGS="-DGGML_CUDA=on" python3 -m pip install --verbose -e .
 
@@ -46,7 +55,7 @@ build.rpc:
 	CMAKE_ARGS="-DGGML_RPC=on" python3 -m pip install --verbose -e .
 
 build.sdist:
-	python3 -m build --sdist
+	python3 -m build --sdist --verbose
 
 deploy.pypi:
 	python3 -m twine upload dist/*
@@ -56,23 +65,29 @@ deploy.gh-docs:
 	mkdocs gh-deploy
 
 test:
-	python3 -m pytest
+	python3 -m pytest --full-trace -v
+
+lint:
+	python3 -m ruff check llama_cpp tests
+	python3 -m ruff format --check llama_cpp tests
+
+format:
+	python3 -m ruff check --fix llama_cpp tests
+	python3 -m ruff format llama_cpp tests
 
 docker:
 	docker build -t llama-cpp-python:latest -f docker/simple/Dockerfile .
 
 run-server:
-	uvicorn --factory llama.server:app --host ${HOST} --port ${PORT}
+	python3 -m llama_cpp.server --model ${MODEL}
 
 clean:
-	- cd vendor/llama.cpp && make clean
-	- cd vendor/llama.cpp && rm libllama.so
 	- rm -rf _skbuild
-	- rm llama_cpp/*.so
-	- rm llama_cpp/*.dylib
-	- rm llama_cpp/*.metal
-	- rm llama_cpp/*.dll
-	- rm llama_cpp/*.lib
+	- rm llama_cpp/lib/*.so
+	- rm llama_cpp/lib/*.dylib
+	- rm llama_cpp/lib/*.metal
+	- rm llama_cpp/lib/*.dll
+	- rm llama_cpp/lib/*.lib
 
 .PHONY: \
 	update \
@@ -84,5 +99,7 @@ clean:
 	build.sdist \
 	deploy.pypi \
 	deploy.gh-docs \
+	lint \
+	format \
 	docker \
 	clean
diff --git a/README.md b/README.md
index dbaec50775..c02df9eed0 100644
--- a/README.md
+++ b/README.md
@@ -9,7 +9,7 @@
 [![PyPI](https://img.shields.io/pypi/v/llama-cpp-python)](https://pypi.org/project/llama-cpp-python/)
 [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/llama-cpp-python)](https://pypi.org/project/llama-cpp-python/)
 [![PyPI - License](https://img.shields.io/pypi/l/llama-cpp-python)](https://pypi.org/project/llama-cpp-python/)
-[![PyPI - Downloads](https://img.shields.io/pypi/dm/llama-cpp-python)](https://pypi.org/project/llama-cpp-python/)
+[![PyPI - Downloads](https://static.pepy.tech/badge/llama-cpp-python/month)](https://pepy.tech/projects/llama-cpp-python)
 [![Github All Releases](https://img.shields.io/github/downloads/abetlen/llama-cpp-python/total.svg?label=Github%20Downloads)]()
 
 Simple Python bindings for **@ggerganov's** [`llama.cpp`](https://github.com/ggerganov/llama.cpp) library.
@@ -59,7 +59,7 @@ pip install llama-cpp-python \
 
 ### Installation Configuration
 
-`llama.cpp` supports a number of hardware acceleration backends to speed up inference as well as backend specific options. See the [llama.cpp README](https://github.com/ggerganov/llama.cpp#build) for a full list.
+`llama.cpp` supports a number of hardware acceleration backends to speed up inference as well as backend specific options. See the [llama.cpp README](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md) for a full list.
 
 All `llama.cpp` cmake build options can be set via the `CMAKE_ARGS` environment variable or via the `--config-settings / -C` cli flag during installation.
 
@@ -125,7 +125,8 @@ CMAKE_ARGS="-DGGML_CUDA=on" pip install llama-cpp-python
 
 It is also possible to install a pre-built wheel with CUDA support. As long as your system meets some requirements:
 
-- CUDA Version is 12.1, 12.2, 12.3, 12.4 or 12.5
+- CUDA Version is 11.8, 12.1, 12.2, 12.3, 12.4, 12.5, 13.0 or 13.2
+- NVIDIA GPU compute capability is 6.0 through 8.9 for CUDA 11.8 wheels, 6.0 or newer for CUDA 12 wheels, or 7.5 or newer for CUDA 13 wheels
 - Python Version is 3.10, 3.11 or 3.12
 
 ```bash
@@ -134,11 +135,14 @@ pip install llama-cpp-python \
 ```
 
 Where `<cuda-version>` is one of the following:
+- `cu118`: CUDA 11.8
 - `cu121`: CUDA 12.1
 - `cu122`: CUDA 12.2
 - `cu123`: CUDA 12.3
 - `cu124`: CUDA 12.4
 - `cu125`: CUDA 12.5
+- `cu130`: CUDA 13.0
+- `cu132`: CUDA 13.2
 
 For example, to install the CUDA 12.1 wheel:
 
@@ -173,12 +177,28 @@ pip install llama-cpp-python \
 </details>
 
 <details>
-<summary>hipBLAS (ROCm)</summary>
+<summary>HIP (ROCm)</summary>
 
-To install with hipBLAS / ROCm support for AMD cards, set the `GGML_HIPBLAS=on` environment variable before installing:
+To install with HIP / ROCm support for AMD cards, set the `GGML_HIP=on` environment variable before installing:
 
 ```bash
-CMAKE_ARGS="-DGGML_HIPBLAS=on" pip install llama-cpp-python
+CMAKE_ARGS="-DGGML_HIP=on" pip install llama-cpp-python
+```
+
+**Pre-built Wheel (New)**
+
+It is also possible to install a pre-built wheel with ROCm support for Linux:
+
+```bash
+pip install llama-cpp-python \
+  --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/rocm72
+```
+
+Or a pre-built wheel with HIP Radeon support for Windows:
+
+```powershell
+pip install llama-cpp-python `
+  --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/hip-radeon
 ```
 
 </details>
@@ -192,6 +212,15 @@ To install with Vulkan support, set the `GGML_VULKAN=on` environment variable be
 CMAKE_ARGS="-DGGML_VULKAN=on" pip install llama-cpp-python
 ```
 
+**Pre-built Wheel (New)**
+
+It is also possible to install a pre-built wheel with Vulkan support for Linux or Windows:
+
+```bash
+pip install llama-cpp-python \
+  --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/vulkan
+```
+
 </details>
 
 <details>
@@ -269,7 +298,7 @@ To upgrade and rebuild `llama-cpp-python` add `--upgrade --force-reinstall --no-
 
 The high-level API provides a simple managed interface through the [`Llama`](https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama) class.
 
-Below is a short example demonstrating how to use the high-level API to for basic text completion:
+Below is a short example demonstrating how to use the high-level API for basic text completion:
 
 ```python
 from llama_cpp import Llama
@@ -322,13 +351,13 @@ You'll need to install the `huggingface-hub` package to use this feature (`pip i
 
 ```python
 llm = Llama.from_pretrained(
-    repo_id="Qwen/Qwen2-0.5B-Instruct-GGUF",
-    filename="*q8_0.gguf",
+    repo_id="lmstudio-community/Qwen3.5-0.8B-GGUF",
+    filename="*Q8_0.gguf",
     verbose=False
 )
 ```
 
-By default [`from_pretrained`](https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.from_pretrained) will download the model to the huggingface cache directory, you can then manage installed model files with the [`huggingface-cli`](https://huggingface.co/docs/huggingface_hub/en/guides/cli) tool.
+By default [`from_pretrained`](https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama.from_pretrained) will download the model to the huggingface cache directory, you can then manage installed model files with the [`hf`](https://huggingface.co/docs/huggingface_hub/en/guides/cli) tool.
 
 ### Chat Completion
 
@@ -337,7 +366,7 @@ The high-level API also provides a simple interface for chat completion.
 Chat completion requires that the model knows how to format the messages into a single prompt.
 The `Llama` class does this using pre-registered chat formats (ie. `chatml`, `llama-2`, `gemma`, etc) or by providing a custom chat handler object.
 
-The model will will format the messages into a single prompt using the following order of precedence:
+The model will format the messages into a single prompt using the following order of precedence:
   - Use the `chat_handler` if provided
   - Use the `chat_format` if provided
   - Use the `tokenizer.chat_template` from the `gguf` model's metadata (should work for most new models, older models may not have this)
@@ -502,9 +531,16 @@ Below are the supported multi-modal models and their respective chat handlers (P
 | [llava-v1.5-13b](https://huggingface.co/mys/ggml_llava-v1.5-13b) | `Llava15ChatHandler` | `llava-1-5` |
 | [llava-v1.6-34b](https://huggingface.co/cjpais/llava-v1.6-34B-gguf) | `Llava16ChatHandler` | `llava-1-6` |
 | [moondream2](https://huggingface.co/vikhyatk/moondream2) | `MoondreamChatHandler` | `moondream2` |
-| [nanollava](https://huggingface.co/abetlen/nanollava-gguf) | `NanollavaChatHandler` | `nanollava` |
+| [nanollava](https://huggingface.co/abetlen/nanollava-gguf) | `NanoLlavaChatHandler` | `nanollava` |
 | [llama-3-vision-alpha](https://huggingface.co/abetlen/llama-3-vision-alpha-gguf) | `Llama3VisionAlphaChatHandler` | `llama-3-vision-alpha` |
 | [minicpm-v-2.6](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) | `MiniCPMv26ChatHandler` | `minicpm-v-2.6` |
+| [qwen2.5-vl](https://huggingface.co/unsloth/Qwen2.5-VL-3B-Instruct-GGUF) | `Qwen25VLChatHandler` | `qwen2.5-vl` |
+| [gemma-4](https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF) | `Gemma4ChatHandler` | `gemma4` |
+| GGUF models with an mtmd projector and embedded chat template | `MTMDChatHandler` | `mtmd` |
+
+Try Gemma 4 12B in Google Colab -> [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/abetlen/llama-cpp-python/blob/main/examples/colab/notebook.ipynb)
+
+Try Gemma 4 12B QAT in Google Colab -> [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/abetlen/llama-cpp-python/blob/main/examples/colab/Gemma4-12B-QAT.ipynb)
 
 Then you'll need to use a custom chat handler to load the clip model and process the chat messages and images.
 
@@ -684,7 +720,7 @@ For possible options, see [llama_cpp/llama_chat_format.py](llama_cpp/llama_chat_
 If you have `huggingface-hub` installed, you can also use the `--hf_model_repo_id` flag to load a model from the Hugging Face Hub.
 
 ```bash
-python3 -m llama_cpp.server --hf_model_repo_id Qwen/Qwen2-0.5B-Instruct-GGUF --model '*q8_0.gguf'
+python3 -m llama_cpp.server --hf_model_repo_id lmstudio-community/Qwen3.5-0.8B-GGUF --model '*Q8_0.gguf'
 ```
 
 ### Web Server Features
@@ -716,16 +752,20 @@ Below is a short example demonstrating how to use the low-level API to tokenize
 ```python
 import llama_cpp
 import ctypes
-llama_cpp.llama_backend_init(False) # Must be called once at the start of each program
-params = llama_cpp.llama_context_default_params()
+llama_cpp.llama_backend_init()  # Must be called once at the start of each program
+model_params = llama_cpp.llama_model_default_params()
+ctx_params = llama_cpp.llama_context_default_params()
+prompt = b"Q: Name the planets in the solar system? A: "
 # use bytes for char * params
-model = llama_cpp.llama_load_model_from_file(b"./models/7b/llama-model.gguf", params)
-ctx = llama_cpp.llama_new_context_with_model(model, params)
-max_tokens = params.n_ctx
+model = llama_cpp.llama_model_load_from_file(b"./models/7b/llama-model.gguf", model_params)
+ctx = llama_cpp.llama_init_from_model(model, ctx_params)
+vocab = llama_cpp.llama_model_get_vocab(model)
+max_tokens = ctx_params.n_ctx
 # use ctypes arrays for array params
 tokens = (llama_cpp.llama_token * int(max_tokens))()
-n_tokens = llama_cpp.llama_tokenize(ctx, b"Q: Name the planets in the solar system? A: ", tokens, max_tokens, llama_cpp.c_bool(True))
+n_tokens = llama_cpp.llama_tokenize(vocab, prompt, len(prompt), tokens, max_tokens, True, False)
 llama_cpp.llama_free(ctx)
+llama_cpp.llama_model_free(model)
 ```
 
 Check out the [examples folder](examples/low_level_api) for more examples of using the low-level API.
@@ -738,6 +778,7 @@ If you find any issues with the documentation, please open an issue or submit a
 ## Development
 
 This package is under active development and I welcome any contributions.
+See [CONTRIBUTING.md](CONTRIBUTING.md) for contribution workflow, PR title, changelog, testing, and style guidelines.
 
 To get started, clone the repository and install the package in editable / development mode:
 
@@ -751,16 +792,44 @@ pip install --upgrade pip
 # Install with pip
 pip install -e .
 
+# install development tooling (tests, docs, ruff)
+pip install -e '.[dev]'
+
 # if you want to use the fastapi / openapi server
-pip install -e .[server]
+pip install -e '.[server]'
 
 # to install all optional dependencies
-pip install -e .[all]
+pip install -e '.[all]'
 
 # to clear the local build cache
 make clean
 ```
 
+Now try running the tests
+
+```bash
+pytest
+```
+
+And check formatting / linting before opening a PR:
+
+```bash
+python -m ruff check llama_cpp tests
+python -m ruff format --check llama_cpp tests
+
+# or use the Makefile targets
+make lint
+make format
+```
+
+There's a `Makefile` available with useful targets.
+A typical workflow would look like this:
+
+```bash
+make build
+make test
+```
+
 You can also test out specific commits of `llama.cpp` by checking out the desired commit in the `vendor/llama.cpp` submodule and then running `make clean` and `pip install -e .` again. Any changes in the `llama.h` API will require
 changes to the `llama_cpp/llama_cpp.py` file to match the new API (additional changes may be required elsewhere).
 
diff --git a/docker/simple/Dockerfile b/docker/simple/Dockerfile
index 3594df1a50..22b2335a3e 100644
--- a/docker/simple/Dockerfile
+++ b/docker/simple/Dockerfile
@@ -6,9 +6,11 @@ FROM ${IMAGE}
 
 # Re-declare the ARG after FROM
 ARG IMAGE
+ARG CMAKE_ARGS="-DGGML_NATIVE=off"
 
 # Update and upgrade the existing packages 
 RUN apt-get update && apt-get upgrade -y && apt-get install -y --no-install-recommends \
+    git \
     python3 \
     python3-pip \
     ninja-build \
@@ -25,7 +27,7 @@ RUN python3 -m pip install --upgrade pip
 
 RUN python3 -m pip install --upgrade pip pytest cmake scikit-build setuptools fastapi uvicorn sse-starlette pydantic-settings starlette-context
 
-RUN pip install llama-cpp-python --verbose;
+RUN CC=gcc CXX=g++ CMAKE_ARGS="${CMAKE_ARGS}" pip install . --verbose
 
 # Set environment variable for the host
 ENV HOST=0.0.0.0
diff --git a/docs/server.md b/docs/server.md
index cd6f86c513..9c09a1f1cf 100644
--- a/docs/server.md
+++ b/docs/server.md
@@ -22,6 +22,15 @@ The server can then be started by running the following command:
 python3 -m llama_cpp.server --model <model_path>
 ```
 
+You can also pass chat-template kwargs at model load time from the CLI:
+
+```bash
+python3 -m llama_cpp.server \
+  --model <model_path> \
+  --chat_format chatml \
+  --chat_template_kwargs '{"enable_thinking": true}'
+```
+
 ### Server options
 
 For a full list of options, run:
@@ -147,6 +156,22 @@ The server supports routing requests to multiple models based on the `model` par
 
 At the moment only a single model is loaded into memory at, the server will automatically load and unload models as needed.
 
+For a single-model config, `chat_template_kwargs` can be set directly on the model entry:
+
+```json
+{
+    "models": [
+        {
+            "model": "models/Qwen3.5-0.8B/qwen3.5-0.8b-q8_0.gguf",
+            "chat_format": "chatml",
+            "chat_template_kwargs": {
+                "enable_thinking": true
+            }
+        }
+    ]
+}
+```
+
 ```json
 {
     "host": "0.0.0.0",
diff --git a/examples/batch-processing/server.py b/examples/batch-processing/server.py
index 0b36746f91..2b6fa759e0 100644
--- a/examples/batch-processing/server.py
+++ b/examples/batch-processing/server.py
@@ -6,14 +6,14 @@
 # path = b"../../models/Qwen1.5-0.5B-Chat-GGUF/qwen1_5-0_5b-chat-q8_0.gguf"
 
 # model_params = llama_cpp.llama_model_default_params()
-# model = llama_cpp.llama_load_model_from_file(path, model_params)
+# model = llama_cpp.llama_model_load_from_file(path, model_params)
 
 # if model is None:
 #     raise RuntimeError(f"Failed to load model from file: {path}")
 
 
 # ctx_params = llama_cpp.llama_context_default_params()
-# ctx = llama_cpp.llama_new_context_with_model(model, ctx_params)
+# ctx = llama_cpp.llama_init_from_model(model, ctx_params)
 
 # if ctx is None:
 #     raise RuntimeError("Failed to create context")
diff --git a/examples/colab/Gemma4-12B-QAT.ipynb b/examples/colab/Gemma4-12B-QAT.ipynb
new file mode 100644
index 0000000000..2cced5a89b
--- /dev/null
+++ b/examples/colab/Gemma4-12B-QAT.ipynb
@@ -0,0 +1,162 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 5,
+ "metadata": {
+  "colab": {
+   "provenance": [],
+   "gpuType": "T4"
+  },
+  "accelerator": "GPU",
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Gemma 4 12B QAT Multimodal Chat\n",
+    "\n",
+    "Run the Gemma 4 12B QAT GGUF model locally in Google Colab with the pre-built CUDA wheel for `llama-cpp-python`.\n",
+    "\n",
+    "Use a GPU runtime before running this notebook: **Runtime > Change runtime type > T4 GPU**.\n",
+    "\n",
+    "Current Colab CUDA images commonly provide CUDA 12 user-space libraries even when `nvidia-smi` reports a CUDA 13-capable driver, so this notebook installs the `cu125` wheel. If your runtime provides `libcudart.so.13`, switch the wheel index URL to `/whl/cu130`.\n"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install --no-cache-dir --upgrade --force-reinstall \\\n",
+    "  \"huggingface-hub>=0.23.0\" \\\n",
+    "  llama-cpp-python \\\n",
+    "  --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu125\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from llama_cpp import Llama\n",
+    "from llama_cpp.llama_chat_format import Gemma4ChatHandler\n",
+    "\n",
+    "MODEL_REPO = \"unsloth/gemma-4-12B-it-qat-GGUF\"\n",
+    "MODEL_FILE = \"gemma-4-12B-it-qat-UD-Q4_K_XL.gguf\"\n",
+    "MMPROJ_FILE = \"mmproj-F16.gguf\"\n",
+    "\n",
+    "chat_handler = Gemma4ChatHandler.from_pretrained(\n",
+    "    repo_id=MODEL_REPO,\n",
+    "    filename=MMPROJ_FILE,\n",
+    "    verbose=False,\n",
+    ")\n",
+    "\n",
+    "llm = Llama.from_pretrained(\n",
+    "    repo_id=MODEL_REPO,\n",
+    "    filename=MODEL_FILE,\n",
+    "    chat_handler=chat_handler,\n",
+    "    n_gpu_layers=-1,\n",
+    "    n_ctx=8192,\n",
+    "    flash_attn=True,\n",
+    "    verbose=False,\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import Image, display\n",
+    "\n",
+    "IMAGE_URL = \"https://raw.githubusercontent.com/ggml-org/llama.cpp/master/tools/mtmd/test-1.jpeg\"\n",
+    "\n",
+    "display(Image(url=IMAGE_URL, width=320))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = llm.create_chat_completion(\n",
+    "    messages=[\n",
+    "        {\n",
+    "            \"role\": \"user\",\n",
+    "            \"content\": [\n",
+    "                {\"type\": \"text\", \"text\": \"Describe this image in one concise sentence.\"},\n",
+    "                {\"type\": \"image_url\", \"image_url\": {\"url\": IMAGE_URL}},\n",
+    "            ],\n",
+    "        }\n",
+    "    ],\n",
+    "    max_tokens=128,\n",
+    "    temperature=0.2,\n",
+    ")\n",
+    "\n",
+    "print(response[\"choices\"][0][\"message\"][\"content\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tools = [\n",
+    "    {\n",
+    "        \"type\": \"function\",\n",
+    "        \"function\": {\n",
+    "            \"name\": \"record_image_observation\",\n",
+    "            \"description\": \"Record a structured observation about an image.\",\n",
+    "            \"parameters\": {\n",
+    "                \"type\": \"object\",\n",
+    "                \"properties\": {\n",
+    "                    \"main_subject\": {\"type\": \"string\"},\n",
+    "                    \"setting\": {\"type\": \"string\"},\n",
+    "                    \"notable_details\": {\n",
+    "                        \"type\": \"array\",\n",
+    "                        \"items\": {\"type\": \"string\"},\n",
+    "                    },\n",
+    "                    \"confidence\": {\"type\": \"number\", \"minimum\": 0, \"maximum\": 1},\n",
+    "                },\n",
+    "                \"required\": [\"main_subject\", \"setting\", \"notable_details\", \"confidence\"],\n",
+    "            },\n",
+    "        },\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "response = llm.create_chat_completion(\n",
+    "    messages=[\n",
+    "        {\n",
+    "            \"role\": \"user\",\n",
+    "            \"content\": [\n",
+    "                {\"type\": \"text\", \"text\": \"Use the provided tool to record a structured observation for this image.\"},\n",
+    "                {\"type\": \"image_url\", \"image_url\": {\"url\": IMAGE_URL}},\n",
+    "            ],\n",
+    "        }\n",
+    "    ],\n",
+    "    tools=tools,\n",
+    "    tool_choice={\"type\": \"function\", \"function\": {\"name\": \"record_image_observation\"}},\n",
+    "    max_tokens=256,\n",
+    "    temperature=0.0,\n",
+    ")\n",
+    "\n",
+    "message = response[\"choices\"][0][\"message\"]\n",
+    "print(message.get(\"tool_calls\", message.get(\"content\")))\n"
+   ]
+  }
+ ]
+}
diff --git a/examples/colab/notebook.ipynb b/examples/colab/notebook.ipynb
new file mode 100644
index 0000000000..8e258b9c03
--- /dev/null
+++ b/examples/colab/notebook.ipynb
@@ -0,0 +1,131 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 5,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "accelerator": "GPU",
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Gemma 4 12B Multimodal Chat\n",
+        "\n",
+        "Run Gemma 4 12B locally in Google Colab with the pre-built CUDA wheel for `llama-cpp-python`.\n",
+        "\n",
+        "Use a GPU runtime before running this notebook: **Runtime > Change runtime type > T4 GPU**.\n",
+        "\n",
+        "Current Colab CUDA images commonly provide CUDA 12 user-space libraries even when `nvidia-smi` reports a CUDA 13-capable driver, so this notebook installs the `cu125` wheel. If your runtime provides `libcudart.so.13`, switch the wheel index URL to `/whl/cu130`.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!pip install --no-cache-dir --upgrade --force-reinstall \\\n",
+        "  \"huggingface-hub>=0.23.0\" \\\n",
+        "  llama-cpp-python \\\n",
+        "  --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu125\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from llama_cpp import Llama\n",
+        "from llama_cpp.llama_chat_format import Gemma4ChatHandler\n",
+        "\n",
+        "MODEL_REPO = \"ggml-org/gemma-4-12B-it-GGUF\"\n",
+        "MODEL_FILE = \"gemma-4-12B-it-Q4_K_M.gguf\"\n",
+        "MMPROJ_FILE = \"mmproj-gemma-4-12B-it-Q8_0.gguf\"\n",
+        "\n",
+        "chat_handler = Gemma4ChatHandler.from_pretrained(\n",
+        "    repo_id=MODEL_REPO,\n",
+        "    filename=MMPROJ_FILE,\n",
+        "    verbose=False,\n",
+        ")\n",
+        "\n",
+        "llm = Llama.from_pretrained(\n",
+        "    repo_id=MODEL_REPO,\n",
+        "    filename=MODEL_FILE,\n",
+        "    chat_handler=chat_handler,\n",
+        "    n_gpu_layers=-1,\n",
+        "    n_ctx=8192,\n",
+        "    flash_attn=True,\n",
+        "    verbose=False,\n",
+        ")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "response = llm.create_chat_completion(\n",
+        "    messages=[\n",
+        "        {\n",
+        "            \"role\": \"user\",\n",
+        "            \"content\": \"What is the capital of France? Answer in one sentence.\",\n",
+        "        }\n",
+        "    ],\n",
+        "    max_tokens=32,\n",
+        "    temperature=0.0,\n",
+        ")\n",
+        "\n",
+        "print(response[\"choices\"][0][\"message\"][\"content\"])\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from IPython.display import Image, display\n",
+        "\n",
+        "IMAGE_URL = \"https://raw.githubusercontent.com/ggml-org/llama.cpp/master/tools/mtmd/test-1.jpeg\"\n",
+        "\n",
+        "display(Image(url=IMAGE_URL, width=320))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "response = llm.create_chat_completion(\n",
+        "    messages=[\n",
+        "        {\n",
+        "            \"role\": \"user\",\n",
+        "            \"content\": [\n",
+        "                {\"type\": \"text\", \"text\": \"Describe this image in one concise sentence.\"},\n",
+        "                {\"type\": \"image_url\", \"image_url\": {\"url\": IMAGE_URL}},\n",
+        "            ],\n",
+        "        }\n",
+        "    ],\n",
+        "    max_tokens=128,\n",
+        "    temperature=0.2,\n",
+        ")\n",
+        "\n",
+        "print(response[\"choices\"][0][\"message\"][\"content\"])\n"
+      ]
+    }
+  ]
+}
diff --git a/examples/gradio_chat/local.py b/examples/gradio_chat/local.py
index e16bf234ab..871d8b09b5 100644
--- a/examples/gradio_chat/local.py
+++ b/examples/gradio_chat/local.py
@@ -4,10 +4,10 @@
 import gradio as gr
 
 llama = llama_cpp.Llama.from_pretrained(
-    repo_id="Qwen/Qwen1.5-0.5B-Chat-GGUF",
-    filename="*q8_0.gguf",
+    repo_id="lmstudio-community/Qwen3.5-0.8B-GGUF",
+    filename="*Q8_0.gguf",
     tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained(
-        "Qwen/Qwen1.5-0.5B"
+        "Qwen/Qwen3.5-0.8B"
     ),
     verbose=False,
 )
diff --git a/examples/hf_pull/main.py b/examples/hf_pull/main.py
index dfed17516d..a9ca424d19 100644
--- a/examples/hf_pull/main.py
+++ b/examples/hf_pull/main.py
@@ -3,10 +3,10 @@
 
 
 llama = llama_cpp.Llama.from_pretrained(
-    repo_id="Qwen/Qwen1.5-0.5B-Chat-GGUF",
-    filename="*q8_0.gguf",
+    repo_id="lmstudio-community/Qwen3.5-0.8B-GGUF",
+    filename="*Q8_0.gguf",
     tokenizer=llama_cpp.llama_tokenizer.LlamaHFTokenizer.from_pretrained(
-        "Qwen/Qwen1.5-0.5B"
+        "Qwen/Qwen3.5-0.8B"
     ),
     verbose=False,
 )
diff --git a/examples/low_level_api/low_level_api_chat_cpp.py b/examples/low_level_api/low_level_api_chat_cpp.py
index 39081be17a..20f7a158ac 100644
--- a/examples/low_level_api/low_level_api_chat_cpp.py
+++ b/examples/low_level_api/low_level_api_chat_cpp.py
@@ -79,19 +79,22 @@ def __init__(self, params: GptParams) -> None:
         self.lparams.use_mlock = self.params.use_mlock
         self.lparams.use_mmap = self.params.use_mmap
 
-        self.model = llama_cpp.llama_load_model_from_file(
+        self.model = llama_cpp.llama_model_load_from_file(
             self.params.model.encode("utf8"), self.lparams
         )
+        self.vocab = llama_cpp.llama_model_get_vocab(self.model)
 
         # Context Params.
         self.cparams = llama_cpp.llama_context_default_params()
 
-        self.ctx = llama_cpp.llama_new_context_with_model(self.model, self.cparams)
+        self.ctx = llama_cpp.llama_init_from_model(self.model, self.cparams)
         if not self.ctx:
             raise RuntimeError(f"error: failed to load model '{self.params.model}'")
 
         if self.params.ignore_eos:
-            self.params.logit_bias[llama_cpp.llama_token_eos()] = -float("inf")
+            self.params.logit_bias[llama_cpp.llama_vocab_eos(self.vocab)] = -float(
+                "inf"
+            )
 
         if len(self.params.lora_adapter) > 0:
             if (
@@ -153,7 +156,7 @@ def __init__(self, params: GptParams) -> None:
                 _session_tokens = (llama_cpp.llama_token * (self.params.n_ctx))()
                 _n_token_count_out = llama_cpp.c_size_t()
                 if (
-                    llama_cpp.llama_load_session_file(
+                    llama_cpp.llama_state_load_file(
                         self.ctx,
                         self.params.path_session.encode("utf8"),
                         _session_tokens,
@@ -314,7 +317,7 @@ def __init__(self, params: GptParams) -> None:
     def _tokenize(self, prompt, bos=True):
         _arr = (llama_cpp.llama_token * ((len(prompt) + 1) * 4))()
         _n = llama_cpp.llama_tokenize(
-            self.model,
+            self.vocab,
             prompt.encode("utf8", errors="ignore"),
             len(prompt),
             _arr,
@@ -406,7 +409,7 @@ def generate(self):
             if len(self.embd_inp) <= self.input_consumed:  # && !is_interacting
                 # out of user input, sample next token
                 top_k = (
-                    llama_cpp.llama_n_vocab(self.ctx)
+                    llama_cpp.llama_vocab_n_tokens(self.vocab)
                     if self.params.top_k <= 0
                     else self.params.top_k
                 )
@@ -419,7 +422,7 @@ def generate(self):
                 # optionally save the session on first sample (for faster prompt loading next time)
                 if len(self.params.path_session) > 0 and self.need_to_save_session:
                     self.need_to_save_session = False
-                    llama_cpp.llama_save_session_file(
+                    llama_cpp.llama_state_save_file(
                         self.ctx,
                         self.params.path_session.encode("utf8"),
                         (llama_cpp.llama_token * len(self.session_tokens))(
@@ -431,7 +434,7 @@ def generate(self):
                 id = 0
 
                 logits = llama_cpp.llama_get_logits(self.ctx)
-                n_vocab = llama_cpp.llama_n_vocab(self.model)
+                n_vocab = llama_cpp.llama_vocab_n_tokens(self.vocab)
 
                 # Apply params.logit_bias map
                 for key, value in self.params.logit_bias.items():
@@ -448,7 +451,7 @@ def generate(self):
                 )
 
                 # Apply penalties
-                nl_logit = logits[llama_cpp.llama_token_nl(self.ctx)]
+                nl_logit = logits[llama_cpp.llama_vocab_nl(self.vocab)]
                 last_n_repeat = min(len(self.last_n_tokens), repeat_last_n, self.n_ctx)
 
                 _arr = (llama_cpp.llama_token * last_n_repeat)(
@@ -470,7 +473,7 @@ def generate(self):
                 # 	last_n_repeat, llama_cpp.c_float(self.params.frequency_penalty), llama_cpp.c_float(self.params.presence_penalty))
 
                 if not self.params.penalize_nl:
-                    logits[llama_cpp.llama_token_nl()] = nl_logit
+                    logits[llama_cpp.llama_vocab_nl(self.vocab)] = nl_logit
 
                 if self.params.temp <= 0:
                     # Greedy sampling
@@ -539,7 +542,7 @@ def generate(self):
 
                 # replace end of text token with newline token when in interactive mode
                 if (
-                    id == llama_cpp.llama_token_eos(self.ctx)
+                    id == llama_cpp.llama_vocab_eos(self.vocab)
                     and self.params.interactive
                     and not self.params.instruct
                 ):
@@ -599,8 +602,8 @@ def generate(self):
                     break
 
             # end of text token
-            if len(self.embd) > 0 and self.embd[-1] == llama_cpp.llama_token_eos(
-                self.ctx
+            if len(self.embd) > 0 and self.embd[-1] == llama_cpp.llama_vocab_eos(
+                self.vocab
             ):
                 if not self.params.instruct:
                     for i in self.llama_token_eot:
@@ -636,7 +639,7 @@ def token_to_str(self, token_id: int) -> bytes:
         size = 32
         buffer = (ctypes.c_char * size)()
         n = llama_cpp.llama_token_to_piece(
-            self.model, llama_cpp.llama_token(token_id), buffer, size
+            self.vocab, llama_cpp.llama_token(token_id), buffer, size, 0, False
         )
         assert n <= size
         return bytes(buffer[:n])
diff --git a/examples/low_level_api/low_level_api_llama_cpp.py b/examples/low_level_api/low_level_api_llama_cpp.py
index ba3545771d..9fb3424ec0 100644
--- a/examples/low_level_api/low_level_api_llama_cpp.py
+++ b/examples/low_level_api/low_level_api_llama_cpp.py
@@ -4,7 +4,7 @@
 
 import llama_cpp
 
-llama_cpp.llama_backend_init(numa=False)
+llama_cpp.llama_backend_init()
 
 N_THREADS = multiprocessing.cpu_count()
 MODEL_PATH = os.environ.get("MODEL", "../models/7B/ggml-model.bin")
@@ -13,8 +13,9 @@
 
 lparams = llama_cpp.llama_model_default_params()
 cparams = llama_cpp.llama_context_default_params()
-model = llama_cpp.llama_load_model_from_file(MODEL_PATH.encode("utf-8"), lparams)
-ctx = llama_cpp.llama_new_context_with_model(model, cparams)
+model = llama_cpp.llama_model_load_from_file(MODEL_PATH.encode("utf-8"), lparams)
+ctx = llama_cpp.llama_init_from_model(model, cparams)
+vocab = llama_cpp.llama_model_get_vocab(model)
 
 # determine the required inference memory per token:
 tmp = [0, 1, 2, 3]
@@ -28,13 +29,13 @@
 
 embd_inp = (llama_cpp.llama_token * (len(prompt) + 1))()
 n_of_tok = llama_cpp.llama_tokenize(
-    model=model,
-    text=bytes(str(prompt), "utf-8"),
-    text_len=len(embd_inp),
+    vocab=vocab,
+    text=prompt,
+    text_len=len(prompt),
     tokens=embd_inp,
-    n_max_tokens=len(embd_inp),
-    add_bos=False,
-    special=False,
+    n_tokens_max=len(embd_inp),
+    add_special=False,
+    parse_special=False,
 )
 embd_inp = embd_inp[:n_of_tok]
 
@@ -70,7 +71,7 @@
     embd = []
     if len(embd_inp) <= input_consumed:
         logits = llama_cpp.llama_get_logits(ctx)
-        n_vocab = llama_cpp.llama_n_vocab(model)
+        n_vocab = llama_cpp.llama_vocab_n_tokens(vocab)
 
         _arr = (llama_cpp.llama_token_data * n_vocab)(
             *[
@@ -114,7 +115,7 @@
             size = 32
             buffer = (ctypes.c_char * size)()
             n = llama_cpp.llama_token_to_piece(
-                model, llama_cpp.llama_token(id), buffer, size
+                vocab, llama_cpp.llama_token(id), buffer, size, 0, False
             )
             assert n <= size
             print(
@@ -123,7 +124,7 @@
                 flush=True,
             )
 
-    if len(embd) > 0 and embd[-1] == llama_cpp.llama_token_eos(ctx):
+    if len(embd) > 0 and embd[-1] == llama_cpp.llama_vocab_eos(vocab):
         break
 
 print()
@@ -131,3 +132,4 @@
 llama_cpp.llama_print_timings(ctx)
 
 llama_cpp.llama_free(ctx)
+llama_cpp.llama_model_free(model)
diff --git a/examples/notebooks/Batching.ipynb b/examples/notebooks/Batching.ipynb
index 73b28c7444..0b36cd9c3a 100644
--- a/examples/notebooks/Batching.ipynb
+++ b/examples/notebooks/Batching.ipynb
@@ -6,6 +6,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import ctypes\n",
     "import llama_cpp"
    ]
   },
@@ -13,18 +14,7 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "ggml_init_cublas: GGML_CUDA_FORCE_MMQ:   no\n",
-      "ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes\n",
-      "ggml_init_cublas: found 1 CUDA devices:\n",
-      "  Device 0: NVIDIA GeForce RTX 2060, compute capability 7.5\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "llama_cpp.llama_backend_init(numa=False)"
    ]
@@ -38,363 +28,104 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "llama_model_loader: loaded meta data with 16 key-value pairs and 291 tensors from ../../models/mistral-7b-v0.1-GGUF/ggml-model-Q4_K.gguf (version GGUF V2)\n",
-      "llama_model_loader: - tensor    0:                token_embd.weight q4_K     [  4096, 32000,     1,     1 ]\n",
-      "llama_model_loader: - tensor    1:               output_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor    2:                    output.weight q6_K     [  4096, 32000,     1,     1 ]\n",
-      "llama_model_loader: - tensor    3:              blk.0.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor    4:              blk.0.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor    5:              blk.0.attn_v.weight q6_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor    6:         blk.0.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor    7:            blk.0.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor    8:            blk.0.ffn_down.weight q6_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor    9:              blk.0.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor   10:           blk.0.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor   11:            blk.0.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor   12:              blk.1.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   13:              blk.1.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor   14:              blk.1.attn_v.weight q6_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor   15:         blk.1.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   16:            blk.1.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor   17:            blk.1.ffn_down.weight q6_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   18:              blk.1.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor   19:           blk.1.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor   20:            blk.1.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor   21:              blk.2.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   22:              blk.2.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor   23:              blk.2.attn_v.weight q6_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor   24:         blk.2.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   25:            blk.2.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor   26:            blk.2.ffn_down.weight q6_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   27:              blk.2.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor   28:           blk.2.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor   29:            blk.2.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor   30:              blk.3.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   31:              blk.3.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor   32:              blk.3.attn_v.weight q6_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor   33:         blk.3.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   34:            blk.3.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor   35:            blk.3.ffn_down.weight q6_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   36:              blk.3.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor   37:           blk.3.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor   38:            blk.3.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor   39:              blk.4.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   40:              blk.4.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor   41:              blk.4.attn_v.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor   42:         blk.4.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   43:            blk.4.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor   44:            blk.4.ffn_down.weight q4_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   45:              blk.4.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor   46:           blk.4.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor   47:            blk.4.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor   48:              blk.5.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   49:              blk.5.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor   50:              blk.5.attn_v.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor   51:         blk.5.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   52:            blk.5.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor   53:            blk.5.ffn_down.weight q4_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   54:              blk.5.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor   55:           blk.5.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor   56:            blk.5.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor   57:              blk.6.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   58:              blk.6.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor   59:              blk.6.attn_v.weight q6_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor   60:         blk.6.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   61:            blk.6.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor   62:            blk.6.ffn_down.weight q6_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   63:              blk.6.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor   64:           blk.6.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor   65:            blk.6.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor   66:              blk.7.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   67:              blk.7.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor   68:              blk.7.attn_v.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor   69:         blk.7.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   70:            blk.7.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor   71:            blk.7.ffn_down.weight q4_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   72:              blk.7.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor   73:           blk.7.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor   74:            blk.7.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor   75:              blk.8.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   76:              blk.8.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor   77:              blk.8.attn_v.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor   78:         blk.8.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   79:            blk.8.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor   80:            blk.8.ffn_down.weight q4_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   81:              blk.8.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor   82:           blk.8.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor   83:            blk.8.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor   84:              blk.9.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   85:              blk.9.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor   86:              blk.9.attn_v.weight q6_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor   87:         blk.9.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   88:            blk.9.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor   89:            blk.9.ffn_down.weight q6_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   90:              blk.9.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor   91:           blk.9.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor   92:            blk.9.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor   93:             blk.10.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   94:             blk.10.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor   95:             blk.10.attn_v.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor   96:        blk.10.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   97:           blk.10.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor   98:           blk.10.ffn_down.weight q4_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor   99:             blk.10.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  100:          blk.10.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  101:           blk.10.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  102:             blk.11.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  103:             blk.11.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  104:             blk.11.attn_v.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  105:        blk.11.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  106:           blk.11.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  107:           blk.11.ffn_down.weight q4_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  108:             blk.11.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  109:          blk.11.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  110:           blk.11.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  111:             blk.12.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  112:             blk.12.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  113:             blk.12.attn_v.weight q6_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  114:        blk.12.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  115:           blk.12.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  116:           blk.12.ffn_down.weight q6_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  117:             blk.12.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  118:          blk.12.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  119:           blk.12.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  120:             blk.13.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  121:             blk.13.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  122:             blk.13.attn_v.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  123:        blk.13.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  124:           blk.13.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  125:           blk.13.ffn_down.weight q4_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  126:             blk.13.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  127:          blk.13.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  128:           blk.13.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  129:             blk.14.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  130:             blk.14.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  131:             blk.14.attn_v.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  132:        blk.14.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  133:           blk.14.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  134:           blk.14.ffn_down.weight q4_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  135:             blk.14.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  136:          blk.14.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  137:           blk.14.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  138:             blk.15.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  139:             blk.15.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  140:             blk.15.attn_v.weight q6_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  141:        blk.15.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  142:           blk.15.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  143:           blk.15.ffn_down.weight q6_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  144:             blk.15.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  145:          blk.15.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  146:           blk.15.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  147:             blk.16.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  148:             blk.16.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  149:             blk.16.attn_v.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  150:        blk.16.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  151:           blk.16.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  152:           blk.16.ffn_down.weight q4_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  153:             blk.16.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  154:          blk.16.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  155:           blk.16.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  156:             blk.17.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  157:             blk.17.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  158:             blk.17.attn_v.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  159:        blk.17.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  160:           blk.17.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  161:           blk.17.ffn_down.weight q4_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  162:             blk.17.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  163:          blk.17.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  164:           blk.17.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  165:             blk.18.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  166:             blk.18.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  167:             blk.18.attn_v.weight q6_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  168:        blk.18.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  169:           blk.18.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  170:           blk.18.ffn_down.weight q6_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  171:             blk.18.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  172:          blk.18.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  173:           blk.18.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  174:             blk.19.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  175:             blk.19.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  176:             blk.19.attn_v.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  177:        blk.19.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  178:           blk.19.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  179:           blk.19.ffn_down.weight q4_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  180:             blk.19.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  181:          blk.19.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  182:           blk.19.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  183:             blk.20.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  184:             blk.20.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  185:             blk.20.attn_v.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  186:        blk.20.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  187:           blk.20.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  188:           blk.20.ffn_down.weight q4_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  189:             blk.20.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  190:          blk.20.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  191:           blk.20.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  192:             blk.21.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  193:             blk.21.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  194:             blk.21.attn_v.weight q6_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  195:        blk.21.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  196:           blk.21.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  197:           blk.21.ffn_down.weight q6_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  198:             blk.21.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  199:          blk.21.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  200:           blk.21.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  201:             blk.22.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  202:             blk.22.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  203:             blk.22.attn_v.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  204:        blk.22.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  205:           blk.22.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  206:           blk.22.ffn_down.weight q4_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  207:             blk.22.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  208:          blk.22.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  209:           blk.22.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  210:             blk.23.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  211:             blk.23.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  212:             blk.23.attn_v.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  213:        blk.23.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  214:           blk.23.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  215:           blk.23.ffn_down.weight q4_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  216:             blk.23.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  217:          blk.23.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  218:           blk.23.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  219:             blk.24.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  220:             blk.24.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  221:             blk.24.attn_v.weight q6_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  222:        blk.24.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  223:           blk.24.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  224:           blk.24.ffn_down.weight q6_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  225:             blk.24.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  226:          blk.24.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  227:           blk.24.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  228:             blk.25.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  229:             blk.25.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  230:             blk.25.attn_v.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  231:        blk.25.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  232:           blk.25.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  233:           blk.25.ffn_down.weight q4_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  234:             blk.25.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  235:          blk.25.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  236:           blk.25.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  237:             blk.26.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  238:             blk.26.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  239:             blk.26.attn_v.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  240:        blk.26.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  241:           blk.26.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  242:           blk.26.ffn_down.weight q4_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  243:             blk.26.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  244:          blk.26.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  245:           blk.26.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  246:             blk.27.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  247:             blk.27.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  248:             blk.27.attn_v.weight q6_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  249:        blk.27.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  250:           blk.27.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  251:           blk.27.ffn_down.weight q6_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  252:             blk.27.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  253:          blk.27.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  254:           blk.27.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  255:             blk.28.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  256:             blk.28.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  257:             blk.28.attn_v.weight q6_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  258:        blk.28.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  259:           blk.28.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  260:           blk.28.ffn_down.weight q6_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  261:             blk.28.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  262:          blk.28.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  263:           blk.28.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  264:             blk.29.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  265:             blk.29.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  266:             blk.29.attn_v.weight q6_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  267:        blk.29.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  268:           blk.29.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  269:           blk.29.ffn_down.weight q6_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  270:             blk.29.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  271:          blk.29.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  272:           blk.29.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  273:             blk.30.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  274:             blk.30.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  275:             blk.30.attn_v.weight q6_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  276:        blk.30.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  277:           blk.30.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  278:           blk.30.ffn_down.weight q6_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  279:             blk.30.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  280:          blk.30.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  281:           blk.30.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  282:             blk.31.attn_q.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  283:             blk.31.attn_k.weight q4_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  284:             blk.31.attn_v.weight q6_K     [  4096,  1024,     1,     1 ]\n",
-      "llama_model_loader: - tensor  285:        blk.31.attn_output.weight q4_K     [  4096,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  286:           blk.31.ffn_gate.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  287:           blk.31.ffn_down.weight q6_K     [ 14336,  4096,     1,     1 ]\n",
-      "llama_model_loader: - tensor  288:             blk.31.ffn_up.weight q4_K     [  4096, 14336,     1,     1 ]\n",
-      "llama_model_loader: - tensor  289:          blk.31.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - tensor  290:           blk.31.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
-      "llama_model_loader: - kv   0:                       general.architecture str     \n",
-      "llama_model_loader: - kv   1:                               general.name str     \n",
-      "llama_model_loader: - kv   2:                       llama.context_length u32     \n",
-      "llama_model_loader: - kv   3:                     llama.embedding_length u32     \n",
-      "llama_model_loader: - kv   4:                          llama.block_count u32     \n",
-      "llama_model_loader: - kv   5:                  llama.feed_forward_length u32     \n",
-      "llama_model_loader: - kv   6:                 llama.rope.dimension_count u32     \n",
-      "llama_model_loader: - kv   7:                 llama.attention.head_count u32     \n",
-      "llama_model_loader: - kv   8:              llama.attention.head_count_kv u32     \n",
-      "llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32     \n",
-      "llama_model_loader: - kv  10:                          general.file_type u32     \n",
-      "llama_model_loader: - kv  11:                       tokenizer.ggml.model str     \n",
-      "llama_model_loader: - kv  12:                      tokenizer.ggml.tokens arr     \n",
-      "llama_model_loader: - kv  13:                      tokenizer.ggml.scores arr     \n",
-      "llama_model_loader: - kv  14:                  tokenizer.ggml.token_type arr     \n",
-      "llama_model_loader: - kv  15:               general.quantization_version u32     \n",
+      "llama_model_loader: loaded meta data with 20 key-value pairs and 291 tensors from /workspaces/llama-cpp-python/mistral-7b-v0.1.Q2_K.gguf (version GGUF V2)\n",
+      "llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n",
+      "llama_model_loader: - kv   0:                       general.architecture str              = llama\n",
+      "llama_model_loader: - kv   1:                               general.name str              = mistralai_mistral-7b-v0.1\n",
+      "llama_model_loader: - kv   2:                       llama.context_length u32              = 32768\n",
+      "llama_model_loader: - kv   3:                     llama.embedding_length u32              = 4096\n",
+      "llama_model_loader: - kv   4:                          llama.block_count u32              = 32\n",
+      "llama_model_loader: - kv   5:                  llama.feed_forward_length u32              = 14336\n",
+      "llama_model_loader: - kv   6:                 llama.rope.dimension_count u32              = 128\n",
+      "llama_model_loader: - kv   7:                 llama.attention.head_count u32              = 32\n",
+      "llama_model_loader: - kv   8:              llama.attention.head_count_kv u32              = 8\n",
+      "llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010\n",
+      "llama_model_loader: - kv  10:                       llama.rope.freq_base f32              = 10000.000000\n",
+      "llama_model_loader: - kv  11:                          general.file_type u32              = 10\n",
+      "llama_model_loader: - kv  12:                       tokenizer.ggml.model str              = llama\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "llama_model_loader: - kv  13:                      tokenizer.ggml.tokens arr[str,32000]   = [\"<unk>\", \"<s>\", \"</s>\", \"<0x00>\", \"<...\n",
+      "llama_model_loader: - kv  14:                      tokenizer.ggml.scores arr[f32,32000]   = [0.000000, 0.000000, 0.000000, 0.0000...\n",
+      "llama_model_loader: - kv  15:                  tokenizer.ggml.token_type arr[i32,32000]   = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...\n",
+      "llama_model_loader: - kv  16:                tokenizer.ggml.bos_token_id u32              = 1\n",
+      "llama_model_loader: - kv  17:                tokenizer.ggml.eos_token_id u32              = 2\n",
+      "llama_model_loader: - kv  18:            tokenizer.ggml.unknown_token_id u32              = 0\n",
+      "llama_model_loader: - kv  19:               general.quantization_version u32              = 2\n",
       "llama_model_loader: - type  f32:   65 tensors\n",
-      "llama_model_loader: - type q4_K:  193 tensors\n",
-      "llama_model_loader: - type q6_K:   33 tensors\n",
-      "llm_load_vocab: special tokens definition check successful ( 259/32000 ).\n",
+      "llama_model_loader: - type q2_K:   65 tensors\n",
+      "llama_model_loader: - type q3_K:  160 tensors\n",
+      "llama_model_loader: - type q6_K:    1 tensors\n",
+      "llm_load_vocab: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect\n",
+      "llm_load_vocab: special tokens cache size = 3\n",
+      "llm_load_vocab: token to piece cache size = 0.1637 MB\n",
       "llm_load_print_meta: format           = GGUF V2\n",
       "llm_load_print_meta: arch             = llama\n",
       "llm_load_print_meta: vocab type       = SPM\n",
       "llm_load_print_meta: n_vocab          = 32000\n",
       "llm_load_print_meta: n_merges         = 0\n",
-      "llm_load_print_meta: n_ctx_train      = 4096\n",
+      "llm_load_print_meta: vocab_only       = 0\n",
+      "llm_load_print_meta: n_ctx_train      = 32768\n",
       "llm_load_print_meta: n_embd           = 4096\n",
+      "llm_load_print_meta: n_layer          = 32\n",
       "llm_load_print_meta: n_head           = 32\n",
       "llm_load_print_meta: n_head_kv        = 8\n",
-      "llm_load_print_meta: n_layer          = 32\n",
       "llm_load_print_meta: n_rot            = 128\n",
+      "llm_load_print_meta: n_swa            = 0\n",
+      "llm_load_print_meta: n_embd_head_k    = 128\n",
+      "llm_load_print_meta: n_embd_head_v    = 128\n",
       "llm_load_print_meta: n_gqa            = 4\n",
+      "llm_load_print_meta: n_embd_k_gqa     = 1024\n",
+      "llm_load_print_meta: n_embd_v_gqa     = 1024\n",
       "llm_load_print_meta: f_norm_eps       = 0.0e+00\n",
       "llm_load_print_meta: f_norm_rms_eps   = 1.0e-05\n",
       "llm_load_print_meta: f_clamp_kqv      = 0.0e+00\n",
       "llm_load_print_meta: f_max_alibi_bias = 0.0e+00\n",
+      "llm_load_print_meta: f_logit_scale    = 0.0e+00\n",
       "llm_load_print_meta: n_ff             = 14336\n",
+      "llm_load_print_meta: n_expert         = 0\n",
+      "llm_load_print_meta: n_expert_used    = 0\n",
+      "llm_load_print_meta: causal attn      = 1\n",
+      "llm_load_print_meta: pooling type     = 0\n",
+      "llm_load_print_meta: rope type        = 0\n",
+      "llm_load_print_meta: rope scaling     = linear\n",
       "llm_load_print_meta: freq_base_train  = 10000.0\n",
       "llm_load_print_meta: freq_scale_train = 1\n",
+      "llm_load_print_meta: n_ctx_orig_yarn  = 32768\n",
+      "llm_load_print_meta: rope_finetuned   = unknown\n",
+      "llm_load_print_meta: ssm_d_conv       = 0\n",
+      "llm_load_print_meta: ssm_d_inner      = 0\n",
+      "llm_load_print_meta: ssm_d_state      = 0\n",
+      "llm_load_print_meta: ssm_dt_rank      = 0\n",
+      "llm_load_print_meta: ssm_dt_b_c_rms   = 0\n",
       "llm_load_print_meta: model type       = 7B\n",
-      "llm_load_print_meta: model ftype      = mostly Q4_K - Medium\n",
+      "llm_load_print_meta: model ftype      = Q2_K - Medium\n",
       "llm_load_print_meta: model params     = 7.24 B\n",
-      "llm_load_print_meta: model size       = 4.07 GiB (4.83 BPW) \n",
-      "llm_load_print_meta: general.name   = LLaMA v2\n",
-      "llm_load_print_meta: BOS token = 1 '<s>'\n",
-      "llm_load_print_meta: EOS token = 2 '</s>'\n",
-      "llm_load_print_meta: UNK token = 0 '<unk>'\n",
-      "llm_load_print_meta: LF token  = 13 '<0x0A>'\n",
-      "llm_load_tensors: ggml ctx size =    0.10 MB\n",
-      "llm_load_tensors: using CUDA for GPU acceleration\n",
-      "llm_load_tensors: mem required  =   70.41 MB\n",
-      "llm_load_tensors: offloading 32 repeating layers to GPU\n",
-      "llm_load_tensors: offloading non-repeating layers to GPU\n",
-      "llm_load_tensors: offloaded 35/35 layers to GPU\n",
-      "llm_load_tensors: VRAM used: 4095.05 MB\n",
-      ".................................................................................................\n"
+      "llm_load_print_meta: model size       = 2.87 GiB (3.41 BPW) \n",
+      "llm_load_print_meta: general.name     = mistralai_mistral-7b-v0.1\n",
+      "llm_load_print_meta: BOS token        = 1 '<s>'\n",
+      "llm_load_print_meta: EOS token        = 2 '</s>'\n",
+      "llm_load_print_meta: UNK token        = 0 '<unk>'\n",
+      "llm_load_print_meta: LF token         = 13 '<0x0A>'\n",
+      "llm_load_print_meta: EOG token        = 2 '</s>'\n",
+      "llm_load_print_meta: max token length = 48\n",
+      "llm_load_tensors: ggml ctx size =    0.14 MiB\n",
+      "llm_load_tensors:        CPU buffer size =  2939.57 MiB\n",
+      "..................................................................................................\n"
      ]
     }
    ],
    "source": [
     "params = llama_cpp.llama_model_default_params()\n",
     "params.n_gpu_layers = 35\n",
-    "model = llama_cpp.llama_load_model_from_file(\n",
-    "    b\"../../models/mistral-7b-v0.1-GGUF/ggml-model-Q4_K.gguf\", params=params\n",
-    ")  # Update this to whatever"
+    "model = llama_cpp.llama_model_load_from_file(\n",
+    "    b\"/workspaces/llama-cpp-python/mistral-7b-v0.1.Q2_K.gguf\", params\n",
+    ")  # Update this to whatever\n",
+    "vocab = llama_cpp.llama_model_get_vocab(model)"
    ]
   },
   {
@@ -406,7 +137,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1, 1014, 2936, 9060, 285, 1142]\n",
+      "[1, 415, 2936, 9060, 285, 1142]\n",
       "58\n"
      ]
     }
@@ -419,7 +150,7 @@
     "\n",
     "tokens = (llama_cpp.llama_token * n_ctx)()\n",
     "tokens_len = llama_cpp.llama_tokenize(\n",
-    "    model, prompt, len(prompt), tokens, len(tokens), True, True\n",
+    "    vocab, prompt, len(prompt), tokens, len(tokens), True, True\n",
     ")\n",
     "print(tokens[:tokens_len])\n",
     "\n",
@@ -436,17 +167,18 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "llama_new_context_with_model: n_ctx      = 58\n",
+      "llama_new_context_with_model: n_ctx      = 64\n",
+      "llama_new_context_with_model: n_batch    = 32\n",
+      "llama_new_context_with_model: n_ubatch   = 32\n",
+      "llama_new_context_with_model: flash_attn = 0\n",
       "llama_new_context_with_model: freq_base  = 10000.0\n",
       "llama_new_context_with_model: freq_scale = 1\n",
-      "llama_kv_cache_init: offloading v cache to GPU\n",
-      "llama_kv_cache_init: offloading k cache to GPU\n",
-      "llama_kv_cache_init: VRAM kv self = 7.25 MB\n",
-      "llama_new_context_with_model: kv self size  =    7.25 MB\n",
-      "llama_build_graph: non-view tensors processed: 740/740\n",
-      "llama_new_context_with_model: compute buffer total size = 10.63 MB\n",
-      "llama_new_context_with_model: VRAM scratch buffer: 4.51 MB\n",
-      "llama_new_context_with_model: total VRAM used: 4106.81 MB (model: 4095.05 MB, context: 11.76 MB)\n"
+      "llama_kv_cache_init:        CPU KV buffer size =     8.00 MiB\n",
+      "llama_new_context_with_model: KV self size  =    8.00 MiB, K (f16):    4.00 MiB, V (f16):    4.00 MiB\n",
+      "llama_new_context_with_model:        CPU  output buffer size =     0.12 MiB\n",
+      "llama_new_context_with_model:        CPU compute buffer size =     5.01 MiB\n",
+      "llama_new_context_with_model: graph nodes  = 1030\n",
+      "llama_new_context_with_model: graph splits = 1\n"
      ]
     }
    ],
@@ -457,7 +189,7 @@
     "ctx_params.n_batch = max(n_len, n_parallel)\n",
     "ctx_params.n_threads = 1\n",
     "ctx_params.n_threads_batch = 1\n",
-    "ctx = llama_cpp.llama_new_context_with_model(model, ctx_params)"
+    "ctx = llama_cpp.llama_init_from_model(model, ctx_params)"
    ]
   },
   {
@@ -476,7 +208,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import ctypes\n",
+    "\n",
     "\n",
     "batch.n_tokens = tokens_len\n",
     "for i in range(tokens_len):\n",
@@ -502,10 +234,35 @@
     "    llama_cpp.llama_kv_cache_seq_cp(ctx, 0, i, 0, batch.n_tokens)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Initialize sampler chain with default parameters\n",
+    "sparams = llama_cpp.llama_sampler_chain_default_params()\n",
+    "sampler_chain = llama_cpp.llama_sampler_chain_init(sparams)\n",
+    "\n",
+    "# Add top_k, top_p, temperature, and final distribution-based sampler\n",
+    "llama_cpp.llama_sampler_chain_add(sampler_chain, llama_cpp.llama_sampler_init_top_k(40))\n",
+    "llama_cpp.llama_sampler_chain_add(sampler_chain, llama_cpp.llama_sampler_init_top_p(0.9, 1))\n",
+    "llama_cpp.llama_sampler_chain_add(sampler_chain, llama_cpp.llama_sampler_init_temp(0.4))\n",
+    "llama_cpp.llama_sampler_chain_add(sampler_chain, llama_cpp.llama_sampler_init_dist(1234))  # Final \"dist\" sampler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -514,61 +271,59 @@
       "7\n",
       "[' j', ' jumped']\n",
       "8\n",
-      "[' jumps', ' jumped over']\n",
+      "[' j over', ' jumped over']\n",
       "9\n",
-      "[' jumps over', ' jumped over the']\n",
+      "[' j over the', ' jumped over the']\n",
       "10\n",
-      "[' jumps over the', ' jumped over the lazy']\n",
+      "[' j over the lazy', ' jumped over the lazy']\n",
       "11\n",
-      "[' jumps over the lazy', ' jumped over the lazy dog']\n",
+      "[' j over the lazy dog', ' jumped over the lazy dog']\n",
       "12\n",
-      "[' jumps over the lazy dog', ' jumped over the lazy dog.']\n",
+      "[' j over the lazy dog.', ' jumped over the lazy dog\\n']\n",
       "13\n",
-      "[' jumps over the lazy dog.', ' jumped over the lazy dog.\\n']\n",
+      "[' j over the lazy dog. También', ' jumped over the lazy dog\\nGroupLayout']\n",
       "14\n",
-      "[' jumps over the lazy dog.\\n', ' jumped over the lazy dog.\\n\\n']\n",
+      "[' j over the lazy dog. También:', ' jumped over the lazy dog\\nGroupLayouting']\n",
       "15\n",
-      "[' jumps over the lazy dog.\\n\\n', ' jumped over the lazy dog.\\n\\nThe']\n",
+      "[' j over the lazy dog. También: is', ' jumped over the lazy dog\\nGroupLayouting is']\n",
       "16\n",
-      "[' jumps over the lazy dog.\\n\\nI', ' jumped over the lazy dog.\\n\\nThe quick']\n",
+      "[' j over the lazy dog. También: is a', ' jumped over the lazy dog\\nGroupLayouting is a']\n",
       "17\n",
-      "[' jumps over the lazy dog.\\n\\nI’', ' jumped over the lazy dog.\\n\\nThe quick brown']\n",
+      "[' j over the lazy dog. También: is a technique', ' jumped over the lazy dog\\nGroupLayouting is a common']\n",
       "18\n",
-      "[' jumps over the lazy dog.\\n\\nI’m', ' jumped over the lazy dog.\\n\\nThe quick brown f']\n",
+      "[' j over the lazy dog. También: is a technique practice', ' jumped over the lazy dog\\nGroupLayouting is a common practice']\n",
       "19\n",
-      "[' jumps over the lazy dog.\\n\\nI’m not', ' jumped over the lazy dog.\\n\\nThe quick brown fox']\n",
+      "[' j over the lazy dog. También: is a technique practice in', ' jumped over the lazy dog\\nGroupLayouting is a common practice in']\n",
       "20\n",
-      "[' jumps over the lazy dog.\\n\\nI’m not sure', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped']\n",
+      "[' j over the lazy dog. También: is a technique practice in the', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the']\n",
       "21\n",
-      "[' jumps over the lazy dog.\\n\\nI’m not sure if', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over']\n",
+      "[' j over the lazy dog. También: is a technique practice in the real', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media']\n",
       "22\n",
-      "[' jumps over the lazy dog.\\n\\nI’m not sure if that', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the']\n",
+      "[' j over the lazy dog. También: is a technique practice in the real-', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media industry']\n",
       "23\n",
-      "[' jumps over the lazy dog.\\n\\nI’m not sure if that’', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy']\n",
+      "[' j over the lazy dog. También: is a technique practice in the real-.', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media industry.']\n",
       "24\n",
-      "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog']\n",
+      "[' j over the lazy dog. También: is a technique practice in the real-. We', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media industry. However']\n",
       "25\n",
-      "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.']\n",
+      "[' j over the lazy dog. También: is a technique practice in the real-. We,', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media industry. However,']\n",
       "26\n",
-      "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the most', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.\\n']\n",
+      "[' j over the lazy dog. También: is a technique practice in the real-. We, when', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media industry. However, there']\n",
       "27\n",
-      "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the most famous', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.\\n\\n']\n",
+      "[' j over the lazy dog. También: is a technique practice in the real-. We, when is', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media industry. However, there has']\n",
       "28\n",
-      "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the most famous sentence', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.\\n\\nThe']\n",
+      "[' j over the lazy dog. También: is a technique practice in the real-. We, when is been', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media industry. However, there has been']\n",
       "29\n",
-      "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the most famous sentence in', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.\\n\\nThe quick']\n",
+      "[' j over the lazy dog. También: is a technique practice in the real-. We, when is been little', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media industry. However, there has been little']\n",
       "30\n",
-      "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the most famous sentence in the', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.\\n\\nThe quick brown']\n",
+      "[' j over the lazy dog. También: is a technique practice in the real-. We, when is been little research', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media industry. However, there has been little emp']\n",
       "31\n",
-      "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the most famous sentence in the English', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.\\n\\nThe quick brown f']\n",
+      "[' j over the lazy dog. También: is a technique practice in the real-. We, when is been little researchirical', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media industry. However, there has been little empirical']\n",
       "32\n",
-      "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the most famous sentence in the English language', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.\\n\\nThe quick brown fox']\n"
+      "[' j over the lazy dog. También: is a technique practice in the real-. We, when is been little researchirical research', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media industry. However, there has been little empirical research']\n"
      ]
     }
    ],
    "source": [
-    "import ctypes\n",
-    "\n",
     "streams = [\"\"] * n_parallel\n",
     "i_batch = [batch.n_tokens - 1] * n_parallel\n",
     "\n",
@@ -581,36 +336,17 @@
     "        if i_batch[i] < 0:\n",
     "            continue\n",
     "\n",
-    "        n_vocab = llama_cpp.llama_n_vocab(model)\n",
-    "        logits = llama_cpp.llama_get_logits_ith(ctx, i_batch[i])\n",
-    "\n",
-    "        candidates = (llama_cpp.llama_token_data * n_vocab)()\n",
-    "\n",
-    "        for token_id in range(n_vocab):\n",
-    "            candidates[token_id].id = token_id\n",
-    "            candidates[token_id].logit = logits[token_id]\n",
-    "            candidates[token_id].p = 0.0\n",
+    "        # Sample the next token using the sampler chain\n",
+    "        new_token_id = llama_cpp.llama_sampler_sample(sampler_chain, ctx, -1)\n",
     "\n",
-    "        candidates_p = llama_cpp.llama_token_data_array(\n",
-    "            candidates, len(candidates), False\n",
-    "        )\n",
-    "\n",
-    "        top_k = 40\n",
-    "        top_p = 0.9\n",
-    "        temp = 0.4\n",
-    "\n",
-    "        llama_cpp.llama_sample_top_k(ctx, ctypes.byref(candidates_p), top_k, 1)\n",
-    "        llama_cpp.llama_sample_top_p(ctx, ctypes.byref(candidates_p), top_p, 1)\n",
-    "        llama_cpp.llama_sample_temp(ctx, ctypes.byref(candidates_p), temp)\n",
-    "\n",
-    "        new_token_id = llama_cpp.llama_sample_token(ctx, ctypes.byref(candidates_p))\n",
-    "\n",
-    "        if new_token_id == llama_cpp.llama_token_eos(ctx) or n_cur == n_len:\n",
+    "        if new_token_id == llama_cpp.llama_vocab_eos(vocab) or n_cur == n_len:\n",
     "            i_batch[i] = -1\n",
     "            continue\n",
     "\n",
     "        buf = (ctypes.c_char * 32)()\n",
-    "        outlen = llama_cpp.llama_token_to_piece(model, new_token_id, buf, len(buf))\n",
+    "        \n",
+    "        # Convert token ID to text\n",
+    "        outlen = llama_cpp.llama_token_to_piece(vocab, new_token_id, buf, len(buf), 0, False)\n",
     "        streams[i] += bytes(buf[:outlen]).decode(\"utf-8\")\n",
     "\n",
     "        batch.token[batch.n_tokens] = new_token_id\n",
@@ -637,14 +373,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[' jumps over the lazy dog.\\n\\nI’m not sure if that’s the most famous sentence in the English language', ' jumped over the lazy dog.\\n\\nThe quick brown fox jumped over the lazy dog.\\n\\nThe quick brown fox']\n"
+      "[' j over the lazy dog. También: is a technique practice in the real-. We, when is been little researchirical research', ' jumped over the lazy dog\\nGroupLayouting is a common practice in the media industry. However, there has been little empirical research']\n"
      ]
     }
    ],
@@ -654,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -663,7 +399,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -672,16 +408,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
-    "llama_cpp.llama_free_model(model)"
+    "llama_cpp.llama_model_free(model)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -705,7 +441,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -719,7 +455,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5+"
+   "version": "3.12.1"
   },
   "orig_nbformat": 4
  },
diff --git a/examples/server/README.md b/examples/server/README.md
new file mode 100644
index 0000000000..b2819a244f
--- /dev/null
+++ b/examples/server/README.md
@@ -0,0 +1,479 @@
+# Server Example
+
+This example is an updated OpenAI-compatible web server that depends only on the low-level C bindings.
+It supports batched inference, prompt caching, response parsing, `/v1/responses`, `/v1/embeddings`, disk sequence caching, MTP, LoRA, and multimodal image/audio inputs.
+
+## Setup
+
+The server is a [`uv` inline script](https://docs.astral.sh/uv/guides/scripts/), so `uv` can create the script environment and install the Python dependencies automatically.
+
+```bash
+cd examples/server
+uv run --script server.py -C configs/qwen3.5-0.8b.json
+```
+
+Use `uv run --extra-index-url` to pull a pre-built `llama-cpp-python` binary wheel instead of building from source.
+
+```bash
+cd examples/server
+uv run \
+  --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu \
+  --script server.py -C configs/qwen3.5-0.8b.json
+```
+
+Pick the wheel index that matches the backend you want.
+
+| Backend | Wheel index |
+| --- | --- |
+| CPU | `https://abetlen.github.io/llama-cpp-python/whl/cpu` |
+| CUDA 11.8 | `https://abetlen.github.io/llama-cpp-python/whl/cu118` |
+| CUDA 12.1 | `https://abetlen.github.io/llama-cpp-python/whl/cu121` |
+| CUDA 12.2 | `https://abetlen.github.io/llama-cpp-python/whl/cu122` |
+| CUDA 12.3 | `https://abetlen.github.io/llama-cpp-python/whl/cu123` |
+| CUDA 12.4 | `https://abetlen.github.io/llama-cpp-python/whl/cu124` |
+| CUDA 12.5 | `https://abetlen.github.io/llama-cpp-python/whl/cu125` |
+| CUDA 13.0 | `https://abetlen.github.io/llama-cpp-python/whl/cu130` |
+| CUDA 13.2 | `https://abetlen.github.io/llama-cpp-python/whl/cu132` |
+| Metal | `https://abetlen.github.io/llama-cpp-python/whl/metal` |
+| ROCm | `https://abetlen.github.io/llama-cpp-python/whl/rocm72` |
+| Vulkan | `https://abetlen.github.io/llama-cpp-python/whl/vulkan` |
+
+See the repository installation section for the full [pre-built wheel requirements](../../README.md#supported-backends).
+
+## Model Configs
+
+The smallest checked-in example uses Qwen3.5 0.8B so the server can be started on a normal development machine.
+
+| Config | Model | Notes |
+| --- | --- | --- |
+| [`configs/bge-small-en-v1.5.json`](configs/bge-small-en-v1.5.json) | [`CompendiumLabs/bge-small-en-v1.5-gguf`](https://huggingface.co/CompendiumLabs/bge-small-en-v1.5-gguf) | Small embedding model config for `/v1/embeddings`. |
+| [`configs/qwen3.5-0.8b.json`](configs/qwen3.5-0.8b.json) | [`lmstudio-community/Qwen3.5-0.8B-GGUF`](https://huggingface.co/lmstudio-community/Qwen3.5-0.8B-GGUF) | Default small multimodal example. |
+| [`configs/gemma-4-12b-it-qat.json`](configs/gemma-4-12b-it-qat.json) | [`unsloth/gemma-4-12B-it-qat-GGUF`](https://huggingface.co/unsloth/gemma-4-12B-it-qat-GGUF) | Larger Gemma 4 QAT multimodal config with projector. |
+| [`configs/qwen3.6-27b.json`](configs/qwen3.6-27b.json) | [`unsloth/Qwen3.6-27B-GGUF`](https://huggingface.co/unsloth/Qwen3.6-27B-GGUF) | Larger Qwen3.6 multimodal config. |
+| [`configs/qwen3.6-35b-a3b.json`](configs/qwen3.6-35b-a3b.json) | [`unsloth/Qwen3.6-35B-A3B-GGUF`](https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF) | Larger Qwen3.6 MoE multimodal config. |
+| [`configs/gpt-oss-120b.json`](configs/gpt-oss-120b.json) | [`ggml-org/gpt-oss-120b-GGUF`](https://huggingface.co/ggml-org/gpt-oss-120b-GGUF) | Large text-only split-GGUF config. |
+
+The larger model configs default to `n_gpu_layers: -1` and `flash_attn: true`.
+
+## Client Examples
+
+Point an OpenAI-compatible client at the local `/v1` base URL.
+
+### Chat Completions
+
+```python
+from openai import OpenAI
+
+client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="not-used")
+
+response = client.chat.completions.create(
+    model="qwen3.5-0.8b-vl",
+    messages=[{"role": "user", "content": "What is the capital of France?"}],
+)
+print(response.choices[0].message.content)
+```
+
+### Responses API
+
+```python
+from openai import OpenAI
+
+client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="not-used")
+
+response = client.responses.create(
+    model="qwen3.5-0.8b-vl",
+    input="Write one sentence about why prefix caching helps batched inference.",
+)
+print(response.output_text)
+```
+
+### Embeddings
+
+Start the server with an embedding config before calling `/v1/embeddings`.
+
+```bash
+cd examples/server
+uv run --script server.py -C configs/bge-small-en-v1.5.json
+```
+
+```python
+from openai import OpenAI
+
+client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="not-used")
+
+response = client.embeddings.create(
+    model="bge-small-en-v1.5",
+    input=["The food was delicious.", "The meal was excellent."],
+)
+print(len(response.data[0].embedding))
+```
+
+## API Surface
+
+| Endpoint | Purpose | Reference |
+| --- | --- | --- |
+| `POST /v1/completions` | Legacy text completions with streaming, stop sequences, logprobs, penalties, seeds, and grammar-backed JSON output. | [OpenAI Completions API](https://platform.openai.com/docs/api-reference/completions) |
+| `POST /v1/embeddings` | OpenAI-compatible embeddings for embedding-mode GGUF models, including string inputs, token inputs, base64 output, and dimensions truncation. | [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings) |
+| `POST /v1/chat/completions` | Chat completions with streaming, tools, forced tool choice, reasoning parsing, multimodal content parts, and structured response parsing. | [OpenAI Chat API](https://platform.openai.com/docs/api-reference/chat) |
+| `POST /v1/responses` | Stateless Responses API compatibility for clients that use response items and response events. | [OpenAI Responses API](https://platform.openai.com/docs/api-reference/responses) |
+| `WS /v1/responses` | Stateful websocket Responses transport with per-connection `previous_response_id` replay. | [OpenAI Responses API](https://platform.openai.com/docs/api-reference/responses) |
+| `GET /v1/models` | Returns the configured model alias. | [OpenAI Models API](https://platform.openai.com/docs/api-reference/models) |
+| `GET /healthz` | Returns a simple typed health response. | |
+| `GET /metrics` | Exposes scheduler, cache, draft, and model metrics in Prometheus text format. | [Prometheus exposition format](https://prometheus.io/docs/instrumenting/exposition_formats/) |
+
+## Config Overview
+
+Config files have three top-level sections.
+
+```json
+{
+  "server": {},
+  "model": {},
+  "disk_cache": {}
+}
+```
+
+| Section | Required | Purpose |
+| --- | --- | --- |
+| `server` | No | Uvicorn host and port settings. |
+| `model` | Yes | Model source, llama.cpp runtime settings, chat formatting, LoRA, MTMD, draft decoding, and output parsing. |
+| `disk_cache` | No | Optional serialized sequence cache for repeated prompt prefixes. |
+
+## `server`
+
+Use `server.host` and `server.port` to choose the bind address.
+
+```json
+{
+  "server": {
+    "host": "0.0.0.0",
+    "port": 8000
+  }
+}
+```
+
+| Field | Default | Notes |
+| --- | --- | --- |
+| `host` | `127.0.0.1` | Use `0.0.0.0` to expose the server on the network. |
+| `port` | `8000` | Passed directly to `uvicorn.run()`. |
+
+## `model` Source
+
+Load a local GGUF with `path` or download a GGUF from Hugging Face with `from_pretrained`.
+
+```json
+{
+  "model": {
+    "alias": "qwen3.5-0.8b-vl",
+    "from_pretrained": {
+      "repo_id": "lmstudio-community/Qwen3.5-0.8B-GGUF",
+      "filename": "Qwen3.5-0.8B-Q8_0.gguf"
+    }
+  }
+}
+```
+
+| Field | Notes |
+| --- | --- |
+| `path` | Local GGUF path. |
+| `from_pretrained.repo_id` | Hugging Face model repository. |
+| `from_pretrained.filename` | File name or glob pattern for the GGUF. |
+| `from_pretrained.additional_files` | Extra files to download from the same repository. |
+| `from_pretrained.cache_dir` | Optional Hugging Face cache directory. |
+| `alias` | Model id returned by `/v1/models` and used by OpenAI-compatible clients. |
+
+See the [Hugging Face Hub download guide](https://huggingface.co/docs/huggingface_hub/guides/download) for cache behavior and repository file resolution.
+
+## llama.cpp Runtime Settings
+
+Most model runtime fields map to `llama_model_params` or `llama_context_params` in [`llama.h`](https://github.com/ggml-org/llama.cpp/blob/master/include/llama.h).
+
+```json
+{
+  "model": {
+    "n_ctx": 32768,
+    "n_seq_max": 64,
+    "n_batch": 128,
+    "n_ubatch": 128,
+    "threads": 4,
+    "threads_batch": 8,
+    "kv_unified": true,
+    "use_mmap": true,
+    "use_mlock": true
+  }
+}
+```
+
+| Field | Purpose |
+| --- | --- |
+| `n_ctx` | Total context size. |
+| `n_seq_max` | Maximum number of concurrent llama.cpp sequence ids. |
+| `n_batch` | Logical batch capacity. |
+| `n_ubatch` | Physical microbatch capacity. |
+| `threads` | Decode thread count. |
+| `threads_batch` | Prefill and batch thread count. |
+| `kv_unified` | Selects unified or per-sequence memory layout. |
+| `embedding` | Overrides embedding mode; omit to auto-detect pooled embedding GGUFs from model metadata. |
+| `pooling_type` | Overrides pooled embedding behavior for embedding models, such as `1` for mean pooling. |
+| `store_logits` | Keeps logits after decode when needed by sampling or diagnostics. |
+| `use_mmap` | Memory maps model weights. |
+| `use_mlock` | Attempts to lock model pages into RAM. |
+
+GPU and backend-related fields are passed through to llama.cpp when set.
+
+```json
+{
+  "model": {
+    "n_gpu_layers": -1,
+    "split_mode": 1,
+    "main_gpu": 0,
+    "tensor_split": [1.0],
+    "flash_attn": true,
+    "offload_kqv": true,
+    "op_offload": true
+  }
+}
+```
+
+## Chat Template
+
+`model.chat_template` is a Jinja chat template compatible with the style used by [Hugging Face chat templates](https://huggingface.co/docs/transformers/chat_templating).
+
+```json
+{
+  "model": {
+    "chat_template": "{{ bos_token }}{{ messages[0].content }}{{ eos_token }}"
+  }
+}
+```
+
+Use an array of strings when the template is too large to read or edit as one JSON string.
+
+```json
+{
+  "model": {
+    "chat_template": [
+      "{{ bos_token }}",
+      "{{ messages[0].content }}",
+      "{{ eos_token }}"
+    ]
+  }
+}
+```
+
+The checked-in [`configs/qwen3.5-0.8b.json`](configs/qwen3.5-0.8b.json) includes a Qwen3.5 template with reasoning text, tool calls, forced tool choice, image markers, and video markers.
+
+## Response Parsing
+
+`model.response_schema` parses generated text into OpenAI-compatible fields with JSON Schema plus the Hugging Face `x-regex` extensions.
+
+```json
+{
+  "model": {
+    "response_schema": {
+      "type": "object",
+      "properties": {
+        "role": {"const": "assistant"},
+        "content": {
+          "type": "string",
+          "x-regex": "^(.*)$"
+        }
+      },
+      "required": ["role"]
+    }
+  }
+}
+```
+
+Use `x-regex-iterator` and `x-regex-key-value` to parse repeated tool-call blocks.
+
+See [Hugging Face response parsing](https://huggingface.co/docs/transformers/chat_response_parsing) and the [JSON Schema reference](https://json-schema.org/understanding-json-schema/reference) for the underlying schema concepts.
+
+## Multimodal `model.mtmd`
+
+`model.mtmd` loads a llama.cpp multimodal projector and enables OpenAI-style image, audio, and video content parts.
+
+```json
+{
+  "model": {
+    "mtmd": {
+      "mmproj_from_pretrained": {
+        "repo_id": "lmstudio-community/Qwen3.5-0.8B-GGUF",
+        "filename": "mmproj-Qwen3.5-0.8B-BF16.gguf"
+      },
+      "embedding_cache": {
+        "path": ".cache/mtmd-embeddings",
+        "max_bytes": 1073741824
+      },
+      "batch_max_tokens": 1024,
+      "image_max_bytes": 20971520,
+      "audio_max_bytes": 104857600,
+      "video_max_bytes": 536870912,
+      "image_timeout_seconds": 10.0
+    }
+  }
+}
+```
+
+| Field | Purpose |
+| --- | --- |
+| `mmproj_path` | Local multimodal projector path. |
+| `mmproj_from_pretrained` | Hugging Face projector source. |
+| `embedding_cache.path` | Directory for cached image, audio, and video embeddings. |
+| `embedding_cache.max_bytes` | Maximum embedding cache size. |
+| `batch_max_tokens` | Maximum number of media output tokens per MTMD projector-side encode batch. |
+| `image_max_bytes` | Maximum image payload size. |
+| `audio_max_bytes` | Maximum audio payload size. |
+| `video_max_bytes` | Maximum video payload size. |
+| `image_timeout_seconds` | Timeout for remote image, audio, and video URL fetches. |
+
+Send image inputs with OpenAI chat content parts.
+
+```json
+{
+  "messages": [
+    {
+      "role": "user",
+      "content": [
+        {"type": "text", "text": "Describe this image."},
+        {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}
+      ]
+    }
+  ]
+}
+```
+
+Send audio inputs as a URL or as base64 `input_audio` content.
+
+```json
+{
+  "messages": [
+    {
+      "role": "user",
+      "content": [
+        {"type": "text", "text": "Transcribe this audio."},
+        {"type": "input_audio", "input_audio": {"data": "...base64...", "format": "wav"}}
+      ]
+    }
+  ]
+}
+```
+
+## Media Loading Policy
+
+Remote `http:` and `https:` media URLs are unrestricted unless `allowed_media_domains` is set.
+
+```json
+{
+  "model": {
+    "mtmd": {
+      "allowed_media_domains": ["example.com", "static.example.com"]
+    }
+  }
+}
+```
+
+Local `file:` media URLs are disabled unless `allowed_local_media_path` is set.
+
+```json
+{
+  "model": {
+    "mtmd": {
+      "allowed_local_media_path": "/srv/llama-cpp-python/media"
+    }
+  }
+}
+```
+
+`allowed_media_domains` matches exact hostnames and does not allow wildcard patterns.
+
+## LoRA `model.loras`
+
+Load LoRA adapters once at startup from local files or Hugging Face.
+
+```json
+{
+  "model": {
+    "loras": [
+      {
+        "from_pretrained": {
+          "repo_id": "example/qwen-lora-gguf",
+          "filename": "adapter.gguf"
+        },
+        "scale": 1.0
+      }
+    ]
+  }
+}
+```
+
+The current implementation does not hot-swap LoRAs per request.
+
+## Draft Decoding
+
+Set `model.draft_model` to enable speculative draft providers.
+
+```json
+{
+  "model": {
+    "draft_model": "prompt-lookup-decoding",
+    "draft_model_num_pred_tokens": 8,
+    "draft_model_max_ngram_size": 4
+  }
+}
+```
+
+### Multi-Token Prediction (MTP)
+
+Use MTP when the loaded model and llama.cpp build expose the required draft state.
+
+```json
+{
+  "model": {
+    "draft_model": "draft-mtp",
+    "draft_model_num_pred_tokens": 2,
+    "draft_model_threads": 4,
+    "draft_model_threads_batch": 8
+  }
+}
+```
+
+By default `draft-mtp` creates the MTP context from the target model.
+Set `draft_model_path` or `draft_model_from_pretrained` when the model uses a separate assistant GGUF.
+
+```json
+{
+  "model": {
+    "draft_model": "draft-mtp",
+    "draft_model_num_pred_tokens": 2,
+    "draft_model_from_pretrained": {
+      "repo_id": "example/gemma-assistant-GGUF",
+      "filename": "assistant.gguf"
+    }
+  }
+}
+```
+
+MTP currently applies to text-only requests.
+
+## Disk Sequence Cache
+
+`disk_cache` stores serialized llama.cpp sequence state for repeated prompt prefixes.
+
+```json
+{
+  "disk_cache": {
+    "path": ".cache/sequences",
+    "max_bytes": 1073741824,
+    "min_tokens": 128
+  }
+}
+```
+
+| Field | Purpose |
+| --- | --- |
+| `path` | Directory for cached sequence files. |
+| `max_bytes` | Maximum cache size before background cleanup removes entries. |
+| `min_tokens` | Minimum prefix length that is worth saving. |
+
+The cache is versioned by model and context compatibility data and should be treated as ephemeral.
diff --git a/examples/server/configs/bge-small-en-v1.5.json b/examples/server/configs/bge-small-en-v1.5.json
new file mode 100644
index 0000000000..3fc8016df8
--- /dev/null
+++ b/examples/server/configs/bge-small-en-v1.5.json
@@ -0,0 +1,22 @@
+{
+  "server": {
+    "host": "0.0.0.0",
+    "port": 8000
+  },
+  "model": {
+    "alias": "bge-small-en-v1.5",
+    "from_pretrained": {
+      "repo_id": "CompendiumLabs/bge-small-en-v1.5-gguf",
+      "filename": "bge-small-en-v1.5-q4_k_m.gguf"
+    },
+    "n_ctx": 512,
+    "n_seq_max": 16,
+    "n_batch": 512,
+    "n_ubatch": 512,
+    "threads": 4,
+    "threads_batch": 8,
+    "kv_unified": true,
+    "store_logits": false,
+    "use_mmap": true
+  }
+}
diff --git a/examples/server/configs/gemma-4-12b-it-qat.json b/examples/server/configs/gemma-4-12b-it-qat.json
new file mode 100644
index 0000000000..60ffd2a987
--- /dev/null
+++ b/examples/server/configs/gemma-4-12b-it-qat.json
@@ -0,0 +1,451 @@
+{
+  "server": {
+    "host": "0.0.0.0",
+    "port": 8000
+  },
+  "model": {
+    "alias": "gemma-4-12b-it-qat",
+    "from_pretrained": {
+      "repo_id": "unsloth/gemma-4-12B-it-qat-GGUF",
+      "filename": "gemma-4-12B-it-qat-UD-Q4_K_XL.gguf"
+    },
+    "mtmd": {
+      "mmproj_from_pretrained": {
+        "repo_id": "unsloth/gemma-4-12B-it-qat-GGUF",
+        "filename": "mmproj-BF16.gguf"
+      }
+    },
+    "n_ctx": 32768,
+    "max_output_tokens": 4096,
+    "n_seq_max": 8,
+    "n_batch": 512,
+    "n_ubatch": 512,
+    "threads": 8,
+    "threads_batch": 8,
+    "kv_unified": true,
+    "store_logits": false,
+    "use_mmap": true,
+    "use_mlock": false,
+    "n_gpu_layers": -1,
+    "flash_attn": true,
+    "response_schema": {
+      "type": "object",
+      "properties": {
+        "role": {
+          "const": "assistant"
+        },
+        "reasoning_content": {
+          "type": "string",
+          "x-regex": "^(?:<\\|turn>model\\n)?<\\|channel>thought\\n(.*?)<channel\\|>"
+        },
+        "content": {
+          "type": "string",
+          "x-regex": "^(?:<\\|turn>model\\n)?(?:(?:<\\|channel>thought\\n).*?<channel\\|>)?(.*?)(?=<\\|tool_call>|<turn\\|>|$)"
+        },
+        "tool_calls": {
+          "type": "array",
+          "x-regex-iterator": "<\\|tool_call>(call:[^\\{\\[]+[\\{\\[].*?[\\}\\]])<tool_call\\|>",
+          "items": {
+            "type": "object",
+            "properties": {
+              "type": {
+                "const": "function"
+              },
+              "function": {
+                "type": "object",
+                "properties": {
+                  "name": {
+                    "type": "string",
+                    "x-regex": "^call:(\\w+)[\\{\\[]"
+                  },
+                  "arguments": {
+                    "type": "object",
+                    "x-regex": "^call:\\w+(\\{.*\\}|\\[.*\\])$",
+                    "x-parser": "gemma4-tool-call",
+                    "additionalProperties": true
+                  }
+                },
+                "required": [
+                  "name",
+                  "arguments"
+                ]
+              }
+            },
+            "required": [
+              "type",
+              "function"
+            ]
+          }
+        }
+      },
+      "required": [
+        "role"
+      ]
+    },
+    "chat_template": [
+      "{%- macro format_parameters(properties, required, filter_keys=false) -%}\n",
+      "    {%- set standard_keys = ['description', 'type', 'properties', 'required', 'nullable'] -%}\n",
+      "    {%- set ns = namespace(found_first=false) -%}\n",
+      "    {%- for key, value in properties | dictsort -%}\n",
+      "        {%- set add_comma = false -%}\n",
+      "        {%- if not filter_keys or key not in standard_keys -%}\n",
+      "            {%- if ns.found_first %},{% endif -%}\n",
+      "            {%- set ns.found_first = true -%}\n",
+      "            {{ key }}:{\n",
+      "            {%- if value['description'] -%}\n",
+      "                description:<|\"|>{{ value['description'] }}<|\"|>\n",
+      "                {%- set add_comma = true -%}\n",
+      "            {%- endif -%}\n",
+      "            {%- if value['type'] | upper == 'STRING' -%}\n",
+      "                {%- if value['enum'] -%}\n",
+      "                    {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}\n",
+      "                    enum:{{ format_argument(value['enum']) }}\n",
+      "                {%- endif -%}\n",
+      "            {%- elif value['type'] | upper == 'ARRAY' -%}\n",
+      "                {%- if value['items'] is mapping and value['items'] -%}\n",
+      "                    {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}\n",
+      "                    items:{\n",
+      "                    {%- set ns_items = namespace(found_first=false) -%}\n",
+      "                    {%- for item_key, item_value in value['items'] | dictsort -%}\n",
+      "                        {%- if item_value is not none -%}\n",
+      "                            {%- if ns_items.found_first %},{% endif -%}\n",
+      "                            {%- set ns_items.found_first = true -%}\n",
+      "                            {%- if item_key == 'properties' -%}\n",
+      "                                properties:{\n",
+      "                                {%- if item_value is mapping -%}\n",
+      "                                    {{- format_parameters(item_value, value['items']['required'] | default([])) -}}\n",
+      "                                {%- endif -%}\n",
+      "                                }\n",
+      "                            {%- elif item_key == 'required' -%}\n",
+      "                                required:[\n",
+      "                                {%- for req_item in item_value -%}\n",
+      "                                    <|\"|>{{- req_item -}}<|\"|>\n",
+      "                                    {%- if not loop.last %},{% endif -%}\n",
+      "                                {%- endfor -%}\n",
+      "                                ]\n",
+      "                            {%- elif item_key == 'type' -%}\n",
+      "                                {%- if item_value is string -%}\n",
+      "                                    type:{{ format_argument(item_value | upper) }}\n",
+      "                                {%- else -%}\n",
+      "                                    type:{{ format_argument(item_value | map('upper') | list) }}\n",
+      "                                {%- endif -%}\n",
+      "                            {%- else -%}\n",
+      "                                {{ item_key }}:{{ format_argument(item_value) }}\n",
+      "                            {%- endif -%}\n",
+      "                        {%- endif -%}\n",
+      "                    {%- endfor -%}\n",
+      "                    }\n",
+      "                {%- endif -%}\n",
+      "            {%- endif -%}\n",
+      "            {%- if value['nullable'] %}\n",
+      "                {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}\n",
+      "                nullable:true\n",
+      "            {%- endif -%}\n",
+      "            {%- if value['type'] | upper == 'OBJECT' -%}\n",
+      "                {%- if value['properties'] is defined and value['properties'] is mapping -%}\n",
+      "                    {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}\n",
+      "                    properties:{\n",
+      "                    {{- format_parameters(value['properties'], value['required'] | default([])) -}}\n",
+      "                    }\n",
+      "                {%- elif value is mapping -%}\n",
+      "                    {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}\n",
+      "                    properties:{\n",
+      "                    {{- format_parameters(value, value['required'] | default([]), filter_keys=true) -}}\n",
+      "                    }\n",
+      "                {%- endif -%}\n",
+      "                {%- if value['required'] -%}\n",
+      "                    {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}\n",
+      "                    required:[\n",
+      "                    {%- for item in value['required'] | default([]) -%}\n",
+      "                        <|\"|>{{- item -}}<|\"|>\n",
+      "                        {%- if not loop.last %},{% endif -%}\n",
+      "                    {%- endfor -%}\n",
+      "                    ]\n",
+      "                {%- endif -%}\n",
+      "            {%- endif -%}\n",
+      "            {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%}\n",
+      "            type:<|\"|>{{ value['type'] | upper }}<|\"|>}\n",
+      "        {%- endif -%}\n",
+      "    {%- endfor -%}\n",
+      "{%- endmacro -%}\n",
+      "{%- macro format_function_declaration(tool_data) -%}\n",
+      "    declaration:{{- tool_data['function']['name'] -}}{description:<|\"|>{{- tool_data['function']['description'] -}}<|\"|>\n",
+      "    {%- set params = tool_data['function']['parameters'] -%}\n",
+      "    {%- if params -%}\n",
+      "        ,parameters:{\n",
+      "        {%- if params['properties'] -%}\n",
+      "            properties:{ {{- format_parameters(params['properties'], params['required']) -}} },\n",
+      "        {%- endif -%}\n",
+      "        {%- if params['required'] -%}\n",
+      "            required:[\n",
+      "            {%- for item in params['required'] -%}\n",
+      "                <|\"|>{{- item -}}<|\"|>\n",
+      "                {{- ',' if not loop.last -}}\n",
+      "            {%- endfor -%}\n",
+      "            ],\n",
+      "        {%- endif -%}\n",
+      "        {%- if params['type'] -%}\n",
+      "            type:<|\"|>{{- params['type'] | upper -}}<|\"|>}\n",
+      "        {%- endif -%}\n",
+      "    {%- endif -%}\n",
+      "    {%- if 'response' in tool_data['function'] -%}\n",
+      "        {%- set response_declaration = tool_data['function']['response'] -%}\n",
+      "        ,response:{\n",
+      "        {%- if response_declaration['description'] -%}\n",
+      "            description:<|\"|>{{- response_declaration['description'] -}}<|\"|>,\n",
+      "        {%- endif -%}\n",
+      "        {%- if response_declaration['type'] | upper == 'OBJECT' -%}\n",
+      "            type:<|\"|>{{- response_declaration['type'] | upper -}}<|\"|>}\n",
+      "        {%- endif -%}\n",
+      "    {%- endif -%}\n",
+      "    }\n",
+      "{%- endmacro -%}\n",
+      "{%- macro format_argument(argument, escape_keys=True) -%}\n",
+      "    {%- if argument is string -%}\n",
+      "        {{- '<|\"|>' + argument + '<|\"|>' -}}\n",
+      "    {%- elif argument is boolean -%}\n",
+      "        {{- 'true' if argument else 'false' -}}\n",
+      "    {%- elif argument is mapping -%}\n",
+      "        {{- '{' -}}\n",
+      "        {%- set ns = namespace(found_first=false) -%}\n",
+      "        {%- for key, value in argument | dictsort -%}\n",
+      "            {%- if ns.found_first %},{% endif -%}\n",
+      "            {%- set ns.found_first = true -%}\n",
+      "            {%- if escape_keys -%}\n",
+      "                {{- '<|\"|>' + key + '<|\"|>' -}}\n",
+      "            {%- else -%}\n",
+      "                {{- key -}}\n",
+      "            {%- endif -%}\n",
+      "            :{{- format_argument(value, escape_keys=escape_keys) -}}\n",
+      "        {%- endfor -%}\n",
+      "        {{- '}' -}}\n",
+      "    {%- elif argument is sequence -%}\n",
+      "        {{- '[' -}}\n",
+      "        {%- for item in argument -%}\n",
+      "            {{- format_argument(item, escape_keys=escape_keys) -}}\n",
+      "            {%- if not loop.last %},{% endif -%}\n",
+      "        {%- endfor -%}\n",
+      "        {{- ']' -}}\n",
+      "    {%- else -%}\n",
+      "        {{- argument -}}\n",
+      "    {%- endif -%}\n",
+      "{%- endmacro -%}\n",
+      "{%- macro strip_thinking(text) -%}\n",
+      "    {%- set ns = namespace(result='') -%}\n",
+      "    {%- for part in text.split('<channel|>') -%}\n",
+      "        {%- if '<|channel>' in part -%}\n",
+      "            {%- set ns.result = ns.result + part.split('<|channel>')[0] -%}\n",
+      "        {%- else -%}\n",
+      "            {%- set ns.result = ns.result + part -%}\n",
+      "        {%- endif -%}\n",
+      "    {%- endfor -%}\n",
+      "    {{- ns.result | trim -}}\n",
+      "{%- endmacro -%}\n",
+      "\n",
+      "{%- macro format_tool_response_block(tool_name, response) -%}\n",
+      "    {{- '<|tool_response>' -}}\n",
+      "    {%- if response is mapping -%}\n",
+      "        {{- 'response:' + tool_name + '{' -}}\n",
+      "        {%- for key, value in response | dictsort -%}\n",
+      "            {{- key -}}:{{- format_argument(value, escape_keys=False) -}}\n",
+      "            {%- if not loop.last %},{% endif -%}\n",
+      "        {%- endfor -%}\n",
+      "        {{- '}' -}}\n",
+      "    {%- else -%}\n",
+      "        {{- 'response:' + tool_name + '{value:' + format_argument(response, escape_keys=False) + '}' -}}\n",
+      "    {%- endif -%}\n",
+      "    {{- '<tool_response|>' -}}\n",
+      "{%- endmacro -%}\n",
+      "\n",
+      "{%- set ns = namespace(prev_message_type=None) -%}\n",
+      "{%- set loop_messages = messages -%}\n",
+      "{{- bos_token -}}\n",
+      "{#- Handle System/Tool Definitions Block -#}\n",
+      "{%- if (enable_thinking is defined and enable_thinking) or tools or messages[0]['role'] in ['system', 'developer'] -%}\n",
+      "    {{- '<|turn>system\\n' -}}\n",
+      "    {#- Inject Thinking token at the very top of the FIRST system turn -#}\n",
+      "    {%- if enable_thinking is defined and enable_thinking -%}\n",
+      "        {{- '<|think|>\\n' -}}\n",
+      "        {%- set ns.prev_message_type = 'think' -%}\n",
+      "    {%- endif -%}\n",
+      "    {%- if messages[0]['role'] in ['system', 'developer'] -%}\n",
+      "        {%- if messages[0]['content'] is string -%}\n",
+      "            {{- messages[0]['content'] | trim -}}\n",
+      "        {%- elif messages[0]['content'] is sequence -%}\n",
+      "            {%- for item in messages[0]['content'] -%}\n",
+      "                {{- item['text'] | trim + ' '-}}\n",
+      "            {%- endfor -%}\n",
+      "        {%- endif -%}\n",
+      "        {%- set loop_messages = messages[1:] -%}\n",
+      "    {%- endif -%}\n",
+      "    {%- if tools -%}\n",
+      "        {%- for tool in tools %}\n",
+      "            {{- '<|tool>' -}}\n",
+      "            {{- format_function_declaration(tool) | trim -}}\n",
+      "            {{- '<tool|>' -}}\n",
+      "        {%- endfor %}\n",
+      "        {%- set ns.prev_message_type = 'tool' -%}\n",
+      "    {%- endif -%}\n",
+      "    {{- '<turn|>\\n' -}}\n",
+      "{%- endif %}\n",
+      "\n",
+      "{#- Pre-scan: find last user message index for reasoning guard -#}\n",
+      "{%- set ns_turn = namespace(last_user_idx=-1) -%}\n",
+      "{%- for i in range(loop_messages | length) -%}\n",
+      "    {%- if loop_messages[i]['role'] == 'user' -%}\n",
+      "        {%- set ns_turn.last_user_idx = i -%}\n",
+      "    {%- endif -%}\n",
+      "{%- endfor -%}\n",
+      "\n",
+      "{#- Loop through messages -#}\n",
+      "{%- for message in loop_messages -%}\n",
+      "    {%- if message['role'] != 'tool' -%}\n",
+      "    {%- set ns.prev_message_type = None -%}\n",
+      "    {%- set role = 'model' if message['role'] == 'assistant' else message['role'] -%}\n",
+      "    {#- Detect continuation: suppress duplicate <|turn>model when previous non-tool message was also assistant -#}\n",
+      "    {%- set prev_nt = namespace(role=None, found=false) -%}\n",
+      "    {%- if loop.index0 > 0 -%}\n",
+      "        {%- for j in range(loop.index0 - 1, -1, -1) -%}\n",
+      "            {%- if not prev_nt.found -%}\n",
+      "                {%- if loop_messages[j]['role'] != 'tool' -%}\n",
+      "                    {%- set prev_nt.role = loop_messages[j]['role'] -%}\n",
+      "                    {%- set prev_nt.found = true -%}\n",
+      "                {%- endif -%}\n",
+      "            {%- endif -%}\n",
+      "        {%- endfor -%}\n",
+      "    {%- endif -%}\n",
+      "    {%- set continue_same_model_turn = (role == 'model' and prev_nt.role == 'assistant') -%}\n",
+      "    {%- if not continue_same_model_turn -%}\n",
+      "        {{- '<|turn>' + role + '\\n' }}\n",
+      "    {%- endif -%}\n",
+      "\n",
+      "    {#- Render reasoning/reasoning_content as thinking channel -#}\n",
+      "    {%- set thinking_text = message.get('reasoning') or message.get('reasoning_content') -%}\n",
+      "    {%- if thinking_text and loop.index0 > ns_turn.last_user_idx and message.get('tool_calls') -%}\n",
+      "        {{- '<|channel>thought\\n' + thinking_text + '\\n<channel|>' -}}\n",
+      "    {%- endif -%}\n",
+      "\n",
+      "            {%- if message['tool_calls'] -%}\n",
+      "                {%- for tool_call in message['tool_calls'] -%}\n",
+      "                    {%- set function = tool_call['function'] -%}\n",
+      "                    {{- '<|tool_call>call:' + function['name'] + '{' -}}\n",
+      "                    {%- if function['arguments'] is mapping -%}\n",
+      "                        {%- set ns_args = namespace(found_first=false) -%}\n",
+      "                        {%- for key, value in function['arguments'] | dictsort -%}\n",
+      "                            {%- if ns_args.found_first %},{% endif -%}\n",
+      "                            {%- set ns_args.found_first = true -%}\n",
+      "                            {{- key -}}:{{- format_argument(value, escape_keys=False) -}}\n",
+      "                        {%- endfor -%}\n",
+      "                    {%- elif function['arguments'] is string -%}\n",
+      "                        {{- function['arguments'] -}}\n",
+      "                    {%- endif -%}\n",
+      "                    {{- '}<tool_call|>' -}}\n",
+      "                {%- endfor -%}\n",
+      "                {%- set ns.prev_message_type = 'tool_call' -%}\n",
+      "            {%- endif -%}\n",
+      "\n",
+      "            {%- set ns_tr_out = namespace(flag=false) -%}\n",
+      "            {%- if message.get('tool_responses') -%}\n",
+      "                {#- Legacy: tool_responses embedded on the assistant message (Google/Gemma native) -#}\n",
+      "                {%- for tool_response in message['tool_responses'] -%}\n",
+      "                    {{- format_tool_response_block(tool_response['name'] | default('unknown'), tool_response['response']) -}}\n",
+      "                    {%- set ns_tr_out.flag = true -%}\n",
+      "                    {%- set ns.prev_message_type = 'tool_response' -%}\n",
+      "                {%- endfor -%}\n",
+      "            {%- elif message.get('tool_calls') -%}\n",
+      "                {#- OpenAI Chat Completions: forward-scan consecutive role:tool messages -#}\n",
+      "                {%- set ns_tool_scan = namespace(stopped=false) -%}\n",
+      "                {%- for k in range(loop.index0 + 1, loop_messages | length) -%}\n",
+      "                    {%- if ns_tool_scan.stopped -%}\n",
+      "                    {%- elif loop_messages[k]['role'] != 'tool' -%}\n",
+      "                        {%- set ns_tool_scan.stopped = true -%}\n",
+      "                    {%- else -%}\n",
+      "                        {%- set follow = loop_messages[k] -%}\n",
+      "                        {#- Resolve tool_call_id to function name -#}\n",
+      "                        {%- set ns_tname = namespace(name=follow.get('name') | default('unknown')) -%}\n",
+      "                        {%- for tc in message['tool_calls'] -%}\n",
+      "                            {%- if tc.get('id') == follow.get('tool_call_id') -%}\n",
+      "                                {%- set ns_tname.name = tc['function']['name'] -%}\n",
+      "                            {%- endif -%}\n",
+      "                        {%- endfor -%}\n",
+      "                        {#- Handle content as string or content-parts array -#}\n",
+      "                        {%- set tool_body = follow.get('content') -%}\n",
+      "                        {%- if tool_body is string -%}\n",
+      "                            {{- format_tool_response_block(ns_tname.name, tool_body) -}}\n",
+      "                        {%- elif tool_body is sequence and tool_body is not string -%}\n",
+      "                            {%- set ns_txt = namespace(s='') -%}\n",
+      "                            {%- for part in tool_body -%}\n",
+      "                                {%- if part.get('type') == 'text' -%}\n",
+      "                                    {%- set ns_txt.s = ns_txt.s + (part.get('text') | default('')) -%}\n",
+      "                                {%- endif -%}\n",
+      "                            {%- endfor -%}\n",
+      "                            {{- format_tool_response_block(ns_tname.name, ns_txt.s) -}}\n",
+      "                            {%- for part in tool_body -%}\n",
+      "                                {%- if part.get('type') == 'image' -%}\n",
+      "                                    {{- '<|image|>' -}}\n",
+      "                                {%- elif part.get('type') == 'audio' -%}\n",
+      "                                    {{- '<|audio|>' -}}\n",
+      "                                {%- elif part.get('type') == 'video' -%}\n",
+      "                                    {{- '<|video|>' -}}\n",
+      "                                {%- endif -%}\n",
+      "                            {%- endfor -%}\n",
+      "                        {%- else -%}\n",
+      "                            {{- format_tool_response_block(ns_tname.name, tool_body) -}}\n",
+      "                        {%- endif -%}\n",
+      "                        {%- set ns_tr_out.flag = true -%}\n",
+      "                        {%- set ns.prev_message_type = 'tool_response' -%}\n",
+      "                    {%- endif -%}\n",
+      "                {%- endfor -%}\n",
+      "            {%- endif -%}\n",
+      "\n",
+      "            {%- set captured_content -%}\n",
+      "            {%- if message['content'] is string -%}\n",
+      "                {%- if role == 'model' -%}\n",
+      "                    {{- strip_thinking(message['content']) -}}\n",
+      "                {%- else -%}\n",
+      "                    {{- message['content'] | trim -}}\n",
+      "                {%- endif -%}\n",
+      "            {%- elif message['content'] is sequence -%}\n",
+      "                {%- for item in message['content'] -%}\n",
+      "                    {%- if item['type'] == 'text' -%}\n",
+      "                        {%- if role == 'model' -%}\n",
+      "                            {{- strip_thinking(item['text']) -}}\n",
+      "                        {%- else -%}\n",
+      "                            {{- item['text'] | trim -}}\n",
+      "                        {%- endif -%}\n",
+      "                    {%- elif item['type'] == 'image' -%}\n",
+      "                        {{- '<|image|>' -}}\n",
+      "                        {%- set ns.prev_message_type = 'image' -%}\n",
+      "                    {%- elif item['type'] == 'audio' -%}\n",
+      "                        {{- '<|audio|>' -}}\n",
+      "                        {%- set ns.prev_message_type = 'audio' -%}\n",
+      "                    {%- elif item['type'] == 'video' -%}\n",
+      "                        {{- '<|video|>' -}}\n",
+      "                        {%- set ns.prev_message_type = 'video' -%}\n",
+      "                    {%- endif -%}\n",
+      "                {%- endfor -%}\n",
+      "            {%- endif -%}\n",
+      "            {%- endset -%}\n",
+      "\n",
+      "            {{- captured_content -}}\n",
+      "            {%- set has_content = captured_content | trim | length > 0 -%}\n",
+      "\n",
+      "        {%- if ns.prev_message_type == 'tool_call' and not ns_tr_out.flag -%}\n",
+      "            {{- '<|tool_response>' -}}\n",
+      "        {%- elif not (ns_tr_out.flag and not has_content) -%}\n",
+      "            {{- '<turn|>\\n' -}}\n",
+      "        {%- endif -%}\n",
+      "    {%- endif -%}\n",
+      "{%- endfor -%}\n",
+      "\n",
+      "{%- if add_generation_prompt -%}\n",
+      "    {%- if ns.prev_message_type != 'tool_response' and ns.prev_message_type != 'tool_call' -%}\n",
+      "        {{- '<|turn>model\\n' -}}\n",
+      "        {%- if not enable_thinking | default(false) -%}\n",
+      "            {{- '<|channel>thought\\n<channel|>' -}}\n",
+      "        {%- endif -%}\n",
+      "    {%- endif -%}\n",
+      "{%- endif -%}"
+    ]
+  }
+}
diff --git a/examples/server/configs/gpt-oss-120b.json b/examples/server/configs/gpt-oss-120b.json
new file mode 100644
index 0000000000..64aaf040d7
--- /dev/null
+++ b/examples/server/configs/gpt-oss-120b.json
@@ -0,0 +1,431 @@
+{
+  "server": {
+    "host": "0.0.0.0",
+    "port": 8000
+  },
+  "model": {
+    "alias": "gpt-oss-120b",
+    "from_pretrained": {
+      "repo_id": "ggml-org/gpt-oss-120b-GGUF",
+      "filename": "gpt-oss-120b-mxfp4-00001-of-00003.gguf",
+      "additional_files": [
+        "gpt-oss-120b-mxfp4-00002-of-00003.gguf",
+        "gpt-oss-120b-mxfp4-00003-of-00003.gguf"
+      ]
+    },
+    "n_ctx": 32768,
+    "max_output_tokens": 4096,
+    "n_seq_max": 4,
+    "n_batch": 512,
+    "n_ubatch": 512,
+    "threads": 8,
+    "threads_batch": 8,
+    "kv_unified": true,
+    "store_logits": false,
+    "use_mmap": true,
+    "use_mlock": false,
+    "n_gpu_layers": -1,
+    "flash_attn": true,
+    "response_schema": {
+      "type": "object",
+      "properties": {
+        "role": {
+          "const": "assistant"
+        },
+        "reasoning_content": {
+          "type": "string",
+          "x-regex": "^(?:<\\|start\\|>assistant)?<\\|channel\\|>analysis<\\|message\\|>(.*?)(?=<\\|end\\|>|<\\|start\\|>assistant|$)"
+        },
+        "content": {
+          "type": "string",
+          "x-regex": "^(?:.*?<\\|start\\|>assistant)?<\\|channel\\|>final<\\|message\\|>(.*?)(?=<\\|return\\|>|<\\|end\\|>|$)"
+        },
+        "tool_calls": {
+          "type": "array",
+          "x-regex-iterator": "(<\\|start\\|>assistant<\\|channel\\|>commentary to=functions\\.\\w+ <\\|constrain\\|>json<\\|message\\|>.*?<\\|call\\|>)",
+          "items": {
+            "type": "object",
+            "properties": {
+              "type": {
+                "const": "function"
+              },
+              "function": {
+                "type": "object",
+                "properties": {
+                  "name": {
+                    "type": "string",
+                    "x-regex": "^<\\|start\\|>assistant<\\|channel\\|>commentary to=functions\\.(\\w+) <\\|constrain\\|>json<\\|message\\|>"
+                  },
+                  "arguments": {
+                    "type": "object",
+                    "x-regex": "^<\\|start\\|>assistant<\\|channel\\|>commentary to=functions\\.\\w+ <\\|constrain\\|>json<\\|message\\|>(.*?)<\\|call\\|>$",
+                    "x-parser": "json",
+                    "additionalProperties": true
+                  }
+                },
+                "required": [
+                  "name",
+                  "arguments"
+                ]
+              }
+            },
+            "required": [
+              "type",
+              "function"
+            ]
+          }
+        }
+      },
+      "required": [
+        "role"
+      ]
+    },
+    "chat_template": [
+      "{#-\n",
+      "  In addition to the normal inputs of `messages` and `tools`, this template also accepts the\n",
+      "  following kwargs:\n",
+      "  - \"builtin_tools\": A list, can contain \"browser\" and/or \"python\".\n",
+      "  - \"model_identity\": A string that optionally describes the model identity.\n",
+      "  - \"reasoning_effort\": A string that describes the reasoning effort, defaults to \"medium\".\n",
+      " #}\n",
+      "\n",
+      "{#- Tool Definition Rendering ============================================== #}\n",
+      "{%- macro render_typescript_type(param_spec, required_params, is_nullable=false) -%}\n",
+      "    {%- if param_spec.type == \"array\" -%}\n",
+      "        {%- if param_spec['items'] -%}\n",
+      "            {%- if param_spec['items']['type'] == \"string\" -%}\n",
+      "                {{- \"string[]\" }}\n",
+      "            {%- elif param_spec['items']['type'] == \"number\" -%}\n",
+      "                {{- \"number[]\" }}\n",
+      "            {%- elif param_spec['items']['type'] == \"integer\" -%}\n",
+      "                {{- \"number[]\" }}\n",
+      "            {%- elif param_spec['items']['type'] == \"boolean\" -%}\n",
+      "                {{- \"boolean[]\" }}\n",
+      "            {%- else -%}\n",
+      "                {%- set inner_type = render_typescript_type(param_spec['items'], required_params) -%}\n",
+      "                {%- if inner_type == \"object | object\" or inner_type|length > 50 -%}\n",
+      "                    {{- \"any[]\" }}\n",
+      "                {%- else -%}\n",
+      "                    {{- inner_type + \"[]\" }}\n",
+      "                {%- endif -%}\n",
+      "            {%- endif -%}\n",
+      "            {%- if param_spec.nullable -%}\n",
+      "                {{- \" | null\" }}\n",
+      "            {%- endif -%}\n",
+      "        {%- else -%}\n",
+      "            {{- \"any[]\" }}\n",
+      "            {%- if param_spec.nullable -%}\n",
+      "                {{- \" | null\" }}\n",
+      "            {%- endif -%}\n",
+      "        {%- endif -%}\n",
+      "    {%- elif param_spec.type is defined and param_spec.type is iterable and param_spec.type is not string and param_spec.type is not mapping and param_spec.type[0] is defined -%}\n",
+      "        {#- Handle array of types like [\"object\", \"object\"] from Union[dict, list] #}\n",
+      "        {%- if param_spec.type | length > 1 -%}\n",
+      "            {{- param_spec.type | join(\" | \") }}\n",
+      "        {%- else -%}\n",
+      "            {{- param_spec.type[0] }}\n",
+      "        {%- endif -%}\n",
+      "    {%- elif param_spec.oneOf -%}\n",
+      "        {#- Handle oneOf schemas - check for complex unions and fallback to any #}\n",
+      "        {%- set has_object_variants = false -%}\n",
+      "        {%- for variant in param_spec.oneOf -%}\n",
+      "            {%- if variant.type == \"object\" -%}\n",
+      "                {%- set has_object_variants = true -%}\n",
+      "            {%- endif -%}\n",
+      "        {%- endfor -%}\n",
+      "        {%- if has_object_variants and param_spec.oneOf|length > 1 -%}\n",
+      "            {{- \"any\" }}\n",
+      "        {%- else -%}\n",
+      "            {%- for variant in param_spec.oneOf -%}\n",
+      "                {{- render_typescript_type(variant, required_params) -}}\n",
+      "                {%- if variant.description %}\n",
+      "                    {{- \"// \" + variant.description }}\n",
+      "                {%- endif -%}\n",
+      "                {%- if variant.default is defined %}\n",
+      "                    {{ \"// default: \" + variant.default|tojson }}\n",
+      "                {%- endif -%}\n",
+      "                {%- if not loop.last %}\n",
+      "                    {{- \" | \" }}\n",
+      "                {% endif -%}\n",
+      "            {%- endfor -%}\n",
+      "        {%- endif -%}\n",
+      "    {%- elif param_spec.type == \"string\" -%}\n",
+      "        {%- if param_spec.enum -%}\n",
+      "            {{- '\"' + param_spec.enum|join('\" | \"') + '\"' -}}\n",
+      "        {%- else -%}\n",
+      "            {{- \"string\" }}\n",
+      "            {%- if param_spec.nullable %}\n",
+      "                {{- \" | null\" }}\n",
+      "            {%- endif -%}\n",
+      "        {%- endif -%}\n",
+      "    {%- elif param_spec.type == \"number\" -%}\n",
+      "        {{- \"number\" }}\n",
+      "    {%- elif param_spec.type == \"integer\" -%}\n",
+      "        {{- \"number\" }}\n",
+      "    {%- elif param_spec.type == \"boolean\" -%}\n",
+      "        {{- \"boolean\" }}\n",
+      "\n",
+      "    {%- elif param_spec.type == \"object\" -%}\n",
+      "        {%- if param_spec.properties -%}\n",
+      "            {{- \"{\\n\" }}\n",
+      "            {%- for prop_name, prop_spec in param_spec.properties.items() -%}\n",
+      "                {{- prop_name -}}\n",
+      "                {%- if prop_name not in (param_spec.required or []) -%}\n",
+      "                    {{- \"?\" }}\n",
+      "                {%- endif -%}\n",
+      "                {{- \": \" }}\n",
+      "                {{ render_typescript_type(prop_spec, param_spec.required or []) }}\n",
+      "                {%- if not loop.last -%}\n",
+      "                    {{-\", \" }}\n",
+      "                {%- endif -%}\n",
+      "            {%- endfor -%}\n",
+      "            {{- \"}\" }}\n",
+      "        {%- else -%}\n",
+      "            {{- \"object\" }}\n",
+      "        {%- endif -%}\n",
+      "    {%- else -%}\n",
+      "        {{- \"any\" }}\n",
+      "    {%- endif -%}\n",
+      "{%- endmacro -%}\n",
+      "\n",
+      "{%- macro render_tool_namespace(namespace_name, tools) -%}\n",
+      "    {{- \"## \" + namespace_name + \"\\n\\n\" }}\n",
+      "    {{- \"namespace \" + namespace_name + \" {\\n\\n\" }}\n",
+      "    {%- for tool in tools %}\n",
+      "        {%- set tool = tool.function %}\n",
+      "        {{- \"// \" + tool.description + \"\\n\" }}\n",
+      "        {{- \"type \"+ tool.name + \" = \" }}\n",
+      "        {%- if tool.parameters and tool.parameters.properties %}\n",
+      "            {{- \"(_: {\\n\" }}\n",
+      "            {%- for param_name, param_spec in tool.parameters.properties.items() %}\n",
+      "                {%- if param_spec.description %}\n",
+      "                    {{- \"// \" + param_spec.description + \"\\n\" }}\n",
+      "                {%- endif %}\n",
+      "                {{- param_name }}\n",
+      "                {%- if param_name not in (tool.parameters.required or []) -%}\n",
+      "                    {{- \"?\" }}\n",
+      "                {%- endif -%}\n",
+      "                {{- \": \" }}\n",
+      "                {{- render_typescript_type(param_spec, tool.parameters.required or []) }}\n",
+      "                {%- if param_spec.default is defined -%}\n",
+      "                    {%- if param_spec.enum %}\n",
+      "                        {{- \", // default: \" + param_spec.default }}\n",
+      "                    {%- elif param_spec.oneOf %}\n",
+      "                        {{- \"// default: \" + param_spec.default }}\n",
+      "                    {%- else %}\n",
+      "                        {{- \", // default: \" + param_spec.default|tojson }}\n",
+      "                    {%- endif -%}\n",
+      "                {%- endif -%}\n",
+      "                {%- if not loop.last %}\n",
+      "                    {{- \",\\n\" }}\n",
+      "                {%- else %}\n",
+      "                    {{- \",\\n\" }}\n",
+      "                {%- endif -%}\n",
+      "            {%- endfor %}\n",
+      "            {{- \"}) => any;\\n\\n\" }}\n",
+      "        {%- else -%}\n",
+      "            {{- \"() => any;\\n\\n\" }}\n",
+      "        {%- endif -%}\n",
+      "    {%- endfor %}\n",
+      "    {{- \"} // namespace \" + namespace_name }}\n",
+      "{%- endmacro -%}\n",
+      "\n",
+      "{%- macro render_builtin_tools(browser_tool, python_tool) -%}\n",
+      "    {%- if browser_tool %}\n",
+      "        {{- \"## browser\\n\\n\" }}\n",
+      "        {{- \"// Tool for browsing.\\n\" }}\n",
+      "        {{- \"// The `cursor` appears in brackets before each browsing display: `[{cursor}]`.\\n\" }}\n",
+      "        {{- \"// Cite information from the tool using the following format:\\n\" }}\n",
+      "        {{- \"// `\u3010{cursor}\u2020L{line_start}(-L{line_end})?\u3011`, for example: `\u30106\u2020L9-L11\u3011` or `\u30108\u2020L3\u3011`.\\n\" }}\n",
+      "        {{- \"// Do not quote more than 10 words directly from the tool output.\\n\" }}\n",
+      "        {{- \"// sources=web (default: web)\\n\" }}\n",
+      "        {{- \"namespace browser {\\n\\n\" }}\n",
+      "        {{- \"// Searches for information related to `query` and displays `topn` results.\\n\" }}\n",
+      "        {{- \"type search = (_: {\\n\" }}\n",
+      "        {{- \"query: string,\\n\" }}\n",
+      "        {{- \"topn?: number, // default: 10\\n\" }}\n",
+      "        {{- \"source?: string,\\n\" }}\n",
+      "        {{- \"}) => any;\\n\\n\" }}\n",
+      "        {{- \"// Opens the link `id` from the page indicated by `cursor` starting at line number `loc`, showing `num_lines` lines.\\n\" }}\n",
+      "        {{- \"// Valid link ids are displayed with the formatting: `\u3010{id}\u2020.*\u3011`.\\n\" }}\n",
+      "        {{- \"// If `cursor` is not provided, the most recent page is implied.\\n\" }}\n",
+      "        {{- \"// If `id` is a string, it is treated as a fully qualified URL associated with `source`.\\n\" }}\n",
+      "        {{- \"// If `loc` is not provided, the viewport will be positioned at the beginning of the document or centered on the most relevant passage, if available.\\n\" }}\n",
+      "        {{- \"// Use this function without `id` to scroll to a new location of an opened page.\\n\" }}\n",
+      "        {{- \"type open = (_: {\\n\" }}\n",
+      "        {{- \"id?: number | string, // default: -1\\n\" }}\n",
+      "        {{- \"cursor?: number, // default: -1\\n\" }}\n",
+      "        {{- \"loc?: number, // default: -1\\n\" }}\n",
+      "        {{- \"num_lines?: number, // default: -1\\n\" }}\n",
+      "        {{- \"view_source?: boolean, // default: false\\n\" }}\n",
+      "        {{- \"source?: string,\\n\" }}\n",
+      "        {{- \"}) => any;\\n\\n\" }}\n",
+      "        {{- \"// Finds exact matches of `pattern` in the current page, or the page given by `cursor`.\\n\" }}\n",
+      "        {{- \"type find = (_: {\\n\" }}\n",
+      "        {{- \"pattern: string,\\n\" }}\n",
+      "        {{- \"cursor?: number, // default: -1\\n\" }}\n",
+      "        {{- \"}) => any;\\n\\n\" }}\n",
+      "        {{- \"} // namespace browser\\n\\n\" }}\n",
+      "    {%- endif -%}\n",
+      "\n",
+      "    {%- if python_tool %}\n",
+      "        {{- \"## python\\n\\n\" }}\n",
+      "        {{- \"Use this tool to execute Python code in your chain of thought. The code will not be shown to the user. This tool should be used for internal reasoning, but not for code that is intended to be visible to the user (e.g. when creating plots, tables, or files).\\n\\n\" }}\n",
+      "        {{- \"When you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment. python will respond with the output of the execution or time out after 120.0 seconds. The drive at '/mnt/data' can be used to save and persist user files. Internet access for this session is UNKNOWN. Depends on the cluster.\\n\\n\" }}\n",
+      "    {%- endif -%}\n",
+      "{%- endmacro -%}\n",
+      "\n",
+      "{#- System Message Construction ============================================ #}\n",
+      "{%- macro build_system_message() -%}\n",
+      "    {%- if model_identity is not defined %}\n",
+      "        {%- set model_identity = \"You are ChatGPT, a large language model trained by OpenAI.\" %}\n",
+      "    {%- endif %}\n",
+      "    {{- model_identity + \"\\n\" }}\n",
+      "    {{- \"Knowledge cutoff: 2024-06\\n\" }}\n",
+      "    {{- \"Current date: \" + strftime_now(\"%Y-%m-%d\") + \"\\n\\n\" }}\n",
+      "    {%- if reasoning_effort is not string %}\n",
+      "        {%- set reasoning_effort = \"medium\" %}\n",
+      "    {%- endif %}\n",
+      "    {{- \"Reasoning: \" + reasoning_effort + \"\\n\\n\" }}\n",
+      "    {%- if builtin_tools %}\n",
+      "        {{- \"# Tools\\n\\n\" }}\n",
+      "        {%- set available_builtin_tools = namespace(browser=false, python=false) %}\n",
+      "        {%- for tool in builtin_tools %}\n",
+      "            {%- if tool == \"browser\" %}\n",
+      "                {%- set available_builtin_tools.browser = true %}\n",
+      "            {%- elif tool == \"python\" %}\n",
+      "                {%- set available_builtin_tools.python = true %}\n",
+      "            {%- endif %}\n",
+      "        {%- endfor %}\n",
+      "        {{- render_builtin_tools(available_builtin_tools.browser, available_builtin_tools.python) }}\n",
+      "    {%- endif -%}\n",
+      "    {{- \"# Valid channels: analysis, commentary, final. Channel must be included for every message.\" }}\n",
+      "    {%- if tools -%}\n",
+      "        {{- \"\\nCalls to these tools must go to the commentary channel: 'functions'.\" }}\n",
+      "    {%- endif -%}\n",
+      "{%- endmacro -%}\n",
+      "\n",
+      "{#- Main Template Logic ================================================= #}\n",
+      "{#- Set defaults #}\n",
+      "\n",
+      "{#- Render system message #}\n",
+      "{{- \"<|start|>system<|message|>\" }}\n",
+      "{{- build_system_message() }}\n",
+      "{{- \"<|end|>\" }}\n",
+      "\n",
+      "{#- Extract developer message #}\n",
+      "{%- if messages[0].role == \"developer\" or messages[0].role == \"system\" %}\n",
+      "    {%- set developer_message = messages[0].content %}\n",
+      "    {%- set loop_messages = messages[1:] %}\n",
+      "{%- else %}\n",
+      "    {%- set developer_message = \"\" %}\n",
+      "    {%- set loop_messages = messages %}\n",
+      "{%- endif %}\n",
+      "\n",
+      "{#- Render developer message #}\n",
+      "{%- if developer_message or tools %}\n",
+      "    {{- \"<|start|>developer<|message|>\" }}\n",
+      "    {%- if developer_message %}\n",
+      "        {{- \"# Instructions\\n\\n\" }}\n",
+      "        {{- developer_message }}\n",
+      "        {{- \"\\n\\n\" }}\n",
+      "    {%- endif %}\n",
+      "    {%- if tools -%}\n",
+      "        {{- \"# Tools\\n\\n\" }}\n",
+      "        {{- render_tool_namespace(\"functions\", tools) }}\n",
+      "    {%- endif -%}\n",
+      "    {{- \"<|end|>\" }}\n",
+      "{%- endif %}\n",
+      "\n",
+      "{#- Render messages #}\n",
+      "{%- set last_tool_call = namespace(name=none) %}\n",
+      "{%- for message in loop_messages -%}\n",
+      "    {#- At this point only assistant/user/tool messages should remain #}\n",
+      "    {%- if message.role == 'assistant' -%}\n",
+      "        {#- Checks to ensure the messages are being passed in the format we expect #}\n",
+      "        {%- if \"content\" in message %}\n",
+      "            {%- if \"<|channel|>analysis<|message|>\" in message.content or \"<|channel|>final<|message|>\" in message.content %}\n",
+      "                {{- raise_exception(\"You have passed a message containing <|channel|> tags in the content field. Instead of doing this, you should pass analysis messages (the string between '<|message|>' and '<|end|>') in the 'thinking' field, and final messages (the string between '<|message|>' and '<|end|>') in the 'content' field.\") }}\n",
+      "            {%- endif %}\n",
+      "        {%- endif %}\n",
+      "        {%- if \"thinking\" in message %}\n",
+      "            {%- if \"<|channel|>analysis<|message|>\" in message.thinking or \"<|channel|>final<|message|>\" in message.thinking %}\n",
+      "                {{- raise_exception(\"You have passed a message containing <|channel|> tags in the thinking field. Instead of doing this, you should pass analysis messages (the string between '<|message|>' and '<|end|>') in the 'thinking' field, and final messages (the string between '<|message|>' and '<|end|>') in the 'content' field.\") }}\n",
+      "            {%- endif %}\n",
+      "        {%- endif %}\n",
+      "        {%- if \"tool_calls\" in message %}\n",
+      "            {#- We need very careful handling here - we want to drop the tool call analysis message if the model #}\n",
+      "            {#- has output a later <|final|> message, but otherwise we want to retain it. This is the only case #}\n",
+      "            {#- when we render CoT/analysis messages in inference. #}\n",
+      "            {%- set future_final_message = namespace(found=false) %}\n",
+      "            {%- for future_message in loop_messages[loop.index:] %}\n",
+      "                {%- if future_message.role == 'assistant' and \"tool_calls\" not in future_message %}\n",
+      "                    {%- set future_final_message.found = true %}\n",
+      "                {%- endif %}\n",
+      "            {%- endfor %}\n",
+      "            {#- We assume max 1 tool call per message, and so we infer the tool call name #}\n",
+      "            {#- in \"tool\" messages from the most recent assistant tool call name #}\n",
+      "            {%- set tool_call = message.tool_calls[0] %}\n",
+      "            {%- if tool_call.function %}\n",
+      "                {%- set tool_call = tool_call.function %}\n",
+      "            {%- endif %}\n",
+      "            {%- if message.content and message.thinking %}\n",
+      "                {{- raise_exception(\"Cannot pass both content and thinking in an assistant message with tool calls! Put the analysis message in one or the other, but not both.\") }}\n",
+      "            {%- elif message.content and not future_final_message.found %}\n",
+      "                {{- \"<|start|>assistant<|channel|>analysis<|message|>\" + message.content + \"<|end|>\" }}\n",
+      "            {%- elif message.thinking and not future_final_message.found %}\n",
+      "                {{- \"<|start|>assistant<|channel|>analysis<|message|>\" + message.thinking + \"<|end|>\" }}\n",
+      "            {%- endif %}\n",
+      "            {{- \"<|start|>assistant to=\" }}\n",
+      "            {{- \"functions.\" + tool_call.name + \"<|channel|>commentary \" }}\n",
+      "            {{- (tool_call.content_type if tool_call.content_type is defined else \"json\") + \"<|message|>\" }}\n",
+      "            {{- tool_call.arguments|tojson }}\n",
+      "            {{- \"<|call|>\" }}\n",
+      "            {%- set last_tool_call.name = tool_call.name %}\n",
+      "        {%- elif loop.last and not add_generation_prompt %}\n",
+      "            {#- Only render the CoT if the final turn is an assistant turn and add_generation_prompt is false #}\n",
+      "            {#- This is a situation that should only occur in training, never in inference. #}\n",
+      "            {%- if \"thinking\" in message %}\n",
+      "                {{- \"<|start|>assistant<|channel|>analysis<|message|>\" + message.thinking + \"<|end|>\" }}\n",
+      "            {%- endif %}\n",
+      "            {#- <|return|> indicates the end of generation, but <|end|> does not #}\n",
+      "            {#- <|return|> should never be an input to the model, but we include it as the final token #}\n",
+      "            {#- when training, so the model learns to emit it. #}\n",
+      "            {{- \"<|start|>assistant<|channel|>final<|message|>\" + message.content + \"<|return|>\" }}\n",
+      "        {%- else %}\n",
+      "            {#- CoT is dropped during all previous turns, so we never render it for inference #}\n",
+      "            {{- \"<|start|>assistant<|channel|>final<|message|>\" + message.content + \"<|end|>\" }}\n",
+      "            {%- set last_tool_call.name = none %}\n",
+      "        {%- endif %}\n",
+      "    {%- elif message.role == 'tool' -%}\n",
+      "        {%- if last_tool_call.name is none %}\n",
+      "            {{- raise_exception(\"Message has tool role, but there was no previous assistant message with a tool call!\") }}\n",
+      "        {%- endif %}\n",
+      "        {{- \"<|start|>functions.\" + last_tool_call.name }}\n",
+      "        {{- \" to=assistant<|channel|>commentary<|message|>\" + message.content|tojson + \"<|end|>\" }}\n",
+      "    {%- elif message.role == 'user' -%}\n",
+      "        {{- \"<|start|>user<|message|>\" + message.content + \"<|end|>\" }}\n",
+      "    {%- endif -%}\n",
+      "{%- endfor -%}\n",
+      "\n",
+      "{#- Generation prompt #}\n",
+      "{%- if add_generation_prompt -%}\n",
+      "{%- set forced_tool_name = none %}\n",
+      "{%- if tool_choice is mapping %}\n",
+      "    {%- if tool_choice.function is defined and tool_choice.function.name is defined %}\n",
+      "        {%- set forced_tool_name = tool_choice.function.name %}\n",
+      "    {%- elif tool_choice.name is defined %}\n",
+      "        {%- set forced_tool_name = tool_choice.name %}\n",
+      "    {%- endif %}\n",
+      "{%- elif function_call is mapping and function_call.name is defined %}\n",
+      "    {%- set forced_tool_name = function_call.name %}\n",
+      "{%- endif %}\n",
+      "{%- if forced_tool_name %}\n",
+      "{{- \"<|start|>assistant<|channel|>commentary to=functions.\" + forced_tool_name + \" <|constrain|>json<|message|>\" }}\n",
+      "{%- else %}\n",
+      "<|start|>assistant\n",
+      "{%- endif %}\n",
+      "{%- endif -%}"
+    ]
+  }
+}
diff --git a/examples/server/configs/qwen3.5-0.8b.json b/examples/server/configs/qwen3.5-0.8b.json
new file mode 100644
index 0000000000..c9f2ca944a
--- /dev/null
+++ b/examples/server/configs/qwen3.5-0.8b.json
@@ -0,0 +1,261 @@
+{
+  "server": {
+    "host": "0.0.0.0",
+    "port": 8000
+  },
+  "model": {
+    "alias": "qwen3.5-0.8b-vl",
+    "from_pretrained": {
+      "repo_id": "lmstudio-community/Qwen3.5-0.8B-GGUF",
+      "filename": "Qwen3.5-0.8B-Q8_0.gguf"
+    },
+    "mtmd": {
+      "mmproj_from_pretrained": {
+        "repo_id": "lmstudio-community/Qwen3.5-0.8B-GGUF",
+        "filename": "mmproj-Qwen3.5-0.8B-BF16.gguf"
+      }
+    },
+    "n_ctx": 32768,
+    "max_output_tokens": 4096,
+    "n_seq_max": 64,
+    "n_batch": 128,
+    "n_ubatch": 128,
+    "threads": 4,
+    "threads_batch": 8,
+    "kv_unified": true,
+    "store_logits": false,
+    "use_mmap": true,
+    "use_mlock": true,
+    "response_schema": {
+      "type": "object",
+      "properties": {
+        "role": {
+          "const": "assistant"
+        },
+        "reasoning_content": {
+          "type": "string",
+          "x-regex": "^(?:<\\|im_start\\|>assistant\\n)?(?:<think>\\n)?(.*?)(?=</think>)"
+        },
+        "content": {
+          "type": "string",
+          "x-regex": "^(?:<\\|im_start\\|>assistant\\n)?(?:(?:<think>\\n)?.*?</think>\\s*)?(.*?)(?=\\s*<tool_call>\\n|<\\|im_end\\|>$|$)"
+        },
+        "tool_calls": {
+          "type": "array",
+          "x-regex-iterator": "<tool_call>\\n(.*?)\\n</tool_call>",
+          "items": {
+            "type": "object",
+            "properties": {
+              "type": {
+                "const": "function"
+              },
+              "function": {
+                "type": "object",
+                "properties": {
+                  "name": {
+                    "type": "string",
+                    "x-regex": "^<function=([^>\\n]+)>\\n"
+                  },
+                  "arguments": {
+                    "type": "object",
+                    "x-regex": "^<function=[^>\\n]+>\\n(.*?)\\n</function>$",
+                    "x-regex-key-value": "<parameter=(?P<key>[^>\\n]+)>\\n(?P<value>.*?)\\n</parameter>",
+                    "additionalProperties": true
+                  }
+                },
+                "required": [
+                  "name",
+                  "arguments"
+                ]
+              }
+            },
+            "required": [
+              "type",
+              "function"
+            ]
+          }
+        }
+      },
+      "required": [
+        "role"
+      ]
+    },
+    "chat_template": [
+      "{%- set image_count = namespace(value=0) %}\n",
+      "{%- set video_count = namespace(value=0) %}\n",
+      "{%- if enable_thinking is not defined and reasoning_effort is string %}\n",
+      "    {%- set qwen_reasoning_effort = reasoning_effort|lower %}\n",
+      "    {%- if qwen_reasoning_effort in ['none', 'minimal', 'low'] %}\n",
+      "        {%- set enable_thinking = false %}\n",
+      "    {%- elif qwen_reasoning_effort in ['medium', 'high'] %}\n",
+      "        {%- set enable_thinking = true %}\n",
+      "    {%- endif %}\n",
+      "{%- endif %}\n",
+      "{%- set forced_tool_name = none %}\n",
+      "{%- if tool_choice is mapping %}\n",
+      "    {%- if tool_choice.function is defined and tool_choice.function.name is defined %}\n",
+      "        {%- set forced_tool_name = tool_choice.function.name %}\n",
+      "    {%- elif tool_choice.name is defined %}\n",
+      "        {%- set forced_tool_name = tool_choice.name %}\n",
+      "    {%- endif %}\n",
+      "{%- elif function_call is mapping and function_call.name is defined %}\n",
+      "    {%- set forced_tool_name = function_call.name %}\n",
+      "{%- endif %}\n",
+      "{%- macro render_content(content, do_vision_count, is_system_content=false) %}\n",
+      "    {%- if content is string %}\n",
+      "        {{- content }}\n",
+      "    {%- elif content is iterable and content is not mapping %}\n",
+      "        {%- for item in content %}\n",
+      "            {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}\n",
+      "                {%- if is_system_content %}\n",
+      "                    {{- raise_exception('System message cannot contain images.') }}\n",
+      "                {%- endif %}\n",
+      "                {%- if do_vision_count %}\n",
+      "                    {%- set image_count.value = image_count.value + 1 %}\n",
+      "                {%- endif %}\n",
+      "                {%- if add_vision_id %}\n",
+      "                    {{- 'Picture ' ~ image_count.value ~ ': ' }}\n",
+      "                {%- endif %}\n",
+      "                {{- '<|vision_start|><|image_pad|><|vision_end|>' }}\n",
+      "            {%- elif 'video' in item or item.type == 'video' %}\n",
+      "                {%- if is_system_content %}\n",
+      "                    {{- raise_exception('System message cannot contain videos.') }}\n",
+      "                {%- endif %}\n",
+      "                {%- if do_vision_count %}\n",
+      "                    {%- set video_count.value = video_count.value + 1 %}\n",
+      "                {%- endif %}\n",
+      "                {%- if add_vision_id %}\n",
+      "                    {{- 'Video ' ~ video_count.value ~ ': ' }}\n",
+      "                {%- endif %}\n",
+      "                {{- '<|vision_start|><|video_pad|><|vision_end|>' }}\n",
+      "            {%- elif 'text' in item %}\n",
+      "                {{- item.text }}\n",
+      "            {%- else %}\n",
+      "                {{- raise_exception('Unexpected item type in content.') }}\n",
+      "            {%- endif %}\n",
+      "        {%- endfor %}\n",
+      "    {%- elif content is none or content is undefined %}\n",
+      "        {{- '' }}\n",
+      "    {%- else %}\n",
+      "        {{- raise_exception('Unexpected content type.') }}\n",
+      "    {%- endif %}\n",
+      "{%- endmacro %}\n",
+      "{%- if not messages %}\n",
+      "    {{- raise_exception('No messages provided.') }}\n",
+      "{%- endif %}\n",
+      "{%- if tools and tools is iterable and tools is not mapping %}\n",
+      "    {{- '<|im_start|>system\\n' }}\n",
+      "    {{- \"# Tools\\n\\nYou have access to the following functions:\\n\\n<tools>\" }}\n",
+      "    {%- for tool in tools %}\n",
+      "        {{- \"\\n\" }}\n",
+      "        {{- tool | tojson }}\n",
+      "    {%- endfor %}\n",
+      "    {{- \"\\n</tools>\" }}\n",
+      "    {{- '\\n\\nIf you choose to call a function ONLY reply in the following format with NO suffix:\\n\\n<tool_call>\\n<function=example_function_name>\\n<parameter=example_parameter_1>\\nvalue_1\\n</parameter>\\n<parameter=example_parameter_2>\\nThis is the value for the second parameter\\nthat can span\\nmultiple lines\\n</parameter>\\n</function>\\n</tool_call>\\n\\n<IMPORTANT>\\nReminder:\\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\\n- Required parameters MUST be specified\\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\\n</IMPORTANT>' }}\n",
+      "    {%- if messages[0].role == 'system' %}\n",
+      "        {%- set content = render_content(messages[0].content, false, true)|trim %}\n",
+      "        {%- if content %}\n",
+      "            {{- '\\n\\n' + content }}\n",
+      "        {%- endif %}\n",
+      "    {%- endif %}\n",
+      "    {{- '<|im_end|>\\n' }}\n",
+      "{%- else %}\n",
+      "    {%- if messages[0].role == 'system' %}\n",
+      "        {%- set content = render_content(messages[0].content, false, true)|trim %}\n",
+      "        {{- '<|im_start|>system\\n' + content + '<|im_end|>\\n' }}\n",
+      "    {%- endif %}\n",
+      "{%- endif %}\n",
+      "{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n",
+      "{%- for message in messages[::-1] %}\n",
+      "    {%- set index = (messages|length - 1) - loop.index0 %}\n",
+      "    {%- if ns.multi_step_tool and message.role == \"user\" %}\n",
+      "        {%- set content = render_content(message.content, false)|trim %}\n",
+      "        {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}\n",
+      "            {%- set ns.multi_step_tool = false %}\n",
+      "            {%- set ns.last_query_index = index %}\n",
+      "        {%- endif %}\n",
+      "    {%- endif %}\n",
+      "{%- endfor %}\n",
+      "{%- if ns.multi_step_tool %}\n",
+      "    {{- raise_exception('No user query found in messages.') }}\n",
+      "{%- endif %}\n",
+      "{%- for message in messages %}\n",
+      "    {%- set content = render_content(message.content, true)|trim %}\n",
+      "    {%- if message.role == \"system\" %}\n",
+      "        {%- if not loop.first %}\n",
+      "            {{- raise_exception('System message must be at the beginning.') }}\n",
+      "        {%- endif %}\n",
+      "    {%- elif message.role == \"user\" %}\n",
+      "        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n",
+      "    {%- elif message.role == \"assistant\" %}\n",
+      "        {%- set reasoning_content = '' %}\n",
+      "        {%- if message.reasoning_content is string %}\n",
+      "            {%- set reasoning_content = message.reasoning_content %}\n",
+      "        {%- else %}\n",
+      "            {%- if '</think>' in content %}\n",
+      "                {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n",
+      "                {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n",
+      "            {%- endif %}\n",
+      "        {%- endif %}\n",
+      "        {%- set reasoning_content = reasoning_content|trim %}\n",
+      "        {%- if loop.index0 > ns.last_query_index %}\n",
+      "            {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content + '\\n</think>\\n\\n' + content }}\n",
+      "        {%- else %}\n",
+      "            {{- '<|im_start|>' + message.role + '\\n' + content }}\n",
+      "        {%- endif %}\n",
+      "        {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}\n",
+      "            {%- for tool_call in message.tool_calls %}\n",
+      "                {%- if tool_call.function is defined %}\n",
+      "                    {%- set tool_call = tool_call.function %}\n",
+      "                {%- endif %}\n",
+      "                {%- if loop.first %}\n",
+      "                    {%- if content|trim %}\n",
+      "                        {{- '\\n\\n<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n",
+      "                    {%- else %}\n",
+      "                        {{- '<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n",
+      "                    {%- endif %}\n",
+      "                {%- else %}\n",
+      "                    {{- '\\n<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n",
+      "                {%- endif %}\n",
+      "                {%- if tool_call.arguments is defined %}\n",
+      "                    {%- set arguments = tool_call.arguments | from_json if tool_call.arguments is string else tool_call.arguments %}\n",
+      "                    {%- for args_name, args_value in arguments|items %}\n",
+      "                        {{- '<parameter=' + args_name + '>\\n' }}\n",
+      "                        {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}\n",
+      "                        {{- args_value }}\n",
+      "                        {{- '\\n</parameter>\\n' }}\n",
+      "                    {%- endfor %}\n",
+      "                {%- endif %}\n",
+      "                {{- '</function>\\n</tool_call>' }}\n",
+      "            {%- endfor %}\n",
+      "        {%- endif %}\n",
+      "        {{- '<|im_end|>\\n' }}\n",
+      "    {%- elif message.role == \"tool\" %}\n",
+      "        {%- if loop.previtem and loop.previtem.role != \"tool\" %}\n",
+      "            {{- '<|im_start|>user' }}\n",
+      "        {%- endif %}\n",
+      "        {{- '\\n<tool_response>\\n' }}\n",
+      "        {{- content }}\n",
+      "        {{- '\\n</tool_response>' }}\n",
+      "        {%- if not loop.last and loop.nextitem.role != \"tool\" %}\n",
+      "            {{- '<|im_end|>\\n' }}\n",
+      "        {%- elif loop.last %}\n",
+      "            {{- '<|im_end|>\\n' }}\n",
+      "        {%- endif %}\n",
+      "    {%- else %}\n",
+      "        {{- raise_exception('Unexpected message role.') }}\n",
+      "    {%- endif %}\n",
+      "{%- endfor %}\n",
+      "{%- if add_generation_prompt %}\n",
+      "    {{- '<|im_start|>assistant\\n' }}\n",
+      "    {%- if forced_tool_name %}\n",
+      "        {{- '<tool_call>\\n<function=' + forced_tool_name + '>\\n' }}\n",
+      "    {%- elif enable_thinking is defined and enable_thinking is false %}\n",
+      "        {{- '<think>\\n\\n</think>\\n\\n' }}\n",
+      "    {%- else %}\n",
+      "        {{- '<think>\\n' }}\n",
+      "    {%- endif %}\n",
+      "{%- endif %}"
+    ]
+  }
+}
diff --git a/examples/server/configs/qwen3.6-27b.json b/examples/server/configs/qwen3.6-27b.json
new file mode 100644
index 0000000000..c5b42d8b72
--- /dev/null
+++ b/examples/server/configs/qwen3.6-27b.json
@@ -0,0 +1,263 @@
+{
+  "server": {
+    "host": "0.0.0.0",
+    "port": 8000
+  },
+  "model": {
+    "alias": "qwen3.6-27b",
+    "from_pretrained": {
+      "repo_id": "unsloth/Qwen3.6-27B-GGUF",
+      "filename": "Qwen3.6-27B-Q8_0.gguf"
+    },
+    "mtmd": {
+      "mmproj_from_pretrained": {
+        "repo_id": "unsloth/Qwen3.6-27B-GGUF",
+        "filename": "mmproj-BF16.gguf"
+      }
+    },
+    "n_ctx": 32768,
+    "max_output_tokens": 4096,
+    "n_seq_max": 8,
+    "n_batch": 512,
+    "n_ubatch": 512,
+    "threads": 8,
+    "threads_batch": 8,
+    "kv_unified": true,
+    "store_logits": false,
+    "use_mmap": true,
+    "use_mlock": false,
+    "n_gpu_layers": -1,
+    "flash_attn": true,
+    "response_schema": {
+      "type": "object",
+      "properties": {
+        "role": {
+          "const": "assistant"
+        },
+        "reasoning_content": {
+          "type": "string",
+          "x-regex": "^(?:<\\|im_start\\|>assistant\\n)?(?:<think>\\n)?(.*?)(?=</think>)"
+        },
+        "content": {
+          "type": "string",
+          "x-regex": "^(?:<\\|im_start\\|>assistant\\n)?(?:(?:<think>\\n)?.*?</think>\\s*)?(.*?)(?=\\s*<tool_call>\\n|<\\|im_end\\|>$|$)"
+        },
+        "tool_calls": {
+          "type": "array",
+          "x-regex-iterator": "<tool_call>\\n(.*?)\\n</tool_call>",
+          "items": {
+            "type": "object",
+            "properties": {
+              "type": {
+                "const": "function"
+              },
+              "function": {
+                "type": "object",
+                "properties": {
+                  "name": {
+                    "type": "string",
+                    "x-regex": "^<function=([^>\\n]+)>\\n"
+                  },
+                  "arguments": {
+                    "type": "object",
+                    "x-regex": "^<function=[^>\\n]+>\\n(.*?)\\n</function>$",
+                    "x-regex-key-value": "<parameter=(?P<key>[^>\\n]+)>\\n(?P<value>.*?)\\n</parameter>",
+                    "additionalProperties": true
+                  }
+                },
+                "required": [
+                  "name",
+                  "arguments"
+                ]
+              }
+            },
+            "required": [
+              "type",
+              "function"
+            ]
+          }
+        }
+      },
+      "required": [
+        "role"
+      ]
+    },
+    "chat_template": [
+      "{%- set image_count = namespace(value=0) %}\n",
+      "{%- set video_count = namespace(value=0) %}\n",
+      "{%- if enable_thinking is not defined and reasoning_effort is string %}\n",
+      "    {%- set qwen_reasoning_effort = reasoning_effort|lower %}\n",
+      "    {%- if qwen_reasoning_effort in ['none', 'minimal', 'low'] %}\n",
+      "        {%- set enable_thinking = false %}\n",
+      "    {%- elif qwen_reasoning_effort in ['medium', 'high'] %}\n",
+      "        {%- set enable_thinking = true %}\n",
+      "    {%- endif %}\n",
+      "{%- endif %}\n",
+      "{%- set forced_tool_name = none %}\n",
+      "{%- if tool_choice is mapping %}\n",
+      "    {%- if tool_choice.function is defined and tool_choice.function.name is defined %}\n",
+      "        {%- set forced_tool_name = tool_choice.function.name %}\n",
+      "    {%- elif tool_choice.name is defined %}\n",
+      "        {%- set forced_tool_name = tool_choice.name %}\n",
+      "    {%- endif %}\n",
+      "{%- elif function_call is mapping and function_call.name is defined %}\n",
+      "    {%- set forced_tool_name = function_call.name %}\n",
+      "{%- endif %}\n",
+      "{%- macro render_content(content, do_vision_count, is_system_content=false) %}\n",
+      "    {%- if content is string %}\n",
+      "        {{- content }}\n",
+      "    {%- elif content is iterable and content is not mapping %}\n",
+      "        {%- for item in content %}\n",
+      "            {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}\n",
+      "                {%- if is_system_content %}\n",
+      "                    {{- raise_exception('System message cannot contain images.') }}\n",
+      "                {%- endif %}\n",
+      "                {%- if do_vision_count %}\n",
+      "                    {%- set image_count.value = image_count.value + 1 %}\n",
+      "                {%- endif %}\n",
+      "                {%- if add_vision_id %}\n",
+      "                    {{- 'Picture ' ~ image_count.value ~ ': ' }}\n",
+      "                {%- endif %}\n",
+      "                {{- '<|vision_start|><|image_pad|><|vision_end|>' }}\n",
+      "            {%- elif 'video' in item or item.type == 'video' %}\n",
+      "                {%- if is_system_content %}\n",
+      "                    {{- raise_exception('System message cannot contain videos.') }}\n",
+      "                {%- endif %}\n",
+      "                {%- if do_vision_count %}\n",
+      "                    {%- set video_count.value = video_count.value + 1 %}\n",
+      "                {%- endif %}\n",
+      "                {%- if add_vision_id %}\n",
+      "                    {{- 'Video ' ~ video_count.value ~ ': ' }}\n",
+      "                {%- endif %}\n",
+      "                {{- '<|vision_start|><|video_pad|><|vision_end|>' }}\n",
+      "            {%- elif 'text' in item %}\n",
+      "                {{- item.text }}\n",
+      "            {%- else %}\n",
+      "                {{- raise_exception('Unexpected item type in content.') }}\n",
+      "            {%- endif %}\n",
+      "        {%- endfor %}\n",
+      "    {%- elif content is none or content is undefined %}\n",
+      "        {{- '' }}\n",
+      "    {%- else %}\n",
+      "        {{- raise_exception('Unexpected content type.') }}\n",
+      "    {%- endif %}\n",
+      "{%- endmacro %}\n",
+      "{%- if not messages %}\n",
+      "    {{- raise_exception('No messages provided.') }}\n",
+      "{%- endif %}\n",
+      "{%- if tools and tools is iterable and tools is not mapping %}\n",
+      "    {{- '<|im_start|>system\\n' }}\n",
+      "    {{- \"# Tools\\n\\nYou have access to the following functions:\\n\\n<tools>\" }}\n",
+      "    {%- for tool in tools %}\n",
+      "        {{- \"\\n\" }}\n",
+      "        {{- tool | tojson }}\n",
+      "    {%- endfor %}\n",
+      "    {{- \"\\n</tools>\" }}\n",
+      "    {{- '\\n\\nIf you choose to call a function ONLY reply in the following format with NO suffix:\\n\\n<tool_call>\\n<function=example_function_name>\\n<parameter=example_parameter_1>\\nvalue_1\\n</parameter>\\n<parameter=example_parameter_2>\\nThis is the value for the second parameter\\nthat can span\\nmultiple lines\\n</parameter>\\n</function>\\n</tool_call>\\n\\n<IMPORTANT>\\nReminder:\\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\\n- Required parameters MUST be specified\\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\\n</IMPORTANT>' }}\n",
+      "    {%- if messages[0].role == 'system' %}\n",
+      "        {%- set content = render_content(messages[0].content, false, true)|trim %}\n",
+      "        {%- if content %}\n",
+      "            {{- '\\n\\n' + content }}\n",
+      "        {%- endif %}\n",
+      "    {%- endif %}\n",
+      "    {{- '<|im_end|>\\n' }}\n",
+      "{%- else %}\n",
+      "    {%- if messages[0].role == 'system' %}\n",
+      "        {%- set content = render_content(messages[0].content, false, true)|trim %}\n",
+      "        {{- '<|im_start|>system\\n' + content + '<|im_end|>\\n' }}\n",
+      "    {%- endif %}\n",
+      "{%- endif %}\n",
+      "{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n",
+      "{%- for message in messages[::-1] %}\n",
+      "    {%- set index = (messages|length - 1) - loop.index0 %}\n",
+      "    {%- if ns.multi_step_tool and message.role == \"user\" %}\n",
+      "        {%- set content = render_content(message.content, false)|trim %}\n",
+      "        {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}\n",
+      "            {%- set ns.multi_step_tool = false %}\n",
+      "            {%- set ns.last_query_index = index %}\n",
+      "        {%- endif %}\n",
+      "    {%- endif %}\n",
+      "{%- endfor %}\n",
+      "{%- if ns.multi_step_tool %}\n",
+      "    {{- raise_exception('No user query found in messages.') }}\n",
+      "{%- endif %}\n",
+      "{%- for message in messages %}\n",
+      "    {%- set content = render_content(message.content, true)|trim %}\n",
+      "    {%- if message.role == \"system\" %}\n",
+      "        {%- if not loop.first %}\n",
+      "            {{- raise_exception('System message must be at the beginning.') }}\n",
+      "        {%- endif %}\n",
+      "    {%- elif message.role == \"user\" %}\n",
+      "        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n",
+      "    {%- elif message.role == \"assistant\" %}\n",
+      "        {%- set reasoning_content = '' %}\n",
+      "        {%- if message.reasoning_content is string %}\n",
+      "            {%- set reasoning_content = message.reasoning_content %}\n",
+      "        {%- else %}\n",
+      "            {%- if '</think>' in content %}\n",
+      "                {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n",
+      "                {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n",
+      "            {%- endif %}\n",
+      "        {%- endif %}\n",
+      "        {%- set reasoning_content = reasoning_content|trim %}\n",
+      "        {%- if (preserve_thinking is defined and preserve_thinking is true) or (loop.index0 > ns.last_query_index) %}\n",
+      "            {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content + '\\n</think>\\n\\n' + content }}\n",
+      "        {%- else %}\n",
+      "            {{- '<|im_start|>' + message.role + '\\n' + content }}\n",
+      "        {%- endif %}\n",
+      "        {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}\n",
+      "            {%- for tool_call in message.tool_calls %}\n",
+      "                {%- if tool_call.function is defined %}\n",
+      "                    {%- set tool_call = tool_call.function %}\n",
+      "                {%- endif %}\n",
+      "                {%- if loop.first %}\n",
+      "                    {%- if content|trim %}\n",
+      "                        {{- '\\n\\n<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n",
+      "                    {%- else %}\n",
+      "                        {{- '<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n",
+      "                    {%- endif %}\n",
+      "                {%- else %}\n",
+      "                    {{- '\\n<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n",
+      "                {%- endif %}\n",
+      "                {%- if tool_call.arguments is defined %}\n",
+      "                    {%- set arguments = tool_call.arguments | from_json if tool_call.arguments is string else tool_call.arguments %}\n",
+      "                    {%- for args_name, args_value in arguments|items %}\n",
+      "                        {{- '<parameter=' + args_name + '>\\n' }}\n",
+      "                        {%- set args_value = args_value | string if args_value is string else args_value | tojson | safe %}\n",
+      "                        {{- args_value }}\n",
+      "                        {{- '\\n</parameter>\\n' }}\n",
+      "                    {%- endfor %}\n",
+      "                {%- endif %}\n",
+      "                {{- '</function>\\n</tool_call>' }}\n",
+      "            {%- endfor %}\n",
+      "        {%- endif %}\n",
+      "        {{- '<|im_end|>\\n' }}\n",
+      "    {%- elif message.role == \"tool\" %}\n",
+      "        {%- if loop.previtem and loop.previtem.role != \"tool\" %}\n",
+      "            {{- '<|im_start|>user' }}\n",
+      "        {%- endif %}\n",
+      "        {{- '\\n<tool_response>\\n' }}\n",
+      "        {{- content }}\n",
+      "        {{- '\\n</tool_response>' }}\n",
+      "        {%- if not loop.last and loop.nextitem.role != \"tool\" %}\n",
+      "            {{- '<|im_end|>\\n' }}\n",
+      "        {%- elif loop.last %}\n",
+      "            {{- '<|im_end|>\\n' }}\n",
+      "        {%- endif %}\n",
+      "    {%- else %}\n",
+      "        {{- raise_exception('Unexpected message role.') }}\n",
+      "    {%- endif %}\n",
+      "{%- endfor %}\n",
+      "{%- if add_generation_prompt %}\n",
+      "    {{- '<|im_start|>assistant\\n' }}\n",
+      "    {%- if forced_tool_name %}\n",
+      "        {{- '<tool_call>\\n<function=' + forced_tool_name + '>\\n' }}\n",
+      "    {%- elif enable_thinking is defined and enable_thinking is false %}\n",
+      "        {{- '<think>\\n\\n</think>\\n\\n' }}\n",
+      "    {%- else %}\n",
+      "        {{- '<think>\\n' }}\n",
+      "    {%- endif %}\n",
+      "{%- endif %}"
+    ]
+  }
+}
diff --git a/examples/server/configs/qwen3.6-35b-a3b.json b/examples/server/configs/qwen3.6-35b-a3b.json
new file mode 100644
index 0000000000..10043f9c1a
--- /dev/null
+++ b/examples/server/configs/qwen3.6-35b-a3b.json
@@ -0,0 +1,263 @@
+{
+  "server": {
+    "host": "0.0.0.0",
+    "port": 8000
+  },
+  "model": {
+    "alias": "qwen3.6-35b-a3b",
+    "from_pretrained": {
+      "repo_id": "unsloth/Qwen3.6-35B-A3B-GGUF",
+      "filename": "Qwen3.6-35B-A3B-Q8_0.gguf"
+    },
+    "mtmd": {
+      "mmproj_from_pretrained": {
+        "repo_id": "unsloth/Qwen3.6-35B-A3B-GGUF",
+        "filename": "mmproj-BF16.gguf"
+      }
+    },
+    "n_ctx": 32768,
+    "max_output_tokens": 4096,
+    "n_seq_max": 8,
+    "n_batch": 512,
+    "n_ubatch": 512,
+    "threads": 8,
+    "threads_batch": 8,
+    "kv_unified": true,
+    "store_logits": false,
+    "use_mmap": true,
+    "use_mlock": false,
+    "n_gpu_layers": -1,
+    "flash_attn": true,
+    "response_schema": {
+      "type": "object",
+      "properties": {
+        "role": {
+          "const": "assistant"
+        },
+        "reasoning_content": {
+          "type": "string",
+          "x-regex": "^(?:<\\|im_start\\|>assistant\\n)?(?:<think>\\n)?(.*?)(?=</think>)"
+        },
+        "content": {
+          "type": "string",
+          "x-regex": "^(?:<\\|im_start\\|>assistant\\n)?(?:(?:<think>\\n)?.*?</think>\\s*)?(.*?)(?=\\s*<tool_call>\\n|<\\|im_end\\|>$|$)"
+        },
+        "tool_calls": {
+          "type": "array",
+          "x-regex-iterator": "<tool_call>\\n(.*?)\\n</tool_call>",
+          "items": {
+            "type": "object",
+            "properties": {
+              "type": {
+                "const": "function"
+              },
+              "function": {
+                "type": "object",
+                "properties": {
+                  "name": {
+                    "type": "string",
+                    "x-regex": "^<function=([^>\\n]+)>\\n"
+                  },
+                  "arguments": {
+                    "type": "object",
+                    "x-regex": "^<function=[^>\\n]+>\\n(.*?)\\n</function>$",
+                    "x-regex-key-value": "<parameter=(?P<key>[^>\\n]+)>\\n(?P<value>.*?)\\n</parameter>",
+                    "additionalProperties": true
+                  }
+                },
+                "required": [
+                  "name",
+                  "arguments"
+                ]
+              }
+            },
+            "required": [
+              "type",
+              "function"
+            ]
+          }
+        }
+      },
+      "required": [
+        "role"
+      ]
+    },
+    "chat_template": [
+      "{%- set image_count = namespace(value=0) %}\n",
+      "{%- set video_count = namespace(value=0) %}\n",
+      "{%- if enable_thinking is not defined and reasoning_effort is string %}\n",
+      "    {%- set qwen_reasoning_effort = reasoning_effort|lower %}\n",
+      "    {%- if qwen_reasoning_effort in ['none', 'minimal', 'low'] %}\n",
+      "        {%- set enable_thinking = false %}\n",
+      "    {%- elif qwen_reasoning_effort in ['medium', 'high'] %}\n",
+      "        {%- set enable_thinking = true %}\n",
+      "    {%- endif %}\n",
+      "{%- endif %}\n",
+      "{%- set forced_tool_name = none %}\n",
+      "{%- if tool_choice is mapping %}\n",
+      "    {%- if tool_choice.function is defined and tool_choice.function.name is defined %}\n",
+      "        {%- set forced_tool_name = tool_choice.function.name %}\n",
+      "    {%- elif tool_choice.name is defined %}\n",
+      "        {%- set forced_tool_name = tool_choice.name %}\n",
+      "    {%- endif %}\n",
+      "{%- elif function_call is mapping and function_call.name is defined %}\n",
+      "    {%- set forced_tool_name = function_call.name %}\n",
+      "{%- endif %}\n",
+      "{%- macro render_content(content, do_vision_count, is_system_content=false) %}\n",
+      "    {%- if content is string %}\n",
+      "        {{- content }}\n",
+      "    {%- elif content is iterable and content is not mapping %}\n",
+      "        {%- for item in content %}\n",
+      "            {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}\n",
+      "                {%- if is_system_content %}\n",
+      "                    {{- raise_exception('System message cannot contain images.') }}\n",
+      "                {%- endif %}\n",
+      "                {%- if do_vision_count %}\n",
+      "                    {%- set image_count.value = image_count.value + 1 %}\n",
+      "                {%- endif %}\n",
+      "                {%- if add_vision_id %}\n",
+      "                    {{- 'Picture ' ~ image_count.value ~ ': ' }}\n",
+      "                {%- endif %}\n",
+      "                {{- '<|vision_start|><|image_pad|><|vision_end|>' }}\n",
+      "            {%- elif 'video' in item or item.type == 'video' %}\n",
+      "                {%- if is_system_content %}\n",
+      "                    {{- raise_exception('System message cannot contain videos.') }}\n",
+      "                {%- endif %}\n",
+      "                {%- if do_vision_count %}\n",
+      "                    {%- set video_count.value = video_count.value + 1 %}\n",
+      "                {%- endif %}\n",
+      "                {%- if add_vision_id %}\n",
+      "                    {{- 'Video ' ~ video_count.value ~ ': ' }}\n",
+      "                {%- endif %}\n",
+      "                {{- '<|vision_start|><|video_pad|><|vision_end|>' }}\n",
+      "            {%- elif 'text' in item %}\n",
+      "                {{- item.text }}\n",
+      "            {%- else %}\n",
+      "                {{- raise_exception('Unexpected item type in content.') }}\n",
+      "            {%- endif %}\n",
+      "        {%- endfor %}\n",
+      "    {%- elif content is none or content is undefined %}\n",
+      "        {{- '' }}\n",
+      "    {%- else %}\n",
+      "        {{- raise_exception('Unexpected content type.') }}\n",
+      "    {%- endif %}\n",
+      "{%- endmacro %}\n",
+      "{%- if not messages %}\n",
+      "    {{- raise_exception('No messages provided.') }}\n",
+      "{%- endif %}\n",
+      "{%- if tools and tools is iterable and tools is not mapping %}\n",
+      "    {{- '<|im_start|>system\\n' }}\n",
+      "    {{- \"# Tools\\n\\nYou have access to the following functions:\\n\\n<tools>\" }}\n",
+      "    {%- for tool in tools %}\n",
+      "        {{- \"\\n\" }}\n",
+      "        {{- tool | tojson }}\n",
+      "    {%- endfor %}\n",
+      "    {{- \"\\n</tools>\" }}\n",
+      "    {{- '\\n\\nIf you choose to call a function ONLY reply in the following format with NO suffix:\\n\\n<tool_call>\\n<function=example_function_name>\\n<parameter=example_parameter_1>\\nvalue_1\\n</parameter>\\n<parameter=example_parameter_2>\\nThis is the value for the second parameter\\nthat can span\\nmultiple lines\\n</parameter>\\n</function>\\n</tool_call>\\n\\n<IMPORTANT>\\nReminder:\\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\\n- Required parameters MUST be specified\\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\\n</IMPORTANT>' }}\n",
+      "    {%- if messages[0].role == 'system' %}\n",
+      "        {%- set content = render_content(messages[0].content, false, true)|trim %}\n",
+      "        {%- if content %}\n",
+      "            {{- '\\n\\n' + content }}\n",
+      "        {%- endif %}\n",
+      "    {%- endif %}\n",
+      "    {{- '<|im_end|>\\n' }}\n",
+      "{%- else %}\n",
+      "    {%- if messages[0].role == 'system' %}\n",
+      "        {%- set content = render_content(messages[0].content, false, true)|trim %}\n",
+      "        {{- '<|im_start|>system\\n' + content + '<|im_end|>\\n' }}\n",
+      "    {%- endif %}\n",
+      "{%- endif %}\n",
+      "{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n",
+      "{%- for message in messages[::-1] %}\n",
+      "    {%- set index = (messages|length - 1) - loop.index0 %}\n",
+      "    {%- if ns.multi_step_tool and message.role == \"user\" %}\n",
+      "        {%- set content = render_content(message.content, false)|trim %}\n",
+      "        {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}\n",
+      "            {%- set ns.multi_step_tool = false %}\n",
+      "            {%- set ns.last_query_index = index %}\n",
+      "        {%- endif %}\n",
+      "    {%- endif %}\n",
+      "{%- endfor %}\n",
+      "{%- if ns.multi_step_tool %}\n",
+      "    {{- raise_exception('No user query found in messages.') }}\n",
+      "{%- endif %}\n",
+      "{%- for message in messages %}\n",
+      "    {%- set content = render_content(message.content, true)|trim %}\n",
+      "    {%- if message.role == \"system\" %}\n",
+      "        {%- if not loop.first %}\n",
+      "            {{- raise_exception('System message must be at the beginning.') }}\n",
+      "        {%- endif %}\n",
+      "    {%- elif message.role == \"user\" %}\n",
+      "        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n",
+      "    {%- elif message.role == \"assistant\" %}\n",
+      "        {%- set reasoning_content = '' %}\n",
+      "        {%- if message.reasoning_content is string %}\n",
+      "            {%- set reasoning_content = message.reasoning_content %}\n",
+      "        {%- else %}\n",
+      "            {%- if '</think>' in content %}\n",
+      "                {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n",
+      "                {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n",
+      "            {%- endif %}\n",
+      "        {%- endif %}\n",
+      "        {%- set reasoning_content = reasoning_content|trim %}\n",
+      "        {%- if (preserve_thinking is defined and preserve_thinking is true) or (loop.index0 > ns.last_query_index) %}\n",
+      "            {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content + '\\n</think>\\n\\n' + content }}\n",
+      "        {%- else %}\n",
+      "            {{- '<|im_start|>' + message.role + '\\n' + content }}\n",
+      "        {%- endif %}\n",
+      "        {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}\n",
+      "            {%- for tool_call in message.tool_calls %}\n",
+      "                {%- if tool_call.function is defined %}\n",
+      "                    {%- set tool_call = tool_call.function %}\n",
+      "                {%- endif %}\n",
+      "                {%- if loop.first %}\n",
+      "                    {%- if content|trim %}\n",
+      "                        {{- '\\n\\n<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n",
+      "                    {%- else %}\n",
+      "                        {{- '<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n",
+      "                    {%- endif %}\n",
+      "                {%- else %}\n",
+      "                    {{- '\\n<tool_call>\\n<function=' + tool_call.name + '>\\n' }}\n",
+      "                {%- endif %}\n",
+      "                {%- if tool_call.arguments is defined %}\n",
+      "                    {%- set arguments = tool_call.arguments | from_json if tool_call.arguments is string else tool_call.arguments %}\n",
+      "                    {%- for args_name, args_value in arguments|items %}\n",
+      "                        {{- '<parameter=' + args_name + '>\\n' }}\n",
+      "                        {%- set args_value = args_value | string if args_value is string else args_value | tojson | safe %}\n",
+      "                        {{- args_value }}\n",
+      "                        {{- '\\n</parameter>\\n' }}\n",
+      "                    {%- endfor %}\n",
+      "                {%- endif %}\n",
+      "                {{- '</function>\\n</tool_call>' }}\n",
+      "            {%- endfor %}\n",
+      "        {%- endif %}\n",
+      "        {{- '<|im_end|>\\n' }}\n",
+      "    {%- elif message.role == \"tool\" %}\n",
+      "        {%- if loop.previtem and loop.previtem.role != \"tool\" %}\n",
+      "            {{- '<|im_start|>user' }}\n",
+      "        {%- endif %}\n",
+      "        {{- '\\n<tool_response>\\n' }}\n",
+      "        {{- content }}\n",
+      "        {{- '\\n</tool_response>' }}\n",
+      "        {%- if not loop.last and loop.nextitem.role != \"tool\" %}\n",
+      "            {{- '<|im_end|>\\n' }}\n",
+      "        {%- elif loop.last %}\n",
+      "            {{- '<|im_end|>\\n' }}\n",
+      "        {%- endif %}\n",
+      "    {%- else %}\n",
+      "        {{- raise_exception('Unexpected message role.') }}\n",
+      "    {%- endif %}\n",
+      "{%- endfor %}\n",
+      "{%- if add_generation_prompt %}\n",
+      "    {{- '<|im_start|>assistant\\n' }}\n",
+      "    {%- if forced_tool_name %}\n",
+      "        {{- '<tool_call>\\n<function=' + forced_tool_name + '>\\n' }}\n",
+      "    {%- elif enable_thinking is defined and enable_thinking is false %}\n",
+      "        {{- '<think>\\n\\n</think>\\n\\n' }}\n",
+      "    {%- else %}\n",
+      "        {{- '<think>\\n' }}\n",
+      "    {%- endif %}\n",
+      "{%- endif %}"
+    ]
+  }
+}
diff --git a/examples/server/server.py b/examples/server/server.py
new file mode 100644
index 0000000000..16f8c9f7e5
--- /dev/null
+++ b/examples/server/server.py
@@ -0,0 +1,16343 @@
+#!/usr/bin/env -S uv run --script
+# /// script
+# requires-python = ">=3.8"
+# dependencies = [
+#   "fastapi",
+#   "jinja2",
+#   "llama-cpp-python",
+#   "numpy",
+#   "openai",
+#   "pydantic",
+#   "safetensors",
+#   "uvicorn",
+#   "websockets",
+# ]
+# ///
+
+from __future__ import annotations
+
+import abc
+import os
+import re
+import json
+import math
+import time
+import uuid
+import queue
+import ctypes
+import fnmatch
+import base64
+import hashlib
+import binascii
+import asyncio
+import argparse
+import threading
+import multiprocessing
+import copy
+import shutil
+import inspect
+import sys
+import tempfile
+import urllib.error
+import urllib.parse
+import urllib.request
+
+from pathlib import Path
+from datetime import datetime
+from dataclasses import dataclass, field
+from collections import OrderedDict, deque
+from openai.types.completion import Completion as OpenAICompletion
+from openai.types.completion_choice import (
+    CompletionChoice,
+    Logprobs as CompletionLogprobs,
+)
+from openai.types.completion_usage import CompletionUsage
+from openai.types.chat.chat_completion import (
+    ChatCompletion,
+    Choice as ChatCompletionChoice,
+    ChoiceLogprobs as ChatCompletionChoiceLogprobs,
+)
+from openai.types.chat.chat_completion_chunk import (
+    ChatCompletionChunk,
+    Choice as ChatCompletionChunkChoice,
+    ChoiceDelta,
+    ChoiceDeltaFunctionCall,
+    ChoiceDeltaToolCallFunction,
+    ChoiceDeltaToolCall,
+    ChoiceLogprobs as ChatCompletionChunkChoiceLogprobs,
+)
+from openai.types.chat.chat_completion_message import (
+    ChatCompletionMessage,
+    FunctionCall as ChatCompletionMessageFunctionCall,
+)
+from openai.types.chat.chat_completion_message_tool_call import (
+    ChatCompletionMessageToolCall,
+    Function as ChatCompletionMessageToolCallFunction,
+)
+from openai.types.chat.chat_completion_token_logprob import (
+    ChatCompletionTokenLogprob,
+    TopLogprob,
+)
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Generator,
+    Iterable,
+    List,
+    Optional,
+    Sequence,
+    Tuple,
+    Union,
+    Deque,
+    Literal,
+    Iterator,
+    Protocol,
+    TypedDict,
+    cast,
+    AsyncIterator,
+)
+
+import jinja2
+import uvicorn
+import numpy as np
+
+from fastapi import FastAPI, HTTPException, Request, WebSocket, WebSocketDisconnect
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse, Response, StreamingResponse
+
+from jinja2.sandbox import ImmutableSandboxedEnvironment
+
+from pydantic_core import from_json
+from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
+
+from llama_cpp import llama_cpp  # noqa: E402
+from llama_cpp import llama_cpp_ext  # noqa: E402
+from llama_cpp import mtmd_cpp  # noqa: E402
+
+
+JSON_GBNF = r"""
+root   ::= object
+value  ::= object | array | string | number | ("true" | "false" | "null") ws
+
+object ::=
+  "{" ws (
+            string ":" ws value
+    ("," ws string ":" ws value)*
+  )? "}" ws
+
+array  ::=
+  "[" ws (
+            value
+    ("," ws value)*
+  )? "]" ws
+
+string ::=
+  "\"" (
+    [^"\\\x7F\x00-\x1F] |
+    "\\" (["\\bfnrt] | "u" [0-9a-fA-F]{4})
+  )* "\"" ws
+
+number ::= ("-"? ([0-9] | [1-9] [0-9]{0,15})) ("." [0-9]+)? ([eE] [-+]? [0-9] [1-9]{0,15})? ws
+
+ws ::= | " " | "\n" [ \t]{0,20}
+"""
+
+
+class JsonSchemaConverter:
+    @dataclass(frozen=True)
+    class BuiltinRule:
+        content: str
+        deps: Sequence[str] = ()
+
+    SPACE_RULE = '" "?'
+    INVALID_RULE_CHARS_RE = re.compile(r"[^a-zA-Z0-9-]+")
+    GRAMMAR_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"]')
+    GRAMMAR_LITERAL_ESCAPES = {"\r": "\\r", "\n": "\\n", '"': '\\"'}
+    DOTALL = "[\\U00000000-\\U0010FFFF]"
+    DOT = "[^\\x0A\\x0D]"
+    NON_LITERAL_SET = set("|.()[]{}*+?")
+    ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = set("[]()|{}*+?")
+
+    PRIMITIVE_RULES: Optional[Dict[str, "JsonSchemaConverter.BuiltinRule"]] = None
+    STRING_FORMAT_RULES: Optional[Dict[str, "JsonSchemaConverter.BuiltinRule"]] = None
+    RESERVED_NAMES: Optional[set[str]] = None
+
+    @staticmethod
+    def _build_repetition(
+        item_rule: str,
+        min_items: int,
+        max_items: Optional[int],
+        separator_rule: Optional[str] = None,
+        item_rule_is_literal: bool = False,
+    ) -> str:
+        if not separator_rule:
+            if min_items == 0 and max_items == 1:
+                return f"{item_rule}?"
+            if min_items == 1 and max_items is None:
+                return f"{item_rule}+"
+
+        result = ""
+
+        if min_items > 0:
+            if item_rule_is_literal and separator_rule is None:
+                result = '"' + (item_rule[1:-1] * min_items) + '"'
+            else:
+                result = (f" {separator_rule} " if separator_rule else " ").join(
+                    [item_rule] * min_items
+                )
+
+        def opt_repetitions(up_to_n: int, prefix_with_sep: bool = False) -> str:
+            content = (
+                f"{separator_rule} {item_rule}"
+                if prefix_with_sep and separator_rule
+                else item_rule
+            )
+            if up_to_n == 0:
+                return ""
+            if up_to_n == 1:
+                return f"({content})?"
+            if separator_rule and not prefix_with_sep:
+                return f"({content} {opt_repetitions(up_to_n - 1, prefix_with_sep=True)})?"
+            return (f"({content} " * up_to_n).rstrip() + (")?" * up_to_n)
+
+        if min_items > 0 and max_items != min_items:
+            result += " "
+
+        if max_items is not None:
+            result += opt_repetitions(max_items - min_items, prefix_with_sep=min_items > 0)
+        else:
+            item_operator = f"({separator_rule + ' ' if separator_rule else ''}{item_rule})"
+            if min_items == 0 and separator_rule:
+                result = f"({item_rule} {item_operator}*)?"
+            else:
+                result += f"{item_operator}*"
+
+        return result
+
+    @classmethod
+    def _primitive_rules(cls) -> Dict[str, "JsonSchemaConverter.BuiltinRule"]:
+        if cls.PRIMITIVE_RULES is None:
+            up_to_15_digits = cls._build_repetition("[0-9]", 0, 15)
+            cls.PRIMITIVE_RULES = {
+                "boolean": cls.BuiltinRule('("true" | "false") space', []),
+                "decimal-part": cls.BuiltinRule("[0-9] " + up_to_15_digits, []),
+                "integral-part": cls.BuiltinRule(
+                    "[0-9] | [1-9] " + up_to_15_digits,
+                    [],
+                ),
+                "number": cls.BuiltinRule(
+                    '("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space',
+                    ["integral-part", "decimal-part"],
+                ),
+                "integer": cls.BuiltinRule('("-"? integral-part) space', ["integral-part"]),
+                "value": cls.BuiltinRule(
+                    "object | array | string | number | boolean | null",
+                    ["object", "array", "string", "number", "boolean", "null"],
+                ),
+                "object": cls.BuiltinRule(
+                    '"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space',
+                    ["string", "value"],
+                ),
+                "array": cls.BuiltinRule(
+                    '"[" space ( value ("," space value)* )? "]" space',
+                    ["value"],
+                ),
+                "uuid": cls.BuiltinRule(
+                    r'"\"" '
+                    + ' "-" '.join("[0-9a-fA-F]" * n for n in [8, 4, 4, 4, 12])
+                    + r' "\"" space',
+                    [],
+                ),
+                "char": cls.BuiltinRule(
+                    r'[^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])',
+                    [],
+                ),
+                "string": cls.BuiltinRule(r'"\"" char* "\"" space', ["char"]),
+                "null": cls.BuiltinRule('"null" space', []),
+            }
+        return cls.PRIMITIVE_RULES
+
+    @classmethod
+    def _string_format_rules(cls) -> Dict[str, "JsonSchemaConverter.BuiltinRule"]:
+        if cls.STRING_FORMAT_RULES is None:
+            cls.STRING_FORMAT_RULES = {
+                "date": cls.BuiltinRule(
+                    '[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( "0" [1-9] | [1-2] [0-9] | "3" [0-1] )',
+                    [],
+                ),
+                "time": cls.BuiltinRule(
+                    '([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )',
+                    [],
+                ),
+                "date-time": cls.BuiltinRule('date "T" time', ["date", "time"]),
+                "date-string": cls.BuiltinRule('"\\"" date "\\"" space', ["date"]),
+                "time-string": cls.BuiltinRule('"\\"" time "\\"" space', ["time"]),
+                "date-time-string": cls.BuiltinRule(
+                    '"\\"" date-time "\\"" space',
+                    ["date-time"],
+                ),
+            }
+        return cls.STRING_FORMAT_RULES
+
+    @classmethod
+    def _reserved_names(cls) -> set[str]:
+        if cls.RESERVED_NAMES is None:
+            cls.RESERVED_NAMES = set(
+                ["root", "dot", *cls._primitive_rules().keys(), *cls._string_format_rules().keys()]
+            )
+        return cls.RESERVED_NAMES
+
+    def __init__(
+        self,
+        *,
+        prop_order: Dict[str, int],
+        allow_fetch: bool,
+        dotall: bool,
+        raw_pattern: bool,
+    ):
+        self._prop_order = prop_order
+        self._allow_fetch = allow_fetch
+        self._dotall = dotall
+        self._raw_pattern = raw_pattern
+        self._rules: Dict[str, str] = {"space": self.SPACE_RULE}
+        self._refs: Dict[str, Any] = {}
+        self._refs_being_resolved: set[str] = set()
+
+    def _format_literal(self, literal: str) -> str:
+        escaped = self.GRAMMAR_LITERAL_ESCAPE_RE.sub(
+            lambda match: self.GRAMMAR_LITERAL_ESCAPES[match.group(0)],
+            literal,
+        )
+        return f'"{escaped}"'
+
+    def _add_rule(self, name: str, rule: str) -> str:
+        escaped_name = self.INVALID_RULE_CHARS_RE.sub("-", name)
+        if escaped_name not in self._rules or self._rules[escaped_name] == rule:
+            key = escaped_name
+        else:
+            suffix = 0
+            while (
+                f"{escaped_name}{suffix}" in self._rules
+                and self._rules[f"{escaped_name}{suffix}"] != rule
+            ):
+                suffix += 1
+            key = f"{escaped_name}{suffix}"
+        self._rules[key] = rule
+        return key
+
+    def resolve_refs(self, schema: Dict[str, Any], url: str) -> Dict[str, Any]:
+        def visit(node: Any) -> Any:
+            if isinstance(node, list):
+                return [visit(child) for child in node]
+            if isinstance(node, dict):
+                ref = node.get("$ref")
+                if ref is not None and ref not in self._refs:
+                    if ref.startswith("https://"):
+                        raise ValueError("remote schema fetch is not allowed")
+                    elif ref.startswith("#/"):
+                        target = schema
+                        ref = f"{url}{ref}"
+                        node["$ref"] = ref
+                    else:
+                        raise ValueError(f"Unsupported ref {ref}")
+
+                    for selector in ref.split("#")[-1].split("/")[1:]:
+                        assert target is not None and selector in target, (
+                            f"Error resolving ref {ref}: {selector} not in {target}"
+                        )
+                        target = target[selector]
+                    self._refs[ref] = target
+                else:
+                    for value in node.values():
+                        visit(value)
+            return node
+
+        return cast(Dict[str, Any], visit(schema))
+
+    def _generate_union_rule(self, name: str, alt_schemas: List[Dict[str, Any]]) -> str:
+        return " | ".join(
+            self.visit(alt_schema, f"{name}{'-' if name else 'alternative-'}{index}")
+            for index, alt_schema in enumerate(alt_schemas)
+        )
+
+    def _visit_pattern(self, pattern: str, name: str) -> str:
+        assert pattern.startswith("^") and pattern.endswith("$"), (
+            'Pattern must start with "^" and end with "$"'
+        )
+        pattern = pattern[1:-1]
+        sub_rule_ids: Dict[str, str] = {}
+        index = 0
+        length = len(pattern)
+
+        def to_rule(item: Tuple[str, bool]) -> str:
+            text, is_literal = item
+            return f'"{text}"' if is_literal else text
+
+        def transform() -> Tuple[str, bool]:
+            nonlocal index
+            start = index
+            sequence: List[Tuple[str, bool]] = []
+
+            def get_dot() -> str:
+                rule = self.DOTALL if self._dotall else self.DOT
+                return self._add_rule("dot", rule)
+
+            def join_sequence() -> Tuple[str, bool]:
+                if len(sequence) == 1:
+                    return sequence[0]
+                return (" ".join(to_rule(item) for item in sequence), False)
+
+            while index < length:
+                char = pattern[index]
+                if char == ".":
+                    sequence.append((get_dot(), False))
+                    index += 1
+                elif char == "(":
+                    index += 1
+                    if index < length:
+                        assert pattern[index] != "?", (
+                            f'Unsupported pattern syntax "{pattern[index]}" at index {index} of /{pattern}/'
+                        )
+                    sequence.append((f"({to_rule(transform())})", False))
+                elif char == ")":
+                    index += 1
+                    assert start > 0 and pattern[start - 1] == "(", (
+                        f"Unbalanced parentheses; start = {start}, index = {index}, pattern = {pattern}"
+                    )
+                    return join_sequence()
+                elif char == "[":
+                    square_brackets = char
+                    index += 1
+                    while index < length and pattern[index] != "]":
+                        if pattern[index] == "\\":
+                            square_brackets += pattern[index : index + 2]
+                            index += 2
+                        else:
+                            square_brackets += pattern[index]
+                            index += 1
+                    assert index < length, (
+                        f"Unbalanced square brackets; start = {start}, index = {index}, pattern = {pattern}"
+                    )
+                    square_brackets += "]"
+                    index += 1
+                    sequence.append((square_brackets, False))
+                elif char == "|":
+                    sequence.append(("|", False))
+                    index += 1
+                elif char in ("*", "+", "?"):
+                    sequence[-1] = (to_rule(sequence[-1]) + char, False)
+                    index += 1
+                elif char == "{":
+                    curly_brackets = char
+                    index += 1
+                    while index < length and pattern[index] != "}":
+                        curly_brackets += pattern[index]
+                        index += 1
+                    assert index < length, (
+                        f"Unbalanced curly brackets; start = {start}, index = {index}, pattern = {pattern}"
+                    )
+                    curly_brackets += "}"
+                    index += 1
+                    numbers = [part.strip() for part in curly_brackets[1:-1].split(",")]
+                    min_times = 0
+                    max_times: Optional[int] = None
+                    try:
+                        if len(numbers) == 1:
+                            min_times = int(numbers[0])
+                            max_times = min_times
+                        else:
+                            assert len(numbers) == 2
+                            min_times = int(numbers[0]) if numbers[0] else 0
+                            max_times = int(numbers[1]) if numbers[1] else None
+                    except ValueError as exc:
+                        raise ValueError(
+                            f"Invalid quantifier {curly_brackets} in /{pattern}/"
+                        ) from exc
+
+                    sub, sub_is_literal = sequence[-1]
+                    if not sub_is_literal:
+                        rule_id = sub_rule_ids.get(sub)
+                        if rule_id is None:
+                            rule_id = self._add_rule(f"{name}-{len(sub_rule_ids) + 1}", sub)
+                            sub_rule_ids[sub] = rule_id
+                        sub = rule_id
+
+                    sequence[-1] = (
+                        self._build_repetition(
+                            f'"{sub}"' if sub_is_literal else sub,
+                            min_times,
+                            max_times,
+                            item_rule_is_literal=sub_is_literal,
+                        ),
+                        False,
+                    )
+                else:
+                    literal = ""
+                    while index < length:
+                        if pattern[index] == "\\" and index < length - 1:
+                            next_char = pattern[index + 1]
+                            if next_char in self.ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS:
+                                index += 1
+                                literal += pattern[index]
+                                index += 1
+                            else:
+                                literal += pattern[index : index + 2]
+                                index += 2
+                        elif pattern[index] == '"' and not self._raw_pattern:
+                            literal += '\\"'
+                            index += 1
+                        elif pattern[index] not in self.NON_LITERAL_SET and (
+                            index == length - 1
+                            or literal == ""
+                            or pattern[index + 1] == "."
+                            or pattern[index + 1] not in self.NON_LITERAL_SET
+                        ):
+                            literal += pattern[index]
+                            index += 1
+                        else:
+                            break
+                    if literal:
+                        sequence.append((literal, True))
+
+            return join_sequence()
+
+        return self._add_rule(
+            name,
+            (
+                to_rule(transform())
+                if self._raw_pattern
+                else '"\\"" ' + to_rule(transform()) + ' "\\"" space'
+            ),
+        )
+
+    def _resolve_ref(self, ref: str) -> str:
+        ref_name = ref.split("/")[-1]
+        if ref_name not in self._rules and ref not in self._refs_being_resolved:
+            self._refs_being_resolved.add(ref)
+            resolved = self._refs[ref]
+            ref_name = self.visit(resolved, ref_name)
+            self._refs_being_resolved.remove(ref)
+        return ref_name
+
+    def _generate_constant_rule(self, value: Any) -> str:
+        return self._format_literal(json.dumps(value))
+
+    def visit(self, schema: Dict[str, Any], name: str) -> str:
+        schema_type = schema.get("type")
+        schema_format = schema.get("format")
+        rule_name = name + "-" if name in self._reserved_names() else name or "root"
+
+        ref = schema.get("$ref")
+        if ref is not None:
+            return self._add_rule(rule_name, self._resolve_ref(ref))
+
+        if "oneOf" in schema or "anyOf" in schema:
+            return self._add_rule(
+                rule_name,
+                self._generate_union_rule(name, cast(List[Dict[str, Any]], schema.get("oneOf") or schema["anyOf"])),
+            )
+
+        if isinstance(schema_type, list):
+            return self._add_rule(
+                rule_name,
+                self._generate_union_rule(name, [{"type": entry} for entry in schema_type]),
+            )
+
+        if "const" in schema:
+            return self._add_rule(rule_name, self._generate_constant_rule(schema["const"]))
+
+        if "enum" in schema:
+            rule = " | ".join(self._generate_constant_rule(value) for value in schema["enum"])
+            return self._add_rule(rule_name, rule)
+
+        if schema_type in (None, "object") and (
+            "properties" in schema
+            or ("additionalProperties" in schema and schema["additionalProperties"] is not True)
+        ):
+            required_props = set(schema.get("required", []))
+            property_items = list(cast(Dict[str, Any], schema.get("properties", {})).items())
+            return self._add_rule(
+                rule_name,
+                self._build_object_rule(
+                    property_items, required_props, name, schema.get("additionalProperties")
+                ),
+            )
+
+        if schema_type in (None, "object") and "allOf" in schema:
+            allof_required_props: set[str] = set()
+            allof_property_items: List[Tuple[str, Any]] = []
+
+            def add_component(component_schema: Dict[str, Any], is_required: bool) -> None:
+                component_ref = component_schema.get("$ref")
+                if component_ref is not None:
+                    component_schema = cast(Dict[str, Any], self._refs[component_ref])
+                if "properties" in component_schema:
+                    for prop_name, prop_schema in cast(Dict[str, Any], component_schema["properties"]).items():
+                        allof_property_items.append((prop_name, prop_schema))
+                        if is_required:
+                            allof_required_props.add(prop_name)
+
+            for entry in cast(List[Dict[str, Any]], schema["allOf"]):
+                if "anyOf" in entry:
+                    for alt in cast(List[Dict[str, Any]], entry["anyOf"]):
+                        add_component(alt, is_required=False)
+                else:
+                    add_component(entry, is_required=True)
+
+            return self._add_rule(
+                rule_name,
+                self._build_object_rule(
+                    allof_property_items,
+                    allof_required_props,
+                    name,
+                    additional_properties=[],
+                ),
+            )
+
+        if schema_type in (None, "array") and ("items" in schema or "prefixItems" in schema):
+            items = schema.get("items") or schema["prefixItems"]
+            if isinstance(items, list):
+                return self._add_rule(
+                    rule_name,
+                    '"[" space '
+                    + ' "," space '.join(
+                        self.visit(item, f"{name}{'-' if name else ''}tuple-{index}")
+                        for index, item in enumerate(items)
+                    )
+                    + ' "]" space',
+                )
+            item_rule_name = self.visit(cast(Dict[str, Any], items), f"{name}{'-' if name else ''}item")
+            min_items = int(schema.get("minItems", 0))
+            max_items = cast(Optional[int], schema.get("maxItems"))
+            return self._add_rule(
+                rule_name,
+                '"[" space '
+                + self._build_repetition(
+                    item_rule_name,
+                    min_items,
+                    max_items,
+                    separator_rule='"," space',
+                )
+                + ' "]" space',
+            )
+
+        if schema_type in (None, "string") and "pattern" in schema:
+            return self._visit_pattern(cast(str, schema["pattern"]), rule_name)
+
+        if schema_type in (None, "string") and re.match(r"^uuid[1-5]?$", schema_format or ""):
+            return self._add_primitive(
+                "root" if rule_name == "root" else cast(str, schema_format),
+                self._primitive_rules()["uuid"],
+            )
+
+        if schema_type in (None, "string") and f"{schema_format}-string" in self._string_format_rules():
+            primitive_name = f"{schema_format}-string"
+            return self._add_rule(
+                rule_name,
+                self._add_primitive(primitive_name, self._string_format_rules()[primitive_name]),
+            )
+
+        if schema_type == "string" and ("minLength" in schema or "maxLength" in schema):
+            char_rule = self._add_primitive("char", self._primitive_rules()["char"])
+            min_len = int(schema.get("minLength", 0))
+            max_len = cast(Optional[int], schema.get("maxLength"))
+            return self._add_rule(
+                rule_name,
+                r'"\"" '
+                + self._build_repetition(char_rule, min_len, max_len)
+                + r' "\"" space',
+            )
+
+        if schema_type == "object" or len(schema) == 0:
+            return self._add_rule(
+                rule_name,
+                self._add_primitive("object", self._primitive_rules()["object"]),
+            )
+
+        primitive_rules = self._primitive_rules()
+        assert schema_type in primitive_rules, f"Unrecognized schema: {schema}"
+        return self._add_primitive(
+            "root" if rule_name == "root" else cast(str, schema_type),
+            primitive_rules[cast(str, schema_type)],
+        )
+
+    def _add_primitive(self, name: str, rule: "JsonSchemaConverter.BuiltinRule") -> str:
+        rule_name = self._add_rule(name, rule.content)
+        primitive_rules = self._primitive_rules()
+        string_format_rules = self._string_format_rules()
+        for dependency in rule.deps:
+            dependency_rule = primitive_rules.get(dependency) or string_format_rules.get(
+                dependency
+            )
+            assert dependency_rule is not None, f"Rule {dependency} not known"
+            if dependency not in self._rules:
+                self._add_primitive(dependency, dependency_rule)
+        return rule_name
+
+    def _build_object_rule(
+        self,
+        properties: List[Tuple[str, Any]],
+        required: set[str],
+        name: str,
+        additional_properties: Union[bool, Any],
+    ) -> str:
+        prop_order = self._prop_order
+        sorted_props = [
+            key
+            for _, (key, _) in sorted(
+                enumerate(properties),
+                key=lambda indexed_key: (
+                    prop_order.get(indexed_key[1][0], len(prop_order)),
+                    indexed_key[0],
+                ),
+            )
+        ]
+
+        property_kv_rule_names: Dict[str, str] = {}
+        for prop_name, prop_schema in properties:
+            prop_rule_name = self.visit(
+                cast(Dict[str, Any], prop_schema),
+                f"{name}{'-' if name else ''}{prop_name}",
+            )
+            property_kv_rule_names[prop_name] = self._add_rule(
+                f"{name}{'-' if name else ''}{prop_name}-kv",
+                rf'{self._format_literal(json.dumps(prop_name))} space ":" space {prop_rule_name}',
+            )
+
+        required_props = [key for key in sorted_props if key in required]
+        optional_props = [key for key in sorted_props if key not in required]
+
+        if additional_properties is True or isinstance(additional_properties, dict):
+            sub_name = f"{name}{'-' if name else ''}additional"
+            value_rule = self.visit(
+                {} if additional_properties is True else cast(Dict[str, Any], additional_properties),
+                f"{sub_name}-value",
+            )
+            property_kv_rule_names["*"] = self._add_rule(
+                f"{sub_name}-kv",
+                self._add_primitive("string", self._primitive_rules()["string"])
+                + f' ":" space {value_rule}',
+            )
+            optional_props.append("*")
+
+        rule = '"{" space '
+        rule += ' "," space '.join(property_kv_rule_names[key] for key in required_props)
+
+        if optional_props:
+            if required_props:
+                rule += ' ( "," space ( '
+            else:
+                rule += "( "
+
+            def get_recursive_refs(keys: List[str], first_is_optional: bool) -> str:
+                head, *rest = keys
+                kv_rule_name = property_kv_rule_names[head]
+                result = ""
+                if head == "*":
+                    if first_is_optional:
+                        result = f"({kv_rule_name})?"
+                    else:
+                        result = kv_rule_name
+                elif first_is_optional:
+                    result = f'( "," space {kv_rule_name} )?'
+                else:
+                    result = kv_rule_name
+                if rest:
+                    result += " " + self._add_rule(
+                        f"{name}{'-' if name else ''}{head}-rest",
+                        get_recursive_refs(rest, first_is_optional=True),
+                    )
+                return result
+
+            rule += " | ".join(
+                get_recursive_refs(optional_props[index:], first_is_optional=False)
+                for index in range(len(optional_props))
+            )
+            if required_props:
+                rule += " )"
+            rule += " )?"
+
+        rule += ' "}" space'
+        return rule
+
+    def format_grammar(self) -> str:
+        return "\n".join(
+            f"{name} ::= {rule}"
+            for name, rule in sorted(self._rules.items(), key=lambda item: item[0])
+        )
+
+    @classmethod
+    def to_gbnf(cls, schema: str, prop_order: Optional[List[str]] = None) -> str:
+        property_order = prop_order or []
+        loaded_schema = json.loads(schema)
+        order_index = {name: index for index, name in enumerate(property_order)}
+        converter = cls(
+            prop_order=order_index,
+            allow_fetch=False,
+            dotall=False,
+            raw_pattern=False,
+        )
+        resolved_schema = converter.resolve_refs(loaded_schema, "stdin")
+        converter.visit(resolved_schema, "")
+        return converter.format_grammar()
+
+
+class RadixTrie:
+    __slots__ = ("root", "sequences", "sequence_lengths")
+
+    @dataclass
+    class Node:
+        label: Tuple[int, ...] = ()
+        parent: Optional["RadixTrie.Node"] = None
+        children: Dict[int, "RadixTrie.Node"] = field(default_factory=dict)
+        sequences: set[int] = field(default_factory=set)
+        tails: set[int] = field(default_factory=set)
+
+    def __init__(self) -> None:
+        self.root = RadixTrie.Node()
+        self.sequences: Dict[int, RadixTrie.Node] = {}
+        self.sequence_lengths: Dict[int, int] = {}
+
+    @staticmethod
+    def _pick_sequence(candidates: set[int], preferred_sequences: Optional[Any]) -> int:
+        if preferred_sequences is None:
+            return next(iter(candidates))
+        if isinstance(preferred_sequences, OrderedDict):
+            for sequence_id in reversed(preferred_sequences):
+                if sequence_id in candidates:
+                    return sequence_id
+            return next(iter(candidates))
+        if isinstance(preferred_sequences, (list, tuple)):
+            for sequence_id in reversed(preferred_sequences):
+                if sequence_id in candidates:
+                    return sequence_id
+            return next(iter(candidates))
+        preferred = candidates & preferred_sequences
+        if preferred:
+            return next(iter(preferred))
+        return next(iter(candidates))
+
+    @staticmethod
+    def _common_prefix_len(
+        label: Sequence[int],
+        tokens: Sequence[int],
+        offset: int,
+    ) -> int:
+        limit = min(len(label), len(tokens) - offset)
+        match_len = 0
+        while match_len < limit and label[match_len] == tokens[offset + match_len]:
+            match_len += 1
+        return match_len
+
+    def _split_child(
+        self,
+        parent: "RadixTrie.Node",
+        child: "RadixTrie.Node",
+        prefix_len: int,
+    ) -> "RadixTrie.Node":
+        assert 0 < prefix_len < len(child.label)
+        prefix = child.label[:prefix_len]
+        suffix = child.label[prefix_len:]
+        middle = RadixTrie.Node(
+            label=prefix,
+            parent=parent,
+            sequences=set(child.sequences),
+        )
+        parent.children[prefix[0]] = middle
+        child.label = suffix
+        child.parent = middle
+        middle.children[suffix[0]] = child
+        return middle
+
+    def _locate_prefix_node(
+        self,
+        sequence_id: int,
+        keep_len: int,
+    ) -> "RadixTrie.Node":
+        total_len = self.sequence_lengths[sequence_id]
+        assert 0 <= keep_len <= total_len
+        if keep_len == 0:
+            return self.root
+        node = self.sequences[sequence_id]
+        drop_len = total_len - keep_len
+        while node is not self.root:
+            label_len = len(node.label)
+            if drop_len == 0:
+                return node
+            if drop_len < label_len:
+                parent = node.parent
+                assert parent is not None
+                return self._split_child(parent, node, label_len - drop_len)
+            drop_len -= label_len
+            parent = node.parent
+            assert parent is not None
+            node = parent
+        return self.root
+
+    def extend(
+        self,
+        sequence_id: int,
+        tokens: Sequence[int],
+    ) -> None:
+        assert sequence_id >= 0
+        node = self.sequences.get(sequence_id, self.root)
+        if tokens:
+            node.tails.discard(sequence_id)
+        length = self.sequence_lengths.get(sequence_id, 0)
+        index = 0
+        while index < len(tokens):
+            token = tokens[index]
+            child = node.children.get(token)
+            if child is None:
+                child = RadixTrie.Node(
+                    label=tuple(tokens[index:]),
+                    parent=node,
+                    sequences={sequence_id},
+                )
+                node.children[token] = child
+                node = child
+                length += len(tokens) - index
+                index = len(tokens)
+                break
+            match_len = self._common_prefix_len(child.label, tokens, index)
+            if match_len == len(child.label):
+                child.sequences.add(sequence_id)
+                node = child
+                length += match_len
+                index += match_len
+                continue
+            node = self._split_child(node, child, match_len)
+            node.sequences.add(sequence_id)
+            length += match_len
+            index += match_len
+            if index == len(tokens):
+                break
+            suffix = RadixTrie.Node(
+                label=tuple(tokens[index:]),
+                parent=node,
+                sequences={sequence_id},
+            )
+            node.children[suffix.label[0]] = suffix
+            node = suffix
+            length += len(tokens) - index
+            index = len(tokens)
+            break
+        if node is self.root:
+            self.sequences.pop(sequence_id, None)
+            self.sequence_lengths.pop(sequence_id, None)
+            self.root.tails.discard(sequence_id)
+        else:
+            self.sequences[sequence_id] = node
+            self.sequence_lengths[sequence_id] = length
+            node.tails.add(sequence_id)
+
+    def length(self, sequence_id: int) -> int:
+        return self.sequence_lengths.get(sequence_id, 0)
+
+    def truncate(self, sequence_id: int, keep_len: int) -> None:
+        assert sequence_id >= 0
+        assert sequence_id in self.sequence_lengths
+        assert 0 <= keep_len <= self.sequence_lengths[sequence_id]
+        current_len = self.sequence_lengths[sequence_id]
+        if keep_len == current_len:
+            return
+        boundary = self._locate_prefix_node(sequence_id, keep_len)
+        node = self.sequences.get(sequence_id, self.root)
+        if node is not self.root:
+            node.tails.discard(sequence_id)
+        while node is not boundary:
+            node.sequences.remove(sequence_id)
+            parent = node.parent
+            assert parent is not None
+            if not node.sequences:
+                del parent.children[node.label[0]]
+            node = parent
+        if boundary is self.root:
+            self.sequences.pop(sequence_id, None)
+            self.sequence_lengths.pop(sequence_id, None)
+        else:
+            self.sequences[sequence_id] = boundary
+            self.sequence_lengths[sequence_id] = keep_len
+            boundary.tails.add(sequence_id)
+
+    def copy(self, source_sequence_id: int, dest_sequence_id: int, keep_len: int) -> None:
+        assert source_sequence_id >= 0
+        assert dest_sequence_id >= 0
+        assert source_sequence_id in self.sequence_lengths
+        assert dest_sequence_id not in self.sequence_lengths
+        assert 0 <= keep_len <= self.sequence_lengths[source_sequence_id]
+        if keep_len == 0:
+            self.sequences[dest_sequence_id] = self.root
+            self.sequence_lengths[dest_sequence_id] = 0
+            self.root.tails.add(dest_sequence_id)
+            return
+        node = self._locate_prefix_node(source_sequence_id, keep_len)
+        self.sequences[dest_sequence_id] = node
+        self.sequence_lengths[dest_sequence_id] = keep_len
+        node.tails.add(dest_sequence_id)
+        while node is not self.root:
+            node.sequences.add(dest_sequence_id)
+            parent = node.parent
+            assert parent is not None
+            node = parent
+
+    def tokens(self, sequence_id: int, keep_len: Optional[int] = None) -> List[int]:
+        length = self.sequence_lengths[sequence_id]
+        target_len = length if keep_len is None else keep_len
+        assert 0 <= target_len <= length
+        node = self.sequences[sequence_id]
+        labels: List[Tuple[int, ...]] = []
+        while node is not self.root:
+            labels.append(node.label)
+            parent = node.parent
+            assert parent is not None
+            node = parent
+        values: List[int] = []
+        for label in reversed(labels):
+            values.extend(label)
+        return values[:target_len]
+
+    def longest_prefix(
+        self,
+        tokens: Sequence[int],
+        preferred_sequences: Optional[Any] = None,
+        *,
+        exact_only: bool = False,
+    ) -> Tuple[int, int]:
+        node = self.root
+        longest_sequence_id = -1
+        longest_length = 0
+        index = 0
+        while index < len(tokens):
+            child = node.children.get(tokens[index])
+            if child is None:
+                break
+            match_len = self._common_prefix_len(child.label, tokens, index)
+            if match_len < len(child.label):
+                break
+            node = child
+            index += match_len
+            candidates = node.tails if exact_only else node.sequences
+            if candidates:
+                longest_sequence_id = self._pick_sequence(candidates, preferred_sequences)
+                longest_length = index
+        return longest_sequence_id, longest_length
+
+
+class SequenceHistory:
+    __slots__ = ("_position_lengths", "_root", "_tails", "size")
+
+    @dataclass
+    class Node:
+        token: Optional[int] = None
+        parent: Optional["SequenceHistory.Node"] = None
+        children: Dict[int, "SequenceHistory.Node"] = field(default_factory=dict)
+        sequences: set[int] = field(default_factory=set)
+        position_increment: int = 1
+
+    def __init__(self) -> None:
+        self._root = SequenceHistory.Node()
+        self._tails: Dict[int, SequenceHistory.Node] = {}
+        self._position_lengths: Dict[int, int] = {}
+        self.size = 0
+
+    def extend(
+        self,
+        sequence_id: int,
+        tokens: Sequence[int],
+        position_increments: Optional[Sequence[int]] = None,
+    ) -> None:
+        assert sequence_id >= 0
+        if position_increments is None:
+            position_increments = [1] * len(tokens)
+        assert len(position_increments) == len(tokens)
+        node = self._tails.get(sequence_id, self._root)
+        position_length = self._position_lengths.get(sequence_id, 0)
+        for token, position_increment in zip(tokens, position_increments):
+            position_increment = max(0, int(position_increment))
+            child = node.children.get(sequence_id)
+            if child is None:
+                child = SequenceHistory.Node(
+                    token=token,
+                    parent=node,
+                    position_increment=position_increment,
+                )
+                node.children[sequence_id] = child
+                self.size += 1
+            else:
+                assert child.parent is node
+                assert child.token == token
+                assert child.position_increment == position_increment
+            child.sequences.add(sequence_id)
+            position_length += position_increment
+            node = child
+        if node is self._root:
+            self._tails.pop(sequence_id, None)
+            self._position_lengths.pop(sequence_id, None)
+        else:
+            self._tails[sequence_id] = node
+            self._position_lengths[sequence_id] = position_length
+
+    def position_length(self, sequence_id: int) -> int:
+        return self._position_lengths.get(sequence_id, 0)
+
+    def position_length_for_prefix(self, sequence_id: int, keep_len: int) -> int:
+        if keep_len <= 0 or sequence_id not in self._tails:
+            return 0
+        node = self._tails[sequence_id]
+        increments: List[int] = []
+        while node is not self._root:
+            increments.append(node.position_increment)
+            parent = node.parent
+            assert parent is not None
+            node = parent
+        increments.reverse()
+        assert keep_len <= len(increments)
+        return sum(increments[:keep_len])
+
+    def copy(
+        self,
+        source_sequence_id: int,
+        dest_sequence_id: int,
+        source_length: int,
+        keep_len: int,
+    ) -> None:
+        assert source_sequence_id >= 0
+        assert dest_sequence_id >= 0
+        assert source_sequence_id in self._tails
+        assert dest_sequence_id not in self._tails
+        assert 0 <= keep_len <= source_length
+        node = self._tails[source_sequence_id]
+        path: List[SequenceHistory.Node] = []
+        for _ in range(source_length - keep_len):
+            parent = node.parent
+            assert parent is not None
+            node = parent
+        while node is not self._root:
+            path.append(node)
+            parent = node.parent
+            assert parent is not None
+            node = parent
+        parent = self._root
+        position_length = 0
+        for child in reversed(path):
+            parent.children[dest_sequence_id] = child
+            child.sequences.add(dest_sequence_id)
+            position_length += child.position_increment
+            parent = child
+        if keep_len == 0:
+            self._tails.pop(dest_sequence_id, None)
+            self._position_lengths.pop(dest_sequence_id, None)
+        else:
+            self._tails[dest_sequence_id] = path[0]
+            self._position_lengths[dest_sequence_id] = position_length
+
+    def truncate(
+        self,
+        sequence_id: int,
+        current_length: int,
+        keep_len: int,
+    ) -> None:
+        assert sequence_id >= 0
+        assert sequence_id in self._tails
+        assert 0 <= keep_len <= current_length
+        node = self._tails[sequence_id]
+        drop = current_length - keep_len
+        position_length = self._position_lengths.get(sequence_id, 0)
+        while node is not self._root and drop > 0:
+            node.sequences.remove(sequence_id)
+            parent = node.parent
+            assert parent is not None
+            child = parent.children.get(sequence_id)
+            if child is node:
+                del parent.children[sequence_id]
+            if not node.sequences:
+                self.size -= 1
+            position_length -= node.position_increment
+            node = parent
+            drop -= 1
+        if node is self._root:
+            self._tails.pop(sequence_id, None)
+            self._position_lengths.pop(sequence_id, None)
+        else:
+            self._tails[sequence_id] = node
+            self._position_lengths[sequence_id] = max(0, position_length)
+
+
+class DraftProvider(abc.ABC):
+    @abc.abstractmethod
+    def draft(
+        self,
+        input_ids: np.ndarray,
+        /,
+        *,
+        seq_id: int,
+        max_tokens: Optional[int],
+    ) -> np.ndarray:
+        raise NotImplementedError()
+
+    def can_draft(self, input_length: int, /, *, seq_id: int) -> bool:
+        return True
+
+    def process(self, batch: Any, /) -> None:
+        pass
+
+    def accept(self, seq_id: int, accepted_draft_tokens: int) -> None:
+        pass
+
+    def truncate(self, seq_id: int, keep_len: int) -> None:
+        pass
+
+    def copy_sequence(
+        self,
+        source_seq_id: int,
+        dest_seq_id: int,
+        p0: int,
+        p1: int,
+    ) -> None:
+        pass
+
+    def set_target_processing_enabled(self, enabled: bool) -> None:
+        pass
+
+    def close(self) -> None:
+        pass
+
+
+class PromptLookupDecoding(DraftProvider):
+    def __init__(self, max_ngram_size: int = 2, num_pred_tokens: int = 10) -> None:
+        self._max_ngram_size = max_ngram_size
+        self._num_pred_tokens = num_pred_tokens
+
+    def draft(
+        self,
+        input_ids: np.ndarray,
+        /,
+        *,
+        seq_id: int,
+        max_tokens: Optional[int],
+    ) -> np.ndarray:
+        input_length = input_ids.shape[0]
+        if input_length < 2:
+            return np.array([], dtype=np.intc)
+        num_pred_tokens = self._num_pred_tokens
+        if max_tokens is not None:
+            num_pred_tokens = min(num_pred_tokens, max_tokens)
+        if num_pred_tokens <= 0:
+            return np.array([], dtype=np.intc)
+        max_ngram_size = min(self._max_ngram_size, input_length - 1)
+        for ngram_size in range(max_ngram_size, 0, -1):
+            windows = np.lib.stride_tricks.sliding_window_view(input_ids, (ngram_size,))
+            ngram = input_ids[-ngram_size:]
+            matches = np.all(windows == ngram, axis=1)
+            match_indices = np.nonzero(matches)[0]
+            for index in match_indices:
+                start = index + ngram_size
+                if start >= input_length:
+                    continue
+                end = min(start + num_pred_tokens, input_length)
+                if start < end:
+                    return input_ids[start:end].astype(np.intc, copy=False)
+        return np.array([], dtype=np.intc)
+
+
+class MTPDraftProvider(DraftProvider):
+    batched_draft = True
+    sampled_batch_draft = True
+
+    @dataclass
+    class DraftManyState:
+        result_index: int
+        seq_id: int
+        first_pos: int
+        keep_len: int
+        n_predict: int
+        token: int
+        drafted: List[int]
+        embedding: np.ndarray
+        cache_key: Tuple[Tuple[int, ...], int]
+
+    def __init__(
+        self,
+        *,
+        model: "Model",
+        draft_model: Any,
+        context_params: Any,
+        num_pred_tokens: int,
+        top_k: int,
+        p_min: float,
+    ) -> None:
+        self.target_ctx = model.ctx
+        self.model = draft_model
+        self.n_seq_max = model.n_seq_max
+        self.n_vocab = model.n_vocab
+        self.n_embd = int(llama_cpp.llama_model_n_embd_out(self.model))
+        if self.n_embd <= 0:
+            self.n_embd = int(llama_cpp.llama_model_n_embd(self.model))
+        if self.n_embd != model.n_embd:
+            raise RuntimeError(
+                "MTP draft model output embedding size must match target model "
+                f"embedding size ({self.n_embd} != {model.n_embd})"
+            )
+        self.num_pred_tokens = max(0, int(num_pred_tokens))
+        self.top_k = max(1, int(top_k))
+        self.p_min = max(0.0, min(1.0, float(p_min)))
+        self.ctx = llama_cpp.llama_init_from_model(self.model, context_params)
+        if self.ctx is None:
+            raise RuntimeError("failed to create MTP draft context")
+        ctx_other = llama_cpp_ext.llama_get_ctx_other(self.ctx)
+        self.is_mem_shared = bool(ctx_other and ctx_other == self.target_ctx)
+        self.sampled_batch_draft = not self.is_mem_shared
+        self.n_batch = int(llama_cpp.llama_n_batch(self.ctx))
+        mem = llama_cpp.llama_get_memory(self.ctx)
+        if mem is None:
+            llama_cpp.llama_free(self.ctx)
+            raise RuntimeError("failed to access MTP draft memory")
+        self.mem = mem
+        self.batch = llama_cpp.llama_batch_init(self.n_batch, self.n_embd, 1)
+        self._batch_tokens = (llama_cpp.llama_token * self.n_batch)()
+        self.batch.token = self._batch_tokens
+        self.batch_embeddings = np.ctypeslib.as_array(
+            self.batch.embd,
+            shape=(self.n_batch * self.n_embd,),
+        )
+        self._samplers: List[Any] = []
+        self.pending_h = np.zeros((self.n_seq_max, self.n_embd), dtype=np.float32)
+        self.verify_h: List[np.ndarray] = [
+            np.empty((0, self.n_embd), dtype=np.float32)
+            for _ in range(self.n_seq_max)
+        ]
+        self.verify_h_pos: List[List[int]] = [[] for _ in range(self.n_seq_max)]
+        self.verify_h_rows = [0] * self.n_seq_max
+        self.ready = [False] * self.n_seq_max
+        self.ready_pos = [0] * self.n_seq_max
+        self.context_pos = [0] * self.n_seq_max
+        self.decode_seconds_total = 0.0
+        self.decode_calls_total = 0
+        self.decode_tokens_total = 0
+        self.decode_failures_total = 0
+        self.target_processing_enabled = False
+        self.set_target_processing_enabled(True)
+        llama_cpp_ext.llama_set_embeddings_nextn(
+            self.ctx,
+            True,
+            True,
+        )
+        self._init_samplers()
+
+    def _init_samplers(self) -> None:
+        for seq_id in range(self.n_seq_max):
+            params = llama_cpp.llama_sampler_chain_default_params()
+            params.no_perf = True
+            sampler = llama_cpp.llama_sampler_chain_init(params)
+            if self.top_k > 1:
+                llama_cpp.llama_sampler_chain_add(
+                    sampler,
+                    llama_cpp.llama_sampler_init_top_k(self.top_k),
+                )
+            llama_cpp.llama_sampler_chain_add(
+                sampler,
+                llama_cpp.llama_sampler_init_greedy(),
+            )
+            self._samplers.append(sampler)
+
+    def _passes_p_min(self, output_index: int) -> bool:
+        if self.p_min <= 0.0:
+            return True
+        logits_ptr = llama_cpp.llama_get_logits_ith(self.ctx, output_index)
+        if not logits_ptr:
+            return False
+        logits = np.ctypeslib.as_array(logits_ptr, shape=(self.n_vocab,))
+        n_values = min(self.top_k, self.n_vocab)
+        if n_values <= 0:
+            return False
+        if n_values == self.n_vocab:
+            values = logits
+        else:
+            top_indices = np.argpartition(logits, -n_values)[-n_values:]
+            values = logits[top_indices]
+        max_logit = float(np.max(values))
+        weights = np.exp(values.astype(np.float64, copy=False) - max_logit)
+        total = float(np.sum(weights))
+        if total <= 0.0 or not math.isfinite(total):
+            return False
+        return 1.0 / total >= self.p_min
+
+    def _sample_token(self, output_index: int = 0, *, seq_id: int = 0) -> Optional[int]:
+        if seq_id < 0 or seq_id >= len(self._samplers):
+            return None
+        if not self._passes_p_min(output_index):
+            return None
+        sampler = self._samplers[seq_id]
+        token = int(llama_cpp.llama_sampler_sample(sampler, self.ctx, output_index))
+        if token == llama_cpp.LLAMA_TOKEN_NULL:
+            return None
+        return token
+
+    def _reset_sampler(self, seq_id: int) -> None:
+        if 0 <= seq_id < len(self._samplers):
+            llama_cpp.llama_sampler_reset(self._samplers[seq_id])
+
+    def close(self) -> None:
+        self.set_target_processing_enabled(False)
+        self.batch.token = ctypes.POINTER(llama_cpp.llama_token)()
+        llama_cpp.llama_batch_free(self.batch)
+        llama_cpp.llama_free(self.ctx)
+        for sampler in self._samplers:
+            llama_cpp.llama_sampler_free(sampler)
+        self._samplers.clear()
+
+    def set_target_processing_enabled(self, enabled: bool) -> None:
+        if self.target_processing_enabled == enabled:
+            return
+        llama_cpp_ext.llama_set_embeddings_nextn(
+            self.target_ctx,
+            enabled,
+            False,
+        )
+        self.target_processing_enabled = enabled
+
+    def _clear_batch(self) -> None:
+        self.batch.n_tokens = 0
+
+    def _batch_embeddings(self) -> np.ndarray:
+        return self.batch_embeddings
+
+    def _set_batch_embedding_row(
+        self,
+        row: int,
+        embedding: Union[np.ndarray, ctypes.POINTER(ctypes.c_float)],
+    ) -> None:
+        row_start = row * self.n_embd
+        row_end = row_start + self.n_embd
+        if isinstance(embedding, np.ndarray):
+            self._batch_embeddings()[row_start:row_end] = embedding
+            return
+        self._batch_embeddings()[row_start:row_end] = np.ctypeslib.as_array(
+            embedding,
+            shape=(self.n_embd,),
+        )
+
+    def _add_batch_token(
+        self,
+        *,
+        token: int,
+        pos: int,
+        seq_id: int,
+        logits: bool,
+    ) -> None:
+        slot = int(self.batch.n_tokens)
+        if slot >= self.n_batch:
+            raise RuntimeError("MTP draft batch capacity exceeded")
+        self.batch.token[slot] = int(token)
+        self.batch.pos[slot] = int(pos)
+        self.batch.seq_id[slot][0] = int(seq_id)
+        self.batch.n_seq_id[slot] = 1
+        self.batch.logits[slot] = int(logits)
+        self.batch.n_tokens += 1
+
+    def _try_decode_batch(self) -> bool:
+        n_tokens = int(self.batch.n_tokens)
+        if n_tokens <= 0:
+            return True
+        started_at = time.perf_counter()
+        result = int(llama_cpp.llama_decode(self.ctx, self.batch))
+        self.decode_seconds_total += time.perf_counter() - started_at
+        self.decode_calls_total += 1
+        self.decode_tokens_total += n_tokens
+        if result != 0:
+            self.decode_failures_total += 1
+            return False
+        return True
+
+    def _decode_batch(self) -> None:
+        n_tokens = int(self.batch.n_tokens)
+        if n_tokens <= 0:
+            return
+        started_at = time.perf_counter()
+        result = int(llama_cpp.llama_decode(self.ctx, self.batch))
+        self.decode_seconds_total += time.perf_counter() - started_at
+        self.decode_calls_total += 1
+        self.decode_tokens_total += n_tokens
+        if result != 0:
+            self.decode_failures_total += 1
+            raise RuntimeError(f"MTP draft decode failed with code {result}")
+
+    def metric_definitions(
+        self,
+    ) -> List[Tuple[str, str, str, Union[int, float]]]:
+        decode_tokens_seconds = (
+            self.decode_tokens_total / self.decode_seconds_total
+            if self.decode_seconds_total > 0.0
+            else 0.0
+        )
+        return [
+            (
+                "counter",
+                "batch_server:mtp_decode_seconds_total",
+                "Time spent inside llama_decode() for the MTP draft context.",
+                self.decode_seconds_total,
+            ),
+            (
+                "counter",
+                "batch_server:mtp_decode_calls_total",
+                "Number of llama_decode() calls made by the MTP draft context.",
+                self.decode_calls_total,
+            ),
+            (
+                "counter",
+                "batch_server:mtp_decode_tokens_total",
+                "Number of batch rows decoded by the MTP draft context.",
+                self.decode_tokens_total,
+            ),
+            (
+                "counter",
+                "batch_server:mtp_decode_failures_total",
+                "Number of failed MTP draft context decode calls.",
+                self.decode_failures_total,
+            ),
+            (
+                "gauge",
+                "batch_server:mtp_decode_tokens_seconds",
+                "Average MTP draft context decode throughput in batch rows/s.",
+                decode_tokens_seconds,
+            ),
+        ]
+
+    def can_draft(self, input_length: int, /, *, seq_id: int) -> bool:
+        if (
+            input_length <= 0
+            or seq_id < 0
+            or seq_id >= self.n_seq_max
+            or not self.ready[seq_id]
+        ):
+            return False
+        return self.ready_pos[seq_id] == input_length - 1
+
+    def process(self, batch: Any, /) -> None:
+        n_tokens = int(batch.n_tokens)
+        if (
+            n_tokens <= 0
+            or not self.target_processing_enabled
+            or not bool(batch.token)
+            or bool(batch.embd)
+        ):
+            return
+
+        h_tgt = llama_cpp_ext.llama_get_embeddings_nextn(self.target_ctx)
+        if not h_tgt:
+            raise RuntimeError("missing target nextn embeddings for MTP")
+        h_tgt_rows = np.ctypeslib.as_array(
+            h_tgt,
+            shape=(n_tokens, self.n_embd),
+        )
+
+        previous_row_by_seq: Dict[int, int] = {}
+        first_pos_by_seq: Dict[int, int] = {}
+        target_rows_by_seq: Dict[int, List[int]] = {}
+        aligned_by_seq: Dict[int, bool] = {}
+
+        for start in range(0, n_tokens, self.n_batch):
+            self._process_rows(
+                batch,
+                h_tgt_rows,
+                start,
+                min(start + self.n_batch, n_tokens),
+                previous_row_by_seq,
+                first_pos_by_seq,
+                target_rows_by_seq,
+                aligned_by_seq,
+            )
+
+        for seq_id, rows in target_rows_by_seq.items():
+            if not aligned_by_seq.get(seq_id, False):
+                self.ready[seq_id] = False
+                continue
+            if rows[-1] - rows[0] + 1 == len(rows):
+                target_rows = h_tgt_rows[rows[0] : rows[-1] + 1]
+            else:
+                target_rows = h_tgt_rows[rows].copy()
+            target_positions = [int(batch.pos[source_index]) for source_index in rows]
+            self.verify_h[seq_id] = target_rows
+            self.verify_h_pos[seq_id] = target_positions
+            self.verify_h_rows[seq_id] = len(rows)
+            self.pending_h[seq_id] = target_rows[-1]
+            self.ready[seq_id] = True
+            self.ready_pos[seq_id] = target_positions[-1] + 1
+
+    def _process_rows(
+        self,
+        batch: Any,
+        h_tgt_rows: np.ndarray,
+        start: int,
+        end: int,
+        previous_row_by_seq: Dict[int, int],
+        first_pos_by_seq: Dict[int, int],
+        target_rows_by_seq: Dict[int, List[int]],
+        aligned_by_seq: Dict[int, bool],
+    ) -> None:
+        added_pos_by_seq: Dict[int, int] = {}
+        self._clear_batch()
+        for index in range(start, end):
+            if int(batch.n_seq_id[index]) != 1:
+                raise RuntimeError("MTP requires one sequence id per batch token")
+            seq_id = int(batch.seq_id[index][0])
+            if seq_id < 0 or seq_id >= self.n_seq_max:
+                raise RuntimeError(f"MTP sequence id out of range: {seq_id}")
+            pos = int(batch.pos[index])
+            first_pos = first_pos_by_seq.setdefault(seq_id, pos)
+            aligned = first_pos <= 0 or (
+                self.ready[seq_id] and self.ready_pos[seq_id] == first_pos
+            )
+            aligned_by_seq.setdefault(seq_id, aligned)
+            previous_row = previous_row_by_seq.get(seq_id)
+            if (
+                aligned
+                and not self.is_mem_shared
+                and pos >= 0
+                and pos >= self.context_pos[seq_id]
+            ):
+                slot = int(self.batch.n_tokens)
+                self._add_batch_token(
+                    token=int(batch.token[index]),
+                    pos=pos,
+                    seq_id=seq_id,
+                    logits=False,
+                )
+                if previous_row is None:
+                    self._set_batch_embedding_row(slot, self.pending_h[seq_id])
+                else:
+                    self._set_batch_embedding_row(slot, h_tgt_rows[previous_row])
+                added_pos_by_seq[seq_id] = pos
+            previous_row_by_seq[seq_id] = index
+            target_rows_by_seq.setdefault(seq_id, []).append(index)
+
+        if int(self.batch.n_tokens) > 0:
+            self._decode_batch()
+            for seq_id, pos in added_pos_by_seq.items():
+                self.context_pos[seq_id] = max(self.context_pos[seq_id], pos + 1)
+
+    def draft(
+        self,
+        input_ids: np.ndarray,
+        /,
+        *,
+        seq_id: int,
+        max_tokens: Optional[int],
+    ) -> np.ndarray:
+        if (
+            self.num_pred_tokens <= 0
+            or input_ids.size == 0
+            or seq_id < 0
+            or seq_id >= self.n_seq_max
+            or not self.ready[seq_id]
+        ):
+            return np.array([], dtype=np.intc)
+        n_predict = self.num_pred_tokens
+        if max_tokens is not None:
+            n_predict = min(n_predict, max_tokens)
+        if n_predict <= 0:
+            return np.array([], dtype=np.intc)
+
+        n_past = int(input_ids.size) - 1
+        if self.ready_pos[seq_id] != n_past:
+            return np.array([], dtype=np.intc)
+        first_pos = n_past
+        if first_pos < 0:
+            return np.array([], dtype=np.intc)
+
+        token = int(input_ids[-1])
+        drafted: List[int] = []
+        if not self.is_mem_shared and self.context_pos[seq_id] > first_pos:
+            self.truncate(seq_id, first_pos)
+        if not self.is_mem_shared and self.context_pos[seq_id] < first_pos:
+            self.ready[seq_id] = False
+            return np.array([], dtype=np.intc)
+
+        self._reset_sampler(seq_id)
+        self._clear_batch()
+        self._add_batch_token(
+            token=token,
+            pos=first_pos,
+            seq_id=seq_id,
+            logits=True,
+        )
+        self._set_batch_embedding_row(0, self.pending_h[seq_id])
+        if not self._try_decode_batch():
+            if not self.is_mem_shared:
+                self.truncate(seq_id, first_pos)
+            return np.array([], dtype=np.intc)
+        if not self.is_mem_shared:
+            self.context_pos[seq_id] = first_pos + 1
+
+        while len(drafted) < n_predict:
+            sampled_token = self._sample_token(seq_id=seq_id)
+            if sampled_token is None:
+                break
+            token = sampled_token
+            drafted.append(token)
+            if len(drafted) >= n_predict:
+                break
+            h_row = llama_cpp_ext.llama_get_embeddings_nextn_ith(self.ctx, 0)
+            if not h_row:
+                break
+            self._clear_batch()
+            self._add_batch_token(
+                token=token,
+                pos=first_pos if self.is_mem_shared else first_pos + len(drafted),
+                seq_id=seq_id,
+                logits=True,
+            )
+            self._set_batch_embedding_row(0, h_row)
+            if not self._try_decode_batch():
+                break
+            if not self.is_mem_shared:
+                self.context_pos[seq_id] = first_pos + len(drafted) + 1
+
+        if not drafted:
+            if not self.is_mem_shared:
+                self.truncate(seq_id, n_past)
+            return np.array([], dtype=np.intc)
+        if not self.is_mem_shared:
+            self.truncate(seq_id, n_past)
+        return np.asarray(drafted, dtype=np.intc)
+
+    def draft_many(
+        self,
+        requests: Sequence[Tuple[np.ndarray, int, Optional[int]]],
+        /,
+    ) -> List[np.ndarray]:
+        results = [np.array([], dtype=np.intc) for _ in requests]
+        active: List["MTPDraftProvider.DraftManyState"] = []
+        for result_index, (input_ids, seq_id, max_tokens) in enumerate(requests):
+            if (
+                self.num_pred_tokens <= 0
+                or input_ids.size == 0
+                or seq_id < 0
+                or seq_id >= self.n_seq_max
+                or not self.ready[seq_id]
+            ):
+                continue
+            n_predict = self.num_pred_tokens
+            if max_tokens is not None:
+                n_predict = min(n_predict, max_tokens)
+            if n_predict <= 0:
+                continue
+            if len(active) >= self.n_batch:
+                break
+
+            n_past = int(input_ids.size) - 1
+            if self.ready_pos[seq_id] != n_past:
+                continue
+            first_pos = n_past
+            if first_pos < 0:
+                continue
+            if not self.is_mem_shared and self.context_pos[seq_id] > first_pos:
+                self.truncate(seq_id, first_pos)
+            if not self.is_mem_shared and self.context_pos[seq_id] < first_pos:
+                self.ready[seq_id] = False
+                continue
+            self._reset_sampler(seq_id)
+            active.append(
+                self.DraftManyState(
+                    result_index=result_index,
+                    seq_id=seq_id,
+                    first_pos=first_pos,
+                    keep_len=n_past,
+                    n_predict=n_predict,
+                    token=int(input_ids[-1]),
+                    drafted=[],
+                    embedding=self.pending_h[seq_id],
+                    cache_key=(
+                        tuple(int(token) for token in input_ids.tolist()),
+                        n_predict,
+                    ),
+                )
+            )
+
+        if not active:
+            return results
+
+        touched = list(active)
+        try:
+            if all(state.n_predict == 1 for state in active):
+                grouped: Dict[
+                    Tuple[Tuple[int, ...], int],
+                    List["MTPDraftProvider.DraftManyState"],
+                ] = {}
+                for state in active:
+                    grouped.setdefault(state.cache_key, []).append(state)
+
+                representatives = [states[0] for states in grouped.values()]
+                self._clear_batch()
+                for row, state in enumerate(representatives):
+                    self._add_batch_token(
+                        token=state.token,
+                        pos=state.first_pos,
+                        seq_id=state.seq_id,
+                        logits=True,
+                    )
+                    self._set_batch_embedding_row(row, state.embedding)
+
+                if self._try_decode_batch():
+                    sampled_tokens = [
+                        self._sample_token(row, seq_id=state.seq_id)
+                        for row, state in enumerate(representatives)
+                    ]
+                    for representative, sampled_token in zip(
+                        representatives,
+                        sampled_tokens,
+                    ):
+                        if sampled_token is None:
+                            continue
+                        if not self.is_mem_shared:
+                            self.context_pos[representative.seq_id] = max(
+                                self.context_pos[representative.seq_id],
+                                representative.first_pos + 1,
+                            )
+                        for state in grouped[representative.cache_key]:
+                            state.drafted.append(sampled_token)
+                    active = []
+
+            while active:
+                self._clear_batch()
+                for row, state in enumerate(active):
+                    self._add_batch_token(
+                        token=state.token,
+                        pos=(
+                            state.first_pos
+                            if self.is_mem_shared
+                            else state.first_pos + len(state.drafted)
+                        ),
+                        seq_id=state.seq_id,
+                        logits=True,
+                    )
+                    self._set_batch_embedding_row(row, state.embedding)
+
+                if not self._try_decode_batch():
+                    break
+
+                next_active: List["MTPDraftProvider.DraftManyState"] = []
+                sampled_tokens = [
+                    self._sample_token(row, seq_id=state.seq_id)
+                    for row, state in enumerate(active)
+                ]
+                for row, (state, sampled_token) in enumerate(zip(active, sampled_tokens)):
+                    decoded_pos = (
+                        state.first_pos
+                        if self.is_mem_shared
+                        else state.first_pos + len(state.drafted)
+                    )
+                    if not self.is_mem_shared:
+                        self.context_pos[state.seq_id] = max(
+                            self.context_pos[state.seq_id],
+                            decoded_pos + 1,
+                        )
+                    if sampled_token is None:
+                        continue
+                    h_row_ptr = llama_cpp_ext.llama_get_embeddings_nextn_ith(
+                        self.ctx, row
+                    )
+                    state.drafted.append(sampled_token)
+                    if len(state.drafted) >= state.n_predict:
+                        continue
+                    if not h_row_ptr:
+                        continue
+                    state.token = sampled_token
+                    state.embedding = np.ctypeslib.as_array(
+                        h_row_ptr,
+                        shape=(self.n_embd,),
+                    ).copy()
+                    next_active.append(state)
+                active = next_active
+        finally:
+            if not self.is_mem_shared:
+                for state in touched:
+                    self.truncate(state.seq_id, state.keep_len)
+
+        for state in touched:
+            if state.drafted:
+                results[state.result_index] = np.asarray(
+                    state.drafted,
+                    dtype=np.intc,
+                )
+        return results
+
+    @dataclass(frozen=True)
+    class SampledContextRow:
+        seq_id: int
+        draft_pos: int
+        token: int
+        source_row: Optional[int]
+
+    @dataclass(frozen=True)
+    class SampledPendingRow:
+        update_index: int
+        seq_id: int
+        draft_pos: int
+        token: int
+        source_row: int
+
+    @dataclass(frozen=True)
+    class SampledOutput:
+        update_index: int
+        seq_id: int
+        output_index: int
+        keep_len: int
+        ready_pos: int
+
+    @dataclass(frozen=True)
+    class SampledBatchUpdate:
+        seq_id: int
+        start_pos: int
+        tokens: List[int]
+        row_indices: List[int]
+        target_count: int
+        sample_index: int
+        pending_token: Optional[int]
+        max_tokens: Optional[int]
+
+    @dataclass
+    class SampledBatchPlan:
+        context_rows: List["MTPDraftProvider.SampledContextRow"]
+        pending_rows: List["MTPDraftProvider.SampledPendingRow"]
+        sample_pending_index_by_update: Dict[int, int]
+
+    @dataclass
+    class SampledDraftState:
+        update_index: int
+        seq_id: int
+        keep_len: int
+        pos: int
+        token: int
+        drafted: List[int]
+        n_predict: int
+        embedding: np.ndarray
+
+    def _build_sampled_batch_plan(
+        self,
+        updates: Sequence["MTPDraftProvider.SampledBatchUpdate"],
+        /,
+    ) -> "MTPDraftProvider.SampledBatchPlan":
+        context_rows: List["MTPDraftProvider.SampledContextRow"] = []
+        pending_rows: List["MTPDraftProvider.SampledPendingRow"] = []
+        sample_pending_index_by_update: Dict[int, int] = {}
+
+        for update_index, update in enumerate(updates):
+            seq_id = update.seq_id
+            if seq_id < 0 or seq_id >= self.n_seq_max:
+                continue
+            start_pos = update.start_pos
+            tokens = update.tokens
+            row_indices = update.row_indices
+            target_count = update.target_count
+            sample_index = update.sample_index
+            pending_token = update.pending_token
+            if (
+                pending_token is None
+                or start_pos < 0
+                or target_count <= 0
+                or target_count > len(tokens)
+                or target_count > len(row_indices)
+                or sample_index < 0
+                or sample_index >= target_count
+            ):
+                continue
+
+            for target_index in range(sample_index + 1):
+                mtp_pos = start_pos + target_index
+                source_row = (
+                    None
+                    if target_index == 0
+                    else row_indices[target_index - 1]
+                )
+                context_rows.append(
+                    self.SampledContextRow(
+                        seq_id=seq_id,
+                        draft_pos=mtp_pos,
+                        token=tokens[target_index],
+                        source_row=source_row,
+                    )
+                )
+
+            actual_pos = start_pos + sample_index + 1
+            pending_rows.append(
+                self.SampledPendingRow(
+                    update_index=update_index,
+                    seq_id=seq_id,
+                    draft_pos=actual_pos,
+                    token=pending_token,
+                    source_row=row_indices[sample_index],
+                )
+            )
+            sample_pending_index_by_update[update_index] = len(pending_rows) - 1
+
+        return self.SampledBatchPlan(
+            context_rows=context_rows,
+            pending_rows=pending_rows,
+            sample_pending_index_by_update=sample_pending_index_by_update,
+        )
+
+    def _decode_sampled_context_rows(
+        self,
+        context_rows: Sequence["MTPDraftProvider.SampledContextRow"],
+        h_tgt_rows: np.ndarray,
+        /,
+    ) -> None:
+        self._clear_batch()
+        decoded_context_rows: List[Tuple[int, int]] = []
+        for row in context_rows:
+            if self.is_mem_shared:
+                continue
+            if row.draft_pos < self.context_pos[row.seq_id]:
+                continue
+            if row.source_row is None:
+                embedding = self.pending_h[row.seq_id]
+            else:
+                embedding = h_tgt_rows[row.source_row]
+            self._add_batch_token(
+                token=row.token,
+                pos=row.draft_pos,
+                seq_id=row.seq_id,
+                logits=False,
+            )
+            self._set_batch_embedding_row(int(self.batch.n_tokens) - 1, embedding)
+            decoded_context_rows.append((row.seq_id, row.draft_pos))
+
+        if int(self.batch.n_tokens) > 0:
+            self._decode_batch()
+            for seq_id, draft_pos in decoded_context_rows:
+                self.context_pos[seq_id] = max(
+                    self.context_pos[seq_id],
+                    draft_pos + 1,
+                )
+
+    def _decode_sampled_pending_rows(
+        self,
+        plan: "MTPDraftProvider.SampledBatchPlan",
+        h_tgt_rows: np.ndarray,
+        /,
+    ) -> List["MTPDraftProvider.SampledOutput"]:
+        sampled_outputs: List["MTPDraftProvider.SampledOutput"] = []
+        pending_rows = plan.pending_rows
+
+        self._clear_batch()
+        for pending_index, row in enumerate(pending_rows):
+            if (
+                not self.is_mem_shared
+                and row.draft_pos < self.context_pos[row.seq_id]
+            ):
+                continue
+            is_sample_pending = (
+                pending_index
+                == plan.sample_pending_index_by_update.get(row.update_index)
+            )
+            slot = int(self.batch.n_tokens)
+            self._add_batch_token(
+                token=row.token,
+                pos=row.draft_pos,
+                seq_id=row.seq_id,
+                logits=is_sample_pending,
+            )
+            self._set_batch_embedding_row(slot, h_tgt_rows[row.source_row])
+            if is_sample_pending:
+                sampled_outputs.append(
+                    self.SampledOutput(
+                        update_index=row.update_index,
+                        seq_id=row.seq_id,
+                        output_index=slot,
+                        keep_len=row.draft_pos,
+                        ready_pos=row.draft_pos,
+                    )
+                )
+
+        self._decode_batch()
+        if not self.is_mem_shared:
+            for row in pending_rows:
+                self.context_pos[row.seq_id] = max(
+                    self.context_pos[row.seq_id],
+                    row.draft_pos + 1,
+                )
+
+        return sampled_outputs
+
+    def _start_sampled_draft_states(
+        self,
+        updates: Sequence["MTPDraftProvider.SampledBatchUpdate"],
+        sampled_outputs: Sequence["MTPDraftProvider.SampledOutput"],
+        results: List[np.ndarray],
+        /,
+    ) -> List["MTPDraftProvider.SampledDraftState"]:
+        active: List["MTPDraftProvider.SampledDraftState"] = []
+        for output in sampled_outputs:
+            self._reset_sampler(output.seq_id)
+            sampled_token = self._sample_token(
+                output.output_index,
+                seq_id=output.seq_id,
+            )
+            if sampled_token is None:
+                continue
+            update = updates[output.update_index]
+            seq_id = update.seq_id
+            self.ready[seq_id] = True
+            n_predict = self.num_pred_tokens
+            max_tokens = update.max_tokens
+            if max_tokens is not None:
+                n_predict = min(n_predict, max_tokens)
+            if n_predict <= 0:
+                continue
+            if n_predict > 1:
+                h_row = llama_cpp_ext.llama_get_embeddings_nextn_ith(
+                    self.ctx, output.output_index
+                )
+                if h_row:
+                    active.append(
+                        self.SampledDraftState(
+                            update_index=output.update_index,
+                            seq_id=seq_id,
+                            keep_len=output.keep_len,
+                            pos=(
+                                output.keep_len
+                                if self.is_mem_shared
+                                else output.keep_len + 1
+                            ),
+                            token=sampled_token,
+                            drafted=[sampled_token],
+                            n_predict=n_predict,
+                            embedding=np.ctypeslib.as_array(
+                                h_row,
+                                shape=(self.n_embd,),
+                            ).copy(),
+                        )
+                    )
+            results[output.update_index] = np.asarray([sampled_token], dtype=np.intc)
+
+        return active
+
+    def _extend_sampled_draft_states(
+        self,
+        active: List["MTPDraftProvider.SampledDraftState"],
+        results: List[np.ndarray],
+        cleanup_keep_len_by_seq: Dict[int, int],
+        /,
+    ) -> None:
+        touched = list(active)
+        try:
+            while active:
+                self._clear_batch()
+                for batch_row, state in enumerate(active):
+                    self._add_batch_token(
+                        token=state.token,
+                        pos=state.pos,
+                        seq_id=state.seq_id,
+                        logits=True,
+                    )
+                    self._set_batch_embedding_row(batch_row, state.embedding)
+
+                if not self._try_decode_batch():
+                    break
+
+                next_active: List["MTPDraftProvider.SampledDraftState"] = []
+                sampled_tokens = [
+                    self._sample_token(batch_row, seq_id=state.seq_id)
+                    for batch_row, state in enumerate(active)
+                ]
+                for batch_row, (state, sampled_token) in enumerate(
+                    zip(active, sampled_tokens)
+                ):
+                    if not self.is_mem_shared:
+                        self.context_pos[state.seq_id] = max(
+                            self.context_pos[state.seq_id],
+                            state.pos + 1,
+                        )
+                    if sampled_token is None:
+                        continue
+                    state.drafted.append(sampled_token)
+                    if len(state.drafted) >= state.n_predict:
+                        continue
+                    h_row = llama_cpp_ext.llama_get_embeddings_nextn_ith(
+                        self.ctx, batch_row
+                    )
+                    if not h_row:
+                        continue
+                    state.token = sampled_token
+                    state.embedding = np.ctypeslib.as_array(
+                        h_row,
+                        shape=(self.n_embd,),
+                    ).copy()
+                    if not self.is_mem_shared:
+                        state.pos += 1
+                    next_active.append(state)
+                active = next_active
+        finally:
+            for state in touched:
+                cleanup_keep_len_by_seq[state.seq_id] = state.keep_len
+            if not self.is_mem_shared:
+                for seq_id, keep_len in cleanup_keep_len_by_seq.items():
+                    self._truncate_memory(seq_id, keep_len)
+
+        for state in touched:
+            if state.drafted:
+                results[state.update_index] = np.asarray(
+                    state.drafted,
+                    dtype=np.intc,
+                )
+
+    def process_sampled_batch(
+        self,
+        updates: Sequence["MTPDraftProvider.SampledBatchUpdate"],
+        /,
+    ) -> List[np.ndarray]:
+        results = [np.array([], dtype=np.intc) for _ in updates]
+        if self.num_pred_tokens <= 0 or not updates:
+            return results
+        h_tgt = llama_cpp_ext.llama_get_embeddings_nextn(self.target_ctx)
+        if not h_tgt:
+            raise RuntimeError("missing target nextn embeddings for MTP")
+        n_target_rows = max(
+            (
+                max(update.row_indices) + 1
+                for update in updates
+                if update.row_indices
+            ),
+            default=0,
+        )
+        if n_target_rows <= 0:
+            return results
+        h_tgt_rows = np.ctypeslib.as_array(h_tgt, shape=(n_target_rows, self.n_embd))
+
+        plan = self._build_sampled_batch_plan(updates)
+        if not plan.context_rows and not plan.pending_rows:
+            return results
+        if (
+            len(plan.context_rows) > self.n_batch
+            or len(plan.pending_rows) > self.n_batch
+        ):
+            raise RuntimeError("MTP draft batch capacity exceeded")
+
+        self._decode_sampled_context_rows(plan.context_rows, h_tgt_rows)
+        sampled_outputs = self._decode_sampled_pending_rows(plan, h_tgt_rows)
+        cleanup_keep_len_by_seq: Dict[int, int] = {}
+        for output in sampled_outputs:
+            update = updates[output.update_index]
+            sample_index = update.sample_index
+            sample_source_row = update.row_indices[sample_index]
+            self.pending_h[output.seq_id] = h_tgt_rows[sample_source_row]
+            self.ready[output.seq_id] = True
+            self.ready_pos[output.seq_id] = output.ready_pos
+            cleanup_keep_len_by_seq[output.seq_id] = output.keep_len
+
+        if not self.is_mem_shared:
+            for seq_id, keep_len in cleanup_keep_len_by_seq.items():
+                self._truncate_memory(seq_id, keep_len)
+
+        if sampled_outputs:
+            active = self._start_sampled_draft_states(
+                updates,
+                sampled_outputs,
+                results,
+            )
+            self._extend_sampled_draft_states(
+                active,
+                results,
+                cleanup_keep_len_by_seq,
+            )
+
+        return results
+
+    def accept(self, seq_id: int, accepted_draft_tokens: int) -> None:
+        if seq_id < 0 or seq_id >= self.n_seq_max:
+            return
+        n_rows = self.verify_h_rows[seq_id]
+        if n_rows <= 0:
+            return
+        row = min(max(accepted_draft_tokens, 0), n_rows - 1)
+        self.pending_h[seq_id] = self.verify_h[seq_id][row]
+        self.ready[seq_id] = True
+        self.ready_pos[seq_id] = self.verify_h_pos[seq_id][row] + 1
+
+    def _truncate_memory(self, seq_id: int, keep_len: int) -> None:
+        if seq_id < 0 or seq_id >= self.n_seq_max:
+            return
+        if self.is_mem_shared:
+            self.context_pos[seq_id] = min(self.context_pos[seq_id], keep_len)
+            return
+        if not llama_cpp.llama_memory_seq_rm(
+            self.mem,
+            seq_id,
+            keep_len,
+            -1,
+        ):
+            raise RuntimeError(
+                f"failed to truncate MTP draft sequence {seq_id} at position {keep_len}"
+            )
+        self.context_pos[seq_id] = min(self.context_pos[seq_id], keep_len)
+
+    def truncate(self, seq_id: int, keep_len: int) -> None:
+        if seq_id < 0 or seq_id >= self.n_seq_max:
+            return
+        self._truncate_memory(seq_id, keep_len)
+        if keep_len <= 0:
+            self.pending_h[seq_id].fill(0.0)
+            self.verify_h[seq_id] = np.empty((0, self.n_embd), dtype=np.float32)
+            self.verify_h_pos[seq_id] = []
+            self.verify_h_rows[seq_id] = 0
+            self.ready[seq_id] = False
+            self.ready_pos[seq_id] = 0
+            self.context_pos[seq_id] = 0
+            return
+
+        if self.ready_pos[seq_id] != keep_len:
+            self.ready[seq_id] = False
+
+    def copy_sequence(
+        self,
+        source_seq_id: int,
+        dest_seq_id: int,
+        p0: int,
+        p1: int,
+    ) -> None:
+        if (
+            source_seq_id < 0
+            or source_seq_id >= self.n_seq_max
+            or dest_seq_id < 0
+            or dest_seq_id >= self.n_seq_max
+        ):
+            return
+        if not self.is_mem_shared:
+            llama_cpp.llama_memory_seq_cp(
+                self.mem,
+                source_seq_id,
+                dest_seq_id,
+                p0,
+                p1,
+            )
+        source_ready_pos = self.ready_pos[source_seq_id]
+        copied_full_ready_state = p1 < 0 or p1 == source_ready_pos
+        if self.ready[source_seq_id] and copied_full_ready_state:
+            self.pending_h[dest_seq_id] = self.pending_h[source_seq_id]
+            self.verify_h[dest_seq_id] = self.verify_h[source_seq_id].copy()
+            self.verify_h_pos[dest_seq_id] = list(self.verify_h_pos[source_seq_id])
+            self.verify_h_rows[dest_seq_id] = self.verify_h_rows[source_seq_id]
+            self.ready[dest_seq_id] = True
+            self.ready_pos[dest_seq_id] = source_ready_pos
+            self.context_pos[dest_seq_id] = min(
+                self.context_pos[source_seq_id],
+                source_ready_pos,
+            )
+            return
+
+        self.pending_h[dest_seq_id].fill(0.0)
+        self.verify_h[dest_seq_id] = np.empty((0, self.n_embd), dtype=np.float32)
+        self.verify_h_pos[dest_seq_id] = []
+        self.verify_h_rows[dest_seq_id] = 0
+        self.ready[dest_seq_id] = False
+        self.ready_pos[dest_seq_id] = 0
+        self.context_pos[dest_seq_id] = 0
+
+
+class CompletionRequestCancelledError(RuntimeError):
+    pass
+
+
+class CompletionRequestValidationError(ValueError):
+    pass
+
+
+class CompletionResponseParsingError(RuntimeError):
+    pass
+
+
+def omit_additional_properties(schema: Dict[str, Any]) -> None:
+    schema.pop("additionalProperties", None)
+
+
+class CompletionChunkLogprobs(TypedDict):
+    tokens: List[str]
+    text_offset: List[int]
+    token_logprobs: List[Optional[float]]
+    top_logprobs: List[Optional[Dict[str, float]]]
+
+
+class CompletionChunkChoice(TypedDict):
+    text: str
+    index: int
+    logprobs: Optional[CompletionChunkLogprobs]
+    finish_reason: Optional[str]
+
+
+class CompletionChunk(TypedDict):
+    id: str
+    object: Literal["text_completion"]
+    created: int
+    model: str
+    choices: List[CompletionChunkChoice]
+
+
+CompletionStream = Generator[CompletionChunk, None, OpenAICompletion]
+CompletionPrompt = Union[str, List[int], List[str], List[List[int]]]
+EmbeddingInput = Union[str, List[str], List[int], List[List[int]]]
+
+
+class CreateCompletionRequest(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    prompt: CompletionPrompt = ""
+    suffix: Optional[str] = None
+    max_tokens: Optional[int] = Field(default=16, ge=0)
+    temperature: float = 0.8
+    top_p: float = Field(default=0.95, ge=0.0, le=1.0)
+    echo: bool = False
+    stop: Optional[Union[str, List[str]]] = None
+    stream: bool = False
+    logprobs: Optional[int] = Field(default=None, ge=0)
+    presence_penalty: Optional[float] = Field(default=0.0, ge=-2.0, le=2.0)
+    frequency_penalty: Optional[float] = Field(default=0.0, ge=-2.0, le=2.0)
+    logit_bias: Optional[Dict[str, float]] = None
+    seed: Optional[int] = None
+    model: Optional[str] = None
+    n: int = Field(default=1, ge=1)
+    best_of: Optional[int] = Field(default=None, ge=1)
+    user: Optional[str] = None
+
+    @field_validator("logit_bias")
+    @classmethod
+    def validate_logit_bias(cls, value: Optional[Dict[str, float]]) -> Optional[Dict[str, float]]:
+        if value is None:
+            return None
+        result: Dict[str, float] = {}
+        for key, bias in value.items():
+            int(key)
+            result[key] = float(bias)
+        return result
+
+    @model_validator(mode="after")
+    def validate_after(self) -> "CreateCompletionRequest":
+        if self.best_of is None:
+            self.best_of = self.n
+        if self.best_of < self.n:
+            raise ValueError("best_of must be greater than or equal to n")
+        if self.stream and self.best_of > 1:
+            raise ValueError("best_of is not supported for streaming completions")
+        if len(self.normalized_prompt()) > 1 and self.stream:
+            raise ValueError("streaming does not support multiple prompts")
+        return self
+
+    def normalized_prompt(self) -> List[Union[str, List[int]]]:
+        if isinstance(self.prompt, str):
+            return [self.prompt]
+        if all(isinstance(token, int) for token in self.prompt):
+            return [cast(List[int], self.prompt)]
+        if all(isinstance(prompt, str) for prompt in self.prompt):
+            return cast(List[Union[str, List[int]]], list(cast(List[str], self.prompt)))
+        if all(
+            isinstance(prompt, list)
+            and all(isinstance(token, int) for token in prompt)
+            for prompt in self.prompt
+        ):
+            return cast(
+                List[Union[str, List[int]]],
+                list(cast(List[List[int]], self.prompt)),
+            )
+        raise ValueError("prompt must be a string, token ids, list of strings, or list of token-id lists")
+
+
+class CreateEmbeddingRequest(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    input: EmbeddingInput
+    model: str
+    encoding_format: Literal["float", "base64"] = "float"
+    dimensions: Optional[int] = Field(default=None, ge=1)
+    user: Optional[str] = None
+
+    @staticmethod
+    def _validate_text_input(text: str) -> str:
+        if text == "":
+            raise ValueError("embedding input must not contain empty strings")
+        return text
+
+    @staticmethod
+    def _validate_token_input(tokens: List[int]) -> List[int]:
+        if not tokens:
+            raise ValueError("embedding token input must not be empty")
+        if len(tokens) > 2048:
+            raise ValueError("embedding token input must not exceed 2048 tokens")
+        return tokens
+
+    @model_validator(mode="after")
+    def validate_after(self) -> "CreateEmbeddingRequest":
+        self.normalized_input()
+        return self
+
+    def normalized_input(self) -> List[Union[str, List[int]]]:
+        if isinstance(self.input, str):
+            return [self._validate_text_input(self.input)]
+        if all(isinstance(token, int) for token in self.input):
+            return [self._validate_token_input(cast(List[int], self.input))]
+        if all(isinstance(item, str) for item in self.input):
+            if len(self.input) > 2048:
+                raise ValueError("embedding input array must not exceed 2048 items")
+            return [
+                self._validate_text_input(item)
+                for item in cast(List[str], self.input)
+            ]
+        if all(
+            isinstance(item, list)
+            and all(isinstance(token, int) for token in item)
+            for item in self.input
+        ):
+            if len(self.input) > 2048:
+                raise ValueError("embedding input array must not exceed 2048 items")
+            return [
+                self._validate_token_input(item)
+                for item in cast(List[List[int]], self.input)
+            ]
+        raise ValueError(
+            "embedding input must be a string, list of strings, token ids, or list of token-id lists"
+        )
+
+
+class EmbeddingDataResponse(BaseModel):
+    object: Literal["embedding"] = "embedding"
+    embedding: Union[List[float], str]
+    index: int
+
+
+class EmbeddingUsageResponse(BaseModel):
+    prompt_tokens: int
+    total_tokens: int
+
+
+class CreateEmbeddingResponse(BaseModel):
+    object: Literal["list"] = "list"
+    data: List[EmbeddingDataResponse]
+    model: str
+    usage: EmbeddingUsageResponse
+
+    @staticmethod
+    def encode_embedding(
+        embedding: Sequence[float],
+        encoding_format: Literal["float", "base64"],
+        dimensions: Optional[int],
+    ) -> Union[List[float], str]:
+        if dimensions is not None:
+            if dimensions > len(embedding):
+                raise CompletionRequestValidationError(
+                    f"dimensions ({dimensions}) exceeds embedding size ({len(embedding)})"
+                )
+            embedding = embedding[:dimensions]
+        if encoding_format == "float":
+            return [float(value) for value in embedding]
+        array = np.asarray(embedding, dtype=np.float32)
+        return base64.b64encode(array.tobytes()).decode("ascii")
+
+    @classmethod
+    def from_embeddings(
+        cls,
+        *,
+        model: str,
+        embeddings: Sequence[Sequence[float]],
+        total_tokens: int,
+        encoding_format: Literal["float", "base64"],
+        dimensions: Optional[int],
+    ) -> "CreateEmbeddingResponse":
+        return cls(
+            data=[
+                EmbeddingDataResponse(
+                    embedding=cls.encode_embedding(
+                        embedding,
+                        encoding_format,
+                        dimensions,
+                    ),
+                    index=index,
+                )
+                for index, embedding in enumerate(embeddings)
+            ],
+            model=model,
+            usage=EmbeddingUsageResponse(
+                prompt_tokens=total_tokens,
+                total_tokens=total_tokens,
+            ),
+        )
+
+
+class ChatCompletionFunctionCall(BaseModel):
+    model_config = ConfigDict(
+        extra="allow",
+        json_schema_extra=omit_additional_properties,
+    )
+
+    name: str
+    arguments: Optional[str] = None
+
+
+class ChatCompletionToolCall(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    id: Optional[str] = None
+    type: Literal["function"] = "function"
+    function: ChatCompletionFunctionCall
+
+
+class ChatCompletionRequestMessage(BaseModel):
+    model_config = ConfigDict(
+        extra="allow",
+        json_schema_extra=omit_additional_properties,
+    )
+
+    role: Literal["system", "developer", "user", "assistant", "tool", "function"] = Field(
+        default="user"
+    )
+    content: Optional[Union[str, List[Dict[str, Any]]]] = Field(default="")
+    name: Optional[str] = Field(default=None)
+    tool_call_id: Optional[str] = Field(default=None)
+    function_call: Optional[ChatCompletionFunctionCall] = Field(default=None)
+    tool_calls: Optional[List[ChatCompletionToolCall]] = Field(default=None)
+
+
+class ChatTemplateFunctionDefinition(TypedDict, total=False):
+    name: str
+    description: Optional[str]
+    parameters: Optional[Dict[str, Any]]
+    strict: Optional[bool]
+    content_type: Optional[str]
+
+
+class ChatTemplateTool(TypedDict, total=False):
+    type: Literal["function"]
+    original_type: str
+    function: ChatTemplateFunctionDefinition
+
+
+class ChatTemplateFunctionCall(TypedDict, total=False):
+    name: str
+
+
+class ChatTemplateToolChoice(TypedDict, total=False):
+    type: Literal["function"]
+    function: ChatTemplateFunctionCall
+
+
+class ChatTemplateResponseFormatJsonSchema(TypedDict, total=False):
+    name: Optional[str]
+    description: Optional[str]
+    schema: Dict[str, Any]
+    strict: Optional[bool]
+
+
+class ChatTemplateResponseFormat(TypedDict, total=False):
+    type: Literal["text", "json_object", "json_schema"]
+    schema: Dict[str, Any]
+    json_schema: ChatTemplateResponseFormatJsonSchema
+
+
+class ChatCompletionFunctionDefinition(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    name: str
+    description: Optional[str] = None
+    parameters: Optional[Dict[str, Any]] = None
+
+    def to_template_function(self) -> ChatTemplateFunctionDefinition:
+        function: ChatTemplateFunctionDefinition = {"name": self.name}
+        if self.description is not None:
+            function["description"] = self.description
+        if self.parameters is not None:
+            function["parameters"] = self.parameters
+        return function
+
+
+class ChatCompletionToolFunction(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    name: str
+    description: Optional[str] = None
+    parameters: Optional[Dict[str, Any]] = None
+    strict: Optional[bool] = None
+
+    def to_template_function(self) -> ChatTemplateFunctionDefinition:
+        function: ChatTemplateFunctionDefinition = {"name": self.name}
+        if self.description is not None:
+            function["description"] = self.description
+        if self.parameters is not None:
+            function["parameters"] = self.parameters
+        if self.strict is not None:
+            function["strict"] = self.strict
+        return function
+
+
+class ChatCompletionTool(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    type: Literal["function"]
+    function: ChatCompletionToolFunction
+
+    def to_template_tool(self) -> ChatTemplateTool:
+        return {
+            "type": self.type,
+            "function": self.function.to_template_function(),
+        }
+
+
+class ChatCompletionFunctionCallOption(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    name: str
+
+    def to_template_function_call(self) -> ChatTemplateFunctionCall:
+        return {"name": self.name}
+
+
+class ChatCompletionToolChoiceObject(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    type: Literal["function"]
+    function: ChatCompletionFunctionCallOption
+
+    def to_template_tool_choice(self) -> ChatTemplateToolChoice:
+        return {
+            "type": self.type,
+            "function": self.function.to_template_function_call(),
+        }
+
+
+class ChatCompletionResponseFormatJsonSchema(BaseModel):
+    model_config = ConfigDict(extra="ignore", populate_by_name=True)
+
+    name: Optional[str] = None
+    description: Optional[str] = None
+    schema_: Optional[Dict[str, Any]] = Field(default=None, alias="schema")
+    strict: Optional[bool] = None
+
+    def to_template_json_schema(self) -> ChatTemplateResponseFormatJsonSchema:
+        json_schema: ChatTemplateResponseFormatJsonSchema = {}
+        if self.name is not None:
+            json_schema["name"] = self.name
+        if self.description is not None:
+            json_schema["description"] = self.description
+        if self.schema_ is not None:
+            json_schema["schema"] = self.schema_
+        if self.strict is not None:
+            json_schema["strict"] = self.strict
+        return json_schema
+
+
+class ChatCompletionResponseFormat(BaseModel):
+    model_config = ConfigDict(extra="ignore", populate_by_name=True)
+
+    type: Literal["text", "json_object", "json_schema"]
+    schema_: Optional[Dict[str, Any]] = Field(default=None, alias="schema")
+    json_schema: Optional[ChatCompletionResponseFormatJsonSchema] = None
+
+    def to_template_response_format(self) -> ChatTemplateResponseFormat:
+        response_format: ChatTemplateResponseFormat = {"type": self.type}
+        if self.schema_ is not None:
+            response_format["schema"] = self.schema_
+        if self.json_schema is not None:
+            response_format["json_schema"] = self.json_schema.to_template_json_schema()
+        return response_format
+
+
+class CreateChatCompletionRequest(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    messages: List[ChatCompletionRequestMessage] = Field(default_factory=list)
+    max_tokens: Optional[int] = Field(default=None, ge=0)
+    temperature: float = 0.8
+    top_p: float = Field(default=0.95, ge=0.0, le=1.0)
+    stop: Optional[Union[str, List[str]]] = None
+    stream: bool = False
+    logprobs: Optional[bool] = Field(default=False)
+    top_logprobs: Optional[int] = Field(default=None, ge=0)
+    presence_penalty: Optional[float] = Field(default=0.0, ge=-2.0, le=2.0)
+    frequency_penalty: Optional[float] = Field(default=0.0, ge=-2.0, le=2.0)
+    logit_bias: Optional[Dict[str, float]] = None
+    seed: Optional[int] = None
+    model: Optional[str] = None
+    n: int = Field(default=1, ge=1)
+    user: Optional[str] = None
+    functions: Optional[List[ChatCompletionFunctionDefinition]] = None
+    function_call: Optional[
+        Union[Literal["none", "auto"], ChatCompletionFunctionCallOption]
+    ] = None
+    tools: Optional[List[ChatCompletionTool]] = None
+    tool_choice: Optional[
+        Union[Literal["none", "auto", "required"], ChatCompletionToolChoiceObject]
+    ] = None
+    response_format: Optional[ChatCompletionResponseFormat] = None
+    reasoning_effort: Optional[
+        Literal["low", "medium", "high", "minimal", "none"]
+    ] = None
+
+    @field_validator("logit_bias")
+    @classmethod
+    def validate_logit_bias(cls, value: Optional[Dict[str, float]]) -> Optional[Dict[str, float]]:
+        if value is None:
+            return None
+        result: Dict[str, float] = {}
+        for key, bias in value.items():
+            int(key)
+            result[key] = float(bias)
+        return result
+
+    @model_validator(mode="after")
+    def validate_after(self) -> "CreateChatCompletionRequest":
+        if self.top_logprobs is not None and not self.logprobs:
+            raise ValueError("top_logprobs requires logprobs=true")
+        return self
+
+
+class ResponsesFunctionTool(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    type: Literal["function"]
+    name: str
+    description: Optional[str] = None
+    parameters: Optional[Dict[str, Any]] = None
+    strict: Optional[bool] = None
+
+    def to_chat_template_tool(self) -> ChatTemplateTool:
+        return {
+            "type": "function",
+            "function": {
+                "name": self.name,
+                "description": self.description,
+                "parameters": self.parameters,
+                "strict": self.strict,
+            },
+        }
+
+
+class ResponsesCustomToolFormat(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    type: Optional[str] = None
+    syntax: Optional[str] = None
+    definition: Optional[str] = None
+
+
+class ResponsesCustomTool(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    type: Literal["custom"]
+    name: str
+    description: Optional[str] = None
+    format: Optional[ResponsesCustomToolFormat] = None
+    strict: Optional[bool] = None
+
+    def to_chat_template_tool(self) -> ChatTemplateTool:
+        tool_format = self.format
+        syntax = tool_format.syntax if tool_format is not None else None
+        definition = tool_format.definition if tool_format is not None else None
+        description = self.description or ""
+        text_tool_guidance = (
+            "This is a text tool. When calling it, the "
+            "`function.arguments` field itself must be the raw input string. "
+            "Do not wrap the input in JSON and do not use an object such as "
+            "'{\"input\": \"...\"}' or '{\"patch\": \"...\"}'."
+        )
+        if isinstance(syntax, str) and isinstance(definition, str):
+            if description:
+                description = (
+                    f"{description}\n\n{text_tool_guidance}\n\n"
+                    f"{syntax}:\n{definition}"
+                )
+            else:
+                description = f"{text_tool_guidance}\n\n{syntax}:\n{definition}"
+        elif description:
+            description = f"{description}\n\n{text_tool_guidance}"
+        else:
+            description = text_tool_guidance
+        return {
+            "type": "function",
+            "original_type": "custom",
+            "function": {
+                "name": self.name,
+                "description": description or None,
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "input": {
+                            "type": "string",
+                            "description": (
+                                "Raw input text for this tool. "
+                                "For apply_patch, put the full patch here."
+                            ),
+                        },
+                    },
+                    "required": ["input"],
+                    "additionalProperties": False,
+                },
+                "strict": self.strict,
+                "content_type": "text",
+            },
+        }
+
+
+class ResponsesWebSearchTool(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    type: Literal["web_search"]
+
+
+class ResponsesNamespaceTool(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    type: Literal["namespace"]
+
+
+class ResponsesImageGenerationTool(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    type: Literal["image_generation"]
+
+
+ResponsesToolDefinition = Union[
+    ResponsesFunctionTool,
+    ResponsesCustomTool,
+    ResponsesWebSearchTool,
+    ResponsesNamespaceTool,
+    ResponsesImageGenerationTool,
+]
+
+
+class ResponsesToolChoiceObject(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    type: Literal["function", "custom"]
+    name: str
+
+    def to_chat_template_tool_choice(self) -> ChatTemplateToolChoice:
+        return {
+            "type": "function",
+            "function": {
+                "name": self.name,
+            },
+        }
+
+
+ResponsesToolChoice = Union[
+    Literal["auto", "none", "required"],
+    ResponsesToolChoiceObject,
+]
+
+
+class ResponsesTextFormatJsonSchema(BaseModel):
+    model_config = ConfigDict(extra="ignore", populate_by_name=True)
+
+    schema_: Dict[str, Any] = Field(alias="schema")
+
+    def to_template_json_schema(self) -> ChatTemplateResponseFormatJsonSchema:
+        return {"schema": self.schema_}
+
+
+class ResponsesTextFormat(BaseModel):
+    model_config = ConfigDict(extra="ignore", populate_by_name=True)
+
+    type: Literal["text", "json_object", "json_schema"]
+    schema_: Optional[Dict[str, Any]] = Field(default=None, alias="schema")
+    json_schema: Optional[ResponsesTextFormatJsonSchema] = None
+
+    def to_chat_response_format(self) -> ChatTemplateResponseFormat:
+        response_format: ChatTemplateResponseFormat = {"type": self.type}
+        if self.schema_ is not None:
+            response_format["schema"] = self.schema_
+        if self.json_schema is not None:
+            response_format["json_schema"] = self.json_schema.to_template_json_schema()
+        return response_format
+
+
+class ResponsesTextOptions(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    format: Optional[ResponsesTextFormat] = None
+
+
+class ResponsesReasoningOptions(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    effort: Optional[Literal["low", "medium", "high"]] = None
+
+
+class CreateResponseRequest(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    input: Any = ""
+    instructions: Optional[str] = None
+    max_output_tokens: Optional[int] = Field(default=None, ge=0)
+    temperature: Optional[float] = Field(default=0.8, ge=0.0, le=2.0)
+    top_p: Optional[float] = Field(default=0.95, ge=0.0, le=1.0)
+    stream: bool = False
+    top_logprobs: Optional[int] = Field(default=None, ge=0)
+    model: Optional[str] = None
+    tools: Optional[List["ResponsesToolDefinition"]] = None
+    tool_choice: Optional["ResponsesToolChoice"] = None
+    parallel_tool_calls: bool = True
+    text: Optional["ResponsesTextOptions"] = None
+    reasoning: Optional["ResponsesReasoningOptions"] = None
+    metadata: Optional[Dict[str, str]] = None
+    user: Optional[str] = None
+    previous_response_id: Optional[str] = None
+    conversation: Optional[str] = None
+    store: Optional[bool] = None
+    truncation: Optional[Literal["auto", "disabled"]] = None
+
+
+class ResponseCreateWebSocketRequest(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
+    type: Literal["response.create"]
+    model: Optional[str] = None
+    input: Any = ""
+    instructions: Optional[str] = None
+    max_output_tokens: Optional[int] = Field(default=None, ge=0)
+    temperature: Optional[float] = Field(default=0.8, ge=0.0, le=2.0)
+    top_p: Optional[float] = Field(default=0.95, ge=0.0, le=1.0)
+    stream: bool = True
+    top_logprobs: Optional[int] = Field(default=None, ge=0)
+    tools: Optional[List["ResponsesToolDefinition"]] = None
+    tool_choice: Optional["ResponsesToolChoice"] = None
+    parallel_tool_calls: bool = True
+    text: Optional["ResponsesTextOptions"] = None
+    reasoning: Optional["ResponsesReasoningOptions"] = None
+    metadata: Optional[Dict[str, str]] = None
+    user: Optional[str] = None
+    previous_response_id: Optional[str] = None
+    conversation: Optional[str] = None
+    store: Optional[bool] = None
+    truncation: Optional[Literal["auto", "disabled"]] = None
+    generate: Optional[bool] = None
+
+    def to_create_response_request(self) -> CreateResponseRequest:
+        return CreateResponseRequest.model_validate(
+            self.model_dump(mode="python", exclude={"type"})
+        )
+
+
+class ModelCardResponse(BaseModel):
+    id: str
+    object: Literal["model"] = "model"
+    created: int
+    owned_by: str
+
+
+class ModelListResponse(BaseModel):
+    object: Literal["list"] = "list"
+    data: List[ModelCardResponse]
+
+
+class HealthzResponse(BaseModel):
+    status: Literal["ok"] = "ok"
+
+
+@dataclass
+class ResponsesWebSocketState:
+    input_items: List[Any]
+    output_items: List[Dict[str, Any]]
+
+
+class ConfigFile(BaseModel):
+    class ServerOptions(BaseModel):
+        host: str = "127.0.0.1"
+        port: int = 8000
+
+    class DiskCacheOptions(BaseModel):
+        path: str
+        max_bytes: int = Field(ge=0)
+        min_tokens: int = Field(default=128, ge=1)
+
+    class FromPretrainedOptions(BaseModel):
+        repo_id: str
+        filename: str
+        additional_files: Optional[List[str]] = None
+        local_dir: Optional[str] = None
+        local_dir_use_symlinks: Union[bool, Literal["auto"]] = "auto"
+        cache_dir: Optional[str] = None
+
+        @staticmethod
+        def _pattern_has_glob(pattern: str) -> bool:
+            return any(char in pattern for char in "*?[]")
+
+        @staticmethod
+        def _subfolder_for_repo_file(repo_file: str) -> Optional[str]:
+            subfolder = str(Path(repo_file).parent)
+            return None if subfolder == "." else subfolder
+
+        def _cached_repo_file_list(self) -> List[str]:
+            from huggingface_hub import scan_cache_dir
+
+            try:
+                cache_info = scan_cache_dir(self.cache_dir)
+            except Exception:
+                return []
+
+            cached_files: set[str] = set()
+            for repo in cache_info.repos:
+                if repo.repo_type != "model" or repo.repo_id != self.repo_id:
+                    continue
+                for revision in repo.revisions:
+                    for cached_file in revision.files:
+                        cached_files.add(cached_file.file_name)
+            return sorted(cached_files)
+
+        def _download_repo_file(self, repo_file: str) -> str:
+            from huggingface_hub import hf_hub_download
+
+            filename = Path(repo_file).name
+            subfolder = self._subfolder_for_repo_file(repo_file)
+
+            download_kwargs: Dict[str, Any] = dict(
+                repo_id=self.repo_id,
+                filename=filename,
+                subfolder=subfolder,
+                local_dir=self.local_dir,
+                cache_dir=self.cache_dir,
+            )
+            hf_hub_download_signature = inspect.signature(hf_hub_download)
+            supports_local_dir_use_symlinks = (
+                "local_dir_use_symlinks" in hf_hub_download_signature.parameters
+                or any(
+                    parameter.kind is inspect.Parameter.VAR_KEYWORD
+                    for parameter in hf_hub_download_signature.parameters.values()
+                )
+            )
+            if supports_local_dir_use_symlinks:
+                download_kwargs["local_dir_use_symlinks"] = self.local_dir_use_symlinks
+
+            try:
+                resolved_path = hf_hub_download(**download_kwargs)
+            except Exception as exc:
+                try:
+                    cached_path = cast(
+                        str,
+                        hf_hub_download(
+                            repo_id=self.repo_id,
+                            filename=filename,
+                            subfolder=subfolder,
+                            cache_dir=self.cache_dir,
+                            local_files_only=True,
+                        ),
+                    )
+                except Exception:
+                    raise exc
+
+                if self.local_dir is None:
+                    return cached_path
+
+                resolved_path = os.path.join(self.local_dir, repo_file)
+                resolved_parent = os.path.dirname(resolved_path)
+                if resolved_parent:
+                    os.makedirs(resolved_parent, exist_ok=True)
+                shutil.copy2(cached_path, resolved_path)
+
+            return cast(str, resolved_path)
+
+        @staticmethod
+        def _match_single_file(pattern: str, file_list: Sequence[str]) -> str:
+            matching_files = [file for file in file_list if fnmatch.fnmatch(file, pattern)]
+            if len(matching_files) == 0:
+                raise ValueError(
+                    f"No file found matching {pattern}\n\n"
+                    f"Available Files:\n{json.dumps(list(file_list))}"
+                )
+            if len(matching_files) > 1:
+                raise ValueError(
+                    f"Multiple files found matching {pattern}\n\n"
+                    f"Available Files:\n{json.dumps(list(file_list))}"
+                )
+            return matching_files[0]
+
+        def resolve_model_path(self) -> str:
+            try:
+                from huggingface_hub import HfFileSystem
+                from huggingface_hub.utils import validate_repo_id
+            except ImportError as exc:
+                try:
+                    from huggingface_hub import HfFileSystem
+                    from huggingface_hub.utils._validators import validate_repo_id
+                except ImportError:
+                    raise ImportError(
+                        "model.from_pretrained requires the huggingface-hub package. "
+                        "You can install it with `pip install huggingface-hub`."
+                    ) from exc
+
+            validate_repo_id(self.repo_id)
+            requested_patterns = [self.filename, *(self.additional_files or [])]
+            if not any(self._pattern_has_glob(pattern) for pattern in requested_patterns):
+                model_path = self._download_repo_file(self.filename)
+                if self.additional_files:
+                    for additional_file_name in self.additional_files:
+                        self._download_repo_file(additional_file_name)
+                return model_path
+
+            try:
+                hffs = HfFileSystem()
+                files = [
+                    file["name"] if isinstance(file, dict) else file
+                    for file in hffs.ls(self.repo_id, recursive=True)
+                ]
+                file_list = [str(Path(file).relative_to(self.repo_id)) for file in files]
+            except Exception as exc:
+                file_list = self._cached_repo_file_list()
+                if not file_list:
+                    raise exc
+
+            matching_file = self._match_single_file(self.filename, file_list)
+            model_path = self._download_repo_file(matching_file)
+
+            if self.additional_files:
+                for additional_file_name in self.additional_files:
+                    matching_additional_file = self._match_single_file(
+                        additional_file_name, file_list
+                    )
+                    self._download_repo_file(matching_additional_file)
+
+            return model_path
+
+    class LoraOptions(BaseModel):
+        path: Optional[str] = None
+        from_pretrained: Optional["ConfigFile.FromPretrainedOptions"] = None
+        scale: float = 1.0
+
+        @model_validator(mode="after")
+        def validate_source(self) -> "ConfigFile.LoraOptions":
+            if (self.path is None) == (self.from_pretrained is None):
+                raise ValueError("exactly one of lora.path or lora.from_pretrained is required")
+            return self
+
+        def resolve_path(self) -> str:
+            if self.from_pretrained is not None:
+                return self.from_pretrained.resolve_model_path()
+            assert self.path is not None
+            return self.path
+
+    class MTMDEmbeddingCacheOptions(BaseModel):
+        path: str
+        max_bytes: int = Field(ge=0)
+
+    class MTMDOptions(BaseModel):
+        mmproj_path: Optional[str] = None
+        mmproj_from_pretrained: Optional["ConfigFile.FromPretrainedOptions"] = None
+        embedding_cache: Optional["ConfigFile.MTMDEmbeddingCacheOptions"] = None
+        allowed_media_domains: Optional[List[str]] = None
+        allowed_local_media_path: Optional[str] = None
+        batch_max_tokens: int = Field(default=1024, ge=1)
+        image_max_bytes: int = Field(default=20 * 1024 * 1024, ge=1)
+        audio_max_bytes: int = Field(default=100 * 1024 * 1024, ge=1)
+        video_max_bytes: int = Field(default=512 * 1024 * 1024, ge=1)
+        image_timeout_seconds: float = Field(default=10.0, gt=0.0)
+
+        @model_validator(mode="after")
+        def validate_source(self) -> "ConfigFile.MTMDOptions":
+            if (self.mmproj_path is None) == (self.mmproj_from_pretrained is None):
+                raise ValueError(
+                    "exactly one of model.mtmd.mmproj_path or "
+                    "model.mtmd.mmproj_from_pretrained is required"
+                )
+            return self
+
+        def resolve_mmproj_path(self) -> str:
+            if self.mmproj_from_pretrained is not None:
+                return self.mmproj_from_pretrained.resolve_model_path()
+            assert self.mmproj_path is not None
+            return self.mmproj_path
+
+    class ModelOptions(BaseModel):
+        path: Optional[str] = None
+        alias: Optional[str] = None
+        chat_template: Optional[str] = None
+        from_pretrained: Optional["ConfigFile.FromPretrainedOptions"] = None
+        loras: List["ConfigFile.LoraOptions"] = Field(default_factory=list)
+        mtmd: Optional["ConfigFile.MTMDOptions"] = None
+        n_gpu_layers: Optional[int] = None
+        split_mode: Optional[int] = None
+        main_gpu: Optional[int] = None
+        tensor_split: Optional[List[float]] = None
+        vocab_only: Optional[bool] = None
+        use_mmap: Optional[bool] = None
+        use_mlock: Optional[bool] = None
+        kv_overrides: Optional[Dict[str, Union[bool, int, float, str]]] = None
+        n_ctx: Optional[int] = None
+        n_batch: Optional[int] = None
+        n_ubatch: Optional[int] = None
+        n_seq_max: Optional[int] = None
+        threads: Optional[int] = Field(
+            default_factory=lambda: max(multiprocessing.cpu_count(), 1)
+        )
+        threads_batch: Optional[int] = Field(
+            default_factory=lambda: max(multiprocessing.cpu_count(), 1)
+        )
+        rope_scaling_type: Optional[int] = None
+        pooling_type: Optional[int] = None
+        attention_type: Optional[int] = None
+        embedding: Optional[bool] = None
+        rope_freq_base: Optional[float] = None
+        rope_freq_scale: Optional[float] = None
+        yarn_ext_factor: Optional[float] = None
+        yarn_attn_factor: Optional[float] = None
+        yarn_beta_fast: Optional[float] = None
+        yarn_beta_slow: Optional[float] = None
+        yarn_orig_ctx: Optional[int] = None
+        offload_kqv: Optional[bool] = None
+        flash_attn: Optional[bool] = None
+        op_offload: Optional[bool] = None
+        swa_full: Optional[bool] = None
+        no_perf: Optional[bool] = None
+        type_k: Optional[int] = None
+        type_v: Optional[int] = None
+        max_seq_len: Optional[int] = None
+        max_output_tokens: Optional[int] = Field(default=None, ge=0)
+        kv_unified: bool = True
+        draft_model: Optional[Literal["prompt-lookup-decoding", "draft-mtp"]] = None
+        draft_model_path: Optional[str] = None
+        draft_model_from_pretrained: Optional[
+            "ConfigFile.FromPretrainedOptions"
+        ] = None
+        draft_model_num_pred_tokens: int = 16
+        draft_model_max_ngram_size: int = 2
+        draft_model_top_k: int = Field(default=1, ge=1)
+        draft_model_p_min: float = Field(default=0.0, ge=0.0, le=1.0)
+        draft_model_max_batch_size: Optional[int] = Field(default=None, ge=1)
+        draft_model_threads: Optional[int] = Field(default=None, gt=0)
+        draft_model_threads_batch: Optional[int] = Field(default=None, gt=0)
+        response_schema: Optional[Dict[str, Any]] = None
+        store_logits: bool = True
+
+        @model_validator(mode="after")
+        def validate_source(self) -> "ConfigFile.ModelOptions":
+            if (self.path is None) == (self.from_pretrained is None):
+                raise ValueError("exactly one of model.path or model.from_pretrained is required")
+            if (
+                self.draft_model_path is not None
+                and self.draft_model_from_pretrained is not None
+            ):
+                raise ValueError(
+                    "model.draft_model_path and model.draft_model_from_pretrained "
+                    "are mutually exclusive"
+                )
+            return self
+
+        def resolve_draft_model_path(self) -> Optional[str]:
+            if self.draft_model_from_pretrained is not None:
+                return self.draft_model_from_pretrained.resolve_model_path()
+            return self.draft_model_path
+
+        @field_validator("chat_template", mode="before")
+        @classmethod
+        def normalize_chat_template(cls, value: Any) -> Any:
+            if isinstance(value, list):
+                if not all(isinstance(item, str) for item in value):
+                    raise TypeError("model.chat_template list entries must be strings")
+                return "".join(value)
+            return value
+
+        def resolve_model_path(self) -> str:
+            if self.from_pretrained is not None:
+                return self.from_pretrained.resolve_model_path()
+            assert self.path is not None
+            return self.path
+
+    server: "ConfigFile.ServerOptions" = Field(default_factory=lambda: ConfigFile.ServerOptions())
+    model: "ConfigFile.ModelOptions"
+    disk_cache: Optional["ConfigFile.DiskCacheOptions"] = None
+
+    @classmethod
+    def load(cls, path: str) -> "ConfigFile":
+        return cls.model_validate_json(Path(path).read_text())
+
+
+ConfigFile.model_rebuild()
+
+
+@dataclass(frozen=True)
+class MediaInput:
+    kind: Literal["image", "audio", "video"]
+    url: Optional[str] = None
+    data: Optional[str] = None
+    format: Optional[str] = None
+
+
+class Jinja2ChatFormatter:
+    def __init__(self, template: str, *, bos_token: str, eos_token: str) -> None:
+        self._eos_token = eos_token
+        self._bos_token = bos_token
+        self._template_text = template
+        environment = ImmutableSandboxedEnvironment(
+            loader=jinja2.BaseLoader(),
+            trim_blocks=True,
+            lstrip_blocks=True,
+        )
+        environment.filters["from_json"] = self._from_json
+        self._template = environment.from_string(template)
+
+    @staticmethod
+    def _from_json(value: Any) -> Any:
+        if isinstance(value, str):
+            return json.loads(value)
+        return value
+
+    @staticmethod
+    def media_inputs_from_messages(
+        messages: Sequence[ChatCompletionRequestMessage],
+    ) -> List[MediaInput]:
+        media_inputs: List[MediaInput] = []
+        for message in messages:
+            content = message.content
+            if not isinstance(content, list):
+                continue
+            for part in content:
+                if not isinstance(part, dict):
+                    continue
+                part_type = part.get("type")
+                if part_type in {"image_url", "input_image", "image"}:
+                    image_url = part.get("image_url") or part.get("url")
+                    if isinstance(image_url, str):
+                        media_inputs.append(MediaInput(kind="image", url=image_url))
+                    elif isinstance(image_url, dict) and isinstance(image_url.get("url"), str):
+                        media_inputs.append(MediaInput(kind="image", url=cast(str, image_url["url"])))
+                    else:
+                        raise ValueError("image_url content part requires a URL string")
+                    continue
+                if part_type == "audio_url":
+                    audio_url = part.get("audio_url")
+                    if isinstance(audio_url, str):
+                        media_inputs.append(MediaInput(kind="audio", url=audio_url))
+                    elif isinstance(audio_url, dict) and isinstance(audio_url.get("url"), str):
+                        media_inputs.append(MediaInput(kind="audio", url=cast(str, audio_url["url"])))
+                    else:
+                        raise ValueError("audio_url content part requires a URL string")
+                    continue
+                if part_type == "input_audio":
+                    input_audio = part.get("input_audio")
+                    if isinstance(input_audio, dict):
+                        data = input_audio.get("data")
+                        audio_format = input_audio.get("format")
+                    else:
+                        data = part.get("data")
+                        audio_format = part.get("format")
+                    if not isinstance(data, str):
+                        raise ValueError("input_audio content part requires base64 data")
+                    if audio_format is not None and not isinstance(audio_format, str):
+                        raise ValueError("input_audio format must be a string")
+                    media_inputs.append(
+                        MediaInput(
+                            kind="audio",
+                            data=data,
+                            format=cast(Optional[str], audio_format),
+                        )
+                    )
+                    continue
+                if part_type in {"video_url", "input_video", "video"}:
+                    input_video = part.get("input_video")
+                    if isinstance(input_video, dict):
+                        data = input_video.get("data")
+                        video_url = input_video.get("video_url") or input_video.get("url")
+                        video_format = input_video.get("format")
+                    else:
+                        data = part.get("data")
+                        video_url = part.get("video_url") or part.get("url")
+                        video_format = part.get("format")
+                    if isinstance(video_url, dict):
+                        video_url = video_url.get("url")
+                    if isinstance(data, str):
+                        if video_format is not None and not isinstance(video_format, str):
+                            raise ValueError("input_video format must be a string")
+                        media_inputs.append(
+                            MediaInput(
+                                kind="video",
+                                data=data,
+                                format=cast(Optional[str], video_format),
+                            )
+                        )
+                    elif isinstance(video_url, str):
+                        media_inputs.append(MediaInput(kind="video", url=video_url))
+                    else:
+                        raise ValueError("video content part requires base64 data or a URL string")
+                    continue
+        return media_inputs
+
+    @staticmethod
+    def _literal_content_for_template(
+        text: str,
+        media_marker: Optional[str],
+    ) -> str:
+        if media_marker is not None and media_marker in text:
+            raise ValueError("message content contains reserved multimodal marker text")
+        return text
+
+    @staticmethod
+    def _content_part_for_template(
+        part: Any,
+        media_marker: Optional[str],
+    ) -> str:
+        if isinstance(part, str):
+            return Jinja2ChatFormatter._literal_content_for_template(part, media_marker)
+        if not isinstance(part, dict):
+            raise ValueError("content parts must be strings or objects")
+        part_type = part.get("type")
+        if part_type in {
+            "image_url",
+            "input_image",
+            "image",
+            "audio_url",
+            "input_audio",
+            "video_url",
+            "input_video",
+            "video",
+        }:
+            if media_marker is None:
+                raise ValueError("multimodal content requires model.mtmd")
+            return media_marker
+        if part_type in {"text", "input_text"}:
+            text = part.get("text")
+            if not isinstance(text, str):
+                raise ValueError(f"content part {part_type!r} requires string text")
+            return Jinja2ChatFormatter._literal_content_for_template(text, media_marker)
+        raise ValueError(f"unsupported content part type: {part_type!r}")
+
+    @staticmethod
+    def _messages_for_template(
+        messages: Sequence[ChatCompletionRequestMessage],
+        media_marker: Optional[str],
+    ) -> List[Dict[str, Any]]:
+        converted: List[Dict[str, Any]] = []
+        for message in messages:
+            data = message.model_dump(exclude_none=True)
+            content = data.get("content")
+            if isinstance(content, list):
+                data["content"] = "".join(
+                    Jinja2ChatFormatter._content_part_for_template(
+                        part,
+                        media_marker,
+                    )
+                    for part in content
+                )
+            elif isinstance(content, str):
+                data["content"] = Jinja2ChatFormatter._literal_content_for_template(
+                    content,
+                    media_marker,
+                )
+            converted.append(data)
+        return converted
+
+    def _render(
+        self,
+        *,
+        messages: List[ChatCompletionRequestMessage],
+        media_marker: Optional[str] = None,
+        functions: Optional[List[ChatTemplateFunctionDefinition]] = None,
+        function_call: Optional[Union[str, ChatTemplateFunctionCall]] = None,
+        tools: Optional[List[ChatTemplateTool]] = None,
+        tool_choice: Optional[Union[str, ChatTemplateToolChoice]] = None,
+        reasoning_effort: Optional[str] = None,
+        add_generation_prompt: bool,
+        strftime_now: Callable[[str], str],
+    ) -> str:
+        def raise_exception(message: str) -> None:
+            raise ValueError(message)
+
+        return cast(
+            str,
+            self._template.render(
+                messages=self._messages_for_template(messages, media_marker),
+                eos_token=self._eos_token,
+                bos_token=self._bos_token,
+                raise_exception=raise_exception,
+                add_generation_prompt=add_generation_prompt,
+                functions=functions,
+                function_call=function_call,
+                tools=tools,
+                tool_choice=tool_choice,
+                reasoning_effort=reasoning_effort,
+                strftime_now=strftime_now,
+            ),
+        )
+
+    def format(
+        self,
+        *,
+        messages: List[ChatCompletionRequestMessage],
+        media_marker: Optional[str] = None,
+        functions: Optional[List[ChatTemplateFunctionDefinition]] = None,
+        function_call: Optional[Union[str, ChatTemplateFunctionCall]] = None,
+        tools: Optional[List[ChatTemplateTool]] = None,
+        tool_choice: Optional[Union[str, ChatTemplateToolChoice]] = None,
+        reasoning_effort: Optional[str] = None,
+    ) -> Tuple[str, str, List[str]]:
+        render_time = datetime.now()
+
+        def strftime_now(format_string: str) -> str:
+            return render_time.strftime(format_string)
+
+        chat_template = self._render(
+            messages=messages,
+            media_marker=media_marker,
+            functions=functions,
+            function_call=function_call,
+            tools=tools,
+            tool_choice=tool_choice,
+            reasoning_effort=reasoning_effort,
+            add_generation_prompt=False,
+            strftime_now=strftime_now,
+        )
+        prompt = self._render(
+            messages=messages,
+            media_marker=media_marker,
+            functions=functions,
+            function_call=function_call,
+            tools=tools,
+            tool_choice=tool_choice,
+            reasoning_effort=reasoning_effort,
+            add_generation_prompt=True,
+            strftime_now=strftime_now,
+        )
+        generation_prompt = prompt[len(chat_template) :] if prompt.startswith(chat_template) else ""
+        stop = [self._eos_token] if self._eos_token else []
+        return prompt, generation_prompt, stop
+
+
+@dataclass
+class Token:
+    token: int
+    text_bytes: bytes
+    token_logprob: Optional[float]
+    top_logprobs: Optional[Dict[str, float]]
+
+    @classmethod
+    def from_token(
+        cls,
+        *,
+        model: Model,
+        prev_tokens: Sequence[int],
+        prev_text_bytes: Optional[Union[bytes, bytearray]] = None,
+        token: int,
+    ) -> "Token":
+        return cls(
+            token=token,
+            text_bytes=(
+                model.token_bytes_with_prev_bytes(prev_tokens, prev_text_bytes, token)
+                if prev_text_bytes is not None
+                else model.token_bytes_with_prev(prev_tokens, token)
+            ),
+            token_logprob=None,
+            top_logprobs=None,
+        )
+
+    @classmethod
+    def from_logits(
+        cls,
+        *,
+        model: Model,
+        formatter: OpenAIFormatter,
+        prev_tokens: Sequence[int],
+        prev_text_bytes: Optional[Union[bytes, bytearray]] = None,
+        token: int,
+        logits: np.ndarray,
+        logprobs_count: Optional[int],
+        need_token_logprob: bool = False,
+    ) -> "Token":
+        text_bytes = (
+            model.token_bytes_with_prev_bytes(prev_tokens, prev_text_bytes, token)
+            if prev_text_bytes is not None
+            else model.token_bytes_with_prev(prev_tokens, token)
+        )
+        if not model.store_logits:
+            return cls(
+                token=token,
+                text_bytes=text_bytes,
+                token_logprob=None,
+                top_logprobs=None,
+            )
+        if logprobs_count is None and not need_token_logprob:
+            return cls(
+                token=token,
+                text_bytes=text_bytes,
+                token_logprob=None,
+                top_logprobs=None,
+            )
+        logprobs = CompletionScheduler.logits_to_logprobs(logits)
+        token_logprob = float(logprobs[token])
+        top_logprobs: Optional[Dict[str, float]] = None
+        if logprobs_count is not None:
+            top_count = min(max(logprobs_count, 0), model.n_vocab)
+            if top_count > 0:
+                top_indices = np.argpartition(logprobs, -top_count)[-top_count:]
+                top_indices = top_indices[np.argsort(logprobs[top_indices])[::-1]]
+                top_logprobs = {
+                    formatter.decode_text(model.token_bytes(int(index))): float(
+                        logprobs[int(index)]
+                    )
+                    for index in top_indices
+                }
+            else:
+                top_logprobs = {}
+            top_logprobs[formatter.decode_text(text_bytes)] = token_logprob
+        return cls(
+            token=token,
+            text_bytes=text_bytes,
+            token_logprob=token_logprob,
+            top_logprobs=top_logprobs,
+        )
+
+
+@dataclass
+class Completion:
+    request_id: str
+    index: int
+    seq_id: int
+    sampler: "Sampler"
+    prompt_tokens: List[int]
+    prompt_length: int
+    prompt_text: str
+    multimodal_prompt: bool
+    max_total_tokens: int
+    stop_sequences: List[bytes]
+    logprobs: Optional[int]
+    completion_tokens: List[int] = field(default_factory=list)
+    token_records: List[Token] = field(default_factory=list)
+    rendered_bytes: bytearray = field(default_factory=bytearray)
+    detokenized_prefix_bytes: bytearray = field(default_factory=bytearray)
+    pending_input_tokens: List[int] = field(default_factory=list)
+    draft_tokens: List[int] = field(default_factory=list)
+    pending_finish_reason: Optional[str] = None
+    returned_token_count: int = 0
+    finished: bool = False
+    finish_reason: Optional[str] = None
+    score_sum: float = 0.0
+    rank_by_score: bool = False
+
+    @property
+    def total_tokens(self) -> int:
+        return self.prompt_length + len(self.completion_tokens)
+
+    @property
+    def completion_token_count(self) -> int:
+        return len(self.completion_tokens)
+
+    @property
+    def needs_token_logprob(self) -> bool:
+        return self.logprobs is not None or self.rank_by_score
+
+    @property
+    def max_stop_sequence_length(self) -> int:
+        return max((len(stop) for stop in self.stop_sequences), default=0)
+
+
+@dataclass
+class PromptSegment:
+    @dataclass
+    class Media:
+        embeddings: np.ndarray
+        positions: np.ndarray
+        non_causal: bool = False
+
+    kind: Literal["text", "image", "audio", "video"]
+    start_pos: int
+    n_pos: int
+    identity_tokens: List[int]
+    decode_start_pos: int
+    decode_n_pos: int
+    text_tokens: List[int] = field(default_factory=list)
+    media: Optional["PromptSegment.Media"] = None
+
+    @property
+    def end_pos(self) -> int:
+        return self.start_pos + self.n_pos
+
+    @property
+    def batch_rows(self) -> int:
+        if self.kind != "text":
+            if self.media is None:
+                return 0
+            return int(self.media.embeddings.shape[0])
+        return len(self.text_tokens)
+
+    @property
+    def decoder_position_increments(self) -> List[int]:
+        if not self.identity_tokens:
+            return []
+        if self.kind == "text":
+            return [1] * len(self.identity_tokens)
+        return [*([0] * (len(self.identity_tokens) - 1)), self.decode_n_pos]
+
+    def rows_for_capacity(self, offset: int, capacity: int) -> int:
+        if capacity <= 0:
+            return 0
+        return min(capacity, self.end_pos - self.start_pos - offset)
+
+    def media_slice(
+        self,
+        row_offset: int,
+        row_count: int,
+    ) -> Tuple[np.ndarray, np.ndarray, List[int]]:
+        if self.media is None:
+            raise RuntimeError("media segment is missing embeddings or positions")
+        row_start = row_offset
+        row_end = row_offset + row_count
+        embeddings = self.media.embeddings[row_start:row_end]
+        if len(self.media.positions) == self.batch_rows:
+            positions = self.media.positions[row_start:row_end]
+        else:
+            positions = (
+                self.media.positions.reshape(4, self.batch_rows)[:, row_start:row_end]
+                .reshape(-1)
+            )
+        return (
+            embeddings,
+            positions,
+            self.decoder_position_increments[row_start:row_end],
+        )
+
+
+@dataclass
+class PromptPlan:
+    text: str
+    generation_prompt: str
+    text_tokens: List[int]
+    identity_tokens: List[int]
+    segments: List[PromptSegment]
+    text_token_index_by_pos: Dict[int, int] = field(default_factory=dict)
+
+    @classmethod
+    def from_tokens(
+        cls,
+        text: str,
+        tokens: List[int],
+        *,
+        generation_prompt: str = "",
+    ) -> "PromptPlan":
+        segment = PromptSegment(
+            kind="text",
+            start_pos=0,
+            n_pos=len(tokens),
+            identity_tokens=list(tokens),
+            decode_start_pos=0,
+            decode_n_pos=len(tokens),
+            text_tokens=list(tokens),
+        )
+        return cls(
+            text=text,
+            generation_prompt=generation_prompt,
+            text_tokens=list(tokens),
+            identity_tokens=list(tokens),
+            segments=[segment] if tokens else [],
+            text_token_index_by_pos={pos: pos for pos in range(len(tokens))},
+        )
+
+    @property
+    def length(self) -> int:
+        return len(self.identity_tokens)
+
+    @property
+    def eval_token_count(self) -> int:
+        return self.length
+
+    def position_increments_up_to(self, pos: int) -> List[int]:
+        increments: List[int] = []
+        for segment in self.segments:
+            if pos <= segment.start_pos:
+                break
+            take = min(pos, segment.end_pos) - segment.start_pos
+            if take <= 0:
+                continue
+            increments.extend(segment.decoder_position_increments[:take])
+            if pos <= segment.end_pos:
+                break
+        return increments
+
+    def is_boundary(self, pos: int) -> bool:
+        if pos <= 0 or pos >= self.length:
+            return True
+        return any(segment.start_pos == pos or segment.end_pos == pos for segment in self.segments)
+
+    def is_reusable_boundary(self, pos: int) -> bool:
+        if pos <= 0 or pos >= self.length:
+            return True
+        if self.is_boundary(pos):
+            return True
+        return self.segment_at(pos).kind == "text"
+
+    def clamp_to_reusable_boundary(self, pos: int) -> int:
+        if pos <= 0:
+            return 0
+        if pos >= self.length:
+            return self.length
+        if self.is_reusable_boundary(pos):
+            return pos
+        segment = self.segment_at(pos)
+        return segment.start_pos
+
+    def decoder_pos_up_to(self, pos: int) -> int:
+        if not self.is_reusable_boundary(pos):
+            raise RuntimeError("decoder position requested inside a non-reusable media segment")
+        if pos <= 0:
+            return 0
+        if pos >= self.length:
+            if not self.segments:
+                return 0
+            segment = self.segments[-1]
+            return segment.decode_start_pos + segment.decode_n_pos
+        segment = self.segment_at(pos)
+        if segment.kind == "text":
+            return segment.decode_start_pos + (pos - segment.start_pos)
+        if pos == segment.start_pos:
+            return segment.decode_start_pos
+        if pos == segment.end_pos:
+            return segment.decode_start_pos + segment.decode_n_pos
+        raise RuntimeError("decoder position requested inside a media segment")
+
+    def segment_at(self, pos: int) -> PromptSegment:
+        for segment in self.segments:
+            if segment.start_pos <= pos < segment.end_pos:
+                return segment
+        raise RuntimeError(f"missing prompt segment at position {pos}")
+
+    def has_text_token_at(self, pos: int) -> bool:
+        return pos in self.text_token_index_by_pos
+
+    def text_token_at(self, pos: int) -> int:
+        return self.text_tokens[self.text_token_index_by_pos[pos]]
+
+    def prev_text_tokens_at(self, pos: int) -> List[int]:
+        return self.text_tokens[: self.text_token_index_by_pos[pos]]
+
+
+@dataclass(frozen=True)
+class PreparedCompletionParts:
+    payload: CreateCompletionRequest
+    prompt_text: str
+    generation_prompt: str
+    prompt_plan: PromptPlan
+    grammar_text: Optional[str]
+    tool_name: Optional[str]
+
+
+@dataclass(frozen=True)
+class ResponsesChatRequestParts:
+    messages: List[ChatCompletionRequestMessage]
+    max_tokens: Optional[int]
+    temperature: float
+    top_p: float
+    stream: bool
+    logprobs: bool
+    top_logprobs: Optional[int]
+    model: Optional[str]
+    user: Optional[str]
+    tools: Optional[List[ChatTemplateTool]]
+    tool_choice: Optional[Union[Literal["auto", "none", "required"], ChatTemplateToolChoice]]
+    response_format: Optional[ChatTemplateResponseFormat]
+    reasoning_effort: Optional[str]
+
+
+@dataclass
+class SchedulerMetrics:
+    started_at: float = field(default_factory=time.time)
+    requests_submitted_total: int = 0
+    requests_admitted_total: int = 0
+    requests_completed_total: int = 0
+    requests_cancelled_total: int = 0
+    requests_failed_total: int = 0
+    prompt_tokens_total: int = 0
+    prompt_seconds_total: float = 0.0
+    tokens_predicted_total: int = 0
+    tokens_predicted_seconds_total: float = 0.0
+    scheduler_step_seconds_total: float = 0.0
+    process_batch_seconds_total: float = 0.0
+    sample_seconds_total: float = 0.0
+    draft_seconds_total: float = 0.0
+    draft_process_seconds_total: float = 0.0
+    draft_generate_seconds_total: float = 0.0
+    draft_sampled_batch_seconds_total: float = 0.0
+    draft_process_calls_total: int = 0
+    draft_generate_calls_total: int = 0
+    draft_sampled_batch_calls_total: int = 0
+    draft_batches_verified_total: int = 0
+    draft_target_tokens_verified_total: int = 0
+    draft_target_tokens_wasted_total: int = 0
+    draft_tokens_reused_as_pending_total: int = 0
+    n_decode_total: int = 0
+    scheduler_step_calls_total: int = 0
+    process_batch_calls_total: int = 0
+    sample_calls_total: int = 0
+    n_tokens_max: int = 0
+    n_busy_slots_total: int = 0
+    checkpoint_hits_total: int = 0
+    checkpoint_saves_total: int = 0
+    checkpoint_evictions_total: int = 0
+    sequence_cache_hits_total: int = 0
+    sequence_cache_save_requests_total: int = 0
+    sequence_cache_lookup_failures_total: int = 0
+    sequence_cache_load_failures_total: int = 0
+    sequence_cache_save_failures_total: int = 0
+    sequence_cache_tokens_loaded_total: int = 0
+
+    def observe_decode(
+        self,
+        items: Sequence[Any],
+        elapsed_seconds: float,
+    ) -> None:
+        if not items:
+            return
+        total_tokens = sum(
+            item.batch_token_count
+            for item in items
+        )
+        if total_tokens <= 0:
+            return
+        prompt_tokens = sum(
+            item.batch_token_count
+            for item in items
+            if getattr(item, "kind", None) == "prefill"
+        )
+        generation_tokens = total_tokens - prompt_tokens
+        self.n_decode_total += 1
+        self.n_busy_slots_total += len(items)
+        self.n_tokens_max = max(self.n_tokens_max, total_tokens)
+        self.prompt_tokens_total += prompt_tokens
+        if prompt_tokens > 0:
+            self.prompt_seconds_total += elapsed_seconds * (prompt_tokens / total_tokens)
+        if generation_tokens > 0:
+            self.tokens_predicted_seconds_total += elapsed_seconds * (
+                generation_tokens / total_tokens
+            )
+
+    def observe_predicted_token(self) -> None:
+        self.tokens_predicted_total += 1
+
+
+class SequenceCache(Protocol):
+    """Optional external sequence-state cache; storage and eviction stay outside the scheduler."""
+
+    @dataclass(frozen=True)
+    class Match:
+        tokens: Tuple[int, ...]
+        has_prompt_logits: bool = False
+
+        @property
+        def length(self) -> int:
+            return len(self.tokens)
+
+    @dataclass
+    class Load:
+        tokens: List[int]
+        state_bytes: np.ndarray
+        prompt_logits: Optional[np.ndarray] = None
+
+    def lookup(self, tokens: Sequence[int]) -> Optional["SequenceCache.Match"]:
+        ...
+
+    def load(
+        self,
+        match: "SequenceCache.Match",
+    ) -> Optional["SequenceCache.Load"]:
+        ...
+
+    def save(
+        self,
+        tokens: Sequence[int],
+        state_bytes: np.ndarray,
+        prompt_logits: Optional[np.ndarray],
+    ) -> None:
+        ...
+
+
+@dataclass
+class CompletionRequest:
+    payload: CreateCompletionRequest
+    prompt_text: str
+    prompt_tokens: List[int]
+    prompt_plan: PromptPlan
+    effective_max_len: int
+    internal_completion_count: int
+    prompt_visible_start: int
+    id: str = field(default_factory=lambda: f"cmpl-{uuid.uuid4().hex}")
+    created: int = field(default_factory=lambda: int(time.time()))
+    prompt_cursor: int = 0
+    match_sequence_id: int = -1
+    match_length: int = 0
+    sequence_cache_match: Optional[SequenceCache.Match] = None
+    sequence_cache_match_length: int = 0
+    prompt_logits: Optional[np.ndarray] = None
+    base_seq_id: Optional[int] = None
+    sibling_seq_ids: List[int] = field(default_factory=list)
+    completion_seq_ids: List[int] = field(default_factory=list)
+    completions: List[Completion] = field(default_factory=list)
+    admitted: bool = False
+    prompt_done: bool = False
+    prompt_checkpoint_saved: bool = False
+    cancelled: bool = False
+    prompt_records: List[Token] = field(default_factory=list)
+    grammar_text: Optional[str] = None
+    grammar_root: str = "root"
+    chat_tool_name: Optional[str] = None
+    on_stream_chunk: Optional[Callable[[CompletionChunk], None]] = None
+    on_done: Optional[Callable[[OpenAICompletion], None]] = None
+    on_error: Optional[Callable[[BaseException], None]] = None
+
+    @classmethod
+    def from_prepared(
+        cls,
+        *,
+        payload: CreateCompletionRequest,
+        prompt_text: str,
+        prompt_plan: PromptPlan,
+        max_seq_len: int,
+        max_output_tokens: Optional[int],
+        prompt_visible_start: int,
+        prompt_records: Optional[List[Token]] = None,
+        grammar_text: Optional[str] = None,
+        chat_tool_name: Optional[str] = None,
+        on_stream_chunk: Optional[Callable[[CompletionChunk], None]] = None,
+        on_done: Optional[Callable[[OpenAICompletion], None]] = None,
+        on_error: Optional[Callable[[BaseException], None]] = None,
+    ) -> "CompletionRequest":
+        prompt_tokens = list(prompt_plan.identity_tokens)
+        prompt_eval_tokens = prompt_plan.eval_token_count
+        if prompt_eval_tokens > max_seq_len:
+            raise CompletionRequestValidationError("prompt exceeds context window")
+        ctx_limit = prompt_plan.length + (max_seq_len - prompt_eval_tokens)
+        if max_output_tokens is not None:
+            ctx_limit = min(ctx_limit, prompt_plan.length + max_output_tokens)
+        if payload.max_tokens is None:
+            effective_max_len = ctx_limit
+        else:
+            effective_max_len = min(ctx_limit, prompt_plan.length + payload.max_tokens)
+        if effective_max_len < prompt_plan.length:
+            raise CompletionRequestValidationError("prompt exceeds context window")
+        internal_completion_count = payload.best_of if payload.best_of is not None else payload.n
+        request = cls(
+            payload=payload,
+            prompt_text=prompt_text,
+            prompt_tokens=prompt_tokens,
+            prompt_plan=prompt_plan,
+            effective_max_len=effective_max_len,
+            internal_completion_count=internal_completion_count,
+            prompt_visible_start=prompt_visible_start,
+            prompt_records=list(prompt_records or []),
+            grammar_text=grammar_text,
+            chat_tool_name=chat_tool_name,
+            on_stream_chunk=on_stream_chunk,
+            on_done=on_done,
+            on_error=on_error,
+        )
+        return request
+
+    def selected_completions(self) -> List[Completion]:
+        completions = list(self.completions)
+        best_of = self.payload.best_of if self.payload.best_of is not None else self.payload.n
+        if best_of > self.payload.n:
+            completions.sort(
+                key=lambda completion: (
+                    completion.score_sum / max(1, completion.completion_token_count),
+                    completion.score_sum,
+                ),
+                reverse=True,
+            )
+        return sorted(completions[: self.payload.n], key=lambda completion: completion.index)
+
+    def capture_prompt_logprobs(
+        self,
+        *,
+        model: Model,
+        formatter: OpenAIFormatter,
+        output_indices: Sequence[Optional[int]],
+        output_positions: Sequence[int],
+        output_count: int,
+        output_index_to_logits_index: Callable[[Optional[int], int], Optional[int]],
+    ) -> None:
+        if self.payload.logprobs is None or not self.payload.echo:
+            return
+        for token_offset, output_index in enumerate(output_indices):
+            if output_index is None:
+                continue
+            next_pos = output_positions[token_offset] + 1
+            if not self.prompt_plan.has_text_token_at(next_pos):
+                continue
+            text_token_index = self.prompt_plan.text_token_index_by_pos[next_pos]
+            if text_token_index < self.prompt_visible_start:
+                continue
+            logits_index = output_index_to_logits_index(output_index, output_count)
+            assert logits_index is not None
+            next_token = self.prompt_plan.text_token_at(next_pos)
+            record = Token.from_logits(
+                model=model,
+                formatter=formatter,
+                prev_tokens=self.prompt_plan.prev_text_tokens_at(next_pos),
+                token=next_token,
+                logits=model.logits(logits_index),
+                logprobs_count=self.payload.logprobs,
+            )
+            expected_index = text_token_index - self.prompt_visible_start
+            if expected_index == len(self.prompt_records):
+                self.prompt_records.append(record)
+            elif expected_index < len(self.prompt_records):
+                self.prompt_records[expected_index] = record
+            else:
+                raise RuntimeError("prompt logprob order mismatch")
+
+
+class ResponseParser:
+    @dataclass
+    class PartialJsonValue:
+        text: str
+        schema_type: Optional[str] = None
+        complete: bool = False
+
+    @dataclass
+    class PartialJsonObject:
+        value: "OrderedDict[str, Any]"
+        complete: bool = False
+
+    @dataclass
+    class DirectState:
+        deltas: bool
+        pending: str
+        mode: int
+        done: bool
+        saw_tool_calls: bool
+        tool_call_count: int
+        assistant_prefix: str
+        leading_capture_field: Optional[str]
+        leading_capture_start: str
+        leading_capture_end: str
+        leading_capture_strip_after: bool
+        leading_capture_implicit: bool
+        content_end_markers: Tuple[str, ...]
+        trim_before_iterator: bool
+        iterator_start: str
+        iterator_end: str
+        stop_markers: Tuple[str, ...]
+        function_start: str
+        function_name_end: str
+        function_end: str
+        parameter_start: str
+        parameter_name_end: str
+        parameter_end: str
+
+    @dataclass
+    class ItemState:
+        pending: str
+        mode: int
+        tool_call_index: int
+        tool_name: str
+        parameter_count: int
+        visible_tool_call: Optional[Dict[str, Any]]
+        visible_function: Optional[Dict[str, Any]]
+        current_parameter: Optional[str]
+        current_parameter_value: str
+        current_schema_type: Optional[str]
+        current_parameter_schema: Dict[str, Any]
+
+    @dataclass
+    class StreamState:
+        kind: str
+        pending: str
+        mode: str
+        current_item: Optional[Dict[str, Any]]
+        current_segment: Optional[Dict[str, Any]]
+        done: bool
+        saw_tool_calls: bool
+        parsed: Optional[Dict[str, Any]] = None
+        buffer: str = ""
+        tool_call_count: int = 0
+
+    _STREAM_PLAN_CACHE: Dict[int, Tuple[Any, Optional[Dict[str, Any]]]] = {}
+    _TOOL_SCHEMA_CACHE: Dict[int, Tuple[Any, Dict[str, Dict[str, Any]]]] = {}
+    __slots__ = (
+        "_schema",
+        "_tools",
+        "_completion_id",
+        "_choice_index",
+        "_generation_prompt",
+        "_tool_schemas",
+        "_started",
+        "_text_parts",
+        "_message",
+        "_stream_plan",
+        "_direct",
+        "_item",
+        "_stream_state",
+        "_stream_failed",
+    )
+    DIRECT_MODE_ASSISTANT_PREFIX = 0
+    DIRECT_MODE_PRELUDE = 1
+    DIRECT_MODE_LEADING_CAPTURE = 2
+    DIRECT_MODE_CONTENT = 3
+    DIRECT_MODE_TOOL_ITEM = 4
+    DIRECT_MODE_AFTER_TOOL_ITEM = 5
+    ITEM_MODE_FUNCTION_HEADER = 0
+    ITEM_MODE_AFTER_FUNCTION_HEADER = 1
+    ITEM_MODE_PARAMETER_NAME = 2
+    ITEM_MODE_PARAMETER_VALUE = 3
+    ITEM_MODE_AFTER_PARAMETER = 4
+    ITEM_MODE_DONE = 5
+
+    def __init__(
+        self,
+        schema: Dict[str, Any],
+        *,
+        tools: Optional[List[ChatTemplateTool]] = None,
+        completion_id: str = "",
+        choice_index: int = 0,
+        generation_prompt: str = "",
+    ) -> None:
+        self._schema = schema
+        self._tools = tools
+        self._completion_id = completion_id
+        self._choice_index = choice_index
+        self._generation_prompt = generation_prompt
+        self._tool_schemas = self._cached_tool_schema_map(tools)
+        self._started = False
+        self._text_parts: List[str] = []
+        self._message: Dict[str, Any] = {}
+        self._stream_plan = self._cached_stream_plan(schema)
+        self._direct = ResponseParser.DirectState(
+            deltas=bool(self._stream_plan is not None and self._stream_plan.get("direct_deltas")),
+            pending="",
+            mode=self.DIRECT_MODE_PRELUDE,
+            done=False,
+            saw_tool_calls=False,
+            tool_call_count=0,
+            assistant_prefix="",
+            leading_capture_field=None,
+            leading_capture_start="",
+            leading_capture_end="",
+            leading_capture_strip_after=False,
+            leading_capture_implicit=False,
+            content_end_markers=(),
+            trim_before_iterator=False,
+            iterator_start="",
+            iterator_end="",
+            stop_markers=(),
+            function_start="",
+            function_name_end="",
+            function_end="",
+            parameter_start="",
+            parameter_name_end="",
+            parameter_end="",
+        )
+        self._item = ResponseParser.ItemState(
+            pending="",
+            mode=self.ITEM_MODE_FUNCTION_HEADER,
+            tool_call_index=0,
+            tool_name="",
+            parameter_count=0,
+            visible_tool_call=None,
+            visible_function=None,
+            current_parameter=None,
+            current_parameter_value="",
+            current_schema_type=None,
+            current_parameter_schema={},
+        )
+        if self._direct.deltas and self._stream_plan is not None:
+            direct_init = self._stream_plan["direct_init"]
+            (
+                self._direct.assistant_prefix,
+                self._direct.leading_capture_field,
+                self._direct.leading_capture_start,
+                self._direct.leading_capture_end,
+                self._direct.leading_capture_strip_after,
+                self._direct.leading_capture_implicit,
+                self._direct.trim_before_iterator,
+                self._direct.content_end_markers,
+                self._direct.stop_markers,
+                self._direct.iterator_start,
+                self._direct.iterator_end,
+                self._direct.function_start,
+                self._direct.function_name_end,
+                self._direct.function_end,
+                self._direct.parameter_start,
+                self._direct.parameter_name_end,
+                self._direct.parameter_end,
+            ) = direct_init
+            self._direct.mode = (
+                self.DIRECT_MODE_ASSISTANT_PREFIX
+                if self._direct.assistant_prefix
+                else self.DIRECT_MODE_PRELUDE
+            )
+            self._stream_state = None
+        else:
+            self._stream_state = (
+                self._new_stream_state(self._stream_plan) if self._stream_plan is not None else None
+            )
+        self._stream_failed = False
+        if self._generation_prompt and self._stream_plan is not None:
+            success, _ = self._advance_stream_state(self._generation_prompt)
+            if not success:
+                self._stream_failed = True
+
+    @property
+    def started(self) -> bool:
+        return self._started
+
+    @staticmethod
+    def _regex_capture(text: str, pattern: str) -> Optional[Any]:
+        match = re.search(pattern, text, re.S)
+        if match is None:
+            return None
+        group_dict = match.groupdict()
+        if group_dict:
+            return {key: value for key, value in group_dict.items() if value is not None}
+        return match.group(1) if match.lastindex else match.group(0)
+
+    @staticmethod
+    def _gemma4_tool_call_to_json(text: str) -> str:
+        strings: List[str] = []
+
+        def capture(match: re.Match[str]) -> str:
+            strings.append(match.group(1))
+            return f"\x00{len(strings) - 1}\x00"
+
+        stripped = text.strip()
+        if stripped.startswith("[") and stripped.endswith("]"):
+            text = "{" + stripped[1:-1] + "}"
+        text = re.sub(r'<\|"\|>(.*?)<\|"\|>', capture, text, flags=re.S)
+        text = re.sub(r"(?<=[{,])(\w+):", r'"\1":', text)
+        for index, value in enumerate(strings):
+            text = text.replace(f"\x00{index}\x00", json.dumps(value))
+        return text
+
+    @staticmethod
+    def _regex_literal_prefix(pattern: str) -> str:
+        literal, _ = ResponseParser._regex_literal_prefix_and_remainder(pattern)
+        return literal
+
+    @staticmethod
+    def _regex_literal_prefix_and_remainder(pattern: str) -> Tuple[str, str]:
+        literal: List[str] = []
+        index = 0
+        while index < len(pattern):
+            char = pattern[index]
+            if char == "\\":
+                index += 1
+                if index >= len(pattern):
+                    break
+                escaped = pattern[index]
+                if escaped == "n":
+                    literal.append("\n")
+                elif escaped == "t":
+                    literal.append("\t")
+                else:
+                    literal.append(escaped)
+                index += 1
+                continue
+            if char in "[](){}.*+?|^$":
+                break
+            literal.append(char)
+            index += 1
+        return "".join(literal), pattern[index:]
+
+    @staticmethod
+    def _find_regex_group_end(pattern: str, start: int) -> int:
+        depth = 0
+        escaped = False
+        in_character_class = False
+        for index in range(start, len(pattern)):
+            char = pattern[index]
+            if escaped:
+                escaped = False
+                continue
+            if char == "\\":
+                escaped = True
+                continue
+            if char == "[":
+                in_character_class = True
+                continue
+            if char == "]" and in_character_class:
+                in_character_class = False
+                continue
+            if in_character_class:
+                continue
+            if char == "(":
+                depth += 1
+                continue
+            if char == ")":
+                depth -= 1
+                if depth == 0:
+                    return index
+        return -1
+
+    @classmethod
+    def _consume_optional_literal_prefix(
+        cls,
+        pattern: str,
+    ) -> Optional[Tuple[str, str]]:
+        if not pattern.startswith("(?:"):
+            return None
+        group_end = cls._find_regex_group_end(pattern, 0)
+        if group_end < 0 or group_end + 1 >= len(pattern) or pattern[group_end + 1] != "?":
+            return None
+        literal, remainder = cls._regex_literal_prefix_and_remainder(pattern[3:group_end])
+        if not literal or remainder:
+            return None
+        return literal, pattern[group_end + 2 :]
+
+    @staticmethod
+    def _split_regex_alternatives(pattern: str) -> List[str]:
+        alternatives: List[str] = []
+        start = 0
+        depth = 0
+        escaped = False
+        in_character_class = False
+        for index, char in enumerate(pattern):
+            if escaped:
+                escaped = False
+                continue
+            if char == "\\":
+                escaped = True
+                continue
+            if char == "[":
+                in_character_class = True
+                continue
+            if char == "]" and in_character_class:
+                in_character_class = False
+                continue
+            if in_character_class:
+                continue
+            if char == "(":
+                depth += 1
+                continue
+            if char == ")":
+                depth -= 1
+                continue
+            if char == "|" and depth == 0:
+                alternatives.append(pattern[start:index])
+                start = index + 1
+        alternatives.append(pattern[start:])
+        return alternatives
+
+    @classmethod
+    def _regex_lookahead_literal_specs(cls, pattern: str) -> List[Tuple[str, bool]]:
+        if not pattern.startswith("(?="):
+            return []
+        group_end = cls._find_regex_group_end(pattern, 0)
+        if group_end < 0:
+            return []
+        literals: List[Tuple[str, bool]] = []
+        for alternative in cls._split_regex_alternatives(pattern[3:group_end]):
+            strip_leading_whitespace = False
+            while alternative.startswith(r"\s*"):
+                strip_leading_whitespace = True
+                alternative = alternative[3:]
+            if alternative == "$":
+                continue
+            if alternative.endswith("$"):
+                alternative = alternative[:-1]
+            literal, _ = cls._regex_literal_prefix_and_remainder(alternative)
+            if literal:
+                literals.append((literal, strip_leading_whitespace))
+        return literals
+
+    @classmethod
+    def _regex_capture_parts(
+        cls,
+        pattern: str,
+    ) -> Optional[Tuple[str, str]]:
+        normalized = pattern.lstrip("^")
+        captures = [
+            (index, token)
+            for token in ("(.*?)", "(.*)")
+            if (index := normalized.find(token)) >= 0
+        ]
+        if not captures:
+            return None
+        capture_index, capture_token = min(captures, key=lambda item: item[0])
+        return normalized[:capture_index], normalized[capture_index + len(capture_token) :]
+
+    @classmethod
+    def _regex_capture_end_literal_specs(cls, pattern: str) -> List[Tuple[str, bool]]:
+        capture_parts = cls._regex_capture_parts(pattern)
+        if capture_parts is None:
+            return []
+        _, suffix_pattern = capture_parts
+        literal_specs = cls._regex_lookahead_literal_specs(suffix_pattern)
+        if literal_specs:
+            return literal_specs
+        literal, _ = cls._regex_literal_prefix_and_remainder(suffix_pattern)
+        return [(literal, False)] if literal else []
+
+    @classmethod
+    def _regex_capture_end_literals(cls, pattern: str) -> List[str]:
+        return [literal for literal, _ in cls._regex_capture_end_literal_specs(pattern)]
+
+    @classmethod
+    def _regex_leading_capture(
+        cls,
+        *,
+        field_name: str,
+        field_regex: str,
+        content_regex: Optional[str],
+    ) -> Optional[Dict[str, Any]]:
+        capture_parts = cls._regex_capture_parts(field_regex)
+        if capture_parts is None:
+            return None
+        prefix_pattern, _ = capture_parts
+        prefix_pattern = prefix_pattern.lstrip("^")
+        optional_prefix = cls._consume_optional_literal_prefix(prefix_pattern)
+        if optional_prefix is not None:
+            prefix_pattern = optional_prefix[1]
+        implicit_at_start = False
+        optional_capture_start = cls._consume_optional_literal_prefix(prefix_pattern)
+        if optional_capture_start is not None:
+            capture_start, prefix_pattern = optional_capture_start
+            implicit_at_start = True
+        else:
+            capture_start, prefix_pattern = cls._regex_literal_prefix_and_remainder(prefix_pattern)
+        if not capture_start or prefix_pattern:
+            return None
+        end_literals = cls._regex_capture_end_literals(field_regex)
+        if not end_literals:
+            return None
+        capture_end = end_literals[0]
+        strip_after = False
+        if isinstance(content_regex, str):
+            escaped_end = re.escape(capture_end)
+            strip_after = bool(re.search(escaped_end + r"\\s\*", content_regex))
+        return {
+            "field": field_name,
+            "start": capture_start,
+            "end": capture_end,
+            "strip_after": strip_after,
+            "implicit_at_start": implicit_at_start,
+        }
+
+    @staticmethod
+    def _literal_suffix_prefix_length(text: str, literal: str) -> int:
+        max_length = min(len(text), len(literal) - 1)
+        if max_length <= 0:
+            return 0
+        tail = text[-max_length:]
+        if literal[0] not in tail:
+            return 0
+        for prefix_length in range(max_length, 0, -1):
+            if text.endswith(literal[:prefix_length]):
+                return prefix_length
+        return 0
+
+    @classmethod
+    def _consume_until_literal(
+        cls,
+        text: str,
+        literal: str,
+    ) -> Tuple[str, bool, str, str]:
+        if not literal:
+            return text, True, "", ""
+        literal_length = len(literal)
+        search_from = 0
+        first_char = literal[0]
+        while True:
+            marker_index = text.find(first_char, search_from)
+            if marker_index < 0:
+                return text, False, "", ""
+            if text.startswith(literal, marker_index):
+                return text[:marker_index], True, text[marker_index + literal_length :], ""
+            suffix = text[marker_index:]
+            if literal.startswith(suffix):
+                return text[:marker_index], False, "", suffix
+            search_from = marker_index + 1
+
+    @staticmethod
+    def _compile_iterator_pattern(pattern: str) -> Optional[Tuple[str, str]]:
+        if "(.*?)" not in pattern:
+            return None
+        prefix_pattern, suffix_pattern = pattern.split("(.*?)", 1)
+        prefix = ResponseParser._regex_literal_prefix(prefix_pattern.lstrip("^"))
+        suffix = ResponseParser._regex_literal_prefix(suffix_pattern)
+        if not prefix:
+            return None
+        return prefix, suffix
+
+    @classmethod
+    def _compile_iterator_block_pattern(
+        cls,
+        pattern: str,
+    ) -> Optional[Dict[str, Any]]:
+        first_group = pattern.find("(")
+        if first_group < 0:
+            return None
+        depth = 0
+        last_group = -1
+        escaped = False
+        for index in range(first_group, len(pattern)):
+            char = pattern[index]
+            if escaped:
+                escaped = False
+                continue
+            if char == "\\":
+                escaped = True
+                continue
+            if char == "(":
+                depth += 1
+                continue
+            if char == ")":
+                depth -= 1
+                if depth == 0:
+                    last_group = index
+                    break
+        if last_group < first_group:
+            return None
+        start = cls._regex_literal_prefix(pattern[:first_group].lstrip("^"))
+        if not start:
+            return None
+        suffix_pattern = pattern[last_group + 1 :]
+        if suffix_pattern == "":
+            return {"start": start, "end": "", "allow_eof": True}
+        eof_variant = suffix_pattern.endswith("|$)") and suffix_pattern.startswith("(?:")
+        if eof_variant:
+            suffix_pattern = suffix_pattern[3:-3]
+        end = cls._regex_literal_prefix(suffix_pattern)
+        if not end:
+            return None
+        return {"start": start, "end": end, "allow_eof": eof_variant}
+
+    @classmethod
+    def _compile_capture_pattern(
+        cls,
+        pattern: str,
+    ) -> Optional[Dict[str, Any]]:
+        normalized = pattern.lstrip("^")
+        if "(.*?)" in normalized:
+            prefix_pattern, suffix_pattern = normalized.split("(.*?)", 1)
+            start = cls._regex_literal_prefix(prefix_pattern)
+            if not start:
+                return None
+            if suffix_pattern == "":
+                return {"start": start, "end": "", "allow_eof": True}
+            eof_variant = suffix_pattern.endswith("|$)") and suffix_pattern.startswith("(?:")
+            if eof_variant:
+                suffix_pattern = suffix_pattern[3:-3]
+            end = cls._regex_literal_prefix(suffix_pattern)
+            if not end:
+                return None
+            return {"start": start, "end": end, "allow_eof": eof_variant}
+        if "(.*)" in normalized:
+            prefix_pattern, suffix_pattern = normalized.split("(.*)", 1)
+            start = cls._regex_literal_prefix(prefix_pattern)
+            if not start:
+                return None
+            if suffix_pattern == "":
+                return {"start": start, "end": "", "allow_eof": True}
+            eof_variant = suffix_pattern.endswith("|$)") and suffix_pattern.startswith("(?:")
+            if eof_variant:
+                suffix_pattern = suffix_pattern[3:-3]
+            end = cls._regex_literal_prefix(suffix_pattern)
+            if not end:
+                return None
+            return {"start": start, "end": end, "allow_eof": eof_variant}
+        return None
+
+    @classmethod
+    def _compile_word_capture_pattern(
+        cls,
+        pattern: str,
+    ) -> Optional[Dict[str, Any]]:
+        normalized = pattern.lstrip("^")
+        if r"(\w+)" not in normalized:
+            return None
+        prefix_pattern, suffix_pattern = normalized.split(r"(\w+)", 1)
+        start = cls._regex_literal_prefix(prefix_pattern)
+        if not start:
+            return None
+        end = cls._regex_literal_prefix(suffix_pattern) if suffix_pattern else ""
+        return {
+            "kind": "word",
+            "start": start,
+            "end": end,
+        }
+
+    @classmethod
+    def _consume_until_any_literal(
+        cls,
+        text: str,
+        literals: Sequence[str],
+    ) -> Tuple[str, Optional[str], str, str]:
+        for literal in literals:
+            if literal and len(text) < len(literal) and literal.startswith(text):
+                return "", None, "", text
+        literal_first_chars = {literal[0] for literal in literals if literal}
+        if literal_first_chars and not any(char in text for char in literal_first_chars):
+            return text, None, "", ""
+        earliest_index: Optional[int] = None
+        earliest_literal: Optional[str] = None
+        for literal in literals:
+            marker_index = text.find(literal)
+            if marker_index < 0:
+                continue
+            if earliest_index is None or marker_index < earliest_index:
+                earliest_index = marker_index
+                earliest_literal = literal
+        if earliest_index is not None and earliest_literal is not None:
+            return (
+                text[:earliest_index],
+                earliest_literal,
+                text[earliest_index + len(earliest_literal) :],
+                "",
+            )
+        overlap = 0
+        for literal in literals:
+            overlap = max(overlap, cls._literal_suffix_prefix_length(text, literal))
+        if overlap:
+            return text[:-overlap], None, "", text[-overlap:]
+        return text, None, "", ""
+
+    @classmethod
+    def _compile_tool_call_item_plan(
+        cls,
+        item_schema: Dict[str, Any],
+    ) -> Optional[Dict[str, Any]]:
+        item_properties = item_schema.get("properties")
+        if not isinstance(item_properties, dict):
+            return None
+        function_schema = item_properties.get("function")
+        if not isinstance(function_schema, dict):
+            return None
+        function_properties = function_schema.get("properties")
+        if not isinstance(function_properties, dict):
+            return None
+        name_schema = function_properties.get("name")
+        arguments_schema = function_properties.get("arguments")
+        if not isinstance(name_schema, dict) or not isinstance(arguments_schema, dict):
+            return None
+        name_regex = name_schema.get("x-regex")
+        argument_regex = arguments_schema.get("x-regex")
+        argument_key_value = arguments_schema.get("x-regex-key-value")
+        if (
+            name_regex == r"^<function=([^>\n]+)>\n"
+            and argument_regex == r"^<function=[^>\n]+>\n(.*?)\n</function>$"
+            and argument_key_value
+            == r"<parameter=(?P<key>[^>\n]+)>\n(?P<value>.*?)\n</parameter>"
+        ):
+            return {
+                "kind": "tagged-parameters",
+                "schema": item_schema,
+                "function_start": "<function=",
+                "function_name_end": ">\n",
+                "function_end": "</function>",
+                "parameter_start": "<parameter=",
+                "parameter_name_end": ">\n",
+                "parameter_end": "\n</parameter>",
+            }
+        name_capture = (
+            cls._compile_word_capture_pattern(name_regex)
+            if isinstance(name_regex, str)
+            else None
+        )
+        arguments_capture = (
+            cls._compile_capture_pattern(argument_regex)
+            if isinstance(argument_regex, str)
+            else None
+        )
+        if (
+            isinstance(name_capture, dict)
+            and isinstance(arguments_capture, dict)
+            and arguments_schema.get("x-parser") == "json"
+        ):
+            arguments_value_schema = {
+                key: value
+                for key, value in arguments_schema.items()
+                if key != "x-regex"
+            }
+            return {
+                "kind": "json-message",
+                "schema": item_schema,
+                "name_capture": name_capture,
+                "arguments_capture": arguments_capture,
+                "arguments_schema": arguments_value_schema,
+            }
+        return {
+            "kind": "buffered",
+            "schema": item_schema,
+        }
+
+    @classmethod
+    def _compile_segment_message_plan(
+        cls,
+        schema: Dict[str, Any],
+    ) -> Optional[Dict[str, Any]]:
+        if schema.get("type") != "object":
+            return None
+        if isinstance(schema.get("x-regex"), str):
+            return None
+        properties = schema.get("properties")
+        if not isinstance(properties, dict):
+            return None
+        segments: List[Dict[str, Any]] = []
+        field_segment_count = 0
+        for field_name, value_schema in properties.items():
+            if not isinstance(value_schema, dict):
+                continue
+            if field_name == "tool_calls":
+                iterator_pattern = value_schema.get("x-regex-iterator")
+                if not isinstance(iterator_pattern, str):
+                    continue
+                iterator_capture = cls._compile_iterator_block_pattern(iterator_pattern)
+                if not isinstance(iterator_capture, dict) or not iterator_capture["start"]:
+                    return None
+                items_schema = value_schema.get("items")
+                if not isinstance(items_schema, dict):
+                    return None
+                item_plan = cls._compile_tool_call_item_plan(items_schema)
+                if item_plan is None:
+                    return None
+                segments.append(
+                    {
+                        "kind": "iterator",
+                        "field": field_name,
+                        "start": iterator_capture["start"],
+                        "end": iterator_capture["end"],
+                        "allow_eof": iterator_capture["allow_eof"],
+                        "item": item_plan,
+                    }
+                )
+                continue
+            field_regex = value_schema.get("x-regex")
+            if not isinstance(field_regex, str):
+                continue
+            capture = cls._compile_capture_pattern(field_regex)
+            if not isinstance(capture, dict) or not capture["start"]:
+                return None
+            segments.append(
+                {
+                    "kind": "field",
+                    "field": field_name,
+                    "start": capture["start"],
+                    "end": capture["end"],
+                    "allow_eof": capture["allow_eof"],
+                }
+            )
+            field_segment_count += 1
+        if not segments or field_segment_count == 0:
+            return None
+        start_literals = tuple(segment["start"] for segment in segments)
+        if len(start_literals) != len(set(start_literals)):
+            return None
+        return {
+            "kind": "segment-message",
+            "segments": segments,
+            "segment_starts": start_literals,
+            "segments_by_start": {segment["start"]: segment for segment in segments},
+        }
+
+    @classmethod
+    def _compile_tagged_message_plan(
+        cls,
+        schema: Dict[str, Any],
+    ) -> Optional[Dict[str, Any]]:
+        if schema.get("type") != "object":
+            return None
+        properties = schema.get("properties")
+        if not isinstance(properties, dict):
+            return None
+        tool_calls_schema = properties.get("tool_calls")
+        if not isinstance(tool_calls_schema, dict):
+            return None
+        iterator_pattern = tool_calls_schema.get("x-regex-iterator")
+        if not isinstance(iterator_pattern, str):
+            return None
+        iterator = cls._compile_iterator_pattern(iterator_pattern)
+        if iterator is None:
+            iterator_capture = cls._compile_iterator_block_pattern(iterator_pattern)
+            if (
+                not isinstance(iterator_capture, dict)
+                or not iterator_capture["start"]
+                or iterator_capture["allow_eof"]
+            ):
+                return None
+            iterator = (iterator_capture["start"], iterator_capture["end"])
+        items_schema = tool_calls_schema.get("items")
+        if not isinstance(items_schema, dict):
+            return None
+        item_plan = cls._compile_tool_call_item_plan(items_schema)
+        if item_plan is None:
+            return None
+        content_schema = properties.get("content")
+        content_regex = (
+            content_schema.get("x-regex")
+            if isinstance(content_schema, dict)
+            else None
+        )
+        assistant_prefix: Optional[str] = None
+        if isinstance(content_regex, str):
+            optional_prefix = cls._consume_optional_literal_prefix(content_regex.lstrip("^"))
+            if optional_prefix is not None:
+                assistant_prefix = optional_prefix[0]
+        leading_capture: Optional[Dict[str, Any]] = None
+        for field_name, value_schema in properties.items():
+            if not isinstance(value_schema, dict):
+                continue
+            field_regex = value_schema.get("x-regex")
+            if not isinstance(field_regex, str):
+                continue
+            if field_name == "content":
+                continue
+            capture = cls._regex_leading_capture(
+                field_name=field_name,
+                field_regex=field_regex,
+                content_regex=content_regex,
+            )
+            if capture is not None:
+                leading_capture = capture
+                break
+        end_markers: List[str] = []
+        content_end_marker_specs: List[Tuple[str, bool]] = []
+        iterator_start, iterator_end = iterator
+        if "content" in properties:
+            end_markers.append(iterator_start)
+        if isinstance(content_regex, str):
+            content_end_marker_specs = cls._regex_capture_end_literal_specs(content_regex)
+            end_markers.extend(literal for literal, _ in content_end_marker_specs)
+        if not end_markers and iterator_start:
+            end_markers.append(iterator_start)
+        deduped_end_markers = tuple(dict.fromkeys(end_markers))
+        trim_before_iterator = any(
+            literal == iterator_start and strip_leading_whitespace
+            for literal, strip_leading_whitespace in content_end_marker_specs
+        )
+        direct_deltas = item_plan["kind"] == "tagged-parameters"
+        direct_init = (
+            (
+                assistant_prefix or "",
+                leading_capture["field"] if leading_capture is not None else None,
+                leading_capture["start"] if leading_capture is not None else "",
+                leading_capture["end"] if leading_capture is not None else "",
+                bool(leading_capture.get("strip_after")) if leading_capture is not None else False,
+                bool(leading_capture.get("implicit_at_start")) if leading_capture is not None else False,
+                trim_before_iterator,
+                deduped_end_markers,
+                tuple(marker for marker in deduped_end_markers if marker != iterator_start),
+                iterator_start,
+                iterator_end,
+                item_plan["function_start"],
+                item_plan["function_name_end"],
+                item_plan["function_end"],
+                item_plan["parameter_start"],
+                item_plan["parameter_name_end"],
+                item_plan["parameter_end"],
+            )
+            if direct_deltas
+            else None
+        )
+        return {
+            "kind": "tagged-message",
+            "direct_deltas": direct_deltas,
+            "assistant_prefix": assistant_prefix,
+            "leading_capture": leading_capture,
+            "content_field": "content" if "content" in properties else None,
+            "content_end_markers": deduped_end_markers,
+            "trim_before_iterator": trim_before_iterator,
+            "stop_markers": tuple(marker for marker in deduped_end_markers if marker != iterator_start),
+            "direct_init": direct_init,
+            "iterator": {
+                "start": iterator_start,
+                "end": iterator_end,
+                "item": item_plan,
+            },
+        }
+
+    @classmethod
+    def _compile_stream_plan(
+        cls,
+        response_schema: Optional[Dict[str, Any]],
+    ) -> Optional[Dict[str, Any]]:
+        if not isinstance(response_schema, dict):
+            return None
+        if response_schema.get("x-parser") == "json":
+            return {"kind": "json-root"}
+        segment_plan = cls._compile_segment_message_plan(response_schema)
+        if segment_plan is not None:
+            return segment_plan
+        return cls._compile_tagged_message_plan(response_schema)
+
+    @classmethod
+    def _cached_stream_plan(
+        cls,
+        response_schema: Optional[Dict[str, Any]],
+    ) -> Optional[Dict[str, Any]]:
+        if not isinstance(response_schema, dict):
+            return None
+        cache_key = id(response_schema)
+        cached = cls._STREAM_PLAN_CACHE.get(cache_key)
+        if cached is not None and cached[0] is response_schema:
+            return cached[1]
+        plan = cls._compile_stream_plan(response_schema)
+        cls._STREAM_PLAN_CACHE[cache_key] = (response_schema, plan)
+        return plan
+
+    @staticmethod
+    def _tool_schema_map(
+        tools: Optional[List[ChatTemplateTool]],
+    ) -> Dict[str, Dict[str, Any]]:
+        if tools is None:
+            return {}
+        mapping: Dict[str, Dict[str, Any]] = {}
+        for tool in tools:
+            if tool.get("type") != "function":
+                continue
+            function = tool.get("function", {})
+            name = function.get("name")
+            parameters = function.get("parameters")
+            if isinstance(name, str) and isinstance(parameters, dict):
+                mapping[name] = {
+                    "parameters": parameters,
+                    "content_type": function.get("content_type"),
+                }
+        return mapping
+
+    @classmethod
+    def _cached_tool_schema_map(
+        cls,
+        tools: Optional[List[ChatTemplateTool]],
+    ) -> Dict[str, Dict[str, Any]]:
+        if tools is None:
+            return {}
+        cache_key = id(tools)
+        cached = cls._TOOL_SCHEMA_CACHE.get(cache_key)
+        if cached is not None and cached[0] is tools:
+            return cached[1]
+        mapping = cls._tool_schema_map(tools)
+        cls._TOOL_SCHEMA_CACHE[cache_key] = (tools, mapping)
+        return mapping
+
+    def _parameter_schema_for_tool(
+        self, tool_name: str, parameter_name: str
+    ) -> Dict[str, Any]:
+        tool_schema = self._tool_schemas.get(tool_name)
+        if not isinstance(tool_schema, dict):
+            return {}
+        parameters = tool_schema.get("parameters")
+        if not isinstance(parameters, dict):
+            return {}
+        properties = parameters.get("properties")
+        if not isinstance(properties, dict):
+            return {}
+        parameter_schema = properties.get(parameter_name)
+        if not isinstance(parameter_schema, dict):
+            return {}
+        return parameter_schema
+
+    def _tool_content_type(self, tool_name: str) -> Optional[str]:
+        tool_schema = self._tool_schemas.get(tool_name)
+        if not isinstance(tool_schema, dict):
+            return None
+        content_type = tool_schema.get("content_type")
+        if isinstance(content_type, str):
+            return content_type
+        return None
+
+    def _raw_string_tool_arguments(self, tool_name: str, value: str) -> Optional[Dict[str, str]]:
+        if self._tools is None:
+            return None
+        for tool in self._tools:
+            if tool.get("type") != "function":
+                continue
+            function = tool.get("function", {})
+            if function.get("name") != tool_name:
+                continue
+            parameters = function.get("parameters")
+            if not isinstance(parameters, dict):
+                return None
+            required = parameters.get("required")
+            if not isinstance(required, list) or len(required) != 1:
+                return None
+            argument_name = required[0]
+            if not isinstance(argument_name, str):
+                return None
+            properties = parameters.get("properties")
+            if not isinstance(properties, dict):
+                return None
+            argument_schema = properties.get(argument_name)
+            if not isinstance(argument_schema, dict):
+                return None
+            argument_type = argument_schema.get("type")
+            if argument_type == "string" or (
+                isinstance(argument_type, list) and "string" in argument_type
+            ):
+                return {argument_name: value}
+            return None
+        return None
+
+    def _single_string_tool_argument_name(self, tool_name: str) -> Optional[str]:
+        if self._tools is None:
+            return None
+        for tool in self._tools:
+            if tool.get("type") != "function":
+                continue
+            function = tool.get("function", {})
+            if function.get("name") != tool_name:
+                continue
+            parameters = function.get("parameters")
+            if not isinstance(parameters, dict):
+                return None
+            required = parameters.get("required")
+            if not isinstance(required, list) or len(required) != 1:
+                return None
+            argument_name = required[0]
+            if not isinstance(argument_name, str):
+                return None
+            properties = parameters.get("properties")
+            if not isinstance(properties, dict):
+                return None
+            argument_schema = properties.get(argument_name)
+            if not isinstance(argument_schema, dict):
+                return None
+            argument_type = argument_schema.get("type")
+            if argument_type == "string" or (
+                isinstance(argument_type, list) and "string" in argument_type
+            ):
+                return argument_name
+            return None
+        return None
+
+    def _text_tool_argument_from_object(
+        self,
+        tool_name: str,
+        arguments: Dict[str, Any],
+    ) -> Optional[str]:
+        input_value = arguments.get("input")
+        if isinstance(input_value, str):
+            return input_value
+        argument_name = self._single_string_tool_argument_name(tool_name)
+        if argument_name is not None:
+            argument_value = arguments.get(argument_name)
+            if isinstance(argument_value, str):
+                return argument_value
+        if len(arguments) == 1:
+            argument_value = next(iter(arguments.values()))
+            if isinstance(argument_value, str):
+                return argument_value
+        return None
+
+    def _text_tool_arguments(
+        self,
+        tool_name: str,
+        arguments: Any,
+        *,
+        partial: bool,
+    ) -> Optional[str]:
+        if isinstance(arguments, str):
+            parsed_arguments = self._raw_object_tool_arguments(arguments)
+            if parsed_arguments is not None:
+                text = self._text_tool_argument_from_object(tool_name, parsed_arguments)
+                if text is not None:
+                    return text
+                if partial:
+                    return None
+                return json.dumps(parsed_arguments, ensure_ascii=False, separators=(",", ":"))
+            return arguments
+        if isinstance(arguments, ResponseParser.PartialJsonObject):
+            arguments = arguments.value
+        if isinstance(arguments, dict):
+            text = self._text_tool_argument_from_object(tool_name, arguments)
+            if text is not None:
+                return text
+            if partial:
+                return None
+            return json.dumps(arguments, ensure_ascii=False, separators=(",", ":"))
+        if partial:
+            return None
+        return str(arguments)
+
+    @classmethod
+    def _raw_object_tool_arguments(cls, value: str) -> Optional[Dict[str, Any]]:
+        candidates = [value]
+        stripped = value.strip()
+        if stripped.startswith("{{") and stripped.endswith("}}"):
+            candidates.append(stripped[1:-1])
+        for candidate in candidates:
+            normalized = cls._gemma4_tool_call_to_json(candidate)
+            for allow_partial in (False, True):
+                try:
+                    parsed = from_json(normalized, allow_partial=allow_partial)
+                except ValueError:
+                    continue
+                if isinstance(parsed, dict):
+                    return {
+                        key: cls._trim_partial_gemma_quote_marker(value)
+                        if isinstance(value, str)
+                        else value
+                        for key, value in parsed.items()
+                    }
+        return None
+
+    @staticmethod
+    def _trim_partial_gemma_quote_marker(value: str) -> str:
+        quote_marker = '<|"|>'
+        for prefix_length in range(len(quote_marker) - 1, 0, -1):
+            if value.endswith(quote_marker[:prefix_length]):
+                return value[:-prefix_length]
+        return value
+
+    def _has_text_tools(self) -> bool:
+        return any(
+            isinstance(tool_schema, dict) and tool_schema.get("content_type") == "text"
+            for tool_schema in self._tool_schemas.values()
+        )
+
+    @staticmethod
+    def _append_parsed_text(parsed: Dict[str, Any], key: str, text: str) -> None:
+        if not text:
+            return
+        existing = parsed.get(key)
+        if isinstance(existing, str):
+            parsed[key] = existing + text
+        else:
+            parsed[key] = text
+
+    def _append_visible_text(self, key: str, text: str) -> None:
+        if not text:
+            return
+        existing = self._message.get(key)
+        if isinstance(existing, str):
+            self._message[key] = existing + text
+        else:
+            self._message[key] = text
+
+    @staticmethod
+    def _advance_json_scanner(
+        item_state: Dict[str, Any],
+        text: str,
+        *,
+        schema_type: Optional[str],
+    ) -> bool:
+        started = cast(bool, item_state["json_started"])
+        complete = cast(bool, item_state["json_complete"])
+        depth = cast(int, item_state["json_depth"])
+        in_string = cast(bool, item_state["json_in_string"])
+        escaped = cast(bool, item_state["json_escaped"])
+        for char in text:
+            if complete:
+                if not char.isspace():
+                    return False
+                continue
+            if not started:
+                if char.isspace():
+                    continue
+                if schema_type == "object" and char != "{":
+                    return False
+                if schema_type == "array" and char != "[":
+                    return False
+                if char == "{":
+                    started = True
+                    depth = 1
+                    continue
+                if char == "[":
+                    started = True
+                    depth = 1
+                    continue
+                return False
+            if in_string:
+                if escaped:
+                    escaped = False
+                elif char == "\\":
+                    escaped = True
+                elif char == '"':
+                    in_string = False
+                continue
+            if char == '"':
+                in_string = True
+                continue
+            if char in "{[":
+                depth += 1
+                continue
+            if char in "}]":
+                depth -= 1
+                if depth < 0:
+                    return False
+                if depth == 0:
+                    complete = True
+                continue
+        item_state["json_started"] = started
+        item_state["json_complete"] = complete
+        item_state["json_depth"] = depth
+        item_state["json_in_string"] = in_string
+        item_state["json_escaped"] = escaped
+        return True
+
+    def _new_tool_call_state(self, item_plan: Dict[str, Any]) -> Dict[str, Any]:
+        if item_plan["kind"] == "buffered":
+            return {"kind": "buffered", "buffer": ""}
+        if item_plan["kind"] == "json-message":
+            return {
+                "kind": "json-message",
+                "pending": "",
+                "mode": "function-name",
+                "tool_call": {
+                    "type": "function",
+                    "function": {
+                        "name": "",
+                        "arguments": "",
+                    },
+                },
+                "arguments_text": "",
+                "json_started": False,
+                "json_complete": False,
+                "json_depth": 0,
+                "json_in_string": False,
+                "json_escaped": False,
+            }
+        return {
+            "kind": "tagged-parameters",
+            "pending": "",
+            "mode": "function-header",
+            "tool_call": {
+                "type": "function",
+                "function": {
+                    "name": "",
+                    "arguments": ResponseParser.PartialJsonObject(OrderedDict(), complete=False),
+                },
+            },
+            "current_parameter": None,
+            "current_schema_type": None,
+        }
+
+    def _new_stream_state(self, plan: Dict[str, Any]) -> ResponseParser.StreamState:
+        if plan["kind"] == "json-root":
+            return ResponseParser.StreamState(
+                kind="json-root",
+                pending="",
+                mode="prelude",
+                current_item=None,
+                current_segment=None,
+                done=False,
+                saw_tool_calls=False,
+                parsed={"role": "assistant"},
+                buffer="",
+            )
+        if plan.get("direct_deltas"):
+            return ResponseParser.StreamState(
+                kind="tagged-message",
+                pending="",
+                mode="assistant-prefix" if plan.get("assistant_prefix") else "prelude",
+                current_item=None,
+                current_segment=None,
+                done=False,
+                saw_tool_calls=False,
+                tool_call_count=0,
+            )
+        if plan["kind"] == "segment-message":
+            return ResponseParser.StreamState(
+                kind="segment-message",
+                pending="",
+                mode="segment-start",
+                current_item=None,
+                current_segment=None,
+                done=False,
+                saw_tool_calls=False,
+                parsed={"role": "assistant"},
+            )
+        return ResponseParser.StreamState(
+            kind="tagged-message",
+            pending="",
+            mode="assistant-prefix" if plan.get("assistant_prefix") else "prelude",
+            current_item=None,
+            current_segment=None,
+            done=False,
+            saw_tool_calls=False,
+            parsed={"role": "assistant"},
+        )
+
+    def _start_direct_tool_call(self, tool_call_index: int) -> None:
+        self._item.pending = ""
+        self._item.mode = self.ITEM_MODE_FUNCTION_HEADER
+        self._item.tool_call_index = tool_call_index
+        self._item.tool_name = ""
+        self._item.parameter_count = 0
+        tool_calls = self._message.setdefault("tool_calls", [])
+        assert isinstance(tool_calls, list)
+        while len(tool_calls) <= tool_call_index:
+            tool_calls.append({"function": {"name": "", "arguments": ""}})
+        visible_tool_call = tool_calls[tool_call_index]
+        assert isinstance(visible_tool_call, dict)
+        self._item.visible_tool_call = visible_tool_call
+        function = visible_tool_call.setdefault("function", {})
+        assert isinstance(function, dict)
+        self._item.visible_function = function
+        if tool_call_index == 0:
+            self._message["function_call"] = self._item.visible_function
+        self._item.current_parameter = None
+        self._item.current_parameter_value = ""
+        self._item.current_schema_type = None
+        self._item.current_parameter_schema = {}
+
+    def _advance_direct_tool_call_state(self, text: str) -> Tuple[bool, List[Dict[str, Any]]]:
+        deltas: List[Dict[str, Any]] = []
+        mode = self._item.mode
+        tool_call_index = self._item.tool_call_index
+        tool_name = self._item.tool_name
+        parameter_count = self._item.parameter_count
+        visible_tool_call = self._item.visible_tool_call
+        visible_function = self._item.visible_function
+        current_parameter = self._item.current_parameter
+        current_parameter_value = self._item.current_parameter_value
+        current_schema_type = self._item.current_schema_type
+        current_parameter_schema = self._item.current_parameter_schema
+        buffer = self._item.pending + text
+        function_start = self._direct.function_start
+        function_name_end = self._direct.function_name_end
+        function_end = self._direct.function_end
+        parameter_start = self._direct.parameter_start
+        parameter_name_end = self._direct.parameter_name_end
+        parameter_end = self._direct.parameter_end
+
+        while True:
+            if mode == self.ITEM_MODE_FUNCTION_HEADER:
+                if buffer.startswith(function_start):
+                    buffer = buffer[len(function_start) :]
+                elif function_start.startswith(buffer):
+                    self._item.pending = buffer
+                    self._item.mode = mode
+                    return True, deltas
+                else:
+                    self._item.pending = ""
+                    return False, deltas
+                delimiter_index = buffer.find(function_name_end)
+                if delimiter_index < 0:
+                    self._item.pending = function_start + buffer
+                    self._item.mode = mode
+                    return True, deltas
+                function_name = buffer[:delimiter_index]
+                if not function_name:
+                    self._item.pending = ""
+                    return False, deltas
+                tool_name = function_name
+                assert visible_tool_call is not None
+                assert visible_function is not None
+                visible_function["name"] = function_name
+                visible_function["arguments"] = "{"
+                visible_tool_call["id"] = (
+                    f"call_{self._choice_index}_{function_name}_{self._completion_id}_{tool_call_index}"
+                )
+                visible_tool_call["type"] = "function"
+                deltas.append(
+                    {
+                        "tool_calls": [
+                            {
+                                "index": tool_call_index,
+                                "id": (
+                                    f"call_{self._choice_index}_{function_name}_"
+                                    f"{self._completion_id}_{tool_call_index}"
+                                ),
+                                "type": "function",
+                                "function": {"name": function_name, "arguments": "{"},
+                            }
+                        ]
+                    }
+                )
+                buffer = buffer[delimiter_index + len(function_name_end) :]
+                mode = self.ITEM_MODE_AFTER_FUNCTION_HEADER
+                continue
+            if mode == self.ITEM_MODE_AFTER_FUNCTION_HEADER:
+                if buffer.startswith("\n"):
+                    buffer = buffer[1:]
+                    continue
+                if buffer.startswith(parameter_start):
+                    mode = self.ITEM_MODE_PARAMETER_NAME
+                    continue
+                if buffer.startswith(function_end):
+                    buffer = buffer[len(function_end) :]
+                    assert visible_function is not None
+                    visible_function["arguments"] += "}"
+                    deltas.append(
+                        {
+                            "tool_calls": [
+                                {
+                                    "index": tool_call_index,
+                                    "function": {"arguments": "}"},
+                                }
+                            ]
+                        }
+                    )
+                    mode = self.ITEM_MODE_DONE
+                    continue
+                if parameter_start.startswith(buffer) or function_end.startswith(buffer):
+                    self._item.pending = buffer
+                    self._item.mode = mode
+                    self._item.tool_name = tool_name
+                    return True, deltas
+                if not buffer:
+                    self._item.pending = ""
+                    self._item.mode = mode
+                    self._item.tool_name = tool_name
+                    return True, deltas
+                self._item.pending = ""
+                return False, deltas
+            if mode == self.ITEM_MODE_PARAMETER_NAME:
+                if buffer.startswith(parameter_start):
+                    buffer = buffer[len(parameter_start) :]
+                elif parameter_start.startswith(buffer):
+                    self._item.pending = buffer
+                    self._item.mode = mode
+                    self._item.tool_name = tool_name
+                    self._item.parameter_count = parameter_count
+                    return True, deltas
+                else:
+                    self._item.pending = ""
+                    return False, deltas
+                delimiter_index = buffer.find(parameter_name_end)
+                if delimiter_index < 0:
+                    self._item.pending = parameter_start + buffer
+                    self._item.mode = mode
+                    self._item.tool_name = tool_name
+                    self._item.parameter_count = parameter_count
+                    return True, deltas
+                parameter_name = buffer[:delimiter_index]
+                if not parameter_name:
+                    self._item.pending = ""
+                    return False, deltas
+                parameter_schema = self._parameter_schema_for_tool(tool_name, parameter_name)
+                schema_type = parameter_schema.get("type") if isinstance(parameter_schema, dict) else None
+                current_parameter = parameter_name
+                current_parameter_value = ""
+                current_schema_type = schema_type if isinstance(schema_type, str) else None
+                current_parameter_schema = parameter_schema
+                key_prefix = json.dumps(parameter_name, ensure_ascii=False, separators=(",", ":")) + ":"
+                if parameter_count > 0:
+                    key_prefix = "," + key_prefix
+                if schema_type in {None, "string"}:
+                    key_prefix += '"'
+                parameter_count += 1
+                assert visible_function is not None
+                visible_function["arguments"] += key_prefix
+                deltas.append(
+                    {
+                        "tool_calls": [
+                            {
+                                "index": tool_call_index,
+                                "function": {"arguments": key_prefix},
+                            }
+                        ]
+                    }
+                )
+                buffer = buffer[delimiter_index + len(parameter_name_end) :]
+                mode = self.ITEM_MODE_PARAMETER_VALUE
+                continue
+            if mode == self.ITEM_MODE_PARAMETER_VALUE:
+                value_delta, matched, remainder, pending = self._consume_until_literal(buffer, parameter_end)
+                current_parameter_value += value_delta
+                if value_delta:
+                    assert visible_function is not None
+                    visible_function["arguments"] += value_delta
+                    deltas.append(
+                        {
+                            "tool_calls": [
+                                {
+                                    "index": tool_call_index,
+                                    "function": {"arguments": value_delta},
+                                }
+                            ]
+                        }
+                    )
+                if not matched:
+                    self._item.pending = pending
+                    self._item.mode = mode
+                    self._item.tool_name = tool_name
+                    self._item.parameter_count = parameter_count
+                    self._item.current_parameter = current_parameter
+                    self._item.current_parameter_value = current_parameter_value
+                    self._item.current_schema_type = current_schema_type
+                    self._item.current_parameter_schema = current_parameter_schema
+                    return True, deltas
+                self._coerce_tool_argument(
+                    cast(str, current_parameter_value),
+                    cast(Dict[str, Any], current_parameter_schema),
+                    tool_name=tool_name,
+                    argument_name=cast(str, current_parameter),
+                )
+                if current_schema_type in {None, "string"}:
+                    assert visible_function is not None
+                    visible_function["arguments"] += '"'
+                    deltas.append(
+                        {
+                            "tool_calls": [
+                                {
+                                    "index": tool_call_index,
+                                    "function": {"arguments": '"'},
+                                }
+                            ]
+                        }
+                    )
+                current_parameter = None
+                current_parameter_value = ""
+                current_schema_type = None
+                current_parameter_schema = {}
+                buffer = remainder
+                mode = self.ITEM_MODE_AFTER_PARAMETER
+                continue
+            if mode == self.ITEM_MODE_AFTER_PARAMETER:
+                if buffer.startswith("\n"):
+                    buffer = buffer[1:]
+                    continue
+                if buffer.startswith(parameter_start):
+                    mode = self.ITEM_MODE_PARAMETER_NAME
+                    continue
+                if buffer.startswith(function_end):
+                    buffer = buffer[len(function_end) :]
+                    assert visible_function is not None
+                    visible_function["arguments"] += "}"
+                    deltas.append(
+                        {
+                            "tool_calls": [
+                                {
+                                    "index": tool_call_index,
+                                    "function": {"arguments": "}"},
+                                }
+                            ]
+                        }
+                    )
+                    mode = self.ITEM_MODE_DONE
+                    continue
+                if parameter_start.startswith(buffer) or function_end.startswith(buffer):
+                    self._item.pending = buffer
+                    self._item.mode = mode
+                    self._item.tool_name = tool_name
+                    self._item.parameter_count = parameter_count
+                    return True, deltas
+                if not buffer:
+                    self._item.pending = ""
+                    self._item.mode = mode
+                    self._item.tool_name = tool_name
+                    self._item.parameter_count = parameter_count
+                    return True, deltas
+                self._item.pending = ""
+                return False, deltas
+            if mode == self.ITEM_MODE_DONE:
+                if buffer.strip():
+                    self._item.pending = ""
+                    return False, deltas
+                self._item.pending = buffer
+                self._item.mode = mode
+                self._item.tool_name = tool_name
+                self._item.parameter_count = parameter_count
+                return True, deltas
+
+    def _advance_direct_stream_state(self, text: str) -> Tuple[bool, List[Dict[str, Any]]]:
+        deltas: List[Dict[str, Any]] = []
+        mode = self._direct.mode
+        tool_call_count = self._direct.tool_call_count
+        saw_tool_calls = self._direct.saw_tool_calls
+        done = self._direct.done
+        buffer = self._direct.pending + text
+        assistant_prefix = self._direct.assistant_prefix
+        leading_capture_field = self._direct.leading_capture_field
+        leading_capture_start = self._direct.leading_capture_start
+        leading_capture_end = self._direct.leading_capture_end
+        leading_capture_strip_after = self._direct.leading_capture_strip_after
+        leading_capture_implicit = self._direct.leading_capture_implicit
+        iterator_start = self._direct.iterator_start
+        iterator_end = self._direct.iterator_end
+        content_end_markers = self._direct.content_end_markers
+        stop_markers = self._direct.stop_markers
+
+        while True:
+            if mode == self.DIRECT_MODE_ASSISTANT_PREFIX:
+                if buffer.startswith(assistant_prefix):
+                    buffer = buffer[len(assistant_prefix) :]
+                    mode = self.DIRECT_MODE_PRELUDE
+                    continue
+                if assistant_prefix.startswith(buffer):
+                    self._direct.pending = buffer
+                    self._direct.mode = mode
+                    return True, deltas
+                mode = self.DIRECT_MODE_PRELUDE
+                continue
+            if mode == self.DIRECT_MODE_PRELUDE:
+                if leading_capture_field is not None:
+                    if buffer.startswith(leading_capture_start):
+                        buffer = buffer[len(leading_capture_start) :]
+                        mode = self.DIRECT_MODE_LEADING_CAPTURE
+                        continue
+                    if leading_capture_start.startswith(buffer):
+                        self._direct.pending = buffer
+                        self._direct.mode = mode
+                        return True, deltas
+                    if leading_capture_implicit:
+                        tool_index = buffer.find(iterator_start)
+                        end_index = buffer.find(leading_capture_end)
+                        overlap = self._literal_suffix_prefix_length(buffer, leading_capture_end)
+                        if ((end_index >= 0 and (tool_index < 0 or end_index < tool_index)) or overlap):
+                            mode = self.DIRECT_MODE_LEADING_CAPTURE
+                            continue
+                mode = self.DIRECT_MODE_CONTENT
+                continue
+            if mode == self.DIRECT_MODE_LEADING_CAPTURE:
+                segment, matched, remainder, pending = self._consume_until_literal(buffer, leading_capture_end)
+                if segment:
+                    assert leading_capture_field is not None
+                    self._append_visible_text(leading_capture_field, segment)
+                    deltas.append({leading_capture_field: segment})
+                if not matched:
+                    self._direct.pending = pending
+                    self._direct.mode = mode
+                    self._direct.tool_call_count = tool_call_count
+                    self._direct.saw_tool_calls = saw_tool_calls
+                    self._direct.done = done
+                    return True, deltas
+                buffer = remainder.lstrip() if leading_capture_strip_after else remainder
+                mode = self.DIRECT_MODE_CONTENT
+                continue
+            if mode == self.DIRECT_MODE_CONTENT:
+                segment, matched_marker, remainder, pending = self._consume_until_any_literal(
+                    buffer,
+                    content_end_markers,
+                )
+                content_segment = segment.rstrip() if matched_marker == iterator_start and self._direct.trim_before_iterator else segment
+                if content_segment:
+                    self._append_visible_text("content", content_segment)
+                    deltas.append({"content": content_segment})
+                if matched_marker is None:
+                    self._direct.pending = pending
+                    self._direct.mode = mode
+                    self._direct.tool_call_count = tool_call_count
+                    self._direct.saw_tool_calls = saw_tool_calls
+                    self._direct.done = done
+                    return True, deltas
+                if matched_marker == iterator_start:
+                    saw_tool_calls = True
+                    self._start_direct_tool_call(tool_call_count)
+                    tool_call_count += 1
+                    mode = self.DIRECT_MODE_TOOL_ITEM
+                    buffer = remainder
+                    continue
+                done = True
+                self._direct.pending = ""
+                self._direct.mode = mode
+                self._direct.tool_call_count = tool_call_count
+                self._direct.saw_tool_calls = saw_tool_calls
+                self._direct.done = done
+                return True, deltas
+            if mode == self.DIRECT_MODE_TOOL_ITEM:
+                item_text, matched, remainder, pending = self._consume_until_literal(buffer, iterator_end)
+                if item_text:
+                    success, item_deltas = self._advance_direct_tool_call_state(item_text)
+                    if not success:
+                        self._direct.pending = ""
+                        self._direct.mode = mode
+                        self._direct.tool_call_count = tool_call_count
+                        self._direct.saw_tool_calls = saw_tool_calls
+                        self._direct.done = done
+                        return False, deltas
+                    deltas.extend(item_deltas)
+                if not matched:
+                    self._direct.pending = pending
+                    self._direct.mode = mode
+                    self._direct.tool_call_count = tool_call_count
+                    self._direct.saw_tool_calls = saw_tool_calls
+                    self._direct.done = done
+                    return True, deltas
+                buffer = remainder.lstrip()
+                mode = self.DIRECT_MODE_AFTER_TOOL_ITEM
+                continue
+            if mode == self.DIRECT_MODE_AFTER_TOOL_ITEM:
+                if not buffer:
+                    self._direct.pending = ""
+                    self._direct.mode = mode
+                    self._direct.tool_call_count = tool_call_count
+                    self._direct.saw_tool_calls = saw_tool_calls
+                    self._direct.done = done
+                    return True, deltas
+                if leading_capture_field is not None:
+                    if buffer.startswith(leading_capture_start):
+                        buffer = buffer[len(leading_capture_start) :]
+                        mode = self.DIRECT_MODE_LEADING_CAPTURE
+                        continue
+                    if leading_capture_start.startswith(buffer):
+                        self._direct.pending = buffer
+                        self._direct.mode = mode
+                        self._direct.tool_call_count = tool_call_count
+                        self._direct.saw_tool_calls = saw_tool_calls
+                        self._direct.done = done
+                        return True, deltas
+                if buffer.startswith(iterator_start):
+                    saw_tool_calls = True
+                    self._start_direct_tool_call(tool_call_count)
+                    tool_call_count += 1
+                    mode = self.DIRECT_MODE_TOOL_ITEM
+                    buffer = buffer[len(iterator_start) :]
+                    continue
+                for marker in stop_markers:
+                    if buffer.startswith(marker):
+                        done = True
+                        self._direct.pending = ""
+                        self._direct.mode = mode
+                        self._direct.tool_call_count = tool_call_count
+                        self._direct.saw_tool_calls = saw_tool_calls
+                        self._direct.done = done
+                        return True, deltas
+                if any(marker.startswith(buffer) for marker in content_end_markers) or buffer.isspace():
+                    self._direct.pending = buffer
+                    self._direct.mode = mode
+                    self._direct.tool_call_count = tool_call_count
+                    self._direct.saw_tool_calls = saw_tool_calls
+                    self._direct.done = done
+                    return True, deltas
+                self._direct.pending = ""
+                self._direct.mode = mode
+                self._direct.tool_call_count = tool_call_count
+                self._direct.saw_tool_calls = saw_tool_calls
+                self._direct.done = done
+                return False, deltas
+
+    def _advance_tool_call_state(
+        self,
+        item_state: Dict[str, Any],
+        text: str,
+        item_plan: Dict[str, Any],
+    ) -> Tuple[bool, List[Dict[str, Any]]]:
+        deltas: List[Dict[str, Any]] = []
+        if item_plan["kind"] == "buffered":
+            item_state["buffer"] = item_state["buffer"] + text
+            return True, deltas
+        if item_plan["kind"] == "json-message":
+            buffer = item_state["pending"] + text
+            item_state["pending"] = ""
+            while True:
+                mode = item_state["mode"]
+                if mode == "function-name":
+                    name_capture = item_plan["name_capture"]
+                    name_prefix = name_capture["start"]
+                    if buffer.startswith(name_prefix):
+                        remainder = buffer[len(name_prefix) :]
+                    elif name_prefix.startswith(buffer):
+                        item_state["pending"] = buffer
+                        return True, deltas
+                    else:
+                        return False, deltas
+                    name_end = 0
+                    while name_end < len(remainder) and (
+                        remainder[name_end].isalnum() or remainder[name_end] == "_"
+                    ):
+                        name_end += 1
+                    if name_end == 0:
+                        if not remainder:
+                            item_state["pending"] = buffer
+                            return True, deltas
+                        return False, deltas
+                    if name_end == len(remainder):
+                        item_state["pending"] = buffer
+                        return True, deltas
+                    function_name = remainder[:name_end]
+                    item_state["tool_call"]["function"]["name"] = function_name
+                    tool_call_index = cast(int, item_state["tool_call_index"])
+                    deltas.append(
+                        {
+                            "tool_calls": [
+                                {
+                                    "index": tool_call_index,
+                                    "id": (
+                                        f"call_{self._choice_index}_{function_name}_"
+                                        f"{self._completion_id}_{tool_call_index}"
+                                    ),
+                                    "type": "function",
+                                    "function": {"name": function_name},
+                                }
+                            ]
+                        }
+                    )
+                    buffer = remainder[name_end:]
+                    item_state["mode"] = "seek-arguments"
+                    continue
+                if mode == "seek-arguments":
+                    arguments_capture = item_plan["arguments_capture"]
+                    _, matched, remainder, pending = self._consume_until_literal(
+                        buffer,
+                        arguments_capture["start"],
+                    )
+                    if not matched:
+                        item_state["pending"] = pending
+                        return True, deltas
+                    buffer = remainder
+                    item_state["mode"] = "arguments"
+                    continue
+                if mode == "arguments":
+                    if buffer:
+                        item_state["arguments_text"] = item_state["arguments_text"] + buffer
+                        deltas.append(
+                            {
+                                "tool_calls": [
+                                    {
+                                        "index": cast(int, item_state["tool_call_index"]),
+                                        "function": {"arguments": buffer},
+                                    }
+                                ]
+                            }
+                        )
+                    item_state["pending"] = ""
+                    arguments_schema = cast(Dict[str, Any], item_plan["arguments_schema"])
+                    schema_type = arguments_schema.get("type")
+                    if not self._advance_json_scanner(
+                        item_state,
+                        buffer,
+                        schema_type=schema_type if isinstance(schema_type, str) else None,
+                    ):
+                        return False, deltas
+                    return True, deltas
+
+        buffer = item_state["pending"] + text
+        item_state["pending"] = ""
+        while True:
+            mode = item_state["mode"]
+            if mode == "function-header":
+                function_start = item_plan["function_start"]
+                if buffer.startswith(function_start):
+                    buffer = buffer[len(function_start) :]
+                elif function_start.startswith(buffer):
+                    item_state["pending"] = buffer
+                    return True, deltas
+                else:
+                    return False, deltas
+                function_name_end = item_plan["function_name_end"]
+                delimiter_index = buffer.find(function_name_end)
+                if delimiter_index < 0:
+                    item_state["pending"] = function_start + buffer
+                    return True, deltas
+                function_name = buffer[:delimiter_index]
+                if not function_name:
+                    return False, deltas
+                item_state["tool_call"]["function"]["name"] = function_name
+                deltas.append(
+                    {
+                        "tool_calls": [
+                            {
+                                "index": item_state["tool_call_index"],
+                                "id": (
+                                    f"call_{self._choice_index}_{function_name}_"
+                                    f"{self._completion_id}_{item_state['tool_call_index']}"
+                                ),
+                                "type": "function",
+                                "function": {
+                                    "name": function_name,
+                                    "arguments": "{",
+                                },
+                            }
+                        ]
+                    }
+                )
+                buffer = buffer[delimiter_index + len(function_name_end) :]
+                item_state["mode"] = "after-function-header"
+                continue
+            if mode == "after-function-header":
+                if buffer.startswith("\n"):
+                    buffer = buffer[1:]
+                    continue
+                function_end = item_plan["function_end"]
+                parameter_start = item_plan["parameter_start"]
+                if buffer.startswith(parameter_start):
+                    item_state["mode"] = "parameter-name"
+                    continue
+                if buffer.startswith(function_end):
+                    buffer = buffer[len(function_end) :]
+                    item_state["mode"] = "done"
+                    deltas.append(
+                        {
+                            "tool_calls": [
+                                {
+                                    "index": item_state["tool_call_index"],
+                                    "function": {"arguments": "}"},
+                                }
+                            ]
+                        }
+                    )
+                    continue
+                if parameter_start.startswith(buffer) or function_end.startswith(buffer):
+                    item_state["pending"] = buffer
+                    return True, deltas
+                if not buffer:
+                    return True, deltas
+                return False, deltas
+            if mode == "parameter-name":
+                parameter_start = item_plan["parameter_start"]
+                if buffer.startswith(parameter_start):
+                    buffer = buffer[len(parameter_start) :]
+                elif parameter_start.startswith(buffer):
+                    item_state["pending"] = buffer
+                    return True, deltas
+                else:
+                    return False, deltas
+                parameter_name_end = item_plan["parameter_name_end"]
+                delimiter_index = buffer.find(parameter_name_end)
+                if delimiter_index < 0:
+                    item_state["pending"] = parameter_start + buffer
+                    return True, deltas
+                parameter_name = buffer[:delimiter_index]
+                if not parameter_name:
+                    return False, deltas
+                function = item_state["tool_call"]["function"]
+                arguments = cast(ResponseParser.PartialJsonObject, function["arguments"])
+                tool_name = function["name"]
+                parameter_schema = self._parameter_schema_for_tool(tool_name, parameter_name)
+                schema_type = (
+                    parameter_schema.get("type")
+                    if isinstance(parameter_schema, dict)
+                    else None
+                )
+                arguments.value[parameter_name] = ResponseParser.PartialJsonValue(
+                    text="",
+                    schema_type=schema_type if isinstance(schema_type, str) else None,
+                    complete=False,
+                )
+                key_prefix = json.dumps(
+                    parameter_name,
+                    ensure_ascii=False,
+                    separators=(",", ":"),
+                ) + ":"
+                if len(arguments.value) > 1:
+                    key_prefix = "," + key_prefix
+                if schema_type in {None, "string"}:
+                    key_prefix += '"'
+                deltas.append(
+                    {
+                        "tool_calls": [
+                            {
+                                "index": item_state["tool_call_index"],
+                                "function": {"arguments": key_prefix},
+                            }
+                        ]
+                    }
+                )
+                item_state["current_parameter"] = parameter_name
+                item_state["current_schema_type"] = (
+                    schema_type if isinstance(schema_type, str) else None
+                )
+                buffer = buffer[delimiter_index + len(parameter_name_end) :]
+                item_state["mode"] = "parameter-value"
+                continue
+            if mode == "parameter-value":
+                parameter_end = item_plan["parameter_end"]
+                value_delta, matched, remainder, pending = self._consume_until_literal(
+                    buffer,
+                    parameter_end,
+                )
+                function = item_state["tool_call"]["function"]
+                arguments = cast(ResponseParser.PartialJsonObject, function["arguments"])
+                parameter_name = cast(str, item_state["current_parameter"])
+                current_value = arguments.value[parameter_name]
+                assert isinstance(current_value, ResponseParser.PartialJsonValue)
+                current_value.text = current_value.text + value_delta
+                if value_delta:
+                    deltas.append(
+                        {
+                            "tool_calls": [
+                                {
+                                    "index": item_state["tool_call_index"],
+                                    "function": {"arguments": value_delta},
+                                }
+                            ]
+                        }
+                    )
+                if not matched:
+                    item_state["pending"] = pending
+                    return True, deltas
+                tool_name = function["name"]
+                parameter_schema = self._parameter_schema_for_tool(tool_name, parameter_name)
+                self._coerce_tool_argument(
+                    current_value.text,
+                    parameter_schema,
+                    tool_name=tool_name,
+                    argument_name=parameter_name,
+                )
+                current_value.complete = True
+                if current_value.schema_type in {None, "string"}:
+                    deltas.append(
+                        {
+                            "tool_calls": [
+                                {
+                                    "index": item_state["tool_call_index"],
+                                    "function": {"arguments": '"'},
+                                }
+                            ]
+                        }
+                    )
+                item_state["current_parameter"] = None
+                item_state["current_schema_type"] = None
+                buffer = remainder
+                item_state["mode"] = "after-parameter"
+                continue
+            if mode == "after-parameter":
+                if buffer.startswith("\n"):
+                    buffer = buffer[1:]
+                    continue
+                function_end = item_plan["function_end"]
+                parameter_start = item_plan["parameter_start"]
+                if buffer.startswith(parameter_start):
+                    item_state["mode"] = "parameter-name"
+                    continue
+                if buffer.startswith(function_end):
+                    buffer = buffer[len(function_end) :]
+                    cast(
+                        ResponseParser.PartialJsonObject,
+                        item_state["tool_call"]["function"]["arguments"],
+                    ).complete = True
+                    item_state["mode"] = "done"
+                    deltas.append(
+                        {
+                            "tool_calls": [
+                                {
+                                    "index": item_state["tool_call_index"],
+                                    "function": {"arguments": "}"},
+                                }
+                            ]
+                        }
+                    )
+                    continue
+                if parameter_start.startswith(buffer) or function_end.startswith(buffer):
+                    item_state["pending"] = buffer
+                    return True, deltas
+                if not buffer:
+                    return True, deltas
+                return False, deltas
+            if mode == "done":
+                if buffer.strip():
+                    return False, deltas
+                item_state["pending"] = buffer
+                return True, deltas
+
+    def _finish_tool_call_state(
+        self,
+        item_state: Dict[str, Any],
+        item_plan: Dict[str, Any],
+    ) -> Optional[Dict[str, Any]]:
+        if item_plan["kind"] == "buffered":
+            parsed_item = self._parse_response_value(
+                item_state["buffer"],
+                item_plan["schema"],
+                partial=False,
+            )
+            return self._normalize_tool_call_item(parsed_item, partial=False)
+        if item_plan["kind"] == "json-message":
+            if item_state["mode"] != "arguments":
+                return None
+            if item_state["pending"] or not item_state["json_started"] or not item_state["json_complete"]:
+                return None
+            try:
+                arguments = self._parse_response_value(
+                    item_state["arguments_text"],
+                    item_plan["arguments_schema"],
+                    partial=False,
+                )
+            except CompletionResponseParsingError:
+                return None
+            if arguments is None:
+                return None
+            item_state["tool_call"]["function"]["arguments"] = arguments
+            return cast(Dict[str, Any], item_state["tool_call"])
+        if item_state["pending"] or item_state["current_parameter"] is not None:
+            return None
+        if item_state["mode"] not in {"done", "after-function-header"}:
+            return None
+        return cast(Dict[str, Any], item_state["tool_call"])
+
+    def _tool_call_delta(
+        self,
+        *,
+        tool_call: Dict[str, Any],
+        tool_call_index: int,
+        partial: bool,
+    ) -> Dict[str, Any]:
+        function = cast(Dict[str, Any], tool_call["function"])
+        return {
+            "tool_calls": [
+                {
+                    "index": tool_call_index,
+                    "id": (
+                        f"call_{self._choice_index}_{function['name']}_"
+                        f"{self._completion_id}_{tool_call_index}"
+                    ),
+                    "type": tool_call.get("type", "function"),
+                    "function": {
+                        "name": function["name"],
+                        "arguments": self._serialize_tool_arguments(
+                            function["arguments"],
+                            partial=partial,
+                        ),
+                    },
+                }
+            ]
+        }
+
+    def _advance_stream_state(self, text: str) -> Tuple[bool, List[Dict[str, Any]]]:
+        deltas: List[Dict[str, Any]] = []
+        if self._stream_plan is None:
+            return False, deltas
+        if self._direct.deltas:
+            return self._advance_direct_stream_state(text)
+        if self._stream_state is None:
+            return False, deltas
+        if self._stream_plan["kind"] == "segment-message":
+            state = self._stream_state
+            parsed = cast(Dict[str, Any], state.parsed)
+            buffer = state.pending + text
+            state.pending = ""
+            while True:
+                if state.mode == "segment-start":
+                    _, matched_start, remainder, pending = self._consume_until_any_literal(
+                        buffer,
+                        cast(Tuple[str, ...], self._stream_plan["segment_starts"]),
+                    )
+                    if matched_start is None:
+                        state.pending = buffer if not pending else pending
+                        return True, deltas
+                    state.current_segment = cast(
+                        Dict[str, Any],
+                        self._stream_plan["segments_by_start"][matched_start],
+                    )
+                    buffer = remainder
+                    state.mode = (
+                        "segment-tool-item"
+                        if state.current_segment["kind"] == "iterator"
+                        else "segment-field"
+                    )
+                    if state.current_segment["kind"] == "iterator":
+                        item_state = self._new_tool_call_state(state.current_segment["item"])
+                        if item_state["kind"] in {"tagged-parameters", "json-message"}:
+                            tool_calls = cast(
+                                List[Dict[str, Any]],
+                                parsed.setdefault("tool_calls", []),
+                            )
+                            tool_calls.append(item_state["tool_call"])
+                            item_state["tool_call_index"] = len(tool_calls) - 1
+                        state.current_item = item_state
+                        state.saw_tool_calls = True
+                    continue
+                if state.mode == "segment-field":
+                    current_segment = state.current_segment
+                    if not isinstance(current_segment, dict):
+                        return False, deltas
+                    segment_text, matched, remainder, pending = self._consume_until_literal(
+                        buffer,
+                        cast(str, current_segment["end"]),
+                    )
+                    field_name = cast(str, current_segment["field"])
+                    self._append_parsed_text(parsed, field_name, segment_text)
+                    if segment_text:
+                        deltas.append({field_name: segment_text})
+                    if not matched:
+                        state.pending = pending
+                        return True, deltas
+                    buffer = remainder
+                    state.current_segment = None
+                    state.mode = "segment-start"
+                    continue
+                if state.mode == "segment-tool-item":
+                    current_segment = state.current_segment
+                    if not isinstance(current_segment, dict):
+                        return False, deltas
+                    active_item_state = state.current_item
+                    if not isinstance(active_item_state, dict):
+                        return False, deltas
+                    item_text, matched, remainder, pending = self._consume_until_literal(
+                        buffer,
+                        cast(str, current_segment["end"]),
+                    )
+                    if item_text:
+                        success, item_deltas = self._advance_tool_call_state(
+                            active_item_state,
+                            item_text,
+                            cast(Dict[str, Any], current_segment["item"]),
+                        )
+                        if not success:
+                            return False, deltas
+                        deltas.extend(item_deltas)
+                    if not matched:
+                        state.pending = pending
+                        return True, deltas
+                    tool_call = self._finish_tool_call_state(
+                        active_item_state,
+                        cast(Dict[str, Any], current_segment["item"]),
+                    )
+                    if tool_call is None:
+                        return False, deltas
+                    if active_item_state["kind"] == "buffered":
+                        tool_calls = cast(List[Dict[str, Any]], parsed.setdefault("tool_calls", []))
+                        tool_call_index = len(tool_calls)
+                        tool_calls.append(tool_call)
+                        deltas.append(
+                            self._tool_call_delta(
+                                tool_call=tool_call,
+                                tool_call_index=tool_call_index,
+                                partial=False,
+                            )
+                        )
+                    state.current_item = None
+                    state.current_segment = None
+                    state.mode = "segment-start"
+                    buffer = remainder
+                    continue
+                return False, deltas
+        if self._stream_plan["kind"] == "json-root":
+            buffer = self._stream_state.buffer + text
+            try:
+                parsed = self.parse_chat_response(buffer, partial=True)
+            except CompletionResponseParsingError:
+                return False, deltas
+            self._stream_state.buffer = buffer
+            self._stream_state.parsed = parsed
+            return True, deltas
+
+        state = self._stream_state
+        plan = self._stream_plan
+        buffer = state.pending + text
+        state.pending = ""
+        parsed = cast(Dict[str, Any], state.parsed)
+        iterator_start = plan["iterator"]["start"]
+        iterator_end = plan["iterator"]["end"]
+        while True:
+            mode = state.mode
+            if mode == "assistant-prefix":
+                assistant_prefix = plan.get("assistant_prefix")
+                if not assistant_prefix:
+                    state.mode = "prelude"
+                    continue
+                if buffer.startswith(assistant_prefix):
+                    buffer = buffer[len(assistant_prefix) :]
+                    state.mode = "prelude"
+                    continue
+                if assistant_prefix.startswith(buffer):
+                    state.pending = buffer
+                    return True, deltas
+                state.mode = "prelude"
+                continue
+            if mode == "prelude":
+                leading_capture = plan.get("leading_capture")
+                if leading_capture is not None:
+                    capture_start = leading_capture["start"]
+                    if buffer.startswith(capture_start):
+                        buffer = buffer[len(capture_start) :]
+                        state.mode = "leading-capture"
+                        continue
+                    if capture_start.startswith(buffer):
+                        state.pending = buffer
+                        return True, deltas
+                    if leading_capture.get("implicit_at_start"):
+                        capture_end = leading_capture["end"]
+                        tool_index = buffer.find(iterator_start)
+                        end_index = buffer.find(capture_end)
+                        overlap = self._literal_suffix_prefix_length(buffer, capture_end)
+                        if (
+                            end_index >= 0
+                            and (tool_index < 0 or end_index < tool_index)
+                        ) or overlap:
+                            state.mode = "leading-capture"
+                            continue
+                state.mode = "content"
+                continue
+            if mode == "leading-capture":
+                leading_capture = cast(Dict[str, Any], plan["leading_capture"])
+                segment, matched, remainder, pending = self._consume_until_literal(
+                    buffer,
+                    leading_capture["end"],
+                )
+                self._append_parsed_text(parsed, leading_capture["field"], segment)
+                if segment:
+                    deltas.append({leading_capture["field"]: segment})
+                if not matched:
+                    state.pending = pending
+                    return True, deltas
+                buffer = remainder.lstrip() if leading_capture.get("strip_after") else remainder
+                state.mode = "content"
+                continue
+            if mode == "content":
+                content_field = plan.get("content_field")
+                end_markers = cast(List[str], plan["content_end_markers"])
+                segment, matched_marker, remainder, pending = self._consume_until_any_literal(
+                    buffer,
+                    end_markers,
+                )
+                if content_field is not None:
+                    content_segment = (
+                        segment.rstrip()
+                        if matched_marker == iterator_start and plan.get("trim_before_iterator")
+                        else segment
+                    )
+                    self._append_parsed_text(parsed, content_field, content_segment)
+                    if content_segment:
+                        deltas.append({content_field: content_segment})
+                if matched_marker is None:
+                    state.pending = pending
+                    return True, deltas
+                if matched_marker == iterator_start:
+                    item_state = self._new_tool_call_state(plan["iterator"]["item"])
+                    state.saw_tool_calls = True
+                    if item_state["kind"] == "tagged-parameters":
+                        tool_calls = cast(List[Dict[str, Any]], parsed.setdefault("tool_calls", []))
+                        tool_calls.append(item_state["tool_call"])
+                        item_state["tool_call_index"] = len(tool_calls) - 1
+                    state.current_item = item_state
+                    state.mode = "tool-item"
+                    buffer = remainder
+                    continue
+                state.done = True
+                state.pending = ""
+                return True, deltas
+            if mode == "tool-item":
+                current_item_state: Optional[Dict[str, Any]] = state.current_item
+                if not isinstance(current_item_state, dict):
+                    return False, deltas
+                item_text, matched, remainder, pending = self._consume_until_literal(
+                    buffer,
+                    iterator_end,
+                )
+                if item_text:
+                    success, item_deltas = self._advance_tool_call_state(
+                        current_item_state,
+                        item_text,
+                        plan["iterator"]["item"],
+                    )
+                    if not success:
+                        return False, deltas
+                    deltas.extend(item_deltas)
+                if not matched:
+                    state.pending = pending
+                    return True, deltas
+                tool_call = self._finish_tool_call_state(
+                    current_item_state,
+                    plan["iterator"]["item"],
+                )
+                if tool_call is None:
+                    return False, deltas
+                if current_item_state["kind"] == "buffered":
+                    parsed.setdefault("tool_calls", []).append(tool_call)
+                state.current_item = None
+                buffer = remainder.lstrip()
+                state.mode = "after-tool-item"
+                continue
+            if mode == "after-tool-item":
+                end_markers = [
+                    iterator_start,
+                    *[
+                        marker
+                        for marker in cast(List[str], plan["content_end_markers"])
+                        if marker != iterator_start
+                    ],
+                ]
+                if not buffer:
+                    state.pending = ""
+                    return True, deltas
+                leading_capture = plan.get("leading_capture")
+                if leading_capture is not None:
+                    capture_start = leading_capture["start"]
+                    if buffer.startswith(capture_start):
+                        buffer = buffer[len(capture_start) :]
+                        state.mode = "leading-capture"
+                        continue
+                    if capture_start.startswith(buffer):
+                        state.pending = buffer
+                        return True, deltas
+                if buffer.startswith(iterator_start):
+                    item_state = self._new_tool_call_state(plan["iterator"]["item"])
+                    state.saw_tool_calls = True
+                    if item_state["kind"] == "tagged-parameters":
+                        tool_calls = cast(List[Dict[str, Any]], parsed.setdefault("tool_calls", []))
+                        tool_calls.append(item_state["tool_call"])
+                        item_state["tool_call_index"] = len(tool_calls) - 1
+                    state.current_item = item_state
+                    state.mode = "tool-item"
+                    buffer = buffer[len(iterator_start) :]
+                    continue
+                for marker in end_markers[1:]:
+                    if buffer.startswith(marker):
+                        state.done = True
+                        state.pending = ""
+                        return True, deltas
+                if any(marker.startswith(buffer) for marker in end_markers):
+                    state.pending = buffer
+                    return True, deltas
+                if buffer.isspace():
+                    state.pending = buffer
+                    return True, deltas
+                return False, deltas
+
+    @staticmethod
+    def _partial_regex_key_value_item(
+        pattern: str,
+        text: str,
+        *,
+        min_start: int,
+    ) -> Optional[Tuple[str, str]]:
+        value_group = "(?P<value>"
+        value_group_start = pattern.find(value_group)
+        if value_group_start < 0:
+            return None
+        partial_pattern = pattern[:value_group_start] + r"(?P<value>.*)\Z"
+        partial_match: Optional[re.Match[str]] = None
+        for match in re.finditer(partial_pattern, text, re.S):
+            if match.start() < min_start:
+                continue
+            partial_match = match
+        if partial_match is None:
+            return None
+        group_dict = partial_match.groupdict()
+        key = group_dict.get("key")
+        value = group_dict.get("value")
+        if key is None or value is None:
+            return None
+        value_pattern = "(?P<value>.*?)"
+        if value_pattern in pattern:
+            suffix_literal = ResponseParser._regex_literal_prefix(
+                pattern.split(value_pattern, 1)[1]
+            )
+            for suffix_length in range(len(suffix_literal), 0, -1):
+                suffix_prefix = suffix_literal[:suffix_length]
+                if value.endswith(suffix_prefix):
+                    value = value[:-suffix_length]
+                    break
+        return key, value
+
+    def _trim_partial_tool_call_prefix(
+        self,
+        *,
+        response_text: str,
+        parsed: Dict[str, Any],
+    ) -> None:
+        if not isinstance(parsed.get("content"), str):
+            return
+        tool_calls_schema = self._schema.get("properties", {}).get("tool_calls")
+        if not isinstance(tool_calls_schema, dict):
+            return
+        iterator_pattern = tool_calls_schema.get("x-regex-iterator")
+        if not isinstance(iterator_pattern, str) or "(.*?)" not in iterator_pattern:
+            return
+        prefix_pattern = iterator_pattern.split("(.*?)", 1)[0]
+        literal_prefix = self._regex_literal_prefix(prefix_pattern)
+        if not literal_prefix:
+            return
+        content = cast(str, parsed["content"])
+        for prefix_length in range(len(literal_prefix) - 1, 0, -1):
+            prefix = literal_prefix[:prefix_length]
+            if content.endswith(prefix) and response_text.endswith(prefix):
+                trimmed = content[:-prefix_length]
+                parsed["content"] = trimmed if trimmed else None
+                break
+
+    def _parse_response_value(
+        self,
+        text: Any,
+        schema: Dict[str, Any],
+        *,
+        partial: bool,
+    ) -> Any:
+        if "const" in schema:
+            return schema["const"]
+        if text is None:
+            return None
+        node_content: Any = text
+        node_regex = schema.get("x-regex")
+        if node_regex is not None:
+            if not isinstance(node_content, str):
+                raise CompletionResponseParsingError(
+                    "response_schema x-regex requires string input"
+                )
+            captured_content = self._regex_capture(node_content, node_regex)
+            if captured_content is None:
+                if (
+                    partial
+                    and schema.get("type") == "object"
+                    and "x-regex-key-value" in schema
+                ):
+                    captured_content = node_content
+                else:
+                    return None
+            node_content = captured_content
+        node_regex_iterator = schema.get("x-regex-iterator")
+        if node_regex_iterator is not None:
+            if schema.get("type") != "array":
+                raise CompletionResponseParsingError(
+                    "response_schema x-regex-iterator requires array type"
+                )
+            if not isinstance(node_content, str):
+                raise CompletionResponseParsingError(
+                    "response_schema x-regex-iterator requires string input"
+                )
+            array_values = []
+            matches = list(re.finditer(node_regex_iterator, node_content, re.S))
+            for match in matches:
+                item_text = self._regex_capture(match.group(0), node_regex_iterator)
+                if item_text is not None:
+                    array_values.append(item_text)
+            if partial and "(.*?)" in node_regex_iterator:
+                prefix_pattern, suffix_pattern = node_regex_iterator.split("(.*?)", 1)
+                prefix_matches = list(re.finditer(prefix_pattern, node_content, re.S))
+                if prefix_matches:
+                    last_prefix_match = prefix_matches[-1]
+                    if not matches or matches[-1].start() != last_prefix_match.start():
+                        tail = node_content[last_prefix_match.end() :]
+                        if re.search(suffix_pattern, tail, re.S) is None:
+                            array_values.append(tail)
+            if not array_values:
+                return None
+            node_content = array_values
+        node_regex_key_value = schema.get("x-regex-key-value")
+        if node_regex_key_value is not None:
+            if schema.get("type") != "object":
+                raise CompletionResponseParsingError(
+                    "response_schema x-regex-key-value requires object type"
+                )
+            if not isinstance(node_content, str):
+                raise CompletionResponseParsingError(
+                    "response_schema x-regex-key-value requires string input"
+                )
+            key_values: Dict[str, str] = {}
+            matches = list(re.finditer(node_regex_key_value, node_content, re.S))
+            for match in matches:
+                group_dict = match.groupdict()
+                if "key" not in group_dict or "value" not in group_dict:
+                    raise CompletionResponseParsingError(
+                        "response_schema x-regex-key-value must define key and value groups"
+                    )
+                key = group_dict["key"]
+                value = group_dict["value"]
+                if key is None or value is None:
+                    raise CompletionResponseParsingError(
+                        "response_schema x-regex-key-value matched null key or value"
+                    )
+                key_values[key] = value
+            if partial:
+                min_start = matches[-1].end() if matches else 0
+                partial_item = self._partial_regex_key_value_item(
+                    node_regex_key_value,
+                    node_content,
+                    min_start=min_start,
+                )
+                if partial_item is not None:
+                    key_values[partial_item[0]] = partial_item[1]
+            if not key_values:
+                return None
+            node_content = key_values
+        parser_name = schema.get("x-parser")
+        if parser_name is not None:
+            if parser_name != "json":
+                if parser_name != "gemma4-tool-call":
+                    raise CompletionResponseParsingError(
+                        f"unsupported response_schema x-parser: {parser_name}"
+                    )
+                if not isinstance(node_content, str):
+                    raise CompletionResponseParsingError(
+                        "response_schema x-parser='gemma4-tool-call' requires string input"
+                    )
+                node_content = self._gemma4_tool_call_to_json(node_content)
+                parser_name = "json"
+            if parser_name == "json":
+                if not isinstance(node_content, str):
+                    raise CompletionResponseParsingError(
+                        "response_schema x-parser='json' requires string input"
+                    )
+                try:
+                    parsed = from_json(node_content, allow_partial=partial)
+                except ValueError as exc:
+                    if (
+                        self._has_text_tools()
+                        and schema.get("type") == "object"
+                        and schema.get("additionalProperties") is True
+                        and not schema.get("properties")
+                    ):
+                        return node_content
+                    if partial:
+                        return None
+                    raise CompletionResponseParsingError(
+                        "response did not match response_schema JSON parser"
+                    ) from exc
+                stripped_schema = {
+                    key: value
+                    for key, value in schema.items()
+                    if key
+                    not in {
+                        "x-parser",
+                        "x-parser-args",
+                        "x-regex",
+                        "x-regex-iterator",
+                        "x-regex-key-value",
+                    }
+                }
+                return self._parse_response_value(
+                    parsed, stripped_schema, partial=partial
+                )
+        schema_type = schema.get("type")
+        if schema_type == "string":
+            return node_content
+        if schema_type == "array":
+            if isinstance(node_content, list):
+                array_values = []
+                item_schema = schema.get("items", {})
+                for item in node_content:
+                    parsed_item = self._parse_response_value(
+                        item,
+                        item_schema,
+                        partial=partial,
+                    )
+                    if parsed_item is not None:
+                        array_values.append(parsed_item)
+                return array_values
+            return []
+        if schema_type == "object":
+            properties = schema.get("properties", {})
+            if isinstance(node_content, dict):
+                parsed_object: Dict[str, Any] = {}
+                for key, value_schema in properties.items():
+                    value = self._parse_response_value(
+                        node_content.get(key),
+                        value_schema,
+                        partial=partial,
+                    )
+                    if value is None:
+                        continue
+                    if isinstance(value, list) and not value:
+                        continue
+                    parsed_object[key] = value
+                additional_properties = schema.get("additionalProperties")
+                if additional_properties is True:
+                    for key, value in node_content.items():
+                        if key not in parsed_object and key not in properties:
+                            parsed_object[key] = value
+                elif isinstance(additional_properties, dict):
+                    for key, value in node_content.items():
+                        if key in parsed_object or key in properties:
+                            continue
+                        parsed_value = self._parse_response_value(
+                            value,
+                            additional_properties,
+                            partial=partial,
+                        )
+                        if parsed_value is not None:
+                            parsed_object[key] = parsed_value
+                if not partial:
+                    missing = [
+                        key
+                        for key in schema.get("required", [])
+                        if key not in parsed_object
+                    ]
+                    if missing:
+                        raise CompletionResponseParsingError(
+                            f"response did not match response_schema: missing {', '.join(missing)}"
+                        )
+                return parsed_object
+            parsed_object_from_text: Dict[str, Any] = {}
+            for key, value_schema in properties.items():
+                value = self._parse_response_value(
+                    node_content, value_schema, partial=partial
+                )
+                if value is None:
+                    continue
+                if isinstance(value, list) and not value:
+                    continue
+                parsed_object_from_text[key] = value
+            if not partial:
+                missing = [
+                    key
+                    for key in schema.get("required", [])
+                    if key not in parsed_object_from_text
+                ]
+                if missing:
+                    raise CompletionResponseParsingError(
+                        f"response did not match response_schema: missing {', '.join(missing)}"
+                    )
+            return parsed_object_from_text
+        if schema_type == "integer":
+            if isinstance(node_content, int) and not isinstance(node_content, bool):
+                return node_content
+            if partial and isinstance(node_content, str) and not node_content:
+                return None
+            try:
+                return int(node_content)
+            except (TypeError, ValueError):
+                return None
+        if schema_type == "number":
+            if isinstance(node_content, (int, float)) and not isinstance(
+                node_content, bool
+            ):
+                return node_content
+            if partial and isinstance(node_content, str) and not node_content:
+                return None
+            try:
+                return float(node_content)
+            except (TypeError, ValueError):
+                return None
+        if schema_type == "boolean":
+            if isinstance(node_content, bool):
+                return node_content
+            if node_content in {"true", "True", 1, "1"}:
+                return True
+            if node_content in {"false", "False", 0, "0"}:
+                return False
+            return None
+        one_of = schema.get("oneOf")
+        if isinstance(one_of, list):
+            for option in one_of:
+                value = self._parse_response_value(
+                    node_content, option, partial=partial
+                )
+                if value is not None:
+                    return value
+            return None
+        if schema_type is None or schema_type == "any":
+            return node_content
+        return None
+
+    def _coerce_tool_argument(
+        self,
+        raw_value: str,
+        schema: Dict[str, Any],
+        *,
+        tool_name: str,
+        argument_name: str,
+    ) -> Any:
+        if "oneOf" in schema:
+            last_error: Optional[BaseException] = None
+            for variant in schema["oneOf"]:
+                try:
+                    return self._coerce_tool_argument(
+                        raw_value,
+                        variant,
+                        tool_name=tool_name,
+                        argument_name=argument_name,
+                    )
+                except BaseException as exc:
+                    last_error = exc
+            raise CompletionResponseParsingError(
+                f"tool '{tool_name}' argument '{argument_name}' did not match any allowed schema"
+            ) from last_error
+        schema_type = schema.get("type")
+        if isinstance(schema_type, list):
+            last_type_error: Optional[BaseException] = None
+            for variant_type in schema_type:
+                try:
+                    return self._coerce_tool_argument(
+                        raw_value,
+                        {**schema, "type": variant_type},
+                        tool_name=tool_name,
+                        argument_name=argument_name,
+                    )
+                except BaseException as exc:
+                    last_type_error = exc
+            raise CompletionResponseParsingError(
+                f"tool '{tool_name}' argument '{argument_name}' did not match any allowed type"
+            ) from last_type_error
+        if schema_type in {None, "string"}:
+            return raw_value
+        try:
+            decoded = json.loads(raw_value)
+        except json.JSONDecodeError as exc:
+            raise CompletionResponseParsingError(
+                f"tool '{tool_name}' argument '{argument_name}' is not valid JSON for type '{schema_type}'"
+            ) from exc
+        if schema_type == "integer":
+            if isinstance(decoded, bool) or not isinstance(decoded, int):
+                raise CompletionResponseParsingError(
+                    f"tool '{tool_name}' argument '{argument_name}' must be an integer"
+                )
+            return decoded
+        if schema_type == "number":
+            if isinstance(decoded, bool) or not isinstance(decoded, (int, float)):
+                raise CompletionResponseParsingError(
+                    f"tool '{tool_name}' argument '{argument_name}' must be a number"
+                )
+            return decoded
+        if schema_type == "boolean":
+            if not isinstance(decoded, bool):
+                raise CompletionResponseParsingError(
+                    f"tool '{tool_name}' argument '{argument_name}' must be a boolean"
+                )
+            return decoded
+        if schema_type == "object":
+            if not isinstance(decoded, dict):
+                raise CompletionResponseParsingError(
+                    f"tool '{tool_name}' argument '{argument_name}' must be an object"
+                )
+            return decoded
+        if schema_type == "array":
+            if not isinstance(decoded, list):
+                raise CompletionResponseParsingError(
+                    f"tool '{tool_name}' argument '{argument_name}' must be an array"
+                )
+            return decoded
+        return decoded
+
+    def _coerce_tool_arguments(
+        self,
+        tool_name: str,
+        arguments: Dict[str, str],
+        *,
+        partial: bool,
+    ) -> Dict[str, Any]:
+        if self._tool_content_type(tool_name) == "text":
+            raw_input = arguments.get("input", "")
+            if not isinstance(raw_input, str):
+                raw_input = str(raw_input)
+            return {"input": raw_input}
+        if self._tools is None:
+            raise CompletionResponseParsingError(
+                f"response included tool call '{tool_name}' but the request declared no tools"
+            )
+        tool = next(
+            (
+                candidate
+                for candidate in self._tools
+                if candidate.get("type") == "function"
+                and candidate.get("function", {}).get("name") == tool_name
+            ),
+            None,
+        )
+        if tool is None:
+            raise CompletionResponseParsingError(
+                f"response included undeclared tool call '{tool_name}'"
+            )
+        parameters = tool.get("function", {}).get("parameters") or {
+            "type": "object",
+            "properties": {},
+            "required": [],
+        }
+        properties = parameters.get("properties", {})
+        coerced: Dict[str, Any] = {}
+        for argument_name, raw_value in arguments.items():
+            if argument_name not in properties:
+                raise CompletionResponseParsingError(
+                    f"tool '{tool_name}' returned unexpected argument '{argument_name}'"
+                )
+            coerced[argument_name] = self._coerce_tool_argument(
+                raw_value,
+                properties[argument_name],
+                tool_name=tool_name,
+                argument_name=argument_name,
+            )
+        if not partial:
+            missing = [
+                argument_name
+                for argument_name in parameters.get("required", [])
+                if argument_name not in coerced
+            ]
+            if missing:
+                raise CompletionResponseParsingError(
+                    f"tool '{tool_name}' response is missing required arguments: {', '.join(missing)}"
+                )
+        return coerced
+
+    def parse_chat_response(
+        self,
+        response_text: str,
+        *,
+        partial: bool,
+    ) -> Dict[str, Any]:
+        full_response_text = self._generation_prompt + response_text
+        parsed = self._parse_response_value(
+            full_response_text,
+            self._schema,
+            partial=partial,
+        )
+        if not isinstance(parsed, dict):
+            raise CompletionResponseParsingError("response_schema must produce an object")
+        if partial:
+            self._trim_partial_tool_call_prefix(
+                response_text=full_response_text,
+                parsed=parsed,
+            )
+        tool_calls = parsed.get("tool_calls")
+        if isinstance(tool_calls, list):
+            normalized_tool_calls: List[Dict[str, Any]] = []
+            for tool_call in tool_calls:
+                normalized = self._normalize_tool_call_item(tool_call, partial=partial)
+                if normalized is None:
+                    continue
+                normalized_tool_calls.append(normalized)
+            if normalized_tool_calls:
+                parsed["tool_calls"] = normalized_tool_calls
+            else:
+                parsed.pop("tool_calls", None)
+        for field in ("reasoning_content", "thinking"):
+            value = parsed.get(field)
+            if isinstance(value, str) and not value.strip():
+                parsed.pop(field, None)
+        return parsed
+
+    def _normalize_tool_call_item(
+        self,
+        tool_call: Any,
+        *,
+        partial: bool,
+    ) -> Optional[Dict[str, Any]]:
+        if not isinstance(tool_call, dict):
+            if partial:
+                return None
+            raise CompletionResponseParsingError("tool_calls items must be objects")
+        function = tool_call.get("function")
+        if not isinstance(function, dict):
+            if partial:
+                return None
+            raise CompletionResponseParsingError(
+                "tool_calls items must define a function object"
+            )
+        tool_name = function.get("name")
+        if not isinstance(tool_name, str) or not tool_name:
+            if partial:
+                return None
+            raise CompletionResponseParsingError(
+                "tool_calls function name must be a non-empty string"
+            )
+        if self._tool_content_type(tool_name) == "text":
+            arguments = self._text_tool_arguments(
+                tool_name,
+                function.get("arguments", ""),
+                partial=partial,
+            )
+            if arguments is None:
+                return None
+            return {
+                "type": tool_call.get("type", "function"),
+                "function": {
+                    "name": tool_name,
+                    "arguments": arguments,
+                },
+            }
+        arguments = function.get("arguments", {})
+        if isinstance(arguments, str):
+            arguments = self._raw_object_tool_arguments(arguments) or self._raw_string_tool_arguments(
+                tool_name, arguments
+            )
+        if not isinstance(arguments, (dict, ResponseParser.PartialJsonObject)):
+            if partial:
+                return None
+            raise CompletionResponseParsingError(
+                "tool_calls function arguments must parse to an object"
+            )
+        argument_values = (
+            arguments.value
+            if isinstance(arguments, ResponseParser.PartialJsonObject)
+            else arguments
+        )
+        raw_arguments: Dict[str, str] = {}
+        for argument_name, argument_value in argument_values.items():
+            if isinstance(argument_value, ResponseParser.PartialJsonValue):
+                raw_arguments[argument_name] = argument_value.text
+            else:
+                raw_arguments[argument_name] = str(argument_value)
+        normalized_arguments = (
+            arguments
+            if isinstance(arguments, ResponseParser.PartialJsonObject)
+            or any(
+                isinstance(value, ResponseParser.PartialJsonValue)
+                for value in argument_values.values()
+            )
+            else self._coerce_tool_arguments(
+                tool_name,
+                raw_arguments,
+                partial=partial,
+            )
+        )
+        return {
+            "type": tool_call.get("type", "function"),
+            "function": {
+                "name": tool_name,
+                "arguments": normalized_arguments,
+            },
+        }
+
+    @classmethod
+    def _serialize_partial_json_prefix(cls, value: Any) -> str:
+        if isinstance(value, ResponseParser.PartialJsonValue):
+            if value.complete and value.schema_type in {None, "string"}:
+                return json.dumps(value.text, ensure_ascii=False, separators=(",", ":"))
+            if value.schema_type in {None, "string"}:
+                return json.dumps(value.text, ensure_ascii=False, separators=(",", ":"))[:-1]
+            return value.text
+        if isinstance(value, ResponseParser.PartialJsonObject):
+            return cls._serialize_partial_json_prefix(value.value)
+        if isinstance(value, dict):
+            items = list(value.items())
+            if not items:
+                return "{"
+            rendered = ["{"]
+            last_index = len(items) - 1
+            for index, (key, item_value) in enumerate(items):
+                if index > 0:
+                    rendered.append(",")
+                rendered.append(json.dumps(key, ensure_ascii=False, separators=(",", ":")))
+                rendered.append(":")
+                if index == last_index:
+                    rendered.append(cls._serialize_partial_json_prefix(item_value))
+                else:
+                    rendered.append(
+                        json.dumps(item_value, ensure_ascii=False, separators=(",", ":"))
+                    )
+            return "".join(rendered)
+        if isinstance(value, list):
+            if not value:
+                return "["
+            rendered = ["["]
+            last_index = len(value) - 1
+            for index, item_value in enumerate(value):
+                if index > 0:
+                    rendered.append(",")
+                if index == last_index:
+                    rendered.append(cls._serialize_partial_json_prefix(item_value))
+                else:
+                    rendered.append(
+                        json.dumps(item_value, ensure_ascii=False, separators=(",", ":"))
+                    )
+            return "".join(rendered)
+        if isinstance(value, str):
+            return json.dumps(value, ensure_ascii=False, separators=(",", ":"))[:-1]
+        return json.dumps(value, ensure_ascii=False, separators=(",", ":"))
+
+    @classmethod
+    def _contains_partial_json_value(cls, value: Any) -> bool:
+        if isinstance(value, ResponseParser.PartialJsonValue):
+            return True
+        if isinstance(value, ResponseParser.PartialJsonObject):
+            return True
+        if isinstance(value, dict):
+            return any(cls._contains_partial_json_value(item) for item in value.values())
+        if isinstance(value, list):
+            return any(cls._contains_partial_json_value(item) for item in value)
+        return False
+
+    @classmethod
+    def _serialize_partial_json_state(cls, value: Any) -> Tuple[str, bool]:
+        if isinstance(value, ResponseParser.PartialJsonValue):
+            if value.schema_type in {None, "string"}:
+                rendered = json.dumps(value.text, ensure_ascii=False, separators=(",", ":"))
+                if value.complete:
+                    return rendered, True
+                return rendered[:-1], False
+            return value.text, value.complete
+        if isinstance(value, ResponseParser.PartialJsonObject):
+            rendered, children_complete = cls._serialize_partial_json_state(value.value)
+            if value.complete and children_complete:
+                return rendered, True
+            if rendered.endswith("}"):
+                return rendered[:-1], False
+            return rendered, False
+        if isinstance(value, dict):
+            parts = ["{"]
+            items = list(value.items())
+            for index, (key, item_value) in enumerate(items):
+                if index > 0:
+                    parts.append(",")
+                parts.append(json.dumps(key, ensure_ascii=False, separators=(",", ":")))
+                parts.append(":")
+                rendered_item, item_complete = cls._serialize_partial_json_state(item_value)
+                parts.append(rendered_item)
+                if index == len(items) - 1 and not item_complete:
+                    return "".join(parts), False
+            parts.append("}")
+            return "".join(parts), True
+        if isinstance(value, list):
+            parts = ["["]
+            for index, item_value in enumerate(value):
+                if index > 0:
+                    parts.append(",")
+                rendered_item, item_complete = cls._serialize_partial_json_state(item_value)
+                parts.append(rendered_item)
+                if index == len(value) - 1 and not item_complete:
+                    return "".join(parts), False
+            parts.append("]")
+            return "".join(parts), True
+        return json.dumps(value, ensure_ascii=False, separators=(",", ":")), True
+
+    @classmethod
+    def _serialize_tool_arguments(cls, arguments: Any, *, partial: bool = False) -> str:
+        if partial:
+            if cls._contains_partial_json_value(arguments):
+                return cls._serialize_partial_json_state(arguments)[0]
+            return cls._serialize_partial_json_prefix(arguments)
+        if isinstance(arguments, ResponseParser.PartialJsonObject):
+            arguments = arguments.value
+        return json.dumps(arguments, ensure_ascii=False, separators=(",", ":"))
+
+    def _parsed_chat_message(
+        self,
+        *,
+        parsed: Dict[str, Any],
+        partial: bool = False,
+    ) -> Dict[str, Any]:
+        message: Dict[str, Any] = {
+            "role": parsed.get("role", "assistant"),
+        }
+        for key, value in parsed.items():
+            if key in {"role", "content", "tool_calls"}:
+                continue
+            if value is None or value == "":
+                continue
+            message[key] = value
+        content = parsed.get("content")
+        tool_calls = parsed.get("tool_calls")
+        if isinstance(tool_calls, list) and tool_calls:
+            normalized_tool_calls = []
+            for tool_call_index, tool_call in enumerate(tool_calls):
+                function = tool_call["function"]
+                if self._tool_content_type(function["name"]) == "text":
+                    arguments = self._text_tool_arguments(
+                        function["name"],
+                        function["arguments"],
+                        partial=partial,
+                    )
+                    if arguments is None:
+                        continue
+                else:
+                    arguments = self._serialize_tool_arguments(
+                        function["arguments"],
+                        partial=partial,
+                    )
+                normalized_tool_calls.append(
+                    {
+                        "id": f"call_{self._choice_index}_{function['name']}_{self._completion_id}_{tool_call_index}",
+                        "type": tool_call.get("type", "function"),
+                        "function": {
+                            "name": function["name"],
+                            "arguments": arguments,
+                        },
+                    }
+                )
+            message["content"] = content if content not in {None, ""} else None
+            message["tool_calls"] = normalized_tool_calls
+            if len(normalized_tool_calls) == 1:
+                message["function_call"] = dict(normalized_tool_calls[0]["function"])
+            return message
+        message["content"] = content if content is not None else ""
+        return message
+
+    def _message_deltas(
+        self,
+        previous_message: Dict[str, Any],
+        message: Dict[str, Any],
+    ) -> List[Dict[str, Any]]:
+        deltas: List[Dict[str, Any]] = []
+        for key, value in message.items():
+            if key in {"role", "content", "tool_calls", "function_call"}:
+                continue
+            old_value = previous_message.get(key, "")
+            if not isinstance(value, str):
+                if key not in previous_message and value is not None:
+                    deltas.append({key: value})
+                continue
+            if not value:
+                continue
+            if isinstance(old_value, str) and value.startswith(old_value):
+                delta_value = value[len(old_value) :]
+            else:
+                delta_value = value
+            if delta_value:
+                deltas.append({key: delta_value})
+
+        new_content = message.get("content")
+        old_content = previous_message.get("content", "")
+        if isinstance(new_content, str) and new_content:
+            if isinstance(old_content, str) and new_content.startswith(old_content):
+                content_delta = new_content[len(old_content) :]
+            else:
+                content_delta = new_content
+            if content_delta:
+                deltas.append({"content": content_delta})
+
+        new_tool_calls = cast(List[Dict[str, Any]], message.get("tool_calls", []))
+        old_tool_calls = cast(List[Dict[str, Any]], previous_message.get("tool_calls", []))
+        for tool_call_index, tool_call in enumerate(new_tool_calls):
+            old_tool_call = old_tool_calls[tool_call_index] if tool_call_index < len(old_tool_calls) else None
+            delta_tool_call: Dict[str, Any] = {"index": tool_call_index}
+            if old_tool_call is None:
+                delta_tool_call["id"] = tool_call["id"]
+                delta_tool_call["type"] = tool_call["type"]
+            function = cast(Dict[str, Any], tool_call["function"])
+            old_function = (
+                cast(Dict[str, Any], old_tool_call["function"])
+                if old_tool_call is not None
+                else {}
+            )
+            function_delta: Dict[str, Any] = {}
+            if function.get("name") and function.get("name") != old_function.get("name"):
+                function_delta["name"] = function["name"]
+            arguments = cast(str, function.get("arguments", ""))
+            old_arguments = cast(str, old_function.get("arguments", ""))
+            if old_tool_call is None and arguments == "{}":
+                argument_delta = ""
+            elif arguments.startswith(old_arguments):
+                argument_delta = arguments[len(old_arguments) :]
+            else:
+                argument_delta = arguments
+            if argument_delta:
+                function_delta["arguments"] = argument_delta
+            if function_delta:
+                delta_tool_call["function"] = function_delta
+            if len(delta_tool_call) > 1:
+                deltas.append({"tool_calls": [delta_tool_call]})
+        return deltas
+
+    @staticmethod
+    def _apply_message_delta(message: Dict[str, Any], delta: Dict[str, Any]) -> None:
+        if "role" in delta:
+            message["role"] = delta["role"]
+        for key, value in delta.items():
+            if key in {"role", "tool_calls", "function_call"}:
+                continue
+            if isinstance(value, str):
+                existing = message.get(key)
+                if isinstance(existing, str):
+                    message[key] = existing + value
+                else:
+                    message[key] = value
+            else:
+                message[key] = value
+        tool_call_deltas = delta.get("tool_calls")
+        if not isinstance(tool_call_deltas, list):
+            return
+        tool_calls = cast(List[Dict[str, Any]], message.setdefault("tool_calls", []))
+        for tool_delta in tool_call_deltas:
+            if not isinstance(tool_delta, dict):
+                continue
+            index = tool_delta.get("index")
+            if not isinstance(index, int):
+                continue
+            while len(tool_calls) <= index:
+                tool_calls.append({"function": {"name": "", "arguments": ""}})
+            tool_call = tool_calls[index]
+            if "id" in tool_delta:
+                tool_call["id"] = tool_delta["id"]
+            if "type" in tool_delta:
+                tool_call["type"] = tool_delta["type"]
+            function_delta = tool_delta.get("function")
+            if not isinstance(function_delta, dict):
+                continue
+            function = cast(Dict[str, Any], tool_call.setdefault("function", {}))
+            name_delta = function_delta.get("name")
+            if isinstance(name_delta, str):
+                function["name"] = cast(str, function.get("name", "")) + name_delta
+            arguments_delta = function_delta.get("arguments")
+            if isinstance(arguments_delta, str):
+                function["arguments"] = cast(str, function.get("arguments", "")) + arguments_delta
+        if tool_calls:
+            message["function_call"] = dict(cast(Dict[str, Any], tool_calls[0]["function"]))
+
+    def parse_completion_message(self, response_text: str) -> Dict[str, Any]:
+        parsed = self.parse_chat_response(response_text, partial=False)
+        return self._parsed_chat_message(parsed=parsed)
+
+    def _stream_state_message(self, *, partial: bool) -> Dict[str, Any]:
+        assert self._stream_state is not None
+        if self._stream_plan is not None and self._stream_plan.get("direct_deltas"):
+            copied = {
+                key: (
+                    [
+                        {
+                            child_key: (
+                                dict(child_value)
+                                if isinstance(child_value, dict)
+                                else child_value
+                            )
+                            for child_key, child_value in tool_call.items()
+                        }
+                        for tool_call in cast(List[Dict[str, Any]], value)
+                    ]
+                    if key == "tool_calls" and isinstance(value, list)
+                    else value
+                )
+                for key, value in self._message.items()
+            }
+            if copied.get("tool_calls"):
+                copied["function_call"] = dict(
+                    cast(List[Dict[str, Any]], copied["tool_calls"])[0]["function"]
+                )
+            return copied
+        parsed = cast(Dict[str, Any], self._stream_state.parsed)
+        return self._parsed_chat_message(parsed=parsed, partial=partial)
+
+    def _stream_state_complete(self) -> bool:
+        if self._stream_plan is None:
+            return False
+        if self._direct.deltas:
+            return (
+                not self._direct.pending
+                and self._direct.mode in {self.DIRECT_MODE_CONTENT, self.DIRECT_MODE_AFTER_TOOL_ITEM}
+            )
+        if self._stream_state is None:
+            return False
+        if self._stream_plan["kind"] == "json-root":
+            return True
+        if self._stream_plan["kind"] == "segment-message":
+            return (
+                not self._stream_state.pending
+                and self._stream_state.current_item is None
+                and self._stream_state.current_segment is None
+                and self._stream_state.mode == "segment-start"
+            )
+        return (
+            not self._stream_state.pending
+            and self._stream_state.current_item is None
+            and self._stream_state.mode in {"content", "after-tool-item"}
+        )
+
+    @staticmethod
+    def _chat_chunk_payload(
+        *,
+        chunk_id: str,
+        created: int,
+        model: str,
+        index: int,
+        delta: Dict[str, Any],
+        finish_reason: Optional[str],
+        logprobs: Optional[Dict[str, Any]] = None,
+    ) -> Dict[str, Any]:
+        choice: Dict[str, Any] = {
+            "index": index,
+            "delta": delta,
+            "finish_reason": finish_reason,
+        }
+        if logprobs is not None:
+            choice["logprobs"] = logprobs
+        return {
+            "id": "chat" + chunk_id,
+            "object": "chat.completion.chunk",
+            "created": created,
+            "model": model,
+            "choices": [choice],
+        }
+
+    def _chunk_payloads(
+        self,
+        *,
+        chunk_id: str,
+        created: int,
+        model: str,
+        deltas: List[Dict[str, Any]],
+        finish_reason: Optional[str],
+        logprobs: Optional[Dict[str, Any]],
+        leading_delta: Optional[Dict[str, Any]] = None,
+    ) -> List[Dict[str, Any]]:
+        payloads: List[Dict[str, Any]] = []
+        logprobs_sent = False
+        chat_chunk_id = "chat" + chunk_id
+        index = self._choice_index
+        if leading_delta is not None:
+            payloads.append(
+                {
+                    "id": chat_chunk_id,
+                    "object": "chat.completion.chunk",
+                    "created": created,
+                    "model": model,
+                    "choices": [
+                        {
+                            "index": index,
+                            "delta": leading_delta,
+                            "finish_reason": None,
+                        }
+                    ],
+                }
+            )
+        for delta in deltas:
+            payload_logprobs = None
+            if (
+                logprobs is not None
+                and delta.get("role") != "assistant"
+                and not logprobs_sent
+            ):
+                payload_logprobs = logprobs
+                logprobs_sent = True
+            choice: Dict[str, Any] = {
+                "index": index,
+                "delta": delta,
+                "finish_reason": None,
+            }
+            if payload_logprobs is not None:
+                choice["logprobs"] = payload_logprobs
+            payloads.append(
+                {
+                    "id": chat_chunk_id,
+                    "object": "chat.completion.chunk",
+                    "created": created,
+                    "model": model,
+                    "choices": [choice],
+                }
+            )
+        if finish_reason is not None:
+            payloads.append(
+                {
+                    "id": chat_chunk_id,
+                    "object": "chat.completion.chunk",
+                    "created": created,
+                    "model": model,
+                    "choices": [
+                        {
+                            "index": index,
+                            "delta": {},
+                            "finish_reason": finish_reason,
+                        }
+                    ],
+                }
+            )
+        return payloads
+
+    def consume_completion_chunk(
+        self,
+        text: str,
+        *,
+        chunk_id: str,
+        created: int,
+        model: str,
+        finish_reason: Optional[str],
+        logprobs: Optional[Dict[str, Any]] = None,
+    ) -> List[Dict[str, Any]]:
+        role_delta: Optional[Dict[str, Any]] = None
+        if not self._started:
+            self._started = True
+            self._message["role"] = "assistant"
+            role_delta = {"role": "assistant"}
+        self._text_parts.append(text)
+
+        if self._stream_plan is not None and not self._stream_failed:
+            success, stream_deltas = self._advance_stream_state(text)
+            if success:
+                if self._direct.deltas:
+                    if finish_reason is None:
+                        return self._chunk_payloads(
+                            chunk_id=chunk_id,
+                            created=created,
+                            model=model,
+                            deltas=stream_deltas,
+                            finish_reason=None,
+                            logprobs=logprobs,
+                            leading_delta=role_delta,
+                        )
+                    return self._chunk_payloads(
+                        chunk_id=chunk_id,
+                        created=created,
+                        model=model,
+                        deltas=stream_deltas,
+                        finish_reason=(
+                            "tool_calls" if self._direct.saw_tool_calls else finish_reason
+                        ),
+                        logprobs=logprobs,
+                        leading_delta=role_delta,
+                    )
+                elif self._stream_plan["kind"] == "segment-message":
+                    if role_delta is not None:
+                        stream_deltas = [role_delta, *stream_deltas]
+                    for delta in stream_deltas:
+                        self._apply_message_delta(self._message, delta)
+                    if finish_reason is None:
+                        return self._chunk_payloads(
+                            chunk_id=chunk_id,
+                            created=created,
+                            model=model,
+                            deltas=stream_deltas,
+                            finish_reason=None,
+                            logprobs=logprobs,
+                        )
+                    return self._chunk_payloads(
+                        chunk_id=chunk_id,
+                        created=created,
+                        model=model,
+                        deltas=stream_deltas,
+                        finish_reason=(
+                            "tool_calls" if self._message.get("tool_calls") else finish_reason
+                        ),
+                        logprobs=logprobs,
+                    )
+                else:
+                    previous_message = self._message
+                    partial_deltas: List[Dict[str, Any]] = []
+                    assert self._stream_state is not None
+                    parsed = cast(Dict[str, Any], self._stream_state.parsed)
+                    message = self._parsed_chat_message(
+                        parsed=parsed,
+                        partial=finish_reason is None or not self._stream_state_complete(),
+                    )
+                    if finish_reason is None:
+                        if role_delta is not None:
+                            partial_deltas.append(role_delta)
+                        partial_deltas.extend(self._message_deltas(previous_message, message))
+                        self._message = message
+                        return self._chunk_payloads(
+                            chunk_id=chunk_id,
+                            created=created,
+                            model=model,
+                            deltas=partial_deltas,
+                            finish_reason=None,
+                            logprobs=logprobs,
+                        )
+                    if role_delta is not None:
+                        partial_deltas.append(role_delta)
+                    partial_deltas.extend(self._message_deltas(previous_message, message))
+                    self._message = message
+                    return self._chunk_payloads(
+                        chunk_id=chunk_id,
+                        created=created,
+                        model=model,
+                        deltas=partial_deltas,
+                        finish_reason=(
+                            "tool_calls" if message.get("tool_calls") else finish_reason
+                        ),
+                        logprobs=logprobs,
+                    )
+            else:
+                self._stream_failed = True
+
+        response_text = "".join(self._text_parts)
+        parsed = self.parse_chat_response(response_text, partial=finish_reason is None)
+        message = self._parsed_chat_message(parsed=parsed, partial=finish_reason is None)
+        previous_message = self._message
+        deltas: List[Dict[str, Any]] = []
+        if role_delta is not None:
+            deltas.append(role_delta)
+        deltas.extend(self._message_deltas(previous_message, message))
+        self._message = message
+        return self._chunk_payloads(
+            chunk_id=chunk_id,
+            created=created,
+            model=model,
+            deltas=deltas,
+            finish_reason=(
+                "tool_calls"
+                if finish_reason is not None and message.get("tool_calls")
+                else finish_reason
+            ),
+            logprobs=logprobs,
+        )
+
+
+class OpenAIFormatter:
+    @dataclass
+    class ReturnedToken:
+        index: int
+        text_bytes: bytes
+        token: Token
+        text_offset: int
+
+    @dataclass
+    class ResponsesOutputItem:
+        output_index: int
+        item: Dict[str, Any]
+        content_index: Optional[int] = None
+
+    @dataclass
+    class ResponsesStream:
+        body: CreateResponseRequest
+        response_id: str
+        created_at: float
+        model: str
+        output: List[Dict[str, Any]] = field(default_factory=list)
+        sequence_number: int = 0
+        started: bool = False
+        assistant_meta: Dict[str, Any] = field(default_factory=dict)
+        reasoning_item: Optional["OpenAIFormatter.ResponsesOutputItem"] = None
+        message_item: Optional["OpenAIFormatter.ResponsesOutputItem"] = None
+        tool_items: Dict[int, "OpenAIFormatter.ResponsesOutputItem"] = field(
+            default_factory=dict
+        )
+        final_status: Optional[str] = None
+        incomplete_details: Optional[Dict[str, Any]] = None
+
+    def __init__(self, model: Model) -> None:
+        self.model = model
+
+    @staticmethod
+    def decode_text(data: bytes) -> str:
+        return data.decode("utf-8", errors="ignore")
+
+    @staticmethod
+    def encode_sse_payload(payload: BaseModel | Dict[str, Any]) -> bytes:
+        data = (
+            payload.model_dump(mode="json", exclude_none=True)
+            if isinstance(payload, BaseModel)
+            else payload
+        )
+        return (
+            "data: "
+            f"{json.dumps(data, ensure_ascii=False, separators=(',', ':'))}\n\n"
+        ).encode("utf-8")
+
+    @staticmethod
+    def next_stream_chunk(
+        stream: Iterator[CompletionChunk],
+    ) -> Tuple[bool, Optional[CompletionChunk]]:
+        try:
+            return False, next(stream)
+        except StopIteration:
+            return True, None
+
+    @staticmethod
+    def next_stream_output(
+        stream: Iterator[CompletionChunk],
+    ) -> Tuple[bool, Optional[CompletionChunk], Optional[OpenAICompletion]]:
+        try:
+            return False, next(stream), None
+        except StopIteration as stop:
+            return True, None, cast(Optional[OpenAICompletion], stop.value)
+
+    @staticmethod
+    def collect_completion(stream: Iterator[Any]) -> OpenAICompletion:
+        iterator = iter(stream)
+        while True:
+            try:
+                next(iterator)
+            except StopIteration as stop:
+                result = stop.value
+                if result is None:
+                    raise RuntimeError("missing completion result")
+                return cast(OpenAICompletion, result)
+
+    @staticmethod
+    def _tools_for_response_parser(
+        *,
+        functions: Optional[List[ChatTemplateFunctionDefinition]] = None,
+        tools: Optional[List[ChatTemplateTool]] = None,
+    ) -> Optional[List[ChatTemplateTool]]:
+        if functions is not None:
+            return [
+                {
+                    "type": "function",
+                    "function": function,
+                }
+                for function in functions
+            ]
+        return tools
+
+    def _chat_template_text(self) -> str:
+        if self.model.chat_formatter is None:
+            return ""
+        return cast(str, self.model.chat_formatter._template_text)
+
+    def _uses_harmony_channels(self) -> bool:
+        template = self._chat_template_text()
+        return "<|channel|>" in template or "<|recipient|>" in template
+
+    def _uses_reasoning_content(self) -> bool:
+        template = self._chat_template_text()
+        return "reasoning_content" in template or "<think>" in template
+
+    @staticmethod
+    def _chat_message(data: Dict[str, Any]) -> ChatCompletionRequestMessage:
+        return ChatCompletionRequestMessage.model_validate(data)
+
+    def _instructions_role(self) -> Literal["developer", "system"]:
+        return "developer" if "developer" in self._chat_template_text() else "system"
+
+    @staticmethod
+    def _response_reasoning_effort(
+        body: CreateResponseRequest,
+    ) -> Optional[str]:
+        reasoning = body.reasoning
+        if reasoning is None:
+            return None
+        effort = reasoning.effort
+        if effort in {"low", "medium", "high"}:
+            return cast(str, effort)
+        return None
+
+    @staticmethod
+    def _response_text_from_content(content: Any) -> str:
+        if isinstance(content, str):
+            return content
+        if not isinstance(content, list):
+            raise CompletionRequestValidationError("responses input content must be a string or list")
+        parts: List[str] = []
+        for part in content:
+            if not isinstance(part, dict):
+                raise CompletionRequestValidationError("responses content parts must be objects")
+            part_type = part.get("type")
+            if part_type in {"input_text", "output_text", "reasoning_text", "summary_text", "text"}:
+                text = part.get("text")
+                if not isinstance(text, str):
+                    raise CompletionRequestValidationError(
+                        f"responses content part {part_type!r} requires string text"
+                    )
+                parts.append(text)
+                continue
+            raise CompletionRequestValidationError(
+                f"unsupported responses content part type: {part_type!r}"
+            )
+        return "".join(parts)
+
+    @staticmethod
+    def _response_chat_content_from_content(
+        content: Any,
+    ) -> Union[str, List[Dict[str, Any]]]:
+        if isinstance(content, str):
+            return content
+        if not isinstance(content, list):
+            raise CompletionRequestValidationError("responses input content must be a string or list")
+        parts: List[Dict[str, Any]] = []
+        for part in content:
+            if not isinstance(part, dict):
+                raise CompletionRequestValidationError("responses content parts must be objects")
+            part_type = part.get("type")
+            if part_type in {"input_text", "output_text", "reasoning_text", "summary_text", "text"}:
+                text = part.get("text")
+                if not isinstance(text, str):
+                    raise CompletionRequestValidationError(
+                        f"responses content part {part_type!r} requires string text"
+                    )
+                parts.append({"type": "text", "text": text})
+                continue
+            if part_type == "input_image":
+                image_url = part.get("image_url")
+                if not isinstance(image_url, str):
+                    raise CompletionRequestValidationError(
+                        "responses input_image content part requires image_url"
+                    )
+                parts.append({"type": "image_url", "image_url": {"url": image_url}})
+                continue
+            if part_type == "input_audio":
+                input_audio = part.get("input_audio")
+                if isinstance(input_audio, dict):
+                    data = input_audio.get("data")
+                    audio_format = input_audio.get("format")
+                else:
+                    data = part.get("data")
+                    audio_format = part.get("format")
+                if not isinstance(data, str):
+                    raise CompletionRequestValidationError(
+                        "responses input_audio content part requires base64 data"
+                    )
+                if audio_format is not None and not isinstance(audio_format, str):
+                    raise CompletionRequestValidationError(
+                        "responses input_audio format must be a string"
+                    )
+                audio_part: Dict[str, Any] = {"data": data}
+                if audio_format is not None:
+                    audio_part["format"] = audio_format
+                parts.append({"type": "input_audio", "input_audio": audio_part})
+                continue
+            if part_type == "audio_url":
+                audio_url = part.get("audio_url")
+                if not isinstance(audio_url, str):
+                    raise CompletionRequestValidationError(
+                        "responses audio_url content part requires audio_url"
+                    )
+                parts.append({"type": "audio_url", "audio_url": {"url": audio_url}})
+                continue
+            raise CompletionRequestValidationError(
+                f"unsupported responses content part type: {part_type!r}"
+            )
+        if all(part.get("type") == "text" for part in parts):
+            return "".join(cast(str, part.get("text", "")) for part in parts)
+        return parts
+
+    @staticmethod
+    def _response_reasoning_text(item: Dict[str, Any]) -> str:
+        content = item.get("content")
+        if isinstance(content, list) and content:
+            return OpenAIFormatter._response_text_from_content(content)
+        summary = item.get("summary")
+        if isinstance(summary, list) and summary:
+            return OpenAIFormatter._response_text_from_content(summary)
+        return ""
+
+    def _response_reasoning_input_message(
+        self,
+        *,
+        text: str,
+    ) -> ChatCompletionRequestMessage:
+        if self._uses_harmony_channels():
+            return self._chat_message(
+                {
+                    "role": "assistant",
+                    "content": text,
+                    "channel": "analysis",
+                }
+            )
+        if self._uses_reasoning_content():
+            return self._chat_message(
+                {
+                    "role": "assistant",
+                    "content": "",
+                    "reasoning_content": text,
+                }
+            )
+        return self._chat_message(
+            {
+                "role": "assistant",
+                "content": text,
+            }
+        )
+
+    @staticmethod
+    def _response_tool_call_input_message(
+        *,
+        name: str,
+        arguments: str,
+        call_id: str,
+        content_type: Optional[str] = None,
+    ) -> ChatCompletionRequestMessage:
+        function: Dict[str, Any] = {
+            "name": name,
+            "arguments": arguments,
+        }
+        if isinstance(content_type, str) and content_type:
+            function["content_type"] = content_type
+        tool_call = {
+            "id": call_id,
+            "type": "function",
+            "function": function,
+        }
+        message: Dict[str, Any] = {
+            "role": "assistant",
+            "content": None,
+            "tool_calls": [tool_call],
+            "function_call": dict(function),
+        }
+        return OpenAIFormatter._chat_message(message)
+
+    def _response_function_call_input_message(
+        self,
+        item: Dict[str, Any],
+    ) -> ChatCompletionRequestMessage:
+        name = item.get("name")
+        if not isinstance(name, str) or not name:
+            raise CompletionRequestValidationError("function_call input requires name")
+        arguments = item.get("arguments", "")
+        if not isinstance(arguments, str):
+            raise CompletionRequestValidationError("function_call input requires string arguments")
+        call_id = item.get("call_id") or item.get("id") or f"call_{uuid.uuid4().hex}"
+        return self._response_tool_call_input_message(
+            name=name,
+            arguments=arguments,
+            call_id=call_id,
+            content_type="json",
+        )
+
+    def _response_custom_tool_call_input_message(
+        self,
+        item: Dict[str, Any],
+    ) -> ChatCompletionRequestMessage:
+        name = item.get("name")
+        if not isinstance(name, str) or not name:
+            raise CompletionRequestValidationError("custom_tool_call input requires name")
+        input_text = item.get("input", "")
+        if not isinstance(input_text, str):
+            raise CompletionRequestValidationError("custom_tool_call input requires string input")
+        call_id = item.get("call_id") or item.get("id") or f"call_{uuid.uuid4().hex}"
+        content_type = item.get("content_type")
+        if not isinstance(content_type, str) or not content_type:
+            content_type = "text"
+        return self._response_tool_call_input_message(
+            name=name,
+            arguments=input_text,
+            call_id=call_id,
+            content_type=content_type,
+        )
+
+    def responses_input_to_chat_messages(
+        self,
+        body: CreateResponseRequest,
+    ) -> List[ChatCompletionRequestMessage]:
+        if body.conversation is not None:
+            raise CompletionRequestValidationError(
+                "conversation is not supported in stateless /v1/responses"
+            )
+        if body.store:
+            raise CompletionRequestValidationError(
+                "store=true is not supported in stateless /v1/responses"
+            )
+        if body.truncation not in {None, "disabled"}:
+            raise CompletionRequestValidationError(
+                "only truncation='disabled' is supported"
+            )
+
+        messages: List[ChatCompletionRequestMessage] = []
+        if body.instructions is not None:
+            messages.append(
+                self._chat_message(
+                    {
+                        "role": self._instructions_role(),
+                        "content": body.instructions,
+                    }
+                )
+            )
+
+        if isinstance(body.input, str):
+            messages.append(
+                self._chat_message(
+                    {
+                        "role": "user",
+                        "content": body.input,
+                    }
+                )
+            )
+            return messages
+
+        items = body.input
+        if isinstance(items, dict):
+            items = [items]
+        if not isinstance(items, list):
+            raise CompletionRequestValidationError(
+                "responses input must be a string, object, or list"
+            )
+
+        function_names_by_call_id: Dict[str, str] = {}
+        for item in items:
+            if not isinstance(item, dict):
+                raise CompletionRequestValidationError(
+                    "responses input items must be objects"
+                )
+            item_type = item.get("type")
+            if item_type is None and "role" in item:
+                item_type = "message"
+            if item_type == "message":
+                role = item.get("role", "user")
+                if role not in {
+                    "user",
+                    "assistant",
+                    "system",
+                    "developer",
+                    "tool",
+                    "function",
+                }:
+                    raise CompletionRequestValidationError(
+                        f"unsupported responses message role: {role!r}"
+                    )
+                if role == "function":
+                    role = "tool"
+                data: Dict[str, Any] = {
+                    "role": role,
+                    "content": self._response_chat_content_from_content(
+                        item.get("content", "")
+                    ),
+                }
+                phase = item.get("phase")
+                if isinstance(phase, str):
+                    data["phase"] = phase
+                    if role == "assistant" and self._uses_harmony_channels():
+                        if phase == "commentary":
+                            data["channel"] = "commentary"
+                        elif phase == "final_answer":
+                            data["channel"] = "final"
+                messages.append(self._chat_message(data))
+                continue
+            if item_type == "reasoning":
+                text = self._response_reasoning_text(item)
+                if text:
+                    messages.append(self._response_reasoning_input_message(text=text))
+                continue
+            if item_type == "function_call":
+                message = self._response_function_call_input_message(item)
+                call_id = item.get("call_id") or item.get("id")
+                name = item.get("name")
+                if isinstance(call_id, str) and isinstance(name, str):
+                    function_names_by_call_id[call_id] = name
+                messages.append(message)
+                continue
+            if item_type == "custom_tool_call":
+                message = self._response_custom_tool_call_input_message(item)
+                call_id = item.get("call_id") or item.get("id")
+                name = item.get("name")
+                if isinstance(call_id, str) and isinstance(name, str):
+                    function_names_by_call_id[call_id] = name
+                messages.append(message)
+                continue
+            if item_type == "function_call_output":
+                call_id = item.get("call_id")
+                if not isinstance(call_id, str) or not call_id:
+                    raise CompletionRequestValidationError(
+                        "function_call_output input requires call_id"
+                    )
+                tool_output_data: Dict[str, Any] = {
+                    "role": "tool",
+                    "tool_call_id": call_id,
+                    "content": self._response_text_from_content(item.get("output", "")),
+                }
+                name = function_names_by_call_id.get(call_id)
+                if name is not None:
+                    tool_output_data["name"] = name
+                messages.append(self._chat_message(tool_output_data))
+                continue
+            if item_type == "custom_tool_call_output":
+                call_id = item.get("call_id")
+                if not isinstance(call_id, str) or not call_id:
+                    raise CompletionRequestValidationError(
+                        "custom_tool_call_output input requires call_id"
+                    )
+                tool_output_data = {
+                    "role": "tool",
+                    "tool_call_id": call_id,
+                    "content": self._response_text_from_content(item.get("output", "")),
+                }
+                name = function_names_by_call_id.get(call_id)
+                if name is not None:
+                    tool_output_data["name"] = name
+                messages.append(self._chat_message(tool_output_data))
+                continue
+            raise CompletionRequestValidationError(
+                f"unsupported responses input item type: {item_type!r}"
+            )
+        return messages
+
+    @staticmethod
+    def _clone_response_input_items(input_items: Any) -> List[Any]:
+        if isinstance(input_items, list):
+            return copy.deepcopy(input_items)
+        if isinstance(input_items, dict):
+            return [copy.deepcopy(input_items)]
+        return [copy.deepcopy(input_items)]
+
+    @staticmethod
+    def _normalize_response_output_item_for_input(
+        item: Dict[str, Any],
+    ) -> Optional[Dict[str, Any]]:
+        item_type = item.get("type")
+        if item_type == "message":
+            role = item.get("role")
+            if not isinstance(role, str):
+                return None
+            normalized: Dict[str, Any] = {
+                "type": "message",
+                "role": role,
+                "content": copy.deepcopy(item.get("content", [])),
+            }
+            for key in ("id", "phase", "status"):
+                value = item.get(key)
+                if value is not None:
+                    normalized[key] = copy.deepcopy(value)
+            return normalized
+        if item_type == "reasoning":
+            normalized = {
+                "type": "reasoning",
+                "content": copy.deepcopy(item.get("content", [])),
+            }
+            for key in ("id", "summary", "status"):
+                value = item.get(key)
+                if value is not None:
+                    normalized[key] = copy.deepcopy(value)
+            return normalized
+        if item_type == "function_call":
+            normalized = {
+                "type": "function_call",
+                "call_id": copy.deepcopy(item.get("call_id")),
+                "name": copy.deepcopy(item.get("name")),
+                "arguments": copy.deepcopy(item.get("arguments", "")),
+            }
+            namespace = item.get("namespace")
+            item_id = item.get("id")
+            if item_id is not None:
+                normalized["id"] = copy.deepcopy(item_id)
+            if namespace is not None:
+                normalized["namespace"] = copy.deepcopy(namespace)
+            return normalized
+        if item_type == "custom_tool_call":
+            normalized = {
+                "type": "custom_tool_call",
+                "call_id": copy.deepcopy(item.get("call_id")),
+                "name": copy.deepcopy(item.get("name")),
+                "input": copy.deepcopy(item.get("input", "")),
+            }
+            namespace = item.get("namespace")
+            content_type = item.get("content_type")
+            item_id = item.get("id")
+            if item_id is not None:
+                normalized["id"] = copy.deepcopy(item_id)
+            if namespace is not None:
+                normalized["namespace"] = copy.deepcopy(namespace)
+            if content_type is not None:
+                normalized["content_type"] = copy.deepcopy(content_type)
+            return normalized
+        return None
+
+    def _responses_tools_to_chat_tools(
+        self,
+        tools: Optional[List[ResponsesToolDefinition]],
+    ) -> Optional[List[ChatTemplateTool]]:
+        if tools is None:
+            return None
+        chat_tools: List[ChatTemplateTool] = []
+        for tool in tools:
+            if isinstance(
+                tool,
+                (
+                    ResponsesWebSearchTool,
+                    ResponsesNamespaceTool,
+                    ResponsesImageGenerationTool,
+                ),
+            ):
+                continue
+            if isinstance(tool, ResponsesFunctionTool):
+                chat_tools.append(tool.to_chat_template_tool())
+                continue
+            if isinstance(tool, ResponsesCustomTool):
+                chat_tools.append(tool.to_chat_template_tool())
+                continue
+            raise CompletionRequestValidationError(
+                f"unsupported responses tool type: {tool.type!r}"
+            )
+        return chat_tools
+
+    @staticmethod
+    def _responses_tool_choice_to_chat_tool_choice(
+        tool_choice: Optional[ResponsesToolChoice],
+    ) -> Optional[Union[Literal["auto", "none", "required"], ChatTemplateToolChoice]]:
+        if tool_choice is None or isinstance(tool_choice, str):
+            return tool_choice
+        if tool_choice.type in {"function", "custom"}:
+            return tool_choice.to_chat_template_tool_choice()
+        raise CompletionRequestValidationError(
+            f"unsupported responses tool_choice type: {tool_choice.type!r}"
+        )
+
+    @staticmethod
+    def _response_format_type(response_format: Optional[ChatTemplateResponseFormat]) -> Optional[str]:
+        if response_format is None:
+            return None
+        format_type = response_format.get("type")
+        if isinstance(format_type, str):
+            return format_type
+        return None
+
+    @staticmethod
+    def _grammar_for_response_format(
+        response_format: Optional[ChatTemplateResponseFormat],
+    ) -> Optional[str]:
+        format_type = OpenAIFormatter._response_format_type(response_format)
+        if format_type is None or format_type == "text":
+            return None
+        if format_type == "json_object":
+            return JSON_GBNF
+        if format_type == "json_schema":
+            assert response_format is not None
+            schema = response_format.get("schema")
+            if schema is None and isinstance(response_format.get("json_schema"), dict):
+                schema = cast(Dict[str, Any], response_format["json_schema"]).get("schema")
+            if not isinstance(schema, dict):
+                raise CompletionRequestValidationError(
+                    "json_schema response format requires a schema object"
+                )
+            return JsonSchemaConverter.to_gbnf(
+                json.dumps(schema, ensure_ascii=False, separators=(",", ":"))
+            )
+        raise CompletionRequestValidationError(
+            f"unsupported response format type: {format_type!r}"
+        )
+
+    def response_request_to_chat_parts(
+        self,
+        body: CreateResponseRequest,
+    ) -> ResponsesChatRequestParts:
+        chat_tools = self._responses_tools_to_chat_tools(body.tools)
+        response_format = (
+            None
+            if body.text is None or body.text.format is None
+            else body.text.format.to_chat_response_format()
+        )
+        return ResponsesChatRequestParts(
+            messages=self.responses_input_to_chat_messages(body),
+            max_tokens=body.max_output_tokens,
+            temperature=0.8 if body.temperature is None else body.temperature,
+            top_p=0.95 if body.top_p is None else body.top_p,
+            stream=body.stream,
+            logprobs=body.top_logprobs is not None,
+            top_logprobs=body.top_logprobs,
+            model=body.model,
+            user=body.user,
+            tools=chat_tools,
+            tool_choice=self._responses_tool_choice_to_chat_tool_choice(body.tool_choice),
+            response_format=response_format,
+            reasoning_effort=self._response_reasoning_effort(body),
+        )
+
+    def _response_parser(
+        self,
+        *,
+        tools: Optional[List[ChatTemplateTool]] = None,
+        completion_id: str = "",
+        choice_index: int = 0,
+        generation_prompt: str = "",
+    ) -> ResponseParser:
+        if self.model.response_schema is None:
+            raise CompletionResponseParsingError("model does not define response_schema")
+        return ResponseParser(
+            self.model.response_schema,
+            tools=tools,
+            completion_id=completion_id,
+            choice_index=choice_index,
+            generation_prompt=generation_prompt,
+        )
+
+    def parse_chat_response(
+        self,
+        response_text: str,
+        *,
+        tools: Optional[List[ChatTemplateTool]] = None,
+        partial: bool,
+        generation_prompt: str = "",
+    ) -> Dict[str, Any]:
+        return self._response_parser(
+            tools=tools,
+            generation_prompt=generation_prompt,
+        ).parse_chat_response(
+            response_text,
+            partial=partial,
+        )
+
+    def _chat_tool_name_and_grammar(
+        self,
+        *,
+        tools: Optional[List[ChatTemplateTool]],
+        function_call: Optional[Union[Literal["none", "auto"], ChatTemplateFunctionCall]],
+        tool_choice: Optional[Union[Literal["none", "auto", "required"], ChatTemplateToolChoice]],
+        response_format: Optional[ChatTemplateResponseFormat],
+    ) -> Tuple[Optional[str], Optional[str]]:
+        selected_tool_choice: Optional[Union[str, ChatTemplateToolChoice]] = tool_choice
+        if function_call is not None:
+            if isinstance(function_call, str):
+                selected_tool_choice = function_call
+            else:
+                selected_tool_choice = {
+                    "type": "function",
+                    "function": {
+                        "name": function_call["name"],
+                    },
+                }
+        grammar_text = self._grammar_for_response_format(response_format)
+        if not isinstance(selected_tool_choice, dict):
+            return None, grammar_text
+        if tools is None:
+            raise CompletionRequestValidationError("tool choice requires tools")
+        tool_name = selected_tool_choice["function"]["name"]
+        tool = next((tool for tool in tools if tool["function"]["name"] == tool_name), None)
+        if tool is None:
+            raise CompletionRequestValidationError(
+                f"Tool choice '{tool_name}' not found in tools."
+            )
+        if self.model.response_schema is None:
+            return tool_name, JSON_GBNF
+        return tool_name, grammar_text
+
+    def completion_request_from_chat_request(
+        self,
+        body: CreateChatCompletionRequest,
+    ) -> PreparedCompletionParts:
+        functions = (
+            [function.to_template_function() for function in body.functions]
+            if body.functions is not None
+            else None
+        )
+        tools = (
+            [tool.to_template_tool() for tool in body.tools]
+            if body.tools is not None
+            else None
+        )
+        function_call = (
+            body.function_call
+            if body.function_call is None or isinstance(body.function_call, str)
+            else body.function_call.to_template_function_call()
+        )
+        tool_choice = (
+            body.tool_choice
+            if body.tool_choice is None or isinstance(body.tool_choice, str)
+            else body.tool_choice.to_template_tool_choice()
+        )
+        response_format = (
+            body.response_format.to_template_response_format()
+            if body.response_format is not None
+            else None
+        )
+        parser_tools = self._tools_for_response_parser(
+            functions=functions,
+            tools=tools,
+        )
+        try:
+            prompt_text, generation_prompt, prompt_plan, formatter_stop = self.model.build_chat_prompt(
+                body.messages,
+                functions=functions,
+                function_call=function_call,
+                tools=tools,
+                tool_choice=tool_choice,
+                reasoning_effort=body.reasoning_effort,
+            )
+            tool_name, grammar_text = self._chat_tool_name_and_grammar(
+                tools=parser_tools,
+                function_call=function_call,
+                tool_choice=tool_choice,
+                response_format=response_format,
+            )
+        except ValueError as exc:
+            raise CompletionRequestValidationError(str(exc)) from exc
+        request_stop: List[str] = []
+        if body.stop is None:
+            request_stop = []
+        elif isinstance(body.stop, str):
+            request_stop = [body.stop]
+        else:
+            request_stop = list(body.stop)
+        stop_sequences = [stop for stop in [*request_stop, *formatter_stop] if stop]
+        deduped_stop: List[str] = []
+        seen_stop: set[str] = set()
+        for stop in stop_sequences:
+            if stop not in seen_stop:
+                deduped_stop.append(stop)
+                seen_stop.add(stop)
+        payload = CreateCompletionRequest(
+            prompt=prompt_text,
+            max_tokens=body.max_tokens,
+            temperature=body.temperature,
+            top_p=body.top_p,
+            echo=False,
+            stop=deduped_stop or None,
+            stream=body.stream,
+            logprobs=(
+                0 if body.logprobs and body.top_logprobs is None else body.top_logprobs
+            ),
+            presence_penalty=body.presence_penalty,
+            frequency_penalty=body.frequency_penalty,
+            logit_bias=body.logit_bias,
+            seed=body.seed,
+            model=body.model,
+            n=body.n,
+            user=body.user,
+        )
+        return PreparedCompletionParts(
+            payload=payload,
+            prompt_text=prompt_text,
+            generation_prompt=generation_prompt,
+            prompt_plan=prompt_plan,
+            grammar_text=grammar_text,
+            tool_name=tool_name,
+        )
+
+    def completion_request_from_response_chat_parts(
+        self,
+        body: ResponsesChatRequestParts,
+    ) -> PreparedCompletionParts:
+        try:
+            prompt_text, generation_prompt, prompt_plan, formatter_stop = self.model.build_chat_prompt(
+                body.messages,
+                tools=body.tools,
+                tool_choice=body.tool_choice,
+                reasoning_effort=body.reasoning_effort,
+            )
+            tool_name, grammar_text = self._chat_tool_name_and_grammar(
+                tools=body.tools,
+                function_call=None,
+                tool_choice=body.tool_choice,
+                response_format=body.response_format,
+            )
+        except ValueError as exc:
+            raise CompletionRequestValidationError(str(exc)) from exc
+        deduped_stop: List[str] = []
+        seen_stop: set[str] = set()
+        for stop in formatter_stop:
+            if stop and stop not in seen_stop:
+                deduped_stop.append(stop)
+                seen_stop.add(stop)
+        payload = CreateCompletionRequest(
+            prompt=prompt_text,
+            max_tokens=body.max_tokens,
+            temperature=body.temperature,
+            top_p=body.top_p,
+            echo=False,
+            stop=deduped_stop or None,
+            stream=body.stream,
+            logprobs=(
+                0 if body.logprobs and body.top_logprobs is None else body.top_logprobs
+            ),
+            seed=None,
+            model=body.model,
+            n=1,
+            user=body.user,
+        )
+        return PreparedCompletionParts(
+            payload=payload,
+            prompt_text=prompt_text,
+            generation_prompt=generation_prompt,
+            prompt_plan=prompt_plan,
+            grammar_text=grammar_text,
+            tool_name=tool_name,
+        )
+
+    @staticmethod
+    def _response_phase_from_message(message: Dict[str, Any]) -> Optional[str]:
+        phase = message.get("phase")
+        if phase in {"commentary", "final_answer"}:
+            return cast(str, phase)
+        channel = message.get("channel")
+        if channel == "commentary":
+            return "commentary"
+        if channel == "final":
+            return "final_answer"
+        return None
+
+    @staticmethod
+    def _response_reasoning_text_from_message(message: Dict[str, Any]) -> str:
+        thinking = message.get("thinking")
+        if isinstance(thinking, str) and thinking:
+            return thinking
+        reasoning_content = message.get("reasoning_content")
+        if isinstance(reasoning_content, str) and reasoning_content:
+            return reasoning_content
+        if message.get("channel") == "analysis":
+            content = message.get("content")
+            if isinstance(content, str):
+                return content
+        return ""
+
+    @staticmethod
+    def _response_output_text_from_message(message: Dict[str, Any]) -> str:
+        if message.get("channel") == "analysis":
+            return ""
+        content = message.get("content")
+        if isinstance(content, str):
+            return content
+        return ""
+
+    @staticmethod
+    def _response_logprobs_from_chat_logprobs(
+        logprobs: Optional[Dict[str, Any]],
+    ) -> List[Dict[str, Any]]:
+        if logprobs is None:
+            return []
+        content = logprobs.get("content")
+        if not isinstance(content, list):
+            return []
+        response_logprobs: List[Dict[str, Any]] = []
+        for entry in content:
+            if not isinstance(entry, dict):
+                continue
+            token = entry.get("token")
+            logprob = entry.get("logprob")
+            if not isinstance(token, str) or not isinstance(logprob, (int, float)):
+                continue
+            top_logprobs = entry.get("top_logprobs")
+            converted_top: List[Dict[str, Any]] = []
+            if isinstance(top_logprobs, list):
+                for top in top_logprobs:
+                    if not isinstance(top, dict):
+                        continue
+                    converted_top.append(
+                        {
+                            "token": top.get("token"),
+                            "logprob": top.get("logprob"),
+                        }
+                    )
+            response_logprobs.append(
+                {
+                    "token": token,
+                    "logprob": float(logprob),
+                    "top_logprobs": converted_top or None,
+                }
+            )
+        return response_logprobs
+
+    def _response_logprobs_from_completion(
+        self,
+        logprobs: Optional[CompletionLogprobs],
+    ) -> List[Dict[str, Any]]:
+        if logprobs is None:
+            return []
+        response_logprobs: List[Dict[str, Any]] = []
+        tokens = logprobs.tokens or []
+        token_logprobs_list = logprobs.token_logprobs or []
+        top_logprobs_list = logprobs.top_logprobs or []
+        for token, token_logprob, top_logprobs in zip(
+            tokens,
+            token_logprobs_list,
+            top_logprobs_list,
+        ):
+            if token_logprob is None:
+                continue
+            converted_top: List[Dict[str, Any]] = []
+            if top_logprobs is not None:
+                for top_token, top_logprob in top_logprobs.items():
+                    converted_top.append(
+                        {
+                            "token": top_token,
+                            "logprob": float(top_logprob),
+                        }
+                    )
+            response_logprobs.append(
+                {
+                    "token": token,
+                    "logprob": float(token_logprob),
+                    "top_logprobs": converted_top or None,
+                }
+            )
+        return response_logprobs
+
+    @staticmethod
+    def _response_reasoning_item(
+        *,
+        item_id: str,
+        text: str,
+        status: str,
+    ) -> Dict[str, Any]:
+        return {
+            "id": item_id,
+            "type": "reasoning",
+            "summary": [],
+            "content": [
+                {
+                    "type": "reasoning_text",
+                    "text": text,
+                }
+            ],
+            "status": status,
+        }
+
+    @staticmethod
+    def _response_message_item(
+        *,
+        item_id: str,
+        text: str,
+        status: str,
+        phase: Optional[str],
+        logprobs: Optional[List[Dict[str, Any]]] = None,
+    ) -> Dict[str, Any]:
+        content_item: Dict[str, Any] = {
+            "type": "output_text",
+            "text": text,
+            "annotations": [],
+        }
+        if logprobs:
+            content_item["logprobs"] = logprobs
+        item: Dict[str, Any] = {
+            "id": item_id,
+            "type": "message",
+            "role": "assistant",
+            "content": [content_item],
+            "status": status,
+        }
+        if phase is not None:
+            item["phase"] = phase
+        return item
+
+    @staticmethod
+    def _response_function_call_item(
+        *,
+        item_id: str,
+        call_id: str,
+        name: str,
+        arguments: str,
+        status: str,
+    ) -> Dict[str, Any]:
+        item: Dict[str, Any] = {
+            "id": item_id,
+            "type": "function_call",
+            "call_id": call_id,
+            "name": name,
+            "arguments": arguments,
+            "status": status,
+        }
+        if "." in name:
+            namespace, bare_name = name.split(".", 1)
+            item["namespace"] = namespace
+            item["name"] = bare_name
+        return item
+
+    @staticmethod
+    def _response_custom_tool_call_item(
+        *,
+        item_id: str,
+        call_id: str,
+        name: str,
+        input_text: str,
+    ) -> Dict[str, Any]:
+        item: Dict[str, Any] = {
+            "id": item_id,
+            "type": "custom_tool_call",
+            "call_id": call_id,
+            "name": name,
+            "input": input_text,
+        }
+        if "." in name:
+            namespace, bare_name = name.split(".", 1)
+            item["namespace"] = namespace
+            item["name"] = bare_name
+        return item
+
+    @staticmethod
+    def _responses_tool_type_by_name(
+        tools: Optional[List[Any]],
+    ) -> Dict[str, str]:
+        if tools is None:
+            return {}
+        tool_types: Dict[str, str] = {}
+        for tool in tools:
+            if isinstance(tool, BaseModel):
+                tool = tool.model_dump(mode="python", exclude_none=True)
+            if not isinstance(tool, dict):
+                continue
+            tool_type = tool.get("original_type") or tool.get("type")
+            if not isinstance(tool_type, str):
+                continue
+            function = tool.get("function")
+            if isinstance(function, dict):
+                name = function.get("name")
+            else:
+                name = tool.get("name")
+            if isinstance(name, str) and name:
+                tool_types[name] = tool_type
+        return tool_types
+
+    def _response_output_items_from_message(
+        self,
+        *,
+        response_id: str,
+        message: Dict[str, Any],
+        logprobs: Optional[List[Dict[str, Any]]] = None,
+        tools: Optional[List[ChatTemplateTool]] = None,
+    ) -> List[Dict[str, Any]]:
+        items: List[Dict[str, Any]] = []
+        tool_types_by_name = self._responses_tool_type_by_name(tools)
+        reasoning_text = self._response_reasoning_text_from_message(message)
+        if reasoning_text:
+            items.append(
+                self._response_reasoning_item(
+                    item_id=f"rs_{response_id}_0",
+                    text=reasoning_text,
+                    status="completed",
+                )
+            )
+        tool_calls = message.get("tool_calls")
+        if isinstance(tool_calls, list):
+            for tool_call_index, tool_call in enumerate(tool_calls):
+                if not isinstance(tool_call, dict):
+                    continue
+                function = tool_call.get("function")
+                if not isinstance(function, dict):
+                    continue
+                name = function.get("name")
+                arguments = function.get("arguments")
+                if not isinstance(name, str) or not isinstance(arguments, str):
+                    continue
+                call_id = tool_call.get("id")
+                if not isinstance(call_id, str) or not call_id:
+                    call_id = f"call_{response_id}_{tool_call_index}"
+                tool_type = tool_types_by_name.get(name, "function")
+                if tool_type == "custom":
+                    items.append(
+                        self._response_custom_tool_call_item(
+                            item_id=f"fc_{response_id}_{tool_call_index}",
+                            call_id=call_id,
+                            name=name,
+                            input_text=arguments,
+                        )
+                    )
+                    continue
+                items.append(
+                    self._response_function_call_item(
+                        item_id=f"fc_{response_id}_{tool_call_index}",
+                        call_id=call_id,
+                        name=name,
+                        arguments=arguments,
+                        status="completed",
+                    )
+                )
+        output_text = self._response_output_text_from_message(message)
+        if output_text:
+            items.append(
+                self._response_message_item(
+                    item_id=f"msg_{response_id}_0",
+                    text=output_text,
+                    status="completed",
+                    phase=self._response_phase_from_message(message),
+                    logprobs=logprobs,
+                )
+            )
+        return items
+
+    def _responses_usage_from_completion(
+        self,
+        *,
+        usage: Optional[CompletionUsage],
+        output_items: Sequence[Dict[str, Any]],
+    ) -> Optional[Dict[str, Any]]:
+        if usage is None:
+            return None
+        reasoning_tokens = 0
+        for item in output_items:
+            if item.get("type") != "reasoning":
+                continue
+            content = item.get("content")
+            if not isinstance(content, list):
+                continue
+            for part in content:
+                if isinstance(part, dict) and part.get("type") == "reasoning_text":
+                    text = part.get("text")
+                    if isinstance(text, str) and text:
+                        reasoning_tokens += len(
+                            self.model.tokenize(text, add_bos=False, special=True)
+                        )
+        return {
+            "input_tokens": usage.prompt_tokens,
+            "input_tokens_details": {
+                "cached_tokens": 0,
+            },
+            "output_tokens": usage.completion_tokens,
+            "output_tokens_details": {
+                "reasoning_tokens": min(reasoning_tokens, usage.completion_tokens),
+            },
+            "total_tokens": usage.total_tokens,
+        }
+
+    def _response_status_and_incomplete_details(
+        self,
+        *,
+        finish_reason: Optional[str],
+    ) -> Tuple[str, Optional[Dict[str, str]]]:
+        if finish_reason == "length":
+            return "incomplete", {"reason": "max_output_tokens"}
+        return "completed", None
+
+    def _response_object(
+        self,
+        *,
+        body: CreateResponseRequest,
+        response_id: str,
+        created_at: float,
+        model: str,
+        output_items: Sequence[Dict[str, Any]],
+        usage: Optional[Dict[str, Any]],
+        status: str,
+        incomplete_details: Optional[Dict[str, str]],
+        completed_at: Optional[float],
+    ) -> Dict[str, Any]:
+        return {
+            "id": response_id,
+            "object": "response",
+            "created_at": created_at,
+            "completed_at": completed_at,
+            "error": None,
+            "incomplete_details": incomplete_details,
+            "instructions": body.instructions,
+            "metadata": body.metadata,
+            "model": model,
+            "output": list(output_items),
+            "parallel_tool_calls": body.parallel_tool_calls,
+            "reasoning": {
+                "effort": self._response_reasoning_effort(body),
+                "summary": None,
+            },
+            "store": False,
+            "temperature": body.temperature,
+            "tool_choice": (
+                body.tool_choice.model_dump(mode="python", exclude_none=True)
+                if isinstance(body.tool_choice, BaseModel)
+                else body.tool_choice or "auto"
+            ),
+            "tools": (
+                [
+                    tool.model_dump(mode="python", exclude_none=True)
+                    if isinstance(tool, BaseModel)
+                    else tool
+                    for tool in body.tools
+                ]
+                if body.tools
+                else []
+            ),
+            "top_p": body.top_p,
+            "max_output_tokens": body.max_output_tokens,
+            "previous_response_id": None,
+            "status": status,
+            "text": (
+                body.text.model_dump(mode="python", exclude_none=True, by_alias=True)
+                if body.text is not None
+                else {"format": {"type": "text"}}
+            ),
+            "top_logprobs": body.top_logprobs,
+            "truncation": body.truncation,
+            "usage": usage,
+            "user": body.user,
+        }
+
+    def _response_from_chat_message(
+        self,
+        *,
+        body: CreateResponseRequest,
+        response_id: str,
+        created_at: float,
+        model: str,
+        message: Dict[str, Any],
+        finish_reason: Optional[str],
+        usage: Optional[CompletionUsage],
+        logprobs: Optional[List[Dict[str, Any]]] = None,
+    ) -> Dict[str, Any]:
+        status, incomplete_details = self._response_status_and_incomplete_details(
+            finish_reason=finish_reason,
+        )
+        output_items = self._response_output_items_from_message(
+            response_id=response_id,
+            message=message,
+            logprobs=logprobs,
+            tools=cast(Optional[List[Any]], body.tools),
+        )
+        return self._response_object(
+            body=body,
+            response_id=response_id,
+            created_at=created_at,
+            model=model,
+            output_items=output_items,
+            usage=self._responses_usage_from_completion(
+                usage=usage,
+                output_items=output_items,
+            ),
+            status=status,
+            incomplete_details=incomplete_details,
+            completed_at=time.time() if status in {"completed", "incomplete"} else None,
+        )
+
+    def convert_completion_response_to_response(
+        self,
+        completion: OpenAICompletion,
+        body: CreateResponseRequest,
+        tool_name: Optional[str] = None,
+        *,
+        tools: Optional[List[ChatTemplateTool]] = None,
+        generation_prompt: str = "",
+    ) -> Dict[str, Any]:
+        chat_response = self.convert_completion_response_to_chat(
+            completion,
+            tool_name,
+            tools=tools,
+            generation_prompt=generation_prompt,
+        )
+        if isinstance(chat_response, BaseModel):
+            chat_payload = chat_response.model_dump(mode="json", exclude_none=True)
+        else:
+            chat_payload = chat_response
+        choice = cast(Dict[str, Any], chat_payload["choices"][0])
+        message = cast(Dict[str, Any], choice["message"])
+        response_id = "resp_" + completion.id
+        logprobs = self._response_logprobs_from_completion(completion.choices[0].logprobs)
+        return self._response_from_chat_message(
+            body=body,
+            response_id=response_id,
+            created_at=float(completion.created),
+            model=completion.model,
+            message=message,
+            finish_reason=cast(Optional[str], choice.get("finish_reason")),
+            usage=completion.usage,
+            logprobs=logprobs or None,
+        )
+
+    @staticmethod
+    def _response_event(
+        state: "OpenAIFormatter.ResponsesStream",
+        event_type: str,
+        **payload: Any,
+    ) -> Dict[str, Any]:
+        state.sequence_number += 1
+        event = {
+            "type": event_type,
+            "sequence_number": state.sequence_number,
+        }
+        event.update(payload)
+        return event
+
+    def _response_stream_response(
+        self,
+        state: "OpenAIFormatter.ResponsesStream",
+        *,
+        status: str,
+        usage: Optional[Dict[str, Any]],
+        incomplete_details: Optional[Dict[str, str]],
+        completed_at: Optional[float],
+    ) -> Dict[str, Any]:
+        return self._response_object(
+            body=state.body,
+            response_id=state.response_id,
+            created_at=state.created_at,
+            model=state.model,
+            output_items=state.output,
+            usage=usage,
+            status=status,
+            incomplete_details=incomplete_details,
+            completed_at=completed_at,
+        )
+
+    def start_response_stream(
+        self,
+        state: "OpenAIFormatter.ResponsesStream",
+    ) -> List[Dict[str, Any]]:
+        if state.started:
+            return []
+        state.started = True
+        response = self._response_stream_response(
+            state,
+            status="in_progress",
+            usage=None,
+            incomplete_details=None,
+            completed_at=None,
+        )
+        return [
+            self._response_event(state, "response.created", response=response),
+            self._response_event(state, "response.in_progress", response=response),
+        ]
+
+    @staticmethod
+    def _response_item_status(
+        finish_reason: Optional[str],
+    ) -> str:
+        if finish_reason == "length":
+            return "incomplete"
+        return "completed"
+
+    def _add_response_stream_item(
+        self,
+        state: "OpenAIFormatter.ResponsesStream",
+        item: Dict[str, Any],
+        *,
+        content_index: Optional[int] = None,
+    ) -> "OpenAIFormatter.ResponsesOutputItem":
+        item_state = OpenAIFormatter.ResponsesOutputItem(
+            output_index=len(state.output),
+            item=item,
+            content_index=content_index,
+        )
+        state.output.append(item)
+        return item_state
+
+    def _ensure_reasoning_stream_item(
+        self,
+        state: "OpenAIFormatter.ResponsesStream",
+    ) -> Tuple[List[Dict[str, Any]], "OpenAIFormatter.ResponsesOutputItem"]:
+        if state.reasoning_item is not None:
+            return [], state.reasoning_item
+        item = self._response_reasoning_item(
+            item_id=f"rs_{state.response_id}_{len(state.output)}",
+            text="",
+            status="in_progress",
+        )
+        item_state = self._add_response_stream_item(state, item, content_index=0)
+        state.reasoning_item = item_state
+        part = cast(List[Dict[str, Any]], item["content"])[0]
+        return [
+            self._response_event(
+                state,
+                "response.output_item.added",
+                output_index=item_state.output_index,
+                item=copy.deepcopy(item),
+            ),
+            self._response_event(
+                state,
+                "response.content_part.added",
+                item_id=cast(str, item["id"]),
+                output_index=item_state.output_index,
+                content_index=0,
+                part=copy.deepcopy(part),
+            ),
+        ], item_state
+
+    def _ensure_message_stream_item(
+        self,
+        state: "OpenAIFormatter.ResponsesStream",
+    ) -> Tuple[List[Dict[str, Any]], "OpenAIFormatter.ResponsesOutputItem"]:
+        if state.message_item is not None:
+            return [], state.message_item
+        item = self._response_message_item(
+            item_id=f"msg_{state.response_id}_{len(state.output)}",
+            text="",
+            status="in_progress",
+            phase=cast(Optional[str], state.assistant_meta.get("phase")),
+        )
+        item_state = self._add_response_stream_item(state, item, content_index=0)
+        state.message_item = item_state
+        part = cast(List[Dict[str, Any]], item["content"])[0]
+        return [
+            self._response_event(
+                state,
+                "response.output_item.added",
+                output_index=item_state.output_index,
+                item=copy.deepcopy(item),
+            ),
+            self._response_event(
+                state,
+                "response.content_part.added",
+                item_id=cast(str, item["id"]),
+                output_index=item_state.output_index,
+                content_index=0,
+                part=copy.deepcopy(part),
+            ),
+        ], item_state
+
+    def _ensure_tool_stream_item(
+        self,
+        state: "OpenAIFormatter.ResponsesStream",
+        *,
+        tool_call_index: int,
+        call_id: Optional[str],
+        name: Optional[str],
+    ) -> Tuple[List[Dict[str, Any]], "OpenAIFormatter.ResponsesOutputItem"]:
+        existing = state.tool_items.get(tool_call_index)
+        if existing is not None:
+            return [], existing
+        tool_types_by_name = self._responses_tool_type_by_name(state.body.tools)
+        tool_type = tool_types_by_name.get(name or "", "function")
+        item_id = f"fc_{state.response_id}_{tool_call_index}"
+        resolved_call_id = call_id or f"call_{state.response_id}_{tool_call_index}"
+        resolved_name = name or ""
+        if tool_type == "custom":
+            item = self._response_custom_tool_call_item(
+                item_id=item_id,
+                call_id=resolved_call_id,
+                name=resolved_name,
+                input_text="",
+            )
+        else:
+            item = self._response_function_call_item(
+                item_id=item_id,
+                call_id=resolved_call_id,
+                name=resolved_name,
+                arguments="",
+                status="in_progress",
+            )
+        item_state = self._add_response_stream_item(state, item)
+        state.tool_items[tool_call_index] = item_state
+        return [
+            self._response_event(
+                state,
+                "response.output_item.added",
+                output_index=item_state.output_index,
+                item=copy.deepcopy(item),
+            )
+        ], item_state
+
+    def _update_tool_stream_item(
+        self,
+        item: Dict[str, Any],
+        *,
+        call_id: Optional[str],
+        name_delta: Optional[str],
+        arguments_delta: Optional[str],
+    ) -> None:
+        if isinstance(call_id, str) and call_id:
+            item["call_id"] = call_id
+        if isinstance(name_delta, str) and name_delta:
+            current_name = cast(str, item.get("name", ""))
+            if not current_name:
+                item["name"] = name_delta
+            elif name_delta == current_name or name_delta.startswith(current_name):
+                item["name"] = name_delta
+            elif not current_name.endswith(name_delta):
+                item["name"] = current_name + name_delta
+        if isinstance(arguments_delta, str) and arguments_delta:
+            if item.get("type") == "custom_tool_call":
+                raw_arguments = cast(str, item.get("_raw_arguments", "")) + arguments_delta
+                item["_raw_arguments"] = raw_arguments
+                normalized_input = self._normalize_text_tool_payload(raw_arguments)
+                if normalized_input is not None:
+                    item["input"] = normalized_input
+            else:
+                item["arguments"] = (
+                    cast(str, item.get("arguments", "")) + arguments_delta
+                )
+
+    @staticmethod
+    def _normalize_text_tool_payload(payload: str) -> Optional[str]:
+        if payload == "":
+            return ""
+        stripped = payload.lstrip()
+        if not stripped:
+            return ""
+        if stripped[0] not in '{["':
+            return payload
+        try:
+            decoded = json.loads(payload)
+        except Exception:
+            return None
+        if isinstance(decoded, str):
+            return decoded
+        if isinstance(decoded, dict):
+            input_value = decoded.get("input")
+            if isinstance(input_value, str):
+                return input_value
+            if len(decoded) == 1:
+                sole_value = next(iter(decoded.values()))
+                if isinstance(sole_value, str):
+                    return sole_value
+        return payload
+
+    def _finalize_response_stream_items(
+        self,
+        state: "OpenAIFormatter.ResponsesStream",
+        *,
+        finish_reason: Optional[str],
+    ) -> List[Dict[str, Any]]:
+        events: List[Dict[str, Any]] = []
+        item_status = self._response_item_status(finish_reason)
+
+        if (
+            state.reasoning_item is not None
+            and state.reasoning_item.item["status"] == "in_progress"
+        ):
+            item = state.reasoning_item.item
+            item["status"] = item_status
+            part = cast(List[Dict[str, Any]], item["content"])[0]
+            events.append(
+                self._response_event(
+                    state,
+                    "response.reasoning_text.done",
+                    item_id=cast(str, item["id"]),
+                    output_index=state.reasoning_item.output_index,
+                    content_index=cast(int, state.reasoning_item.content_index),
+                    text=part["text"],
+                )
+            )
+            events.append(
+                self._response_event(
+                    state,
+                    "response.content_part.done",
+                    item_id=cast(str, item["id"]),
+                    output_index=state.reasoning_item.output_index,
+                    content_index=cast(int, state.reasoning_item.content_index),
+                    part=part,
+                )
+            )
+            events.append(
+                self._response_event(
+                    state,
+                    "response.output_item.done",
+                    output_index=state.reasoning_item.output_index,
+                    item=item,
+                )
+            )
+
+        if (
+            state.message_item is not None
+            and state.message_item.item["status"] == "in_progress"
+        ):
+            item = state.message_item.item
+            item["status"] = item_status
+            part = cast(List[Dict[str, Any]], item["content"])[0]
+            events.append(
+                self._response_event(
+                    state,
+                    "response.output_text.done",
+                    item_id=cast(str, item["id"]),
+                    output_index=state.message_item.output_index,
+                    content_index=cast(int, state.message_item.content_index),
+                    text=part["text"],
+                    logprobs=part.get("logprobs", []),
+                )
+            )
+            events.append(
+                self._response_event(
+                    state,
+                    "response.content_part.done",
+                    item_id=cast(str, item["id"]),
+                    output_index=state.message_item.output_index,
+                    content_index=cast(int, state.message_item.content_index),
+                    part=part,
+                )
+            )
+            events.append(
+                self._response_event(
+                    state,
+                    "response.output_item.done",
+                    output_index=state.message_item.output_index,
+                    item=item,
+                )
+            )
+
+        for tool_call_index in sorted(state.tool_items):
+            item_state = state.tool_items[tool_call_index]
+            item = item_state.item
+            item.pop("_raw_arguments", None)
+            if (
+                item.get("status") != "in_progress"
+                and item.get("type") != "custom_tool_call"
+            ):
+                continue
+            if item.get("type") == "custom_tool_call":
+                events.append(
+                    self._response_event(
+                        state,
+                        "response.custom_tool_call_input.done",
+                        item_id=cast(str, item["id"]),
+                        output_index=item_state.output_index,
+                        input=cast(str, item.get("input", "")),
+                    )
+                )
+            else:
+                item["status"] = item_status
+                events.append(
+                    self._response_event(
+                        state,
+                        "response.function_call_arguments.done",
+                        item_id=cast(str, item["id"]),
+                        output_index=item_state.output_index,
+                        name=cast(str, item["name"]),
+                        arguments=cast(str, item["arguments"]),
+                    )
+                )
+            events.append(
+                self._response_event(
+                    state,
+                    "response.output_item.done",
+                    output_index=item_state.output_index,
+                    item=item,
+                )
+            )
+
+        state.final_status, state.incomplete_details = (
+            self._response_status_and_incomplete_details(
+                finish_reason=finish_reason,
+            )
+        )
+        return events
+
+    def convert_chat_chunk_to_response_events(
+        self,
+        chunk: ChatCompletionChunk | Dict[str, Any],
+        state: "OpenAIFormatter.ResponsesStream",
+    ) -> List[Dict[str, Any]]:
+        payload = (
+            chunk.model_dump(mode="json", exclude_none=True)
+            if isinstance(chunk, BaseModel)
+            else chunk
+        )
+        events = self.start_response_stream(state)
+        choice = cast(Dict[str, Any], payload["choices"][0])
+        delta = cast(Dict[str, Any], choice.get("delta", {}))
+        finish_reason = cast(Optional[str], choice.get("finish_reason"))
+        logprobs = self._response_logprobs_from_chat_logprobs(
+            cast(Optional[Dict[str, Any]], choice.get("logprobs"))
+        )
+
+        phase = delta.get("phase")
+        if isinstance(phase, str):
+            state.assistant_meta["phase"] = phase
+            if state.message_item is not None:
+                state.message_item.item["phase"] = phase
+
+        reasoning_delta = delta.get("reasoning_content")
+        if not isinstance(reasoning_delta, str) or not reasoning_delta:
+            candidate = delta.get("thinking")
+            reasoning_delta = candidate if isinstance(candidate, str) else None
+        if isinstance(reasoning_delta, str) and reasoning_delta:
+            added, item_state = self._ensure_reasoning_stream_item(state)
+            events.extend(added)
+            part = cast(List[Dict[str, Any]], item_state.item["content"])[0]
+            part["text"] += reasoning_delta
+            events.append(
+                self._response_event(
+                    state,
+                    "response.reasoning_text.delta",
+                    item_id=cast(str, item_state.item["id"]),
+                    output_index=item_state.output_index,
+                    content_index=cast(int, item_state.content_index),
+                    delta=reasoning_delta,
+                )
+            )
+
+        content_delta = delta.get("content")
+        if isinstance(content_delta, str) and content_delta:
+            added, item_state = self._ensure_message_stream_item(state)
+            events.extend(added)
+            part = cast(List[Dict[str, Any]], item_state.item["content"])[0]
+            part["text"] += content_delta
+            if logprobs:
+                cast(List[Dict[str, Any]], part.setdefault("logprobs", [])).extend(
+                    logprobs
+                )
+            events.append(
+                self._response_event(
+                    state,
+                    "response.output_text.delta",
+                    item_id=cast(str, item_state.item["id"]),
+                    output_index=item_state.output_index,
+                    content_index=cast(int, item_state.content_index),
+                    delta=content_delta,
+                    logprobs=logprobs,
+                )
+            )
+
+        tool_calls = delta.get("tool_calls")
+        if isinstance(tool_calls, list):
+            for tool_call in tool_calls:
+                if not isinstance(tool_call, dict):
+                    continue
+                tool_call_index = tool_call.get("index", 0)
+                if not isinstance(tool_call_index, int):
+                    continue
+                function = tool_call.get("function")
+                if not isinstance(function, dict):
+                    continue
+                added, item_state = self._ensure_tool_stream_item(
+                    state,
+                    tool_call_index=tool_call_index,
+                    call_id=cast(Optional[str], tool_call.get("id")),
+                    name=cast(Optional[str], function.get("name")),
+                )
+                events.extend(added)
+                previous_input = cast(str, item_state.item.get("input", ""))
+                self._update_tool_stream_item(
+                    item_state.item,
+                    call_id=cast(Optional[str], tool_call.get("id")),
+                    name_delta=cast(Optional[str], function.get("name")),
+                    arguments_delta=cast(Optional[str], function.get("arguments")),
+                )
+                arguments_delta = function.get("arguments")
+                if isinstance(arguments_delta, str) and arguments_delta:
+                    if item_state.item.get("type") == "custom_tool_call":
+                        current_input = cast(str, item_state.item.get("input", ""))
+                        if not current_input or current_input == previous_input:
+                            continue
+                        delta_text = current_input
+                        if current_input.startswith(previous_input):
+                            delta_text = current_input[len(previous_input) :]
+                        events.append(
+                            self._response_event(
+                                state,
+                                "response.custom_tool_call_input.delta",
+                                item_id=cast(str, item_state.item["id"]),
+                                output_index=item_state.output_index,
+                                delta=delta_text,
+                            )
+                        )
+                        continue
+                    events.append(
+                        self._response_event(
+                            state,
+                            "response.function_call_arguments.delta",
+                            item_id=cast(str, item_state.item["id"]),
+                            output_index=item_state.output_index,
+                            delta=arguments_delta,
+                        )
+                    )
+
+        if finish_reason is not None:
+            events.extend(
+                self._finalize_response_stream_items(
+                    state,
+                    finish_reason=finish_reason,
+                )
+            )
+        return events
+
+    def response_stream_terminal_events(
+        self,
+        state: "OpenAIFormatter.ResponsesStream",
+        completion: Optional[OpenAICompletion],
+    ) -> List[Dict[str, Any]]:
+        if not state.started:
+            state.started = True
+        if completion is not None and state.final_status is None:
+            finish_reason = None
+            if completion.choices:
+                finish_reason = completion.choices[0].finish_reason
+            self._finalize_response_stream_items(state, finish_reason=finish_reason)
+        status = state.final_status or "completed"
+        response = self._response_stream_response(
+            state,
+            status=status,
+            usage=(
+                self._responses_usage_from_completion(
+                    usage=completion.usage if completion is not None else None,
+                    output_items=state.output,
+                )
+            ),
+            incomplete_details=state.incomplete_details,
+            completed_at=time.time() if status in {"completed", "incomplete"} else None,
+        )
+        event_type = "response.incomplete" if status == "incomplete" else "response.completed"
+        return [self._response_event(state, event_type, response=response)]
+
+    def aggregate_completion_results(
+        self,
+        results: Sequence[OpenAICompletion],
+    ) -> OpenAICompletion:
+        choices: List[CompletionChoice] = []
+        prompt_tokens = 0
+        completion_tokens = 0
+        total_tokens = 0
+        for result in results:
+            for choice in result.choices:
+                choices.append(choice.model_copy(update={"index": len(choices)}))
+            assert result.usage is not None
+            prompt_tokens += result.usage.prompt_tokens
+            completion_tokens += result.usage.completion_tokens
+            total_tokens += result.usage.total_tokens
+        return OpenAICompletion(
+            id=f"cmpl-{uuid.uuid4().hex}",
+            object="text_completion",
+            created=int(time.time()),
+            model=self.model.model_path,
+            choices=choices,
+            usage=CompletionUsage(
+                prompt_tokens=prompt_tokens,
+                completion_tokens=completion_tokens,
+                total_tokens=total_tokens,
+            ),
+        )
+
+    @staticmethod
+    def _convert_completion_logprobs_to_chat_choice(
+        logprobs: Optional[CompletionLogprobs],
+    ) -> Optional[ChatCompletionChoiceLogprobs]:
+        if logprobs is None:
+            return None
+        tokens = logprobs.tokens or []
+        token_logprobs_list = logprobs.token_logprobs or []
+        top_logprobs_list = logprobs.top_logprobs or []
+        return ChatCompletionChoiceLogprobs(
+            content=[
+                ChatCompletionTokenLogprob(
+                    token=token,
+                    bytes=None,
+                    logprob=token_logprob if token_logprob is not None else 0.0,
+                    top_logprobs=(
+                        [
+                            TopLogprob(
+                                token=top_token,
+                                logprob=top_logprob,
+                                bytes=None,
+                            )
+                            for top_token, top_logprob in top_logprobs.items()
+                        ]
+                        if top_logprobs is not None
+                        else []
+                    ),
+                )
+                for token, token_logprob, top_logprobs in zip(
+                    tokens,
+                    token_logprobs_list,
+                    top_logprobs_list,
+                )
+            ],
+            refusal=None,
+        )
+
+    @staticmethod
+    def _convert_completion_logprobs_to_chat_chunk(
+        logprobs: Optional[CompletionLogprobs],
+    ) -> Optional[ChatCompletionChunkChoiceLogprobs]:
+        choice_logprobs = OpenAIFormatter._convert_completion_logprobs_to_chat_choice(logprobs)
+        if choice_logprobs is None:
+            return None
+        return ChatCompletionChunkChoiceLogprobs.model_validate(
+            choice_logprobs.model_dump(mode="python", exclude_none=True)
+        )
+
+    @staticmethod
+    def _chat_message_from_completion_choice(
+        completion_id: str,
+        choice: CompletionChoice,
+        tool_name: Optional[str],
+    ) -> ChatCompletionMessage:
+        if tool_name is None:
+            return ChatCompletionMessage(
+                role="assistant",
+                content=choice.text,
+            )
+        tool_id = f"call_{choice.index}_{tool_name}_{completion_id}"
+        arguments = choice.text
+        return ChatCompletionMessage(
+            role="assistant",
+            content=None,
+            function_call=ChatCompletionMessageFunctionCall(
+                name=tool_name,
+                arguments=arguments,
+            ),
+            tool_calls=[
+                ChatCompletionMessageToolCall(
+                    id=tool_id,
+                    type="function",
+                    function=ChatCompletionMessageToolCallFunction(
+                        name=tool_name,
+                        arguments=arguments,
+                    ),
+                )
+            ],
+        )
+
+    @staticmethod
+    def _chat_chunk_payload(
+        *,
+        chunk_id: str,
+        created: int,
+        model: str,
+        index: int,
+        delta: Dict[str, Any],
+        finish_reason: Optional[str],
+        logprobs: Optional[Dict[str, Any]] = None,
+    ) -> Dict[str, Any]:
+        choice: Dict[str, Any] = {
+            "index": index,
+            "delta": delta,
+            "finish_reason": finish_reason,
+        }
+        if logprobs is not None:
+            choice["logprobs"] = logprobs
+        return {
+            "id": "chat" + chunk_id,
+            "object": "chat.completion.chunk",
+            "created": created,
+            "model": model,
+            "choices": [choice],
+        }
+
+    def convert_completion_response_to_chat(
+        self,
+        completion: OpenAICompletion,
+        tool_name: Optional[str] = None,
+        *,
+        functions: Optional[List[ChatTemplateFunctionDefinition]] = None,
+        tools: Optional[List[ChatTemplateTool]] = None,
+        generation_prompt: str = "",
+    ) -> ChatCompletion | Dict[str, Any]:
+        parser_tools = self._tools_for_response_parser(
+            functions=functions,
+            tools=tools,
+        )
+        if self.model.response_schema is not None:
+            choices: List[Dict[str, Any]] = []
+            for choice in completion.choices:
+                parser = self._response_parser(
+                    tools=parser_tools,
+                    completion_id=completion.id,
+                    choice_index=choice.index,
+                    generation_prompt=generation_prompt,
+                )
+                message = parser.parse_completion_message(choice.text)
+                logprobs = self._convert_completion_logprobs_to_chat_choice(choice.logprobs)
+                choices.append(
+                    {
+                        "index": choice.index,
+                        "message": message,
+                        "logprobs": (
+                            logprobs.model_dump(mode="json", exclude_none=True)
+                            if logprobs is not None
+                            else None
+                        ),
+                        "finish_reason": (
+                            "tool_calls"
+                            if message.get("tool_calls")
+                            else choice.finish_reason
+                        ),
+                    }
+                )
+            return {
+                "id": "chat" + completion.id,
+                "object": "chat.completion",
+                "created": completion.created,
+                "model": completion.model,
+                "choices": choices,
+                "usage": (
+                    completion.usage.model_dump(mode="json", exclude_none=True)
+                    if completion.usage is not None
+                    else None
+                ),
+            }
+        return ChatCompletion(
+            id="chat" + completion.id,
+            object="chat.completion",
+            created=completion.created,
+            model=completion.model,
+            choices=[
+                ChatCompletionChoice(
+                    index=choice.index,
+                    message=self._chat_message_from_completion_choice(
+                        completion.id,
+                        choice,
+                        tool_name,
+                    ),
+                    logprobs=self._convert_completion_logprobs_to_chat_choice(choice.logprobs),
+                    finish_reason=cast(Any, (
+                        "tool_calls" if tool_name is not None else choice.finish_reason
+                    )),
+                )
+                for choice in completion.choices
+            ],
+            usage=completion.usage,
+        )
+
+    def convert_completion_chunk_to_chat_chunks(
+        self,
+        chunk: CompletionChunk,
+        started_indices: set[int],
+        tool_name: Optional[str] = None,
+        *,
+        functions: Optional[List[ChatTemplateFunctionDefinition]] = None,
+        tools: Optional[List[ChatTemplateTool]] = None,
+        parsed_states: Optional[Dict[int, Any]] = None,
+        generation_prompt: str = "",
+    ) -> List[ChatCompletionChunk | Dict[str, Any]]:
+        parser_tools = self._tools_for_response_parser(
+            functions=functions,
+            tools=tools,
+        )
+        if self.model.response_schema is not None:
+            parsed_chunks: List[Dict[str, Any]] = []
+            if parsed_states is None:
+                parsed_states = {}
+            for choice in chunk["choices"]:
+                index = choice["index"]
+                parser = parsed_states.get(index)
+                if not isinstance(parser, ResponseParser):
+                    parser = self._response_parser(
+                        tools=parser_tools,
+                        completion_id=chunk["id"],
+                        choice_index=index,
+                        generation_prompt=generation_prompt,
+                    )
+                    parsed_states[index] = parser
+                logprobs = self._convert_completion_logprobs_to_chat_chunk(
+                    CompletionLogprobs.model_validate(choice["logprobs"])
+                    if choice["logprobs"] is not None
+                    else None
+                )
+                parsed_chunks.extend(
+                    parser.consume_completion_chunk(
+                        choice["text"],
+                        chunk_id=chunk["id"],
+                        created=chunk["created"],
+                        model=chunk["model"],
+                        finish_reason=choice["finish_reason"],
+                        logprobs=(
+                            logprobs.model_dump(mode="json", exclude_none=True)
+                            if logprobs is not None
+                            else None
+                        ),
+                    )
+                )
+                if parser.started:
+                    started_indices.add(index)
+            return cast(List[ChatCompletionChunk | Dict[str, Any]], parsed_chunks)
+        chat_chunks: List[ChatCompletionChunk] = []
+        for choice in chunk["choices"]:
+            index = choice["index"]
+            if index not in started_indices:
+                started_indices.add(index)
+                chat_chunks.append(
+                    ChatCompletionChunk(
+                        id="chat" + chunk["id"],
+                        object="chat.completion.chunk",
+                        created=chunk["created"],
+                        model=chunk["model"],
+                        choices=[
+                            ChatCompletionChunkChoice(
+                                index=index,
+                                delta=(
+                                    ChoiceDelta(
+                                        role="assistant",
+                                        content=None,
+                                        function_call=None,
+                                        tool_calls=None,
+                                    )
+                                    if tool_name is not None
+                                    else ChoiceDelta(role="assistant")
+                                ),
+                                logprobs=None,
+                                finish_reason=None,
+                            )
+                        ],
+                    )
+                )
+            if tool_name is not None:
+                delta: ChoiceDelta
+                if choice["finish_reason"] is None:
+                    tool_id = f"call_{index}_{tool_name}_{chunk['id']}"
+                    delta = ChoiceDelta(
+                        role=None,
+                        content=None,
+                        function_call=ChoiceDeltaFunctionCall(
+                            name=tool_name,
+                            arguments=choice["text"],
+                        ),
+                        tool_calls=[
+                            ChoiceDeltaToolCall(
+                                index=0,
+                                id=tool_id,
+                                type="function",
+                                function=ChoiceDeltaToolCallFunction(
+                                    name=tool_name,
+                                    arguments=choice["text"],
+                                ),
+                            )
+                        ],
+                    )
+                else:
+                    delta = ChoiceDelta(
+                        role=None,
+                        content=None,
+                        function_call=None,
+                        tool_calls=None,
+                    )
+                chat_chunks.append(
+                    ChatCompletionChunk(
+                        id="chat" + chunk["id"],
+                        object="chat.completion.chunk",
+                        created=chunk["created"],
+                        model=chunk["model"],
+                        choices=[
+                            ChatCompletionChunkChoice(
+                                index=index,
+                                delta=delta,
+                                logprobs=self._convert_completion_logprobs_to_chat_chunk(
+                                    CompletionLogprobs.model_validate(choice["logprobs"])
+                                    if choice["logprobs"] is not None
+                                    else None
+                                ),
+                                finish_reason=(
+                                    "tool_calls"
+                                    if choice["finish_reason"] is not None
+                                    else None
+                                ),
+                            )
+                        ],
+                    )
+                )
+                continue
+            chat_chunks.append(
+                ChatCompletionChunk(
+                    id="chat" + chunk["id"],
+                    object="chat.completion.chunk",
+                    created=chunk["created"],
+                    model=chunk["model"],
+                    choices=[
+                        ChatCompletionChunkChoice(
+                            index=index,
+                            delta=(
+                                ChoiceDelta(content=choice["text"])
+                                if choice["finish_reason"] is None
+                                else ChoiceDelta()
+                            ),
+                            logprobs=self._convert_completion_logprobs_to_chat_chunk(
+                                CompletionLogprobs.model_validate(choice["logprobs"])
+                                if choice["logprobs"] is not None
+                                else None
+                            ),
+                            finish_reason=cast(Any, choice["finish_reason"]),
+                        )
+                    ],
+                )
+            )
+        return cast(List[ChatCompletionChunk | Dict[str, Any]], chat_chunks)
+
+    def returned_output_end(
+        self,
+        completion: Completion,
+        finish_reason: Optional[str],
+    ) -> int:
+        completion_bytes: bytes | bytearray
+        if completion.rendered_bytes:
+            completion_bytes = completion.rendered_bytes
+        else:
+            completion_bytes = b"".join(record.text_bytes for record in completion.token_records)
+        returned_end = len(completion_bytes)
+        if finish_reason == "stop":
+            stops = [stop for stop in completion.stop_sequences if stop in completion_bytes]
+            if stops:
+                returned_end = min(completion_bytes.index(stop) for stop in stops)
+        elif finish_reason is None:
+            holdback = 0
+            for stop in completion.stop_sequences:
+                for size in range(min(len(stop), returned_end), 0, -1):
+                    if completion_bytes.endswith(stop[:size]):
+                        holdback = max(holdback, size)
+                        break
+            returned_end -= holdback
+        return returned_end
+
+    def returned_tokens(
+        self,
+        completion: Completion,
+        finish_reason: Optional[str],
+        *,
+        start_index: int = 0,
+    ) -> List["OpenAIFormatter.ReturnedToken"]:
+        returned_end = self.returned_output_end(completion, finish_reason)
+        returned_tokens: List[OpenAIFormatter.ReturnedToken] = []
+        prefix_bytes = b""
+        for index, record in enumerate(completion.token_records):
+            token_start = len(prefix_bytes)
+            if token_start >= returned_end:
+                break
+            token_end = token_start + len(record.text_bytes)
+            text_bytes = record.text_bytes
+            if token_end > returned_end:
+                if finish_reason is None:
+                    break
+                text_bytes = text_bytes[: returned_end - token_start]
+            if index >= start_index:
+                returned_tokens.append(
+                    OpenAIFormatter.ReturnedToken(
+                        index=index,
+                        text_bytes=text_bytes,
+                        token=record,
+                        text_offset=len(self.decode_text(prefix_bytes)),
+                    )
+                )
+            prefix_bytes += record.text_bytes
+        return returned_tokens
+
+    def stream_completion_chunks(
+        self,
+        request: CompletionRequest,
+        completion: Completion,
+        finish_reason: Optional[str],
+    ) -> List[CompletionChunk]:
+        returned_tokens = self.returned_tokens(
+            completion,
+            finish_reason,
+            start_index=completion.returned_token_count,
+        )
+        chunks: List[CompletionChunk] = []
+        if completion.logprobs is not None:
+            for returned_token in returned_tokens:
+                token = returned_token.token
+                chunks.append(
+                    {
+                        "id": request.id,
+                        "object": "text_completion",
+                        "created": request.created,
+                        "model": self.model.model_path,
+                        "choices": [
+                            {
+                                "text": self.decode_text(returned_token.text_bytes),
+                                "index": completion.index,
+                                "logprobs": {
+                                    "tokens": [self.decode_text(token.text_bytes)],
+                                    "text_offset": [
+                                        len(completion.prompt_text) + returned_token.text_offset
+                                    ],
+                                    "token_logprobs": [token.token_logprob],
+                                    "top_logprobs": [token.top_logprobs],
+                                },
+                                "finish_reason": None,
+                            }
+                        ],
+                    }
+                )
+                completion.returned_token_count = returned_token.index + 1
+            return chunks
+
+        chunk_tokens: List[OpenAIFormatter.ReturnedToken] = []
+        for returned_token in returned_tokens:
+            chunk_tokens.append(returned_token)
+            chunk_bytes = b"".join(token.text_bytes for token in chunk_tokens)
+            if returned_token.text_bytes != returned_token.token.text_bytes:
+                chunks.append(
+                    {
+                        "id": request.id,
+                        "object": "text_completion",
+                        "created": request.created,
+                        "model": self.model.model_path,
+                        "choices": [
+                            {
+                                "text": self.decode_text(returned_token.text_bytes),
+                                "index": completion.index,
+                                "logprobs": None,
+                                "finish_reason": None,
+                            }
+                        ],
+                    }
+                )
+                completion.returned_token_count = returned_token.index + 1
+                chunk_tokens = []
+                continue
+            try:
+                text = chunk_bytes.decode("utf-8")
+            except UnicodeError:
+                continue
+            chunks.append(
+                {
+                    "id": request.id,
+                    "object": "text_completion",
+                    "created": request.created,
+                    "model": self.model.model_path,
+                    "choices": [
+                        {
+                            "text": text,
+                            "index": completion.index,
+                            "logprobs": None,
+                            "finish_reason": None,
+                        }
+                    ],
+                }
+            )
+            completion.returned_token_count = returned_token.index + 1
+            chunk_tokens = []
+        return chunks
+
+    def completion_finish_chunk(
+        self,
+        request: CompletionRequest,
+        completion: Completion,
+        finish_reason: str,
+    ) -> CompletionChunk:
+        return {
+            "id": request.id,
+            "object": "text_completion",
+            "created": request.created,
+            "model": self.model.model_path,
+            "choices": [
+                {
+                    "text": "",
+                    "index": completion.index,
+                    "logprobs": None,
+                    "finish_reason": finish_reason,
+                }
+            ],
+        }
+
+    def _build_completion_choice(
+        self,
+        request: CompletionRequest,
+        completion: Completion,
+    ) -> CompletionChoice:
+        returned_tokens = self.returned_tokens(completion, completion.finish_reason)
+        text_bytes = b"".join(returned_token.text_bytes for returned_token in returned_tokens)
+        text = self.decode_text(text_bytes)
+        if request.payload.echo:
+            text = completion.prompt_text + text
+        logprobs: Optional[CompletionLogprobs] = None
+        if completion.logprobs is not None:
+            offsets: List[int] = []
+            token_texts: List[str] = []
+            token_logprobs: List[Optional[float]] = []
+            top_logprobs: List[Optional[Dict[str, float]]] = []
+            text_cursor = request.prompt_text if not request.payload.echo else ""
+            if request.payload.echo:
+                for record in request.prompt_records:
+                    offsets.append(len(text_cursor))
+                    token_texts.append(self.decode_text(record.text_bytes))
+                    token_logprobs.append(record.token_logprob)
+                    top_logprobs.append(record.top_logprobs)
+                    text_cursor += self.decode_text(record.text_bytes)
+                text_cursor = request.prompt_text
+            for returned_token in returned_tokens:
+                token = returned_token.token
+                offsets.append(len(completion.prompt_text) + returned_token.text_offset)
+                if request.payload.echo:
+                    offsets[-1] = len(text_cursor)
+                token_texts.append(self.decode_text(token.text_bytes))
+                token_logprobs.append(token.token_logprob)
+                top_logprobs.append(token.top_logprobs)
+                text_cursor += self.decode_text(token.text_bytes)
+            logprobs = CompletionLogprobs.model_construct(
+                text_offset=offsets,
+                token_logprobs=token_logprobs,
+                tokens=token_texts,
+                top_logprobs=top_logprobs,
+            )
+        return CompletionChoice(
+            text=text,
+            index=completion.index,
+            logprobs=logprobs,
+            finish_reason=cast(Any, completion.finish_reason),
+        )
+
+    def build_completion_response(
+        self,
+        request: CompletionRequest,
+        completions: Sequence[Completion],
+    ) -> OpenAICompletion:
+        completion_tokens = sum(completion.completion_token_count for completion in completions)
+        prompt_tokens = request.prompt_plan.eval_token_count
+        return OpenAICompletion(
+            id=request.id,
+            object="text_completion",
+            created=request.created,
+            model=self.model.model_path,
+            choices=[
+                self._build_completion_choice(request, completion)
+                for completion in completions
+            ],
+            usage=CompletionUsage(
+                prompt_tokens=prompt_tokens,
+                completion_tokens=completion_tokens,
+                total_tokens=prompt_tokens + completion_tokens,
+            ),
+        )
+
+
+class Sampler:
+    TOKEN_DATA_DTYPE = np.dtype(
+        [("id", np.intc), ("logit", np.single), ("p", np.single)],
+        align=True,
+    )
+
+    def __init__(
+        self,
+        *,
+        seed: int,
+        vocab: llama_cpp.llama_vocab_p,
+        n_vocab: int,
+        top_p: float,
+        temperature: float,
+        frequency_penalty: float,
+        presence_penalty: float,
+        logit_bias: Optional[Dict[int, float]],
+        grammar_text: Optional[str] = None,
+        grammar_root: str = "root",
+    ) -> None:
+        params = llama_cpp.llama_sampler_chain_default_params()
+        self._sampler = llama_cpp.llama_sampler_chain_init(params)
+        self._closed = False
+        self._sample_logits_size = 0
+        self._sample_logits_token_data: Optional[Any] = None
+        self._sample_logits_token_array: Optional[Any] = None
+        self._sample_logits_recarray: Optional[np.recarray] = None
+        if logit_bias:
+            bias_array = (llama_cpp.llama_logit_bias * len(logit_bias))()
+            for index, (token, bias) in enumerate(logit_bias.items()):
+                bias_array[index].token = ctypes.c_int32(token)
+                bias_array[index].bias = float(bias)
+            llama_cpp.llama_sampler_chain_add(
+                self._sampler,
+                llama_cpp.llama_sampler_init_logit_bias(
+                    n_vocab, len(logit_bias), bias_array
+                ),
+            )
+            self.bias_array = bias_array
+        if frequency_penalty != 0.0 or presence_penalty != 0.0:
+            llama_cpp.llama_sampler_chain_add(
+                self._sampler,
+                llama_cpp.llama_sampler_init_penalties(
+                    64,
+                    1.0,
+                    frequency_penalty,
+                    presence_penalty,
+                ),
+            )
+        if grammar_text is not None:
+            grammar_sampler = llama_cpp.llama_sampler_init_grammar(
+                vocab,
+                grammar_text.encode("utf-8"),
+                grammar_root.encode("utf-8"),
+            )
+            if grammar_sampler is None:
+                raise RuntimeError("failed to initialize grammar sampler")
+            llama_cpp.llama_sampler_chain_add(self._sampler, grammar_sampler)
+        if temperature < 0.0:
+            llama_cpp.llama_sampler_chain_add(
+                self._sampler, llama_cpp.llama_sampler_init_dist(seed)
+            )
+            return
+        if temperature == 0.0:
+            llama_cpp.llama_sampler_chain_add(
+                self._sampler, llama_cpp.llama_sampler_init_greedy()
+            )
+            return
+        min_keep = 1
+        llama_cpp.llama_sampler_chain_add(
+            self._sampler, llama_cpp.llama_sampler_init_top_p(top_p, min_keep)
+        )
+        llama_cpp.llama_sampler_chain_add(
+            self._sampler, llama_cpp.llama_sampler_init_temp(temperature)
+        )
+        llama_cpp.llama_sampler_chain_add(
+            self._sampler, llama_cpp.llama_sampler_init_dist(seed)
+        )
+
+    def sample(self, ctx: llama_cpp.llama_context_p, output_index: int) -> int:
+        return int(llama_cpp.llama_sampler_sample(self._sampler, ctx, output_index))
+
+    def _ensure_sample_logits_buffer(self, size: int) -> None:
+        if size == self._sample_logits_size and self._sample_logits_recarray is not None:
+            return
+        token_data = (llama_cpp.llama_token_data * size)()
+        token_data_address = ctypes.addressof(token_data)
+        recarray = np.recarray(
+            shape=(size,),
+            dtype=self.TOKEN_DATA_DTYPE,
+            buf=cast(
+                Any,
+                (llama_cpp.llama_token_data * size).from_address(token_data_address),
+            ),
+        )
+        recarray.id[:] = np.arange(size, dtype=np.intc)
+        token_array = llama_cpp.llama_token_data_array(
+            data=token_data,
+            size=size,
+            selected=-1,
+            sorted=False,
+        )
+        self._sample_logits_size = size
+        self._sample_logits_token_data = token_data
+        self._sample_logits_token_array = token_array
+        self._sample_logits_recarray = recarray
+
+    def sample_logits(self, logits: np.ndarray) -> int:
+        self._ensure_sample_logits_buffer(len(logits))
+        assert self._sample_logits_recarray is not None
+        assert self._sample_logits_token_array is not None
+        self._sample_logits_recarray.logit[:] = logits
+        self._sample_logits_recarray.p.fill(0.0)
+        self._sample_logits_token_array.selected = -1
+        self._sample_logits_token_array.sorted = False
+        llama_cpp.llama_sampler_apply(
+            self._sampler,
+            cast(Any, ctypes.byref(self._sample_logits_token_array)),
+        )
+        token = int(self._sample_logits_recarray.id[self._sample_logits_token_array.selected])
+        llama_cpp.llama_sampler_accept(self._sampler, token)
+        return token
+
+    def close(self) -> None:
+        if not self._closed:
+            llama_cpp.llama_sampler_free(self._sampler)
+            self._closed = True
+
+
+@dataclass
+class MTMDEmbedding:
+    key: str
+    embeddings: np.ndarray
+
+
+class MTMDEmbeddingCache:
+    _metadata_version = "1"
+
+    def __init__(
+        self,
+        *,
+        path: str,
+        max_bytes: int,
+        model_fingerprint: str,
+        mmproj_fingerprint: str,
+    ) -> None:
+        self.path = Path(path)
+        self.max_bytes = max_bytes
+        self.model_fingerprint = model_fingerprint
+        self.mmproj_fingerprint = mmproj_fingerprint
+        self.path.mkdir(parents=True, exist_ok=True)
+
+    @staticmethod
+    def _safe_open(path: Path) -> Any:
+        try:
+            from safetensors import safe_open
+        except ImportError as exc:
+            raise RuntimeError(
+                "model.mtmd.embedding_cache requires safetensors. "
+                "Install it with `pip install safetensors`."
+            ) from exc
+        return safe_open(str(path), framework="numpy")
+
+    @staticmethod
+    def _save_file(
+        tensors: Dict[str, np.ndarray],
+        path: Path,
+        metadata: Dict[str, str],
+    ) -> None:
+        from safetensors.numpy import save_file
+
+        save_file(tensors, str(path), metadata=metadata)
+
+    @staticmethod
+    def fingerprint_file(path: str) -> str:
+        stat = os.stat(path)
+        payload = f"{Path(path).resolve()}:{stat.st_size}:{stat.st_mtime_ns}"
+        return hashlib.sha256(payload.encode("utf-8")).hexdigest()
+
+    @staticmethod
+    def _build_key(
+        *,
+        model_fingerprint: str,
+        mmproj_fingerprint: str,
+        kind: Literal["image", "audio", "video"],
+        media_bytes: bytes,
+    ) -> str:
+        digest = hashlib.sha256(f"{kind}:".encode("utf-8") + media_bytes).hexdigest()
+        payload = ":".join(
+            [
+                MTMDEmbeddingCache._metadata_version,
+                model_fingerprint,
+                mmproj_fingerprint,
+                kind,
+                digest,
+            ]
+        )
+        return hashlib.sha256(payload.encode("utf-8")).hexdigest()
+
+    def key_for_media(self, kind: Literal["image", "audio", "video"], media_bytes: bytes) -> str:
+        return self._build_key(
+            model_fingerprint=self.model_fingerprint,
+            mmproj_fingerprint=self.mmproj_fingerprint,
+            kind=kind,
+            media_bytes=media_bytes,
+        )
+
+    def _path_for_key(self, key: str) -> Path:
+        return self.path / f"{key}.safetensors"
+
+    def load(self, key: str) -> Optional[MTMDEmbedding]:
+        entry_path = self._path_for_key(key)
+        if not entry_path.exists():
+            return None
+        with self._safe_open(entry_path) as tensors:
+            metadata = tensors.metadata()
+            if metadata.get("version") != self._metadata_version:
+                return None
+            if metadata.get("model") != self.model_fingerprint:
+                return None
+            if metadata.get("mmproj") != self.mmproj_fingerprint:
+                return None
+            embeddings = np.array(tensors.get_tensor("embeddings"), copy=True)
+        return MTMDEmbedding(key=key, embeddings=embeddings.astype(np.float32, copy=False))
+
+    def save(self, key: str, embeddings: np.ndarray) -> None:
+        if self.max_bytes == 0:
+            return
+        tmp_path = self.path / f".{key}.{uuid.uuid4().hex}.tmp"
+        final_path = self._path_for_key(key)
+        metadata = {
+            "version": self._metadata_version,
+            "model": self.model_fingerprint,
+            "mmproj": self.mmproj_fingerprint,
+            "key": key,
+        }
+        self._save_file(
+            {"embeddings": np.ascontiguousarray(embeddings, dtype=np.float32)},
+            tmp_path,
+            metadata,
+        )
+        os.replace(tmp_path, final_path)
+        self.evict_if_needed()
+
+    def evict_if_needed(self) -> None:
+        if self.max_bytes <= 0:
+            return
+        entries = [path for path in self.path.glob("*.safetensors") if path.is_file()]
+        total = sum(path.stat().st_size for path in entries)
+        if total <= self.max_bytes:
+            return
+        for path in sorted(entries, key=lambda item: item.stat().st_mtime_ns):
+            if total <= self.max_bytes:
+                break
+            try:
+                size = path.stat().st_size
+                path.unlink()
+                total -= size
+            except OSError:
+                continue
+
+
+@dataclass
+class MTMDLoadedMedia:
+    media: MediaInput
+    media_bytes: bytes
+    key: str
+    bitmap: Any
+    video_ctx: Optional[Any] = None
+    video_temp_path: Optional[Path] = None
+    video_callback: Optional[Any] = None
+    video_frame_count: int = 0
+    video_frames_used: int = 0
+
+
+class MTMDProcessor:
+    @dataclass
+    class MediaChunk:
+        kind: Literal["image", "audio", "video"]
+        key: str
+        chunk: Any
+        n_tokens: int
+        decode_n_pos: int
+        non_causal: bool
+        embeddings: Optional[np.ndarray] = None
+
+    @dataclass
+    class ParsedChunk:
+        text_tokens: Optional[List[int]] = None
+        media: Optional["MTMDProcessor.MediaChunk"] = None
+
+    def __init__(
+        self,
+        *,
+        model_path: str,
+        llama_model: Any,
+        chat_formatter: Jinja2ChatFormatter,
+        tokenize: Callable[..., List[int]],
+        n_embd_inp: int,
+        n_batch: int,
+        n_ubatch: int,
+        n_threads_batch: int,
+        mmproj_path: str,
+        batch_max_tokens: int,
+        embedding_cache: Optional[MTMDEmbeddingCache],
+        allowed_media_domains: Optional[List[str]],
+        allowed_local_media_path: Optional[str],
+        image_max_bytes: int,
+        audio_max_bytes: int,
+        video_max_bytes: int,
+        image_timeout_seconds: float,
+    ) -> None:
+        self.chat_formatter = chat_formatter
+        self.tokenize = tokenize
+        self.n_embd_inp = n_embd_inp
+        self.n_batch = n_batch
+        self.n_ubatch = n_ubatch
+        self.mmproj_path = mmproj_path
+        self.embedding_cache = embedding_cache
+        self.batch_max_tokens = batch_max_tokens
+        self.model_fingerprint = MTMDEmbeddingCache.fingerprint_file(model_path)
+        self.mmproj_fingerprint = MTMDEmbeddingCache.fingerprint_file(mmproj_path)
+        self.allowed_media_domains = (
+            {domain.lower() for domain in allowed_media_domains}
+            if allowed_media_domains is not None
+            else set()
+        )
+        self.allowed_local_media_path = (
+            Path(allowed_local_media_path).expanduser().resolve()
+            if allowed_local_media_path is not None
+            else None
+        )
+        self.image_max_bytes = image_max_bytes
+        self.audio_max_bytes = audio_max_bytes
+        self.video_max_bytes = video_max_bytes
+        self.image_timeout_seconds = image_timeout_seconds
+        self.lock = threading.Lock()
+        params = mtmd_cpp.mtmd_context_params_default()
+        params.n_threads = max(1, n_threads_batch)
+        params.batch_max_tokens = batch_max_tokens
+        self.ctx = mtmd_cpp.mtmd_init_from_file(
+            mmproj_path.encode("utf-8"),
+            llama_model,
+            params,
+        )
+        if self.ctx is None:
+            raise RuntimeError(f"failed to load MTMD context: {mmproj_path}")
+        self.supports_vision = bool(mtmd_cpp.mtmd_support_vision(self.ctx))
+        self.supports_audio = bool(mtmd_cpp.mtmd_support_audio(self.ctx))
+        self.supports_video = self.supports_vision and bool(
+            mtmd_cpp.mtmd_helper_support_video(self.ctx)
+        )
+        if not self.supports_vision and not self.supports_audio:
+            mtmd_cpp.mtmd_free(self.ctx)
+            self.ctx = None
+            raise RuntimeError(f"MTMD projector does not support image or audio input: {mmproj_path}")
+        media_marker = mtmd_cpp.mtmd_get_marker(self.ctx)
+        if media_marker is None:
+            mtmd_cpp.mtmd_free(self.ctx)
+            self.ctx = None
+            raise RuntimeError(f"MTMD projector does not expose a media marker: {mmproj_path}")
+        self.media_marker = media_marker.decode("utf-8")
+
+    def close(self) -> None:
+        if self.ctx is not None:
+            mtmd_cpp.mtmd_free(self.ctx)
+            self.ctx = None
+
+    def _max_bytes_for_media(self, kind: Literal["image", "audio", "video"]) -> int:
+        if kind == "image":
+            return self.image_max_bytes
+        if kind == "audio":
+            return self.audio_max_bytes
+        return self.video_max_bytes
+
+    def _load_media_file(self, kind: Literal["image", "audio", "video"], media_url: str) -> bytes:
+        if self.allowed_local_media_path is None:
+            raise CompletionRequestValidationError("local media path is not allowed")
+        parsed = urllib.parse.urlsplit(media_url)
+        if parsed.netloc not in {"", "localhost"}:
+            raise CompletionRequestValidationError("local media path is not allowed")
+        path = Path(urllib.parse.unquote(parsed.path)).expanduser().resolve()
+        try:
+            path.relative_to(self.allowed_local_media_path)
+        except ValueError as exc:
+            raise CompletionRequestValidationError("local media path is not allowed") from exc
+        max_bytes = self._max_bytes_for_media(kind)
+        try:
+            if path.stat().st_size > max_bytes:
+                raise CompletionRequestValidationError(f"{kind} exceeds model.mtmd.{kind}_max_bytes")
+            data = path.read_bytes()
+        except OSError as exc:
+            raise CompletionRequestValidationError(f"failed to read local {kind}: {exc}") from exc
+        if len(data) > max_bytes:
+            raise CompletionRequestValidationError(f"{kind} exceeds model.mtmd.{kind}_max_bytes")
+        return data
+
+    def _validate_remote_media_url(self, media_url: str) -> str:
+        parsed = urllib.parse.urlsplit(media_url)
+        if parsed.scheme not in {"http", "https"} or not parsed.hostname:
+            raise CompletionRequestValidationError(
+                "only data:, file:, http:, and https: media URLs are supported"
+            )
+        if parsed.username is not None or parsed.password is not None:
+            raise CompletionRequestValidationError("remote media domain is not allowed")
+        hostname = parsed.hostname.lower()
+        if self.allowed_media_domains and hostname not in self.allowed_media_domains:
+            raise CompletionRequestValidationError("remote media domain is not allowed")
+        return urllib.parse.urlunsplit(parsed)
+
+    @staticmethod
+    def _urlopen_without_redirects(
+        request: urllib.request.Request,
+        *,
+        timeout: float,
+    ) -> Any:
+        class NoRedirectHandler(urllib.request.HTTPRedirectHandler):
+            def redirect_request(self, req, fp, code, msg, headers, newurl):  # type: ignore[no-untyped-def]
+                return None
+
+        opener = urllib.request.build_opener(NoRedirectHandler)
+        return opener.open(request, timeout=timeout)
+
+    def _load_media_url(self, kind: Literal["image", "audio", "video"], media_url: str) -> bytes:
+        max_bytes = self._max_bytes_for_media(kind)
+        if media_url.startswith("data:"):
+            try:
+                _, encoded = media_url.split(",", 1)
+            except ValueError as exc:
+                raise CompletionRequestValidationError(f"invalid data {kind} URL") from exc
+            data = base64.b64decode(encoded, validate=False)
+            if len(data) > max_bytes:
+                raise CompletionRequestValidationError(f"{kind} exceeds model.mtmd.{kind}_max_bytes")
+            return data
+        if media_url.startswith("file:"):
+            return self._load_media_file(kind, media_url)
+        media_url = self._validate_remote_media_url(media_url)
+        request = urllib.request.Request(
+            media_url,
+            headers={"User-Agent": "llama-cpp-python-batch-mtmd/0"},
+        )
+        try:
+            with self._urlopen_without_redirects(request, timeout=self.image_timeout_seconds) as response:
+                data = response.read(max_bytes + 1)
+        except (OSError, TimeoutError, urllib.error.URLError) as exc:
+            raise CompletionRequestValidationError(
+                f"failed to fetch {kind} URL: {exc}"
+            ) from exc
+        if len(data) > max_bytes:
+            raise CompletionRequestValidationError(f"{kind} exceeds model.mtmd.{kind}_max_bytes")
+        return data
+
+    def load_media(self, media: MediaInput) -> bytes:
+        if media.url is not None:
+            return self._load_media_url(media.kind, media.url)
+        if media.kind not in {"audio", "video"} or media.data is None:
+            raise CompletionRequestValidationError(f"{media.kind} input requires a URL")
+        try:
+            data = base64.b64decode(media.data, validate=False)
+        except (ValueError, binascii.Error) as exc:
+            raise CompletionRequestValidationError(f"input_{media.kind} data must be valid base64") from exc
+        max_bytes = self._max_bytes_for_media(media.kind)
+        if len(data) > max_bytes:
+            raise CompletionRequestValidationError(
+                f"{media.kind} exceeds model.mtmd.{media.kind}_max_bytes"
+            )
+        return data
+
+    def _create_loaded_media(
+        self,
+        media: MediaInput,
+        media_bytes: bytes,
+    ) -> MTMDLoadedMedia:
+        key = (
+            self.embedding_cache.key_for_media(media.kind, media_bytes)
+            if self.embedding_cache is not None
+            else MTMDEmbeddingCache._build_key(
+                model_fingerprint=self.model_fingerprint,
+                mmproj_fingerprint=self.mmproj_fingerprint,
+                kind=media.kind,
+                media_bytes=media_bytes,
+            )
+        )
+        if media.kind == "video":
+            return self._create_loaded_video_media(media, media_bytes, key)
+        buffer = (ctypes.c_uint8 * len(media_bytes)).from_buffer_copy(media_bytes)
+        wrapper = mtmd_cpp.mtmd_helper_bitmap_init_from_buf_wrapper(
+            self.ctx,
+            buffer,
+            len(media_bytes),
+            False,
+        )
+        bitmap = wrapper.bitmap
+        if bitmap is None:
+            raise CompletionRequestValidationError(f"failed to create MTMD {media.kind} bitmap")
+        mtmd_cpp.mtmd_bitmap_set_id(bitmap, key.encode("utf-8"))
+        video_frame_count = 0
+        video_ctx = wrapper.video_ctx
+        if video_ctx:
+            video_info = mtmd_cpp.mtmd_helper_video_get_info(video_ctx)
+            video_frame_count = max(0, int(video_info.n_frames))
+        return MTMDLoadedMedia(
+            media=media,
+            media_bytes=media_bytes,
+            key=key,
+            bitmap=bitmap,
+            video_ctx=video_ctx,
+            video_frame_count=video_frame_count,
+        )
+
+    @staticmethod
+    def _video_temp_suffix(media: MediaInput) -> str:
+        extension = (media.format or "mp4").lstrip(".").lower()
+        if not extension or any(not char.isalnum() for char in extension):
+            extension = "video"
+        return f".{extension}"
+
+    def _create_loaded_video_media(
+        self,
+        media: MediaInput,
+        media_bytes: bytes,
+        key: str,
+    ) -> MTMDLoadedMedia:
+        temp_file = tempfile.NamedTemporaryFile(
+            prefix="llama-cpp-python-mtmd-",
+            suffix=self._video_temp_suffix(media),
+            delete=False,
+        )
+        temp_path = Path(temp_file.name)
+        try:
+            with temp_file:
+                temp_file.write(media_bytes)
+            params = mtmd_cpp.mtmd_helper_video_init_params_default()
+            video_ctx = mtmd_cpp.mtmd_helper_video_init(
+                self.ctx,
+                str(temp_path).encode("utf-8"),
+                params,
+            )
+            if video_ctx is None:
+                raise CompletionRequestValidationError("failed to create MTMD video context")
+
+            def read_next(
+                _chunk_index: int,
+                _user_data: Any,
+                out_bitmap: Any,
+                out_text: Any,
+            ) -> int:
+                return int(mtmd_cpp.mtmd_helper_video_read_next(video_ctx, out_bitmap, out_text))
+
+            callback = mtmd_cpp.mtmd_bitmap_lazy_callback(read_next)
+            bitmap = mtmd_cpp.mtmd_bitmap_init_lazy(
+                self.ctx,
+                key.encode("utf-8"),
+                ctypes.c_void_p(),
+                callback,
+            )
+            if bitmap is None:
+                mtmd_cpp.mtmd_helper_video_free(video_ctx)
+                raise CompletionRequestValidationError("failed to create MTMD video bitmap")
+            video_info = mtmd_cpp.mtmd_helper_video_get_info(video_ctx)
+            return MTMDLoadedMedia(
+                media=media,
+                media_bytes=media_bytes,
+                key=key,
+                bitmap=bitmap,
+                video_ctx=video_ctx,
+                video_temp_path=temp_path,
+                video_callback=callback,
+                video_frame_count=max(0, int(video_info.n_frames)),
+            )
+        except Exception:
+            try:
+                temp_path.unlink()
+            except OSError:
+                pass
+            raise
+
+    def _media_identity_tokens(
+        self,
+        kind: Literal["image", "audio", "video"],
+        key: str,
+        n_pos: int,
+    ) -> List[int]:
+        tokens: List[int] = []
+        for index in range(n_pos):
+            digest = hashlib.sha256(f"{kind}:{key}:{index}".encode("utf-8")).digest()
+            tokens.append(-1 - (int.from_bytes(digest[:4], "little") & 0x3FFFFFFF))
+        return tokens
+
+    def _embeddings_from_pointer(self, output: Any, n_tokens: int) -> np.ndarray:
+        flat = np.ctypeslib.as_array(output, shape=(n_tokens * self.n_embd_inp,))
+        return np.array(flat, dtype=np.float32, copy=True).reshape(
+            n_tokens,
+            self.n_embd_inp,
+        )
+
+    def _load_cached_media_chunk(self, media_chunk: "MTMDProcessor.MediaChunk") -> bool:
+        if self.embedding_cache is None:
+            return False
+        cached = self.embedding_cache.load(media_chunk.key)
+        if cached is None or cached.embeddings.shape != (
+            media_chunk.n_tokens,
+            self.n_embd_inp,
+        ):
+            return False
+        media_chunk.embeddings = cached.embeddings
+        return True
+
+    def _save_media_chunk(self, media_chunk: "MTMDProcessor.MediaChunk") -> None:
+        if self.embedding_cache is None or media_chunk.embeddings is None:
+            return
+        self.embedding_cache.save(media_chunk.key, media_chunk.embeddings)
+
+    def _encode_media_batch(
+        self,
+        media_chunks: Sequence["MTMDProcessor.MediaChunk"],
+        start_index: int,
+    ) -> int:
+        batch = mtmd_cpp.mtmd_batch_init(self.ctx)
+        if batch is None:
+            raise CompletionRequestValidationError("failed to create MTMD media batch")
+        try:
+            first = media_chunks[start_index]
+            result = int(mtmd_cpp.mtmd_batch_add_chunk(batch, first.chunk))
+            if result != 0:
+                raise CompletionRequestValidationError(
+                    f"failed to add {first.kind} chunk to MTMD batch: error code {result}"
+                )
+            group = [first]
+            next_index = start_index + 1
+            while next_index < len(media_chunks):
+                candidate = media_chunks[next_index]
+                result = int(mtmd_cpp.mtmd_batch_add_chunk(batch, candidate.chunk))
+                if result == 0:
+                    group.append(candidate)
+                    next_index += 1
+                    continue
+                if result in {2, 3}:
+                    break
+                raise CompletionRequestValidationError(
+                    f"failed to add {candidate.kind} chunk to MTMD batch: error code {result}"
+                )
+            result = int(mtmd_cpp.mtmd_batch_encode(batch))
+            if result != 0:
+                raise CompletionRequestValidationError(
+                    f"failed to encode MTMD media batch: error code {result}"
+                )
+            for media_chunk in group:
+                output = mtmd_cpp.mtmd_batch_get_output_embd(batch, media_chunk.chunk)
+                if output is None:
+                    raise CompletionRequestValidationError(
+                        f"MTMD {media_chunk.kind} encoder returned no embeddings"
+                    )
+                media_chunk.embeddings = self._embeddings_from_pointer(
+                    output,
+                    media_chunk.n_tokens,
+                )
+                self._save_media_chunk(media_chunk)
+            return len(group)
+        finally:
+            mtmd_cpp.mtmd_batch_free(batch)
+
+    def _encode_media_chunks(
+        self,
+        media_chunks: Sequence["MTMDProcessor.MediaChunk"],
+    ) -> None:
+        uncached = [
+            media_chunk
+            for media_chunk in media_chunks
+            if not self._load_cached_media_chunk(media_chunk)
+        ]
+        index = 0
+        while index < len(uncached):
+            index += self._encode_media_batch(uncached, index)
+
+    def _positions_for_chunk(self, chunk: Any, start_pos: int) -> np.ndarray:
+        n_tokens = int(mtmd_cpp.mtmd_input_chunk_get_n_tokens(chunk))
+        if not mtmd_cpp.mtmd_decode_use_mrope(self.ctx):
+            return np.arange(start_pos, start_pos + n_tokens, dtype=np.int32)
+        chunk_type = int(mtmd_cpp.mtmd_input_chunk_get_type(chunk))
+        if chunk_type == mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_AUDIO:
+            positions = np.empty((4, n_tokens), dtype=np.int32)
+            positions[:] = np.arange(start_pos, start_pos + n_tokens, dtype=np.int32)
+            return positions.reshape(-1)
+        image_tokens = mtmd_cpp.mtmd_input_chunk_get_tokens_image(chunk)
+        if image_tokens is None:
+            raise CompletionRequestValidationError("MTMD image chunk has no image tokens")
+        positions = np.empty((4, n_tokens), dtype=np.int32)
+        for index in range(n_tokens):
+            pos = mtmd_cpp.mtmd_image_tokens_get_decoder_pos(
+                image_tokens,
+                llama_cpp.llama_pos(start_pos),
+                index,
+            )
+            positions[0, index] = int(pos.t)
+            positions[1, index] = int(pos.y)
+            positions[2, index] = int(pos.x)
+            positions[3, index] = int(pos.z)
+        return positions.reshape(-1)
+
+    def build_prompt_plan(
+        self,
+        *,
+        messages: List[ChatCompletionRequestMessage],
+        functions: Optional[List[ChatTemplateFunctionDefinition]] = None,
+        function_call: Optional[Union[str, ChatTemplateFunctionCall]] = None,
+        tools: Optional[List[ChatTemplateTool]] = None,
+        tool_choice: Optional[Union[str, ChatTemplateToolChoice]] = None,
+        reasoning_effort: Optional[str] = None,
+    ) -> PromptPlan:
+        media_inputs = Jinja2ChatFormatter.media_inputs_from_messages(messages)
+        if not media_inputs:
+            prompt, generation_prompt, _ = self.chat_formatter.format(
+                messages=messages,
+                functions=functions,
+                function_call=function_call,
+                tools=tools,
+                tool_choice=tool_choice,
+                reasoning_effort=reasoning_effort,
+            )
+            tokens = self.tokenize(prompt, add_bos=False, special=True)
+            return PromptPlan.from_tokens(prompt, tokens, generation_prompt=generation_prompt)
+        if any(media.kind == "image" for media in media_inputs) and not self.supports_vision:
+            raise CompletionRequestValidationError("MTMD projector does not support images")
+        if any(media.kind == "audio" for media in media_inputs) and not self.supports_audio:
+            raise CompletionRequestValidationError("MTMD projector does not support audio")
+        if any(media.kind == "video" for media in media_inputs) and not self.supports_video:
+            raise CompletionRequestValidationError("MTMD projector does not support video")
+        with self.lock:
+            return self._build_prompt_plan_locked(
+                messages=messages,
+                media_inputs=media_inputs,
+                functions=functions,
+                function_call=function_call,
+                tools=tools,
+                tool_choice=tool_choice,
+                reasoning_effort=reasoning_effort,
+            )
+
+    def _build_prompt_plan_locked(
+        self,
+        *,
+        messages: List[ChatCompletionRequestMessage],
+        media_inputs: List[MediaInput],
+        functions: Optional[List[ChatTemplateFunctionDefinition]],
+        function_call: Optional[Union[str, ChatTemplateFunctionCall]],
+        tools: Optional[List[ChatTemplateTool]],
+        tool_choice: Optional[Union[str, ChatTemplateToolChoice]],
+        reasoning_effort: Optional[str],
+    ) -> PromptPlan:
+        prompt, generation_prompt, _ = self.chat_formatter.format(
+            messages=messages,
+            media_marker=self.media_marker,
+            functions=functions,
+            function_call=function_call,
+            tools=tools,
+            tool_choice=tool_choice,
+            reasoning_effort=reasoning_effort,
+        )
+        media_bytes_by_index = [self.load_media(media) for media in media_inputs]
+        loaded_media: List[MTMDLoadedMedia] = []
+        chunks: Optional[Any] = None
+        try:
+            loaded_media = [
+                self._create_loaded_media(media, media_bytes)
+                for media, media_bytes in zip(media_inputs, media_bytes_by_index)
+            ]
+            loaded_media_by_key = {media.key: media for media in loaded_media}
+            video_media = [media for media in loaded_media if media.media.kind == "video"]
+            if len(video_media) > 1 and any(media.video_frame_count <= 0 for media in video_media):
+                raise CompletionRequestValidationError(
+                    "multiple videos require MTMD to report frame counts"
+                )
+            input_text = mtmd_cpp.mtmd_input_text()
+            input_text.text = prompt.encode("utf-8")
+            input_text.add_special = False
+            input_text.parse_special = True
+            chunks = mtmd_cpp.mtmd_input_chunks_init()
+            if chunks is None:
+                raise CompletionRequestValidationError("failed to create MTMD input chunks")
+            bitmap_array = (mtmd_cpp.mtmd_bitmap_p_ctypes * len(loaded_media))(
+                *(media.bitmap for media in loaded_media)
+            )
+            result = int(
+                mtmd_cpp.mtmd_tokenize(
+                    self.ctx,
+                    chunks,
+                    ctypes.byref(input_text),
+                    bitmap_array,
+                    len(loaded_media),
+                )
+            )
+            if result != 0:
+                raise CompletionRequestValidationError(
+                    f"failed to tokenize MTMD prompt: error code {result}"
+                )
+            parsed_chunks: List[MTMDProcessor.ParsedChunk] = []
+            media_chunks: List[MTMDProcessor.MediaChunk] = []
+            video_index = 0
+            used_media_keys = set()
+            n_chunks = int(mtmd_cpp.mtmd_input_chunks_size(chunks))
+            for chunk_index in range(n_chunks):
+                chunk = mtmd_cpp.mtmd_input_chunks_get(chunks, chunk_index)
+                if chunk is None:
+                    continue
+                chunk_type = int(mtmd_cpp.mtmd_input_chunk_get_type(chunk))
+                if chunk_type == mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_TEXT:
+                    n_tokens_out = ctypes.c_size_t()
+                    tokens_ptr = mtmd_cpp.mtmd_input_chunk_get_tokens_text(
+                        chunk,
+                        ctypes.byref(n_tokens_out),
+                    )
+                    tokens = (
+                        [int(tokens_ptr[index]) for index in range(int(n_tokens_out.value))]
+                        if tokens_ptr
+                        else []
+                    )
+                    if tokens:
+                        parsed_chunks.append(
+                            MTMDProcessor.ParsedChunk(text_tokens=tokens)
+                        )
+                    continue
+                if chunk_type == mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_IMAGE:
+                    chunk_kind: Literal["image", "audio"] = "image"
+                    if not self.supports_vision:
+                        raise CompletionRequestValidationError("MTMD projector does not support images")
+                elif chunk_type == mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_AUDIO:
+                    chunk_kind = "audio"
+                    if not self.supports_audio:
+                        raise CompletionRequestValidationError("MTMD projector does not support audio")
+                else:
+                    raise CompletionRequestValidationError("unsupported MTMD input chunk type")
+                chunk_id_bytes = mtmd_cpp.mtmd_input_chunk_get_id(chunk)
+                chunk_id = chunk_id_bytes.decode("utf-8") if chunk_id_bytes else ""
+                media = loaded_media_by_key.get(chunk_id)
+                video_frame_index: Optional[int] = None
+                if media is None and chunk_kind == "image" and video_media:
+                    while (
+                        video_index < len(video_media)
+                        and video_media[video_index].video_frame_count > 0
+                        and video_media[video_index].video_frames_used
+                        >= video_media[video_index].video_frame_count
+                    ):
+                        video_index += 1
+                    if video_index >= len(video_media):
+                        raise CompletionRequestValidationError("MTMD video frame count mismatch")
+                    media = video_media[video_index]
+                    video_frame_index = media.video_frames_used
+                    media.video_frames_used += 1
+                if media is None:
+                    raise CompletionRequestValidationError("MTMD media chunk identity mismatch")
+                if media.media.kind == "video":
+                    if chunk_kind != "image":
+                        raise CompletionRequestValidationError("MTMD video chunk modality mismatch")
+                    kind: Literal["image", "audio", "video"] = "video"
+                    if video_frame_index is None:
+                        video_frame_index = media.video_frames_used
+                        media.video_frames_used += 1
+                    key = hashlib.sha256(
+                        f"{media.key}:frame:{video_frame_index}".encode("utf-8")
+                    ).hexdigest()
+                else:
+                    if media.media.kind != chunk_kind:
+                        raise CompletionRequestValidationError("MTMD media chunk modality mismatch")
+                    kind = media.media.kind
+                    key = media.key
+                used_media_keys.add(media.key)
+                decode_n_pos = int(mtmd_cpp.mtmd_input_chunk_get_n_pos(chunk))
+                if decode_n_pos <= 0:
+                    raise CompletionRequestValidationError("MTMD media chunk has no decoder positions")
+                n_tokens = int(mtmd_cpp.mtmd_input_chunk_get_n_tokens(chunk))
+                if n_tokens <= 0:
+                    raise CompletionRequestValidationError("MTMD media chunk has no embedding tokens")
+                non_causal = bool(mtmd_cpp.mtmd_decode_use_non_causal(self.ctx, chunk))
+                media_chunk = MTMDProcessor.MediaChunk(
+                    kind=kind,
+                    key=key,
+                    chunk=chunk,
+                    n_tokens=n_tokens,
+                    decode_n_pos=decode_n_pos,
+                    non_causal=non_causal,
+                )
+                parsed_chunks.append(MTMDProcessor.ParsedChunk(media=media_chunk))
+                media_chunks.append(media_chunk)
+            if used_media_keys != {media.key for media in loaded_media}:
+                raise CompletionRequestValidationError("not all media inputs were consumed by MTMD")
+            self._encode_media_chunks(media_chunks)
+            segments: List[PromptSegment] = []
+            identity_tokens: List[int] = []
+            text_tokens: List[int] = []
+            text_token_index_by_pos: Dict[int, int] = {}
+            identity_pos = 0
+            decode_pos = 0
+            for parsed_chunk in parsed_chunks:
+                if parsed_chunk.text_tokens is not None:
+                    tokens = parsed_chunk.text_tokens
+                    start_pos = identity_pos
+                    segments.append(
+                        PromptSegment(
+                            kind="text",
+                            start_pos=start_pos,
+                            n_pos=len(tokens),
+                            identity_tokens=list(tokens),
+                            decode_start_pos=decode_pos,
+                            decode_n_pos=len(tokens),
+                            text_tokens=list(tokens),
+                        )
+                    )
+                    for offset, token in enumerate(tokens):
+                        text_token_index_by_pos[start_pos + offset] = len(text_tokens)
+                        text_tokens.append(token)
+                    identity_tokens.extend(tokens)
+                    identity_pos += len(tokens)
+                    decode_pos += len(tokens)
+                    continue
+                media_chunk = parsed_chunk.media
+                if media_chunk is None or media_chunk.embeddings is None:
+                    raise CompletionRequestValidationError("MTMD media chunk has no embeddings")
+                embeddings = media_chunk.embeddings
+                if media_chunk.non_causal and embeddings.shape[0] > min(self.n_batch, self.n_ubatch):
+                    raise CompletionRequestValidationError(
+                        f"non-causal {media_chunk.kind} embedding chunk exceeds model batch limits; "
+                        "increase n_batch and n_ubatch"
+                    )
+                segment_identity = self._media_identity_tokens(
+                    media_chunk.kind,
+                    media_chunk.key,
+                    media_chunk.n_tokens,
+                )
+                positions = self._positions_for_chunk(media_chunk.chunk, decode_pos)
+                segments.append(
+                    PromptSegment(
+                        kind=media_chunk.kind,
+                        start_pos=identity_pos,
+                        n_pos=media_chunk.n_tokens,
+                        identity_tokens=segment_identity,
+                        decode_start_pos=decode_pos,
+                        decode_n_pos=media_chunk.decode_n_pos,
+                        media=PromptSegment.Media(
+                            embeddings=embeddings,
+                            positions=positions,
+                            non_causal=media_chunk.non_causal,
+                        ),
+                    )
+                )
+                identity_tokens.extend(segment_identity)
+                identity_pos += media_chunk.n_tokens
+                decode_pos += media_chunk.decode_n_pos
+            return PromptPlan(
+                text=prompt,
+                generation_prompt=generation_prompt,
+                text_tokens=text_tokens,
+                identity_tokens=identity_tokens,
+                segments=segments,
+                text_token_index_by_pos=text_token_index_by_pos,
+            )
+        finally:
+            if chunks is not None:
+                mtmd_cpp.mtmd_input_chunks_free(chunks)
+            for media in loaded_media:
+                mtmd_cpp.mtmd_bitmap_free(media.bitmap)
+                if media.video_ctx:
+                    mtmd_cpp.mtmd_helper_video_free(media.video_ctx)
+                if media.video_temp_path is not None:
+                    try:
+                        media.video_temp_path.unlink()
+                    except OSError:
+                        pass
+
+
+class Model:
+    @dataclass(frozen=True)
+    class LoraAdapter:
+        path: str
+        scale: float = 1.0
+
+    def __init__(
+        self,
+        *,
+        model_path: str,
+        model_alias: Optional[str] = None,
+        chat_template: Optional[str] = None,
+        loras: Optional[List["Model.LoraAdapter"]] = None,
+        n_gpu_layers: Optional[int] = None,
+        split_mode: Optional[int] = None,
+        main_gpu: Optional[int] = None,
+        tensor_split: Optional[List[float]] = None,
+        vocab_only: Optional[bool] = None,
+        use_mmap: Optional[bool] = None,
+        use_mlock: Optional[bool] = None,
+        kv_overrides: Optional[Dict[str, Union[bool, int, float, str]]] = None,
+        n_ctx: Optional[int],
+        n_batch: Optional[int],
+        n_ubatch: Optional[int] = None,
+        n_seq_max: Optional[int],
+        n_threads: Optional[int],
+        n_threads_batch: Optional[int],
+        rope_scaling_type: Optional[int] = None,
+        pooling_type: Optional[int] = None,
+        attention_type: Optional[int] = None,
+        embedding: Optional[bool] = None,
+        rope_freq_base: Optional[float] = None,
+        rope_freq_scale: Optional[float] = None,
+        yarn_ext_factor: Optional[float] = None,
+        yarn_attn_factor: Optional[float] = None,
+        yarn_beta_fast: Optional[float] = None,
+        yarn_beta_slow: Optional[float] = None,
+        yarn_orig_ctx: Optional[int] = None,
+        offload_kqv: Optional[bool] = None,
+        flash_attn: Optional[bool] = None,
+        op_offload: Optional[bool] = None,
+        swa_full: Optional[bool] = None,
+        no_perf: Optional[bool] = None,
+        type_k: Optional[int] = None,
+        type_v: Optional[int] = None,
+        kv_unified: bool = True,
+        max_seq_len: Optional[int] = None,
+        max_output_tokens: Optional[int] = None,
+        draft_model: Optional[str] = None,
+        draft_model_path: Optional[str] = None,
+        draft_model_num_pred_tokens: int = 16,
+        draft_model_max_ngram_size: int = 2,
+        draft_model_top_k: int = 1,
+        draft_model_p_min: float = 0.0,
+        draft_model_max_batch_size: Optional[int] = None,
+        draft_model_threads: Optional[int] = None,
+        draft_model_threads_batch: Optional[int] = None,
+        response_schema: Optional[Dict[str, Any]] = None,
+        store_logits: bool = True,
+    ) -> None:
+        llama_cpp.llama_backend_init()
+        self.backend_initialized = True
+        self.model_path = model_path
+        self.model_alias = model_alias
+        self.chat_template_override = chat_template
+        self.response_schema = response_schema
+        self.store_logits = store_logits
+        self.max_output_tokens = max_output_tokens
+        self.draft_model_max_batch_size = draft_model_max_batch_size
+        self.draft_provider: Optional[DraftProvider] = None
+        self.loras = list(loras or [])
+        self._lora_adapters: List[Any] = []
+        self._lora_adapter_array: Optional[Any] = None
+        self._lora_scales_array: Optional[Any] = None
+        self.draft_llama_model: Optional[Any] = None
+        model_params, self._c_tensor_split, self._kv_overrides_array = (
+            self.build_model_params(
+                n_gpu_layers=n_gpu_layers,
+                split_mode=split_mode,
+                main_gpu=main_gpu,
+                tensor_split=tensor_split,
+                vocab_only=vocab_only,
+                use_mmap=use_mmap,
+                use_mlock=use_mlock,
+                kv_overrides=kv_overrides,
+            )
+        )
+        llama_model = llama_cpp.llama_model_load_from_file(
+            model_path.encode("utf-8"),
+            model_params,
+        )
+        if llama_model is None:
+            raise RuntimeError(f"failed to load model: {model_path}")
+        self.llama_model = llama_model
+        vocab = llama_cpp.llama_model_get_vocab(llama_model)
+        if vocab is None:
+            raise RuntimeError("failed to access model vocabulary")
+        self.vocab = vocab
+        embedding = self.resolve_embedding_mode(llama_model, embedding)
+        self.embedding = embedding
+        self.has_encoder = bool(llama_cpp.llama_model_has_encoder(llama_model))
+        self.has_decoder = bool(llama_cpp.llama_model_has_decoder(llama_model))
+        if self.has_encoder and not embedding:
+            raise RuntimeError("encoder models are not supported")
+        if not self.has_decoder and not (embedding and self.has_encoder):
+            raise RuntimeError("decoder is required")
+        if llama_cpp.llama_model_is_recurrent(llama_model):
+            self.memory_model = "recurrent"
+        elif llama_cpp.llama_model_is_hybrid(llama_model):
+            self.memory_model = "hybrid"
+        else:
+            self.memory_model = (
+                "attention-unified" if kv_unified else "attention-partitioned"
+        )
+        normalized_draft_model = draft_model
+        required_mtp_batch = max(1, draft_model_num_pred_tokens + 1)
+        if normalized_draft_model == "draft-mtp":
+            if n_batch is not None and n_batch < required_mtp_batch:
+                raise RuntimeError(
+                    "MTP requires n_batch to fit the pending token plus draft tokens "
+                    f"(required {required_mtp_batch}, got {n_batch})"
+                )
+        self.draft_target_batching = normalized_draft_model is not None
+        if (
+            normalized_draft_model is not None
+            and normalized_draft_model != "draft-mtp"
+            and not self.memory_model.startswith("attention")
+        ):
+            raise RuntimeError(
+                "speculative decoding is only supported for attention models"
+            )
+        n_ctx_train = int(llama_cpp.llama_model_n_ctx_train(llama_model))
+
+        context_params = self.build_context_params(
+            n_ctx=n_ctx if n_ctx is not None else n_ctx_train,
+            n_batch=n_batch,
+            n_ubatch=n_ubatch,
+            n_seq_max=n_seq_max,
+            n_threads=n_threads,
+            n_threads_batch=n_threads_batch,
+            rope_scaling_type=rope_scaling_type,
+            pooling_type=pooling_type,
+            attention_type=attention_type,
+            embedding=embedding,
+            rope_freq_base=rope_freq_base,
+            rope_freq_scale=rope_freq_scale,
+            yarn_ext_factor=yarn_ext_factor,
+            yarn_attn_factor=yarn_attn_factor,
+            yarn_beta_fast=yarn_beta_fast,
+            yarn_beta_slow=yarn_beta_slow,
+            yarn_orig_ctx=yarn_orig_ctx,
+            offload_kqv=offload_kqv,
+            flash_attn=flash_attn,
+            op_offload=op_offload,
+            swa_full=swa_full,
+            no_perf=no_perf,
+            type_k=type_k,
+            type_v=type_v,
+            kv_unified=kv_unified,
+            n_rs_seq=None,
+            ctx_type=None,
+        )
+        ctx = llama_cpp.llama_init_from_model(llama_model, context_params)
+        if ctx is None:
+            raise RuntimeError("failed to create context")
+        self.ctx = ctx
+        mem = llama_cpp.llama_get_memory(ctx)
+        if mem is None and not embedding:
+            raise RuntimeError("failed to access model memory")
+        self.mem = mem
+        self.n_ctx = int(llama_cpp.llama_n_ctx(ctx))
+        self.n_ctx_seq = int(llama_cpp.llama_n_ctx_seq(ctx))
+        self.n_seq_max = int(llama_cpp.llama_n_seq_max(ctx))
+        self.n_rs_seq = int(llama_cpp.llama_n_rs_seq(ctx))
+        self.n_batch = int(llama_cpp.llama_n_batch(ctx))
+        self.n_ubatch = int(llama_cpp.llama_n_ubatch(ctx))
+        self.n_threads_batch = (
+            n_threads_batch
+            if n_threads_batch is not None
+            else max(multiprocessing.cpu_count(), 1)
+        )
+        self.mtmd_processor: Optional[MTMDProcessor] = None
+        self._embedding_batch: Optional[llama_cpp.llama_batch] = None
+        self._embedding_batch_refs: List[Any] = []
+        if normalized_draft_model == "draft-mtp" and self.n_batch < required_mtp_batch:
+            raise RuntimeError(
+                "MTP requires runtime n_batch to fit the pending token plus draft tokens "
+                f"(required {required_mtp_batch}, got {self.n_batch})"
+            )
+        self.n_ctx_train = n_ctx_train
+        self.n_vocab = int(llama_cpp.llama_vocab_n_tokens(self.vocab))
+        self.n_embd = int(llama_cpp.llama_model_n_embd(self.llama_model))
+        self.n_embd_inp = int(llama_cpp.llama_model_n_embd_inp(self.llama_model))
+        self.n_embd_out = int(llama_cpp.llama_model_n_embd_out(self.llama_model))
+        if self.n_embd_out <= 0:
+            self.n_embd_out = self.n_embd
+        self.kv_unified = kv_unified
+        self.max_seq_len_limit = min(self.request_context_limit, self.n_ctx_train)
+        if max_seq_len is None:
+            self.max_seq_len = self.max_seq_len_limit
+        else:
+            if max_seq_len <= 0:
+                llama_cpp.llama_free(self.ctx)
+                llama_cpp.llama_model_free(self.llama_model)
+                if self.backend_initialized:
+                    llama_cpp.llama_backend_free()
+                    self.backend_initialized = False
+                raise RuntimeError("max_seq_len must be greater than 0")
+            if max_seq_len > self.max_seq_len_limit:
+                llama_cpp.llama_free(self.ctx)
+                llama_cpp.llama_model_free(self.llama_model)
+                if self.backend_initialized:
+                    llama_cpp.llama_backend_free()
+                    self.backend_initialized = False
+                raise RuntimeError(
+                    f"max_seq_len ({max_seq_len}) exceeds runtime limit ({self.max_seq_len_limit})"
+                )
+            self.max_seq_len = max_seq_len
+        self.batch = llama_cpp.llama_batch_init(self.n_batch, 0, self.n_seq_max)
+        self.bos_token = int(llama_cpp.llama_vocab_bos(self.vocab))
+        self.eos_token = int(llama_cpp.llama_vocab_eos(self.vocab))
+        self.cls_token = self.bos_token
+        self.sep_token = int(llama_cpp.llama_vocab_sep(self.vocab))
+        self.fim_pre_token = int(llama_cpp.llama_vocab_fim_pre(self.vocab))
+        self.fim_mid_token = int(llama_cpp.llama_vocab_fim_mid(self.vocab))
+        self.fim_suf_token = int(llama_cpp.llama_vocab_fim_suf(self.vocab))
+        self.add_bos_token = bool(llama_cpp.llama_vocab_get_add_bos(self.vocab))
+        self.add_eos_token = bool(llama_cpp.llama_vocab_get_add_eos(self.vocab))
+        self.add_space_prefix = (
+            self._meta_value("tokenizer.ggml.add_space_prefix") != "false"
+        )
+        if normalized_draft_model is None:
+            self.draft_provider = None
+        elif normalized_draft_model == "prompt-lookup-decoding":
+            self.draft_provider = PromptLookupDecoding(
+                max_ngram_size=draft_model_max_ngram_size,
+                num_pred_tokens=draft_model_num_pred_tokens,
+            )
+        elif normalized_draft_model == "draft-mtp":
+            draft_llama_model = self.llama_model
+            if draft_model_path is not None:
+                draft_llama_model = llama_cpp.llama_model_load_from_file(
+                    draft_model_path.encode("utf-8"),
+                    model_params,
+                )
+                if draft_llama_model is None:
+                    llama_cpp.llama_batch_free(self.batch)
+                    llama_cpp.llama_free(self.ctx)
+                    self._free_lora_adapters()
+                    llama_cpp.llama_model_free(self.llama_model)
+                    if self.backend_initialized:
+                        llama_cpp.llama_backend_free()
+                        self.backend_initialized = False
+                    raise RuntimeError(f"failed to load MTP draft model: {draft_model_path}")
+                self.draft_llama_model = draft_llama_model
+            if self.n_ubatch < self.n_seq_max:
+                mtp_n_batch = self.n_batch
+            else:
+                mtp_n_batch = min(
+                    self.n_batch,
+                    max(self.n_ubatch, self.n_seq_max, required_mtp_batch),
+                )
+            mtp_context_params = self.build_context_params(
+                n_ctx=self.n_ctx,
+                n_batch=mtp_n_batch,
+                n_ubatch=min(self.n_ubatch, mtp_n_batch),
+                n_seq_max=self.n_seq_max,
+                n_threads=(
+                    draft_model_threads
+                    if draft_model_threads is not None
+                    else n_threads
+                ),
+                n_threads_batch=(
+                    draft_model_threads_batch
+                    if draft_model_threads_batch is not None
+                    else (
+                        draft_model_threads
+                        if draft_model_threads is not None
+                        else n_threads_batch
+                    )
+                ),
+                rope_scaling_type=rope_scaling_type,
+                pooling_type=pooling_type,
+                attention_type=attention_type,
+                embedding=embedding,
+                rope_freq_base=rope_freq_base,
+                rope_freq_scale=rope_freq_scale,
+                yarn_ext_factor=yarn_ext_factor,
+                yarn_attn_factor=yarn_attn_factor,
+                yarn_beta_fast=yarn_beta_fast,
+                yarn_beta_slow=yarn_beta_slow,
+                yarn_orig_ctx=yarn_orig_ctx,
+                offload_kqv=offload_kqv,
+                flash_attn=flash_attn,
+                op_offload=op_offload,
+                swa_full=swa_full,
+                no_perf=no_perf,
+                type_k=type_k,
+                type_v=type_v,
+                kv_unified=kv_unified,
+                n_rs_seq=0,
+                ctx_type=llama_cpp.LLAMA_CONTEXT_TYPE_MTP,
+                n_outputs_max=min(mtp_n_batch, self.n_seq_max),
+                ctx_other=self.ctx,
+            )
+            try:
+                self.draft_provider = MTPDraftProvider(
+                    model=self,
+                    draft_model=draft_llama_model,
+                    context_params=mtp_context_params,
+                    num_pred_tokens=draft_model_num_pred_tokens,
+                    top_k=draft_model_top_k,
+                    p_min=draft_model_p_min,
+                )
+            except BaseException:
+                llama_cpp.llama_batch_free(self.batch)
+                llama_cpp.llama_free(self.ctx)
+                self._free_lora_adapters()
+                if self.draft_llama_model is not None:
+                    llama_cpp.llama_model_free(self.draft_llama_model)
+                    self.draft_llama_model = None
+                llama_cpp.llama_model_free(self.llama_model)
+                if self.backend_initialized:
+                    llama_cpp.llama_backend_free()
+                    self.backend_initialized = False
+                raise
+        else:
+            raise RuntimeError(f"unsupported draft model: {draft_model}")
+        try:
+            self._load_lora_adapters(self.loras)
+            self._apply_lora_adapters(self.ctx, "target")
+            if (
+                isinstance(self.draft_provider, MTPDraftProvider)
+                and self.draft_llama_model is None
+            ):
+                self._apply_lora_adapters(self.draft_provider.ctx, "MTP draft")
+        except BaseException:
+            if self.draft_provider is not None:
+                self.draft_provider.close()
+            llama_cpp.llama_batch_free(self.batch)
+            llama_cpp.llama_free(self.ctx)
+            self._free_lora_adapters()
+            if self.draft_llama_model is not None:
+                llama_cpp.llama_model_free(self.draft_llama_model)
+                self.draft_llama_model = None
+            llama_cpp.llama_model_free(self.llama_model)
+            if self.backend_initialized:
+                llama_cpp.llama_backend_free()
+                self.backend_initialized = False
+            raise
+        self.chat_formatter = self._build_chat_formatter()
+
+    @staticmethod
+    def build_model_params(
+        *,
+        n_gpu_layers: Optional[int],
+        split_mode: Optional[int],
+        main_gpu: Optional[int],
+        tensor_split: Optional[List[float]],
+        vocab_only: Optional[bool],
+        use_mmap: Optional[bool],
+        use_mlock: Optional[bool],
+        kv_overrides: Optional[Dict[str, Union[bool, int, float, str]]],
+    ) -> Tuple[Any, Optional[Any], Optional[Any]]:
+        model_params = llama_cpp.llama_model_default_params()
+        if n_gpu_layers is not None:
+            model_params.n_gpu_layers = 0x7FFFFFFF if n_gpu_layers == -1 else n_gpu_layers
+        if split_mode is not None:
+            model_params.split_mode = split_mode
+        if main_gpu is not None:
+            model_params.main_gpu = main_gpu
+        tensor_split_ref = None
+        if tensor_split is not None:
+            if len(tensor_split) > llama_cpp.LLAMA_MAX_DEVICES:
+                raise ValueError(
+                    "tensor_split exceeds maximum supported devices "
+                    f"({llama_cpp.LLAMA_MAX_DEVICES})"
+                )
+            float_array = ctypes.c_float * llama_cpp.LLAMA_MAX_DEVICES
+            tensor_split_ref = float_array(*tensor_split)
+            model_params.tensor_split = tensor_split_ref
+        if vocab_only is not None:
+            model_params.vocab_only = vocab_only
+        if use_mmap is not None:
+            model_params.use_mmap = use_mmap
+        if use_mlock is not None:
+            model_params.use_mlock = use_mlock
+
+        kv_overrides_ref = None
+        if kv_overrides is not None:
+            kv_overrides_ref = (
+                llama_cpp.llama_model_kv_override * (len(kv_overrides) + 1)
+            )()
+            for index, (key, value) in enumerate(kv_overrides.items()):
+                kv_overrides_ref[index].key = key.encode("utf-8")
+                if isinstance(value, bool):
+                    kv_overrides_ref[index].tag = llama_cpp.LLAMA_KV_OVERRIDE_TYPE_BOOL
+                    kv_overrides_ref[index].value.val_bool = value
+                elif isinstance(value, int):
+                    kv_overrides_ref[index].tag = llama_cpp.LLAMA_KV_OVERRIDE_TYPE_INT
+                    kv_overrides_ref[index].value.val_i64 = value
+                elif isinstance(value, float):
+                    kv_overrides_ref[index].tag = llama_cpp.LLAMA_KV_OVERRIDE_TYPE_FLOAT
+                    kv_overrides_ref[index].value.val_f64 = value
+                elif isinstance(value, str):
+                    encoded = value.encode("utf-8")
+                    if len(encoded) > 128:
+                        raise ValueError(f"kv_overrides value for {key} is too long")
+                    encoded = encoded.ljust(128, b"\0")
+                    kv_overrides_ref[index].tag = llama_cpp.LLAMA_KV_OVERRIDE_TYPE_STR
+                    address = cast(
+                        int,
+                        ctypes.addressof(kv_overrides_ref[index].value)
+                        + cast(
+                            Any,
+                            llama_cpp.llama_model_kv_override_value.val_str,
+                        ).offset,
+                    )
+                    buffer_start = ctypes.cast(address, ctypes.POINTER(ctypes.c_char))
+                    ctypes.memmove(buffer_start, encoded, 128)
+                else:
+                    raise ValueError(f"unsupported kv_override value for {key}: {value!r}")
+            kv_overrides_ref[-1].key = b"\0"
+            model_params.kv_overrides = kv_overrides_ref
+        return model_params, tensor_split_ref, kv_overrides_ref
+
+    @staticmethod
+    def build_context_params(
+        *,
+        n_ctx: Optional[int],
+        n_batch: Optional[int],
+        n_ubatch: Optional[int],
+        n_seq_max: Optional[int],
+        n_threads: Optional[int],
+        n_threads_batch: Optional[int],
+        rope_scaling_type: Optional[int],
+        pooling_type: Optional[int],
+        attention_type: Optional[int],
+        embedding: bool,
+        rope_freq_base: Optional[float],
+        rope_freq_scale: Optional[float],
+        yarn_ext_factor: Optional[float],
+        yarn_attn_factor: Optional[float],
+        yarn_beta_fast: Optional[float],
+        yarn_beta_slow: Optional[float],
+        yarn_orig_ctx: Optional[int],
+        offload_kqv: Optional[bool],
+        flash_attn: Optional[bool],
+        op_offload: Optional[bool],
+        swa_full: Optional[bool],
+        no_perf: Optional[bool],
+        type_k: Optional[int],
+        type_v: Optional[int],
+        kv_unified: bool,
+        n_rs_seq: Optional[int] = None,
+        ctx_type: Optional[int] = None,
+        n_outputs_max: Optional[int] = None,
+        ctx_other: Optional[Any] = None,
+    ) -> Any:
+        context_params = llama_cpp.llama_context_default_params()
+        if n_ctx is not None:
+            context_params.n_ctx = n_ctx
+        if n_batch is not None:
+            context_params.n_batch = min(int(context_params.n_ctx), n_batch)
+        if n_ubatch is not None:
+            context_params.n_ubatch = min(int(context_params.n_batch), n_ubatch)
+        if n_seq_max is not None:
+            context_params.n_seq_max = n_seq_max
+        if n_rs_seq is not None:
+            context_params.n_rs_seq = n_rs_seq
+        if n_threads is not None:
+            context_params.n_threads = n_threads
+        if n_threads_batch is not None:
+            context_params.n_threads_batch = n_threads_batch
+        if ctx_type is not None:
+            context_params.ctx_type = ctx_type
+        if n_outputs_max is not None:
+            context_params.n_outputs_max = n_outputs_max
+        if ctx_other is not None:
+            context_params.ctx_other = ctx_other
+        if rope_scaling_type is not None:
+            context_params.rope_scaling_type = rope_scaling_type
+        if pooling_type is not None:
+            context_params.pooling_type = pooling_type
+        if attention_type is not None:
+            context_params.attention_type = attention_type
+        context_params.embeddings = embedding
+        if rope_freq_base is not None:
+            context_params.rope_freq_base = rope_freq_base
+        if rope_freq_scale is not None:
+            context_params.rope_freq_scale = rope_freq_scale
+        if yarn_ext_factor is not None:
+            context_params.yarn_ext_factor = yarn_ext_factor
+        if yarn_attn_factor is not None:
+            context_params.yarn_attn_factor = yarn_attn_factor
+        if yarn_beta_fast is not None:
+            context_params.yarn_beta_fast = yarn_beta_fast
+        if yarn_beta_slow is not None:
+            context_params.yarn_beta_slow = yarn_beta_slow
+        if yarn_orig_ctx is not None:
+            context_params.yarn_orig_ctx = yarn_orig_ctx
+        if offload_kqv is not None:
+            context_params.offload_kqv = offload_kqv
+        if flash_attn is not None:
+            context_params.flash_attn_type = (
+                llama_cpp.LLAMA_FLASH_ATTN_TYPE_ENABLED
+                if flash_attn
+                else llama_cpp.LLAMA_FLASH_ATTN_TYPE_DISABLED
+            )
+        if op_offload is not None:
+            context_params.op_offload = op_offload
+        if swa_full is not None:
+            context_params.swa_full = swa_full
+        if no_perf is not None:
+            context_params.no_perf = no_perf
+        if type_k is not None:
+            context_params.type_k = type_k
+        if type_v is not None:
+            context_params.type_v = type_v
+        context_params.kv_unified = kv_unified
+        return context_params
+
+    @property
+    def exact_checkpoints_only(self) -> bool:
+        return self.memory_model in {"recurrent", "hybrid"}
+
+    @property
+    def has_attention_budget(self) -> bool:
+        return self.memory_model != "recurrent"
+
+    @property
+    def attention_partitioned(self) -> bool:
+        return self.memory_model == "attention-partitioned"
+
+    @property
+    def request_context_limit(self) -> int:
+        if self.attention_partitioned:
+            return self.n_ctx_seq
+        return self.n_ctx
+
+    def _load_lora_adapters(self, loras: List["Model.LoraAdapter"]) -> None:
+        for lora in loras:
+            adapter = llama_cpp.llama_adapter_lora_init(
+                self.llama_model,
+                lora.path.encode("utf-8"),
+            )
+            if adapter is None:
+                raise RuntimeError(f"failed to load LoRA adapter: {lora.path}")
+            self._lora_adapters.append(adapter)
+
+        if not self._lora_adapters:
+            return
+
+        adapter_array_type = llama_cpp.llama_adapter_lora_p_ctypes * len(
+            self._lora_adapters
+        )
+        scale_array_type = ctypes.c_float * len(self._lora_adapters)
+        self._lora_adapter_array = adapter_array_type(*self._lora_adapters)
+        self._lora_scales_array = scale_array_type(
+            *(float(lora.scale) for lora in loras)
+        )
+
+    def _apply_lora_adapters(self, ctx: Any, context_name: str) -> None:
+        if not self._lora_adapters:
+            return
+        if self._lora_adapter_array is None or self._lora_scales_array is None:
+            raise RuntimeError("LoRA adapter arrays are not initialized")
+        result = llama_cpp.llama_set_adapters_lora(
+            ctx,
+            self._lora_adapter_array,
+            len(self._lora_adapters),
+            self._lora_scales_array,
+        )
+        if result:
+            raise RuntimeError(f"failed to apply LoRA adapters to {context_name} context")
+
+    def _free_lora_adapters(self) -> None:
+        while self._lora_adapters:
+            adapter = self._lora_adapters.pop()
+            llama_cpp.llama_adapter_lora_free(adapter)
+        self._lora_adapter_array = None
+        self._lora_scales_array = None
+
+    def close(self) -> None:
+        if self.mtmd_processor is not None:
+            self.mtmd_processor.close()
+            self.mtmd_processor = None
+        if self.draft_provider is not None:
+            self.draft_provider.close()
+        llama_cpp.llama_batch_free(self.batch)
+        llama_cpp.llama_free(self.ctx)
+        self._free_lora_adapters()
+        if self.draft_llama_model is not None:
+            llama_cpp.llama_model_free(self.draft_llama_model)
+            self.draft_llama_model = None
+        llama_cpp.llama_model_free(self.llama_model)
+        if self.backend_initialized:
+            llama_cpp.llama_backend_free()
+            self.backend_initialized = False
+
+    @staticmethod
+    def _model_meta_key_by_index(llama_model: Any, index: int) -> Optional[str]:
+        capacity = 256
+        while True:
+            buffer = ctypes.create_string_buffer(capacity)
+            count = int(
+                llama_cpp.llama_model_meta_key_by_index(
+                    llama_model,
+                    index,
+                    cast(Any, buffer),
+                    capacity,
+                )
+            )
+            if count < 0:
+                return None
+            if count < capacity:
+                return buffer.value.decode("utf-8", errors="ignore")
+            capacity = count + 1
+
+    @staticmethod
+    def _model_meta_value(llama_model: Any, key: str) -> Optional[str]:
+        encoded = key.encode("utf-8")
+        capacity = 256
+        while True:
+            buffer = ctypes.create_string_buffer(capacity)
+            count = int(
+                llama_cpp.llama_model_meta_val_str(
+                    llama_model,
+                    encoded,
+                    cast(Any, buffer),
+                    capacity,
+                )
+            )
+            if count < 0:
+                return None
+            if count < capacity:
+                return buffer.value.decode("utf-8", errors="ignore")
+            capacity = count + 1
+
+    @staticmethod
+    def _parse_pooling_type(value: str) -> Optional[int]:
+        normalized = value.strip().lower()
+        try:
+            return int(normalized)
+        except ValueError:
+            return {
+                "none": llama_cpp.LLAMA_POOLING_TYPE_NONE,
+                "mean": llama_cpp.LLAMA_POOLING_TYPE_MEAN,
+                "cls": llama_cpp.LLAMA_POOLING_TYPE_CLS,
+                "last": llama_cpp.LLAMA_POOLING_TYPE_LAST,
+                "rank": llama_cpp.LLAMA_POOLING_TYPE_RANK,
+            }.get(normalized)
+
+    @classmethod
+    def detect_embedding_model(cls, llama_model: Any) -> bool:
+        for index in range(int(llama_cpp.llama_model_meta_count(llama_model))):
+            key = cls._model_meta_key_by_index(llama_model, index)
+            if key is None or not key.endswith(".pooling_type"):
+                continue
+            value = cls._model_meta_value(llama_model, key)
+            if value is None:
+                continue
+            pooling_type = cls._parse_pooling_type(value)
+            return pooling_type in {
+                llama_cpp.LLAMA_POOLING_TYPE_MEAN,
+                llama_cpp.LLAMA_POOLING_TYPE_CLS,
+                llama_cpp.LLAMA_POOLING_TYPE_LAST,
+            }
+        return False
+
+    @classmethod
+    def resolve_embedding_mode(
+        cls,
+        llama_model: Any,
+        embedding: Optional[bool],
+    ) -> bool:
+        if embedding is not None:
+            return embedding
+        return cls.detect_embedding_model(llama_model)
+
+    def _meta_value(self, key: str) -> Optional[str]:
+        return self._model_meta_value(self.llama_model, key)
+
+    def _build_chat_formatter(self) -> Optional[Jinja2ChatFormatter]:
+        template_text = self.chat_template_override
+        if template_text is None:
+            template = llama_cpp.llama_model_chat_template(self.llama_model, None)
+            if template:
+                template_text = template.decode("utf-8", errors="ignore")
+        if not template_text:
+            return None
+        bos_token = ""
+        eos_token = ""
+        if self.bos_token != -1:
+            bos_text = llama_cpp.llama_vocab_get_text(self.vocab, self.bos_token)
+            bos_token = bos_text.decode("utf-8", errors="ignore") if bos_text else ""
+        if self.eos_token != -1:
+            eos_text = llama_cpp.llama_vocab_get_text(self.vocab, self.eos_token)
+            eos_token = eos_text.decode("utf-8", errors="ignore") if eos_text else ""
+        return Jinja2ChatFormatter(
+            template=template_text,
+            bos_token=bos_token,
+            eos_token=eos_token,
+        )
+
+    def tokenize(
+        self,
+        text: str,
+        *,
+        add_bos: bool = True,
+        special: bool = True,
+    ) -> List[int]:
+        encoded = text.encode("utf-8")
+        capacity = max(32, len(encoded) + 32)
+        while True:
+            tokens = (llama_cpp.llama_token * capacity)()
+            count = int(
+                llama_cpp.llama_tokenize(
+                    self.vocab,
+                    encoded,
+                    len(encoded),
+                    tokens,
+                    capacity,
+                    add_bos,
+                    special,
+                )
+            )
+            if count >= 0:
+                return [int(tokens[index]) for index in range(count)]
+            capacity = max(capacity * 2, -count)
+
+    def build_prompt_tokens(self, prompt: str, suffix: Optional[str]) -> List[int]:
+        if suffix is None:
+            return self.tokenize(prompt)
+        if min(self.fim_pre_token, self.fim_mid_token, self.fim_suf_token) < 0:
+            raise ValueError("suffix is not supported by this model")
+        bos_tokens = [self.cls_token if self.cls_token != -1 else self.bos_token]
+        eos_tokens = [self.sep_token if self.sep_token != -1 else self.eos_token]
+        if not self.add_bos_token or bos_tokens[:1] == [-1]:
+            bos_tokens = []
+        if not self.add_eos_token and self.sep_token == -1:
+            eos_tokens = []
+        suffix_text = suffix
+        suffix_space_prefix = 0
+        if self.add_space_prefix and suffix_text:
+            suffix_text = "☺" + suffix_text
+            suffix_space_prefix = 2
+        prefix_tokens = [self.fim_pre_token] + self.tokenize(
+            prompt,
+            add_bos=False,
+            special=False,
+        )
+        suffix_tokens = [self.fim_suf_token]
+        if suffix_text:
+            suffix_tokens.extend(
+                self.tokenize(
+                    suffix_text,
+                    add_bos=False,
+                    special=False,
+                )[suffix_space_prefix:]
+            )
+        return bos_tokens + prefix_tokens + suffix_tokens + [self.fim_mid_token] + eos_tokens
+
+    def build_chat_prompt(
+        self,
+        messages: List[ChatCompletionRequestMessage],
+        *,
+        functions: Optional[List[ChatTemplateFunctionDefinition]] = None,
+        function_call: Optional[Union[str, ChatTemplateFunctionCall]] = None,
+        tools: Optional[List[ChatTemplateTool]] = None,
+        tool_choice: Optional[Union[str, ChatTemplateToolChoice]] = None,
+        reasoning_effort: Optional[str] = None,
+    ) -> Tuple[str, str, PromptPlan, List[str]]:
+        if self.chat_formatter is None:
+            raise ValueError("model does not provide a GGUF chat template")
+        if self.mtmd_processor is not None:
+            prompt_plan = self.mtmd_processor.build_prompt_plan(
+                messages=messages,
+                functions=functions,
+                function_call=function_call,
+                tools=tools,
+                tool_choice=tool_choice,
+                reasoning_effort=reasoning_effort,
+            )
+            formatter_stop = [self.chat_formatter._eos_token] if self.chat_formatter._eos_token else []
+            return (
+                prompt_plan.text,
+                prompt_plan.generation_prompt,
+                prompt_plan,
+                formatter_stop,
+            )
+        prompt, generation_prompt, formatter_stop = self.chat_formatter.format(
+            messages=messages,
+            functions=functions,
+            function_call=function_call,
+            tools=tools,
+            tool_choice=tool_choice,
+            reasoning_effort=reasoning_effort,
+        )
+        prompt_tokens = self.tokenize(prompt, add_bos=False, special=True)
+        return (
+            prompt,
+            generation_prompt,
+            PromptPlan.from_tokens(
+                prompt,
+                prompt_tokens,
+                generation_prompt=generation_prompt,
+            ),
+            formatter_stop,
+        )
+
+    def detokenize(self, tokens: Sequence[int]) -> bytes:
+        if not tokens:
+            return b""
+        array = (llama_cpp.llama_token * len(tokens))(*tokens)
+        capacity = max(64, len(tokens) * 16)
+        while True:
+            buffer = ctypes.create_string_buffer(capacity)
+            count = int(
+                llama_cpp.llama_detokenize(
+                    self.vocab,
+                    array,
+                    len(tokens),
+                    cast(Any, buffer),
+                    capacity,
+                    True,
+                    True,
+                )
+            )
+            if count >= 0:
+                return bytes(buffer.raw[:count])
+            capacity = max(capacity * 2, -count)
+
+    def token_bytes(self, token: int) -> bytes:
+        return self.detokenize([token])
+
+    def token_bytes_with_prev(self, prev_tokens: Sequence[int], token: int) -> bytes:
+        current = self.detokenize([*prev_tokens, token])
+        previous = self.detokenize(prev_tokens)
+        return current[len(previous) :]
+
+    def token_bytes_with_prev_bytes(
+        self,
+        prev_tokens: Sequence[int],
+        prev_text_bytes: Union[bytes, bytearray],
+        token: int,
+    ) -> bytes:
+        current = self.detokenize([*prev_tokens, token])
+        return current[len(prev_text_bytes) :]
+
+    def clear_batch(self) -> None:
+        self.batch.n_tokens = 0
+        self._embedding_batch = None
+        self._embedding_batch_refs = []
+
+    def clear_memory(self) -> None:
+        if self.mem is not None:
+            llama_cpp.llama_memory_clear(self.mem, True)
+
+    def add_batch_tokens(
+        self,
+        *,
+        seq_id: int,
+        start_pos: int,
+        tokens: Sequence[int],
+        output_indices: Sequence[Optional[int]],
+    ) -> None:
+        if not tokens:
+            return
+        for index, token in enumerate(tokens):
+            slot = self.batch.n_tokens
+            self.batch.token[slot] = token
+            self.batch.pos[slot] = start_pos + index
+            self.batch.seq_id[slot][0] = seq_id
+            self.batch.n_seq_id[slot] = 1
+            self.batch.logits[slot] = int(output_indices[index] is not None)
+            self.batch.n_tokens += 1
+
+    def add_batch_embeddings(
+        self,
+        *,
+        seq_id: int,
+        embeddings: np.ndarray,
+        positions: np.ndarray,
+        output_indices: Sequence[Optional[int]],
+    ) -> None:
+        if self.batch.n_tokens:
+            raise RuntimeError("cannot mix token and embedding batches")
+        if self._embedding_batch is not None:
+            raise RuntimeError("only one embedding batch is supported per scheduler step")
+        embeddings = np.ascontiguousarray(embeddings, dtype=np.float32)
+        positions = np.ascontiguousarray(positions, dtype=np.int32).reshape(-1)
+        n_tokens = int(embeddings.shape[0])
+        if n_tokens == 0:
+            return
+        if embeddings.ndim != 2 or embeddings.shape[1] != self.n_embd_inp:
+            raise RuntimeError("embedding batch shape does not match model input embedding size")
+        if len(positions) not in {n_tokens, n_tokens * 4}:
+            raise RuntimeError("embedding position length mismatch")
+        if len(output_indices) != n_tokens:
+            raise RuntimeError("embedding output index length mismatch")
+        pos_array = (llama_cpp.llama_pos * len(positions))(
+            *[int(pos) for pos in positions]
+        )
+        n_seq_id_array = (ctypes.c_int32 * n_tokens)(*[1] * n_tokens)
+        seq_id_array = (llama_cpp.llama_seq_id * 1)(llama_cpp.llama_seq_id(seq_id))
+        seq_ids_array = (ctypes.POINTER(llama_cpp.llama_seq_id) * (n_tokens + 1))()
+        for index in range(n_tokens):
+            seq_ids_array[index] = seq_id_array
+        logits_array = (ctypes.c_int8 * n_tokens)(
+            *[int(output_index is not None) for output_index in output_indices]
+        )
+        batch = llama_cpp.llama_batch(
+            n_tokens=n_tokens,
+            token=None,
+            embd=embeddings.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
+            pos=pos_array,
+            n_seq_id=n_seq_id_array,
+            seq_id=seq_ids_array,
+            logits=logits_array,
+        )
+        self._embedding_batch = batch
+        self._embedding_batch_refs = [
+            embeddings,
+            positions,
+            pos_array,
+            n_seq_id_array,
+            seq_id_array,
+            seq_ids_array,
+            logits_array,
+        ]
+
+    def decode(self) -> None:
+        batch = self._embedding_batch if self._embedding_batch is not None else self.batch
+        if self.embedding and self.has_encoder:
+            operation = "llama_encode"
+            result = int(llama_cpp.llama_encode(self.ctx, batch))
+        else:
+            operation = "llama_decode"
+            result = int(llama_cpp.llama_decode(self.ctx, batch))
+        if result != 0:
+            raise RuntimeError(f"{operation} failed with code {result}")
+
+    def process_draft_batch(self) -> None:
+        if self.draft_provider is not None:
+            self.draft_provider.process(self.batch)
+
+    def set_draft_processing_enabled(self, enabled: bool) -> None:
+        if self.draft_provider is not None:
+            self.draft_provider.set_target_processing_enabled(enabled)
+
+    def accept_draft_tokens(self, seq_id: int, accepted_draft_tokens: int) -> None:
+        if self.draft_provider is not None:
+            self.draft_provider.accept(seq_id, accepted_draft_tokens)
+
+    def truncate_draft_sequence(self, seq_id: int, keep_len: int) -> None:
+        if self.draft_provider is not None:
+            self.draft_provider.truncate(seq_id, keep_len)
+
+    def copy_draft_sequence(
+        self,
+        source_seq_id: int,
+        dest_seq_id: int,
+        p0: int,
+        p1: int,
+    ) -> None:
+        if self.draft_provider is not None:
+            self.draft_provider.copy_sequence(source_seq_id, dest_seq_id, p0, p1)
+
+    def logits(self, output_index: int) -> np.ndarray:
+        ptr = llama_cpp.llama_get_logits_ith(self.ctx, output_index)
+        if not ptr:
+            raise RuntimeError(f"missing logits output {output_index}")
+        return np.ctypeslib.as_array(ptr, shape=(self.n_vocab,)).copy()
+
+    def embed(
+        self,
+        inputs: Sequence[Union[str, List[int]]],
+    ) -> Tuple[List[List[float]], int]:
+        if not self.embedding:
+            raise CompletionRequestValidationError(
+                "model.embedding must be true to use /v1/embeddings"
+            )
+        pooling_type = int(llama_cpp.llama_pooling_type(self.ctx))
+        if pooling_type == llama_cpp.LLAMA_POOLING_TYPE_NONE:
+            raise CompletionRequestValidationError(
+                "/v1/embeddings requires a pooled embedding model; "
+                "set model.pooling_type to MEAN, CLS, or LAST"
+            )
+        if pooling_type == llama_cpp.LLAMA_POOLING_TYPE_RANK:
+            raise CompletionRequestValidationError(
+                "/v1/embeddings does not support reranking pooling"
+            )
+        if len(inputs) > 2048:
+            raise CompletionRequestValidationError(
+                "embedding input batch size exceeds 2048"
+            )
+
+        embeddings: List[List[float]] = []
+        total_tokens = 0
+        batch_sizes: List[int] = []
+        batch_token_count = 0
+
+        def decode_embedding_batch() -> None:
+            nonlocal batch_token_count
+            if not batch_sizes:
+                return
+            self.clear_memory()
+            self.decode()
+            self.clear_batch()
+            for seq_id in range(len(batch_sizes)):
+                ptr = llama_cpp.llama_get_embeddings_seq(
+                    self.ctx,
+                    llama_cpp.llama_seq_id(seq_id),
+                )
+                if not ptr:
+                    raise RuntimeError(f"missing embedding output for input {seq_id}")
+                embeddings.append(
+                    np.ctypeslib.as_array(ptr, shape=(self.n_embd_out,)).astype(
+                        float
+                    ).tolist()
+                )
+            batch_sizes.clear()
+            batch_token_count = 0
+
+        try:
+            self.clear_batch()
+            self.clear_memory()
+            for input_item in inputs:
+                tokens = (
+                    self.tokenize(input_item)
+                    if isinstance(input_item, str)
+                    else list(input_item)
+                )
+                n_tokens = len(tokens)
+                if n_tokens == 0:
+                    raise CompletionRequestValidationError(
+                        "embedding input must not be empty"
+                    )
+                if n_tokens > self.n_ctx_seq:
+                    raise CompletionRequestValidationError(
+                        f"embedding input has {n_tokens} tokens, exceeding n_ctx_seq ({self.n_ctx_seq})"
+                    )
+                if n_tokens > self.n_batch:
+                    raise CompletionRequestValidationError(
+                        f"embedding input has {n_tokens} tokens, exceeding n_batch ({self.n_batch})"
+                    )
+                if total_tokens + n_tokens > 300_000:
+                    raise CompletionRequestValidationError(
+                        "embedding request exceeds 300000 total tokens"
+                    )
+                if (
+                    batch_token_count + n_tokens > self.n_batch
+                    or len(batch_sizes) >= self.n_seq_max
+                ):
+                    decode_embedding_batch()
+                seq_id = len(batch_sizes)
+                self.add_batch_tokens(
+                    seq_id=seq_id,
+                    start_pos=0,
+                    tokens=tokens,
+                    output_indices=[0] * n_tokens,
+                )
+                batch_sizes.append(n_tokens)
+                batch_token_count += n_tokens
+                total_tokens += n_tokens
+            decode_embedding_batch()
+        finally:
+            self.clear_batch()
+            self.clear_memory()
+        return embeddings, total_tokens
+
+
+class SequenceDiskCache(SequenceCache):
+    """Directory-backed cache for serialized llama.cpp sequence state."""
+
+    @dataclass
+    class Entry:
+        entry_id: int
+        path: Path
+        tokens: Tuple[int, ...]
+        size_bytes: int
+        has_prompt_logits: bool
+        last_accessed: float
+
+    @dataclass(frozen=True)
+    class Header:
+        tokens: Tuple[int, ...]
+        has_prompt_logits: bool
+
+    @dataclass
+    class Payload:
+        tokens: List[int]
+        state_bytes: np.ndarray
+        prompt_logits: Optional[np.ndarray]
+
+    FORMAT_VERSION = "1"
+
+    TENSOR_TOKENS = "tokens"
+    TENSOR_STATE = "state"
+    TENSOR_PROMPT_LOGITS = "prompt_logits"
+
+    METADATA_FORMAT = "sequence_cache_format"
+    METADATA_COMPATIBILITY_KEY = "compatibility_key"
+
+    def __init__(
+        self,
+        *,
+        path: Union[str, Path],
+        max_bytes: int,
+        compatibility_key: str,
+        min_tokens: int = 128,
+    ) -> None:
+        self.path = Path(path)
+        self.path.mkdir(parents=True, exist_ok=True)
+        self.max_bytes = max(0, int(max_bytes))
+        self.min_tokens = max(1, int(min_tokens))
+        self.compatibility_key = compatibility_key
+        self._metadata = {
+            self.METADATA_FORMAT: self.FORMAT_VERSION,
+            self.METADATA_COMPATIBILITY_KEY: compatibility_key,
+        }
+        self._trie = RadixTrie()
+        self._entries_by_id: Dict[int, SequenceDiskCache.Entry] = {}
+        self._entries_by_tokens: Dict[Tuple[int, ...], SequenceDiskCache.Entry] = {}
+        self._next_entry_id = 0
+        self._size_bytes = 0
+        self._load_entries()
+        self._evict_if_needed()
+
+    @staticmethod
+    def fingerprint_file(path: str) -> str:
+        stat = os.stat(path)
+        payload = f"{Path(path).resolve()}:{stat.st_size}:{stat.st_mtime_ns}"
+        return hashlib.sha256(payload.encode("utf-8")).hexdigest()
+
+    @classmethod
+    def compatibility_key_for_model(cls, model: "Model") -> str:
+        payload: Dict[str, Any] = {
+            "model": cls.fingerprint_file(model.model_path),
+            "memory_model": model.memory_model,
+            "loras": [
+                {
+                    "adapter": cls.fingerprint_file(lora.path),
+                    "scale": float(lora.scale),
+                }
+                for lora in model.loras
+            ],
+        }
+        if model.memory_model == "attention-partitioned":
+            payload["attention_streams"] = model.n_seq_max
+        encoded = json.dumps(
+            payload,
+            sort_keys=True,
+            separators=(",", ":"),
+        ).encode("utf-8")
+        return hashlib.sha256(encoded).hexdigest()
+
+    @staticmethod
+    def _safe_open(path: Path) -> Any:
+        try:
+            from safetensors import safe_open
+        except ImportError as exc:
+            raise ImportError(
+                "disk_cache requires safetensors. Install it with `pip install safetensors`."
+            ) from exc
+
+        return safe_open(str(path), framework="numpy")
+
+    @staticmethod
+    def _save_file(
+        tensors: Dict[str, np.ndarray],
+        path: Path,
+        metadata: Dict[str, str],
+    ) -> None:
+        from safetensors.numpy import save_file
+
+        save_file(tensors, str(path), metadata=metadata)
+
+    def _metadata_compatible(self, metadata: Optional[Dict[str, str]]) -> bool:
+        if metadata is None:
+            return False
+        return all(metadata.get(key) == value for key, value in self._metadata.items())
+
+    def _read_entry_header(self, path: Path) -> Optional["SequenceDiskCache.Header"]:
+        with self._safe_open(path) as tensors:
+            if not self._metadata_compatible(tensors.metadata()):
+                return None
+            tokens_array = tensors.get_tensor(self.TENSOR_TOKENS)
+            has_prompt_logits = self.TENSOR_PROMPT_LOGITS in tensors.keys()
+        tokens = tuple(int(token) for token in tokens_array.tolist())
+        if len(tokens) < self.min_tokens:
+            return None
+        return SequenceDiskCache.Header(
+            tokens=tokens,
+            has_prompt_logits=has_prompt_logits,
+        )
+
+    def _read_entry_payload(
+        self,
+        entry: "SequenceDiskCache.Entry",
+    ) -> "SequenceDiskCache.Payload":
+        with self._safe_open(entry.path) as tensors:
+            tokens = [
+                int(token)
+                for token in tensors.get_tensor(self.TENSOR_TOKENS).tolist()
+            ]
+            state_bytes = np.ascontiguousarray(
+                tensors.get_tensor(self.TENSOR_STATE),
+                dtype=np.uint8,
+            ).copy()
+            prompt_logits = (
+                tensors.get_tensor(self.TENSOR_PROMPT_LOGITS)
+                if entry.has_prompt_logits
+                and self.TENSOR_PROMPT_LOGITS in tensors.keys()
+                else None
+            )
+        return SequenceDiskCache.Payload(
+            tokens=tokens,
+            state_bytes=state_bytes,
+            prompt_logits=prompt_logits,
+        )
+
+    def _write_entry(
+        self,
+        path: Path,
+        tensors: Dict[str, np.ndarray],
+    ) -> None:
+        tmp_path = path.with_name(f"{path.name}.tmp")
+        try:
+            self._save_file(tensors, tmp_path, self._metadata)
+            os.replace(tmp_path, path)
+        except Exception:
+            try:
+                tmp_path.unlink()
+            except FileNotFoundError:
+                pass
+            raise
+
+    def _load_entries(self) -> None:
+        for path in sorted(self.path.glob("*.safetensors")):
+            try:
+                header = self._read_entry_header(path)
+                if header is None:
+                    continue
+                self._add_entry(
+                    path=path,
+                    tokens=header.tokens,
+                    has_prompt_logits=header.has_prompt_logits,
+                    last_accessed=path.stat().st_mtime,
+                )
+            except Exception:  # noqa: BLE001
+                continue
+
+    def _add_entry(
+        self,
+        *,
+        path: Path,
+        tokens: Tuple[int, ...],
+        has_prompt_logits: bool,
+        last_accessed: Optional[float] = None,
+    ) -> None:
+        existing = self._entries_by_tokens.get(tokens)
+        if existing is not None:
+            self._remove_entry(existing)
+        entry_id = self._next_entry_id
+        self._next_entry_id += 1
+        size_bytes = path.stat().st_size
+        entry = SequenceDiskCache.Entry(
+            entry_id=entry_id,
+            path=path,
+            tokens=tokens,
+            size_bytes=size_bytes,
+            has_prompt_logits=has_prompt_logits,
+            last_accessed=last_accessed if last_accessed is not None else time.time(),
+        )
+        self._entries_by_id[entry_id] = entry
+        self._entries_by_tokens[tokens] = entry
+        self._trie.extend(entry_id, tokens)
+        self._size_bytes += size_bytes
+
+    def _remove_entry(self, entry: "SequenceDiskCache.Entry") -> None:
+        if entry.entry_id in self._trie.sequence_lengths:
+            self._trie.truncate(entry.entry_id, 0)
+        self._entries_by_id.pop(entry.entry_id, None)
+        if self._entries_by_tokens.get(entry.tokens) is entry:
+            self._entries_by_tokens.pop(entry.tokens, None)
+        self._size_bytes = max(0, self._size_bytes - entry.size_bytes)
+        try:
+            entry.path.unlink()
+        except FileNotFoundError:
+            pass
+
+    @staticmethod
+    def _touch(entry: "SequenceDiskCache.Entry") -> None:
+        entry.last_accessed = time.time()
+        try:
+            os.utime(entry.path, None)
+        except OSError:
+            pass
+
+    def _oldest_entry(self) -> Optional["SequenceDiskCache.Entry"]:
+        if not self._entries_by_id:
+            return None
+        return min(
+            self._entries_by_id.values(),
+            key=lambda item: (item.last_accessed, item.size_bytes),
+        )
+
+    def _evict_until(self, target_bytes: int) -> None:
+        while self._size_bytes > target_bytes:
+            entry = self._oldest_entry()
+            if entry is None:
+                return
+            self._remove_entry(entry)
+
+    def _evict_if_needed(self) -> None:
+        if self.max_bytes <= 0:
+            for entry in list(self._entries_by_id.values()):
+                self._remove_entry(entry)
+            return
+        if self._size_bytes > self.max_bytes:
+            self._evict_until(self.max_bytes)
+            self._evict_until(int(self.max_bytes * 0.9))
+
+    def lookup(self, tokens: Sequence[int]) -> Optional[SequenceCache.Match]:
+        if not tokens:
+            return None
+        entry_id, _ = self._trie.longest_prefix(tokens)
+        entry = self._entries_by_id.get(entry_id)
+        if entry is None:
+            return None
+        self._touch(entry)
+        return SequenceCache.Match(
+            tokens=entry.tokens,
+            has_prompt_logits=entry.has_prompt_logits,
+        )
+
+    def load(
+        self,
+        match: SequenceCache.Match,
+    ) -> Optional[SequenceCache.Load]:
+        entry = self._entries_by_tokens.get(match.tokens)
+        if entry is None:
+            return None
+
+        payload = self._read_entry_payload(entry)
+        self._touch(entry)
+        return SequenceCache.Load(
+            tokens=payload.tokens,
+            state_bytes=payload.state_bytes,
+            prompt_logits=(
+                np.asarray(payload.prompt_logits, dtype=np.float32)
+                if payload.prompt_logits is not None
+                else None
+            ),
+        )
+
+    def save(
+        self,
+        tokens: Sequence[int],
+        state_bytes: np.ndarray,
+        prompt_logits: Optional[np.ndarray],
+    ) -> None:
+        if len(tokens) < self.min_tokens or self.max_bytes <= 0:
+            return
+        state = np.asarray(state_bytes, dtype=np.uint8)
+        if state.size <= 0:
+            return
+        state = np.ascontiguousarray(state).copy()
+        entry_tokens = tuple(int(token) for token in tokens)
+        tensors: Dict[str, np.ndarray] = {
+            self.TENSOR_TOKENS: np.asarray(entry_tokens, dtype=np.int32),
+            self.TENSOR_STATE: state,
+        }
+        if prompt_logits is not None:
+            tensors[self.TENSOR_PROMPT_LOGITS] = np.asarray(prompt_logits, dtype=np.float32)
+        path = self.path / f"{uuid.uuid4().hex}.safetensors"
+        self._write_entry(path, tensors)
+        self._add_entry(
+            path=path,
+            tokens=entry_tokens,
+            has_prompt_logits=prompt_logits is not None,
+        )
+        self._evict_if_needed()
+
+
+class MemoryPolicy(abc.ABC):
+    def __init__(self, scheduler: CompletionScheduler) -> None:
+        self.scheduler = scheduler
+
+    def reclaim_order(self, best_free: Optional[int]) -> List[int]:
+        reclaim_order = [seq_id for seq_id in self.scheduler.free_sequences if seq_id != best_free]
+        if best_free is not None:
+            reclaim_order.append(best_free)
+        return reclaim_order
+
+    @staticmethod
+    def generation_kv_for_request(
+        request: CompletionRequest,
+        prompt_length: int,
+    ) -> int:
+        return request.internal_completion_count * (
+            request.effective_max_len - prompt_length
+        )
+
+    @staticmethod
+    def attention_kv_required(
+        prompt_kv: int,
+        reused_kv: int,
+        generation_kv: int,
+    ) -> int:
+        return prompt_kv - reused_kv + generation_kv
+
+    def try_set_sequence_cache_match(
+        self,
+        request: CompletionRequest,
+        resident_reuse_len: int,
+        required_sequence_ids: int,
+        required_attn_kv: Optional[int] = None,
+        skip_attention_budget_when_unbounded: bool = False,
+    ) -> bool:
+        cache_match_length = self.scheduler.find_sequence_cache_match(
+            request,
+            resident_reuse_len,
+        )
+        if cache_match_length <= resident_reuse_len:
+            return False
+        has_sequence_budget = len(self.scheduler.unused_sequences) >= required_sequence_ids
+        has_attention_budget = required_attn_kv is None or (
+            skip_attention_budget_when_unbounded
+            and not self.scheduler.model.has_attention_budget
+        ) or self.scheduler.sequence_history.size + required_attn_kv <= self.scheduler.model.n_ctx
+        if has_sequence_budget and has_attention_budget:
+            return True
+        self.scheduler.clear_sequence_cache_match(request)
+        return False
+
+    @abc.abstractmethod
+    def can_admit(self, request: CompletionRequest) -> bool:
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def admit_request(self, request: CompletionRequest) -> None:
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def copy_prompt_state(
+        self,
+        source_sequence_id: int,
+        dest_sequence_id: int,
+        keep_len: int,
+    ) -> None:
+        raise NotImplementedError
+
+
+class AttentionMemoryPolicy(MemoryPolicy):
+    def match_prefix(self, tokens: Sequence[int]) -> Tuple[int, int]:
+        return self.scheduler.radix_trie.longest_prefix(
+            tokens,
+            preferred_sequences=self.scheduler.free_sequences,
+        )
+
+    @staticmethod
+    def reuse_len_for_request(request: CompletionRequest, match_length: int) -> int:
+        needs_generation = (
+            request.payload.max_tokens != 0
+            and request.effective_max_len > len(request.prompt_tokens)
+        )
+        reuse_len = match_length
+        if needs_generation and request.prompt_tokens:
+            reuse_len = min(reuse_len, len(request.prompt_tokens) - 1)
+        return request.prompt_plan.clamp_to_reusable_boundary(reuse_len)
+
+    def admit_request(self, request: CompletionRequest) -> None:
+        match_seq_id = request.match_sequence_id
+        match_length = request.match_length
+        reuse_len = self.reuse_len_for_request(request, match_length)
+        claimable = match_seq_id in self.scheduler.free_sequences
+        if request.sequence_cache_match is not None:
+            base_seq_id = self.scheduler.claim_unused_sequence()
+            reuse_len, request.prompt_logits = self.scheduler.hydrate_sequence_cache_match(
+                request,
+                base_seq_id,
+            )
+        elif claimable:
+            base_seq_id = self.scheduler.claim_free_sequence(match_seq_id)
+            if self.scheduler.radix_trie.length(base_seq_id) > reuse_len:
+                self.scheduler.truncate_sequence(base_seq_id, reuse_len)
+        else:
+            base_seq_id = self.scheduler.claim_unused_sequence()
+            if reuse_len > 0 and match_seq_id >= 0:
+                self.copy_prompt_state(match_seq_id, base_seq_id, reuse_len)
+        sibling_count = request.internal_completion_count - 1
+        sibling_seq_ids: List[int] = []
+        for _ in range(sibling_count):
+            seq_id = self.scheduler.claim_unused_sequence()
+            sibling_seq_ids.append(seq_id)
+        self.scheduler.activate_request(
+            request,
+            base_seq_id=base_seq_id,
+            sibling_seq_ids=sibling_seq_ids,
+        )
+        request.prompt_cursor = reuse_len
+        if request.prompt_cursor == len(request.prompt_tokens):
+            request.prompt_done = True
+            self.scheduler.maybe_save_sequence_cache(request)
+            self.scheduler.start_completions(
+                request,
+                prompt_output_index=None,
+                prompt_logits=request.prompt_logits,
+            )
+
+
+class UnifiedAttentionMemoryPolicy(AttentionMemoryPolicy):
+    def can_admit(self, request: CompletionRequest) -> bool:
+        match_seq_id, match_length = self.match_prefix(request.prompt_tokens)
+        match_length = request.prompt_plan.clamp_to_reusable_boundary(match_length)
+        request.match_sequence_id = match_seq_id
+        request.match_length = match_length
+        claimable = match_seq_id in self.scheduler.free_sequences
+        required_sequence_ids = request.internal_completion_count - int(claimable)
+        prompt_length = len(request.prompt_tokens)
+        prompt_kv = request.prompt_plan.eval_token_count
+        reuse_len = self.reuse_len_for_request(request, match_length)
+        prefix_credit = match_length if claimable else reuse_len
+        prefix_credit_kv = max(0, min(prefix_credit, request.prompt_plan.length))
+        generation_kv = self.generation_kv_for_request(request, prompt_length)
+        if self.try_set_sequence_cache_match(
+            request,
+            resident_reuse_len=reuse_len,
+            required_sequence_ids=request.internal_completion_count,
+            required_attn_kv=self.attention_kv_required(
+                prompt_kv,
+                reused_kv=0,
+                generation_kv=generation_kv,
+            ),
+        ):
+            return True
+        required_kv = self.attention_kv_required(
+            prompt_kv,
+            reused_kv=prefix_credit_kv,
+            generation_kv=generation_kv,
+        )
+        if (
+            len(self.scheduler.unused_sequences) >= required_sequence_ids
+            and self.scheduler.sequence_history.size + required_kv <= self.scheduler.model.n_ctx
+        ):
+            return True
+        best_free = match_seq_id if claimable else None
+        for seq_id in self.reclaim_order(best_free):
+            if len(self.scheduler.unused_sequences) < required_sequence_ids:
+                self.scheduler.delete_free_sequence(seq_id)
+            elif self.scheduler.sequence_history.size + required_kv > self.scheduler.model.n_ctx:
+                if seq_id == best_free and request.match_length > 0:
+                    self.scheduler.truncate_free_sequence(seq_id, request.match_length)
+                else:
+                    self.scheduler.delete_free_sequence(seq_id)
+            if (
+                len(self.scheduler.unused_sequences) >= required_sequence_ids
+                and self.scheduler.sequence_history.size + required_kv <= self.scheduler.model.n_ctx
+            ):
+                request.match_sequence_id, request.match_length = self.match_prefix(
+                    request.prompt_tokens,
+                )
+                request.match_length = request.prompt_plan.clamp_to_reusable_boundary(
+                    request.match_length
+                )
+                return True
+        return False
+
+    def copy_prompt_state(
+        self,
+        source_sequence_id: int,
+        dest_sequence_id: int,
+        keep_len: int,
+    ) -> None:
+        self.scheduler.copy_sequence_state(
+            source_sequence_id,
+            dest_sequence_id,
+            keep_len,
+        )
+
+
+class PartitionedAttentionMemoryPolicy(AttentionMemoryPolicy):
+    def can_admit(self, request: CompletionRequest) -> bool:
+        match_seq_id, match_length = self.match_prefix(request.prompt_tokens)
+        match_length = request.prompt_plan.clamp_to_reusable_boundary(match_length)
+        request.match_sequence_id = match_seq_id
+        request.match_length = match_length
+        claimable = match_seq_id in self.scheduler.free_sequences
+        required_sequence_ids = request.internal_completion_count - int(claimable)
+        reuse_len = self.reuse_len_for_request(request, match_length)
+        if self.try_set_sequence_cache_match(
+            request,
+            resident_reuse_len=reuse_len,
+            required_sequence_ids=request.internal_completion_count,
+        ):
+            return True
+        if len(self.scheduler.unused_sequences) >= required_sequence_ids:
+            return True
+        best_free = match_seq_id if claimable else None
+        for seq_id in self.reclaim_order(best_free):
+            self.scheduler.delete_free_sequence(seq_id)
+            if len(self.scheduler.unused_sequences) >= required_sequence_ids:
+                request.match_sequence_id, request.match_length = self.match_prefix(
+                    request.prompt_tokens,
+                )
+                request.match_length = request.prompt_plan.clamp_to_reusable_boundary(
+                    request.match_length
+                )
+                return True
+        return False
+
+    def copy_prompt_state(
+        self,
+        source_sequence_id: int,
+        dest_sequence_id: int,
+        keep_len: int,
+    ) -> None:
+        self.scheduler.copy_sequence_state(
+            source_sequence_id,
+            dest_sequence_id,
+            keep_len,
+            copy_all_state=True,
+        )
+
+
+class CheckpointMemoryPolicy(MemoryPolicy):
+    def exact_checkpoint_match(self, tokens: Sequence[int]) -> Tuple[int, int]:
+        match_seq_id, match_length = self.scheduler.radix_trie.longest_prefix(
+            tokens,
+            preferred_sequences=self.scheduler.free_sequences,
+            exact_only=True,
+        )
+        if match_seq_id not in self.scheduler.free_sequences:
+            return -1, 0
+        return match_seq_id, match_length
+
+    def can_admit(self, request: CompletionRequest) -> bool:
+        match_seq_id, match_length = self.exact_checkpoint_match(request.prompt_tokens)
+        request.match_sequence_id = match_seq_id
+        request.match_length = match_length
+        claimable = match_seq_id in self.scheduler.free_sequences
+        required_sequence_ids = request.internal_completion_count - int(claimable)
+        prompt_length = len(request.prompt_tokens)
+        prompt_kv = request.prompt_plan.eval_token_count
+        generation_kv = self.generation_kv_for_request(request, prompt_length)
+        if self.try_set_sequence_cache_match(
+            request,
+            resident_reuse_len=match_length,
+            required_sequence_ids=request.internal_completion_count,
+            required_attn_kv=self.attention_kv_required(
+                prompt_kv,
+                reused_kv=0,
+                generation_kv=generation_kv,
+            ),
+            skip_attention_budget_when_unbounded=True,
+        ):
+            return True
+        required_attn_kv = self.attention_kv_required(
+            prompt_kv,
+            reused_kv=max(0, min(match_length, request.prompt_plan.length)),
+            generation_kv=generation_kv,
+        )
+        if len(self.scheduler.unused_sequences) >= required_sequence_ids and (
+            not self.scheduler.model.has_attention_budget
+            or self.scheduler.sequence_history.size + required_attn_kv <= self.scheduler.model.n_ctx
+        ):
+            return True
+        best_free = match_seq_id if claimable else None
+        for seq_id in self.reclaim_order(best_free):
+            self.scheduler.delete_free_sequence(seq_id)
+            if len(self.scheduler.unused_sequences) >= required_sequence_ids and (
+                not self.scheduler.model.has_attention_budget
+                or self.scheduler.sequence_history.size + required_attn_kv <= self.scheduler.model.n_ctx
+            ):
+                request.match_sequence_id, request.match_length = self.exact_checkpoint_match(
+                    request.prompt_tokens,
+                )
+                return True
+        return False
+
+    def admit_request(self, request: CompletionRequest) -> None:
+        match_seq_id = request.match_sequence_id
+        match_length = request.match_length
+        claimable = match_seq_id in self.scheduler.free_sequences
+        if request.sequence_cache_match is not None:
+            base_seq_id = self.scheduler.claim_unused_sequence()
+            match_length, request.prompt_logits = self.scheduler.hydrate_sequence_cache_match(
+                request,
+                base_seq_id,
+            )
+        elif claimable:
+            base_seq_id = self.scheduler.claim_free_sequence(match_seq_id)
+            request.prompt_logits = self.scheduler.checkpoint_logits.get(base_seq_id)
+            self.scheduler.metrics.checkpoint_hits_total += 1
+        else:
+            base_seq_id = self.scheduler.claim_unused_sequence()
+            request.prompt_logits = None
+        sibling_count = request.internal_completion_count - 1
+        sibling_seq_ids: List[int] = []
+        for _ in range(sibling_count):
+            seq_id = self.scheduler.claim_unused_sequence()
+            sibling_seq_ids.append(seq_id)
+        self.scheduler.activate_request(
+            request,
+            base_seq_id=base_seq_id,
+            sibling_seq_ids=sibling_seq_ids,
+        )
+        request.prompt_cursor = match_length
+        if request.prompt_cursor == len(request.prompt_tokens):
+            request.prompt_done = True
+            self.scheduler.maybe_save_prompt_checkpoint(request)
+            self.scheduler.maybe_save_sequence_cache(request)
+            self.scheduler.start_completions(
+                request,
+                prompt_output_index=None,
+                prompt_logits=request.prompt_logits,
+            )
+
+    def copy_prompt_state(
+        self,
+        source_sequence_id: int,
+        dest_sequence_id: int,
+        keep_len: int,
+    ) -> None:
+        self.scheduler.copy_sequence_state(
+            source_sequence_id,
+            dest_sequence_id,
+            keep_len,
+        )
+
+
+class CompletionScheduler:
+    @dataclass
+    class BatchItem:
+        @dataclass
+        class Prefill:
+            embeddings: Optional[np.ndarray] = None
+            positions: Optional[np.ndarray] = None
+            non_causal: bool = False
+            prompt_advance_to: Optional[int] = None
+
+        @dataclass
+        class Decode:
+            completion_index: int
+            pending_count: int
+            accepted_draft_count: int = 0
+            sampled_pending_token: Optional[int] = None
+            rollback_keep_len: Optional[int] = None
+            rollback_accepted_draft_count: int = 0
+            rollback_draft_processed: bool = False
+            deferred_accept_draft_count: Optional[int] = None
+            deferred_truncate_draft_len: Optional[int] = None
+
+        kind: Literal["prefill", "decode"]
+        request_id: str
+        seq_id: int
+        start_pos: int
+        llama_start_pos: int
+        tokens: List[int]
+        identity_tokens: List[int]
+        output_indices: List[Optional[int]]
+        output_positions: List[int]
+        position_increments: List[int]
+        prefill_state: Optional["CompletionScheduler.BatchItem.Prefill"] = None
+        decode_state: Optional["CompletionScheduler.BatchItem.Decode"] = None
+
+        def require_prefill(self) -> "CompletionScheduler.BatchItem.Prefill":
+            if self.prefill_state is None:
+                raise RuntimeError("batch item is not a prefill item")
+            return self.prefill_state
+
+        def require_decode(self) -> "CompletionScheduler.BatchItem.Decode":
+            if self.decode_state is None:
+                raise RuntimeError("batch item is not a decode item")
+            return self.decode_state
+
+        @property
+        def batch_token_count(self) -> int:
+            if (
+                self.prefill_state is not None
+                and self.prefill_state.embeddings is not None
+            ):
+                return int(self.prefill_state.embeddings.shape[0])
+            return len(self.tokens)
+
+        @classmethod
+        def prefill(
+            cls,
+            *,
+            request_id: str,
+            seq_id: int,
+            start_pos: int,
+            llama_start_pos: int,
+            tokens: List[int],
+            identity_tokens: List[int],
+            output_indices: List[Optional[int]],
+            output_positions: List[int],
+            position_increments: List[int],
+            embeddings: Optional[np.ndarray] = None,
+            positions: Optional[np.ndarray] = None,
+            non_causal: bool = False,
+            prompt_advance_to: Optional[int] = None,
+        ) -> "CompletionScheduler.BatchItem":
+            return cls(
+                kind="prefill",
+                request_id=request_id,
+                seq_id=seq_id,
+                start_pos=start_pos,
+                llama_start_pos=llama_start_pos,
+                tokens=tokens,
+                identity_tokens=identity_tokens,
+                output_indices=output_indices,
+                output_positions=output_positions,
+                position_increments=position_increments,
+                prefill_state=cls.Prefill(
+                    embeddings=embeddings,
+                    positions=positions,
+                    non_causal=non_causal,
+                    prompt_advance_to=prompt_advance_to,
+                ),
+            )
+
+        @classmethod
+        def decode(
+            cls,
+            *,
+            request_id: str,
+            seq_id: int,
+            start_pos: int,
+            llama_start_pos: int,
+            tokens: List[int],
+            identity_tokens: List[int],
+            output_indices: List[Optional[int]],
+            output_positions: List[int],
+            position_increments: List[int],
+            completion_index: int,
+            pending_count: int,
+        ) -> "CompletionScheduler.BatchItem":
+            return cls(
+                kind="decode",
+                request_id=request_id,
+                seq_id=seq_id,
+                start_pos=start_pos,
+                llama_start_pos=llama_start_pos,
+                tokens=tokens,
+                identity_tokens=identity_tokens,
+                output_indices=output_indices,
+                output_positions=output_positions,
+                position_increments=position_increments,
+                decode_state=cls.Decode(
+                    completion_index=completion_index,
+                    pending_count=pending_count,
+                ),
+            )
+
+    def __init__(
+        self,
+        model: Model,
+        sequence_cache: Optional[SequenceCache] = None,
+    ) -> None:
+        self.model = model
+        self.sequence_cache = sequence_cache
+        self.formatter = OpenAIFormatter(model)
+        self.radix_trie = RadixTrie()
+        self.sequence_history = SequenceHistory()
+        self.metrics = SchedulerMetrics()
+        self.checkpoint_logits: Dict[int, np.ndarray] = {}
+        self.claimed_sequences: set[int] = set()
+        self.free_sequences: "OrderedDict[int, None]" = OrderedDict()
+        self.unused_sequences: List[int] = list(range(self.model.n_seq_max - 1, -1, -1))
+        self.requests: Dict[str, CompletionRequest] = {}
+        self.pending_requests: Deque[CompletionRequest] = deque()
+        self.active_request_ids: set[str] = set()
+        self.closed = False
+        self.sequence_round_robin = 0
+        self.speculative_stats: Dict[str, int] = {
+            "draft_proposals": 0,
+            "draft_tokens_proposed": 0,
+            "draft_tokens_accepted": 0,
+            "draft_tokens_rejected": 0,
+        }
+        self.draft_acceptance_length_counts: Dict[int, int] = {}
+        self.defer_sampled_draft_processing = False
+        self.memory_policy = self.build_memory_policy()
+
+    def build_memory_policy(self) -> MemoryPolicy:
+        if self.model.exact_checkpoints_only:
+            return CheckpointMemoryPolicy(self)
+        if self.model.attention_partitioned:
+            return PartitionedAttentionMemoryPolicy(self)
+        return UnifiedAttentionMemoryPolicy(self)
+
+    def clear_resident_state(self) -> None:
+        self.model.clear_memory()
+        self.model.clear_batch()
+        self.radix_trie = RadixTrie()
+        self.sequence_history = SequenceHistory()
+        self.checkpoint_logits.clear()
+        self.claimed_sequences.clear()
+        self.free_sequences.clear()
+        self.unused_sequences = list(range(self.model.n_seq_max - 1, -1, -1))
+        for seq_id in range(self.model.n_seq_max):
+            self.model.truncate_draft_sequence(seq_id, 0)
+
+    def create_embedding(
+        self,
+        payload: CreateEmbeddingRequest,
+    ) -> CreateEmbeddingResponse:
+        if not self.is_idle():
+            raise RuntimeError("embedding requests require an idle scheduler")
+        self.clear_resident_state()
+        try:
+            embeddings, total_tokens = self.model.embed(payload.normalized_input())
+            return CreateEmbeddingResponse.from_embeddings(
+                model=payload.model,
+                embeddings=embeddings,
+                total_tokens=total_tokens,
+                encoding_format=payload.encoding_format,
+                dimensions=payload.dimensions,
+            )
+        finally:
+            self.clear_resident_state()
+
+    @staticmethod
+    def request_needs_prompt_logits(request: CompletionRequest) -> bool:
+        return request.payload.max_tokens != 0 and request.effective_max_len > len(
+            request.prompt_tokens
+        )
+
+    @staticmethod
+    def request_needs_uncached_prompt_logprobs(request: CompletionRequest) -> bool:
+        return request.payload.echo and request.payload.logprobs is not None
+
+    @staticmethod
+    def clear_sequence_cache_match(request: CompletionRequest) -> None:
+        request.sequence_cache_match = None
+        request.sequence_cache_match_length = 0
+
+    def can_lookup_sequence_cache(self, request: CompletionRequest) -> bool:
+        return (
+            self.sequence_cache is not None
+            and bool(request.prompt_tokens)
+            and not self.request_needs_uncached_prompt_logprobs(request)
+        )
+
+    def is_sequence_cache_match_usable(
+        self,
+        request: CompletionRequest,
+        match: SequenceCache.Match,
+        resident_reuse_len: int,
+    ) -> bool:
+        match_length = match.length
+        if (
+            match_length <= resident_reuse_len
+            or match_length > len(request.prompt_tokens)
+            or tuple(request.prompt_tokens[:match_length]) != match.tokens
+            or not request.prompt_plan.is_reusable_boundary(match_length)
+        ):
+            return False
+        return not (
+            self.request_needs_prompt_logits(request)
+            and match_length == len(request.prompt_tokens)
+            and not match.has_prompt_logits
+        )
+
+    def find_sequence_cache_match(
+        self,
+        request: CompletionRequest,
+        resident_reuse_len: int,
+    ) -> int:
+        self.clear_sequence_cache_match(request)
+        if not self.can_lookup_sequence_cache(request):
+            return 0
+        assert self.sequence_cache is not None
+        try:
+            match = self.sequence_cache.lookup(request.prompt_tokens)
+        except Exception:  # noqa: BLE001
+            self.metrics.sequence_cache_lookup_failures_total += 1
+            return 0
+        if match is None:
+            return 0
+        if not self.is_sequence_cache_match_usable(
+            request,
+            match,
+            resident_reuse_len,
+        ):
+            return 0
+        request.sequence_cache_match = match
+        request.sequence_cache_match_length = match.length
+        return match.length
+
+    def fail_sequence_cache_load(
+        self,
+        request: CompletionRequest,
+        seq_id: int,
+    ) -> Tuple[int, Optional[np.ndarray]]:
+        llama_cpp.llama_memory_seq_rm(self.model.mem, seq_id, 0, -1)
+        self.model.truncate_draft_sequence(seq_id, 0)
+        self.clear_sequence_cache_match(request)
+        self.metrics.sequence_cache_load_failures_total += 1
+        return 0, None
+
+    def load_sequence_state_bytes(
+        self,
+        seq_id: int,
+        state_bytes: np.ndarray,
+    ) -> bool:
+        state = np.ascontiguousarray(state_bytes, dtype=np.uint8)
+        state_size = int(state.size)
+        if state_size <= 0:
+            return False
+        state_buffer = (ctypes.c_uint8 * state_size).from_buffer(cast(Any, state))
+        loaded_bytes = int(
+            llama_cpp.llama_state_seq_set_data(
+                self.model.ctx,
+                state_buffer,
+                state_size,
+                cast(llama_cpp.llama_seq_id, seq_id),
+            )
+        )
+        return loaded_bytes == state_size
+
+    def save_sequence_state_bytes(self, seq_id: int) -> Optional[np.ndarray]:
+        c_seq_id = cast(llama_cpp.llama_seq_id, seq_id)
+        state_size = int(llama_cpp.llama_state_seq_get_size(self.model.ctx, c_seq_id))
+        if state_size <= 0:
+            return None
+        state_buffer = (ctypes.c_uint8 * state_size)()
+        state_bytes = int(
+            llama_cpp.llama_state_seq_get_data(
+                self.model.ctx,
+                state_buffer,
+                state_size,
+                c_seq_id,
+            )
+        )
+        if state_bytes <= 0:
+            return None
+        return np.ctypeslib.as_array(state_buffer, shape=(state_bytes,)).copy()
+
+    def hydrate_sequence_cache_match(
+        self,
+        request: CompletionRequest,
+        seq_id: int,
+    ) -> Tuple[int, Optional[np.ndarray]]:
+        match = request.sequence_cache_match
+        if self.sequence_cache is None or match is None:
+            return 0, None
+        try:
+            loaded = self.sequence_cache.load(match)
+        except Exception:  # noqa: BLE001
+            return self.fail_sequence_cache_load(request, seq_id)
+        if loaded is None:
+            return self.fail_sequence_cache_load(request, seq_id)
+        tokens = list(loaded.tokens)
+        expected_length = request.sequence_cache_match_length
+        if (
+            len(tokens) != expected_length
+            or tokens != request.prompt_tokens[:expected_length]
+        ):
+            return self.fail_sequence_cache_load(request, seq_id)
+        if (
+            len(tokens) == len(request.prompt_tokens)
+            and self.request_needs_prompt_logits(request)
+            and loaded.prompt_logits is None
+        ):
+            return self.fail_sequence_cache_load(request, seq_id)
+        if not self.load_sequence_state_bytes(seq_id, loaded.state_bytes):
+            return self.fail_sequence_cache_load(request, seq_id)
+        self.radix_trie.extend(seq_id, tokens)
+        self.sequence_history.extend(
+            seq_id,
+            tokens,
+            request.prompt_plan.position_increments_up_to(len(tokens)),
+        )
+        self.model.truncate_draft_sequence(seq_id, 0)
+        self.metrics.sequence_cache_hits_total += 1
+        self.metrics.sequence_cache_tokens_loaded_total += len(tokens)
+        if len(tokens) == len(request.prompt_tokens):
+            return len(tokens), loaded.prompt_logits
+        return len(tokens), None
+
+    def maybe_save_sequence_cache(self, request: CompletionRequest) -> None:
+        if (
+            self.sequence_cache is None
+            or request.base_seq_id is None
+            or not request.prompt_tokens
+            or request.sequence_cache_match_length == len(request.prompt_tokens)
+        ):
+            return
+        state_bytes = self.save_sequence_state_bytes(request.base_seq_id)
+        if state_bytes is None:
+            return
+        try:
+            self.sequence_cache.save(
+                request.prompt_tokens,
+                state_bytes,
+                request.prompt_logits,
+            )
+        except Exception:  # noqa: BLE001
+            self.metrics.sequence_cache_save_failures_total += 1
+            return
+        self.metrics.sequence_cache_save_requests_total += 1
+
+    def close(self) -> None:
+        self.closed = True
+        self.model.close()
+
+    def submit_request(self, request: CompletionRequest) -> str:
+        if self.closed:
+            raise RuntimeError("scheduler closed")
+        self.requests[request.id] = request
+        self.pending_requests.append(request)
+        self.metrics.requests_submitted_total += 1
+        return request.id
+
+    def cancel(self, request_id: str) -> None:
+        request = self.requests.get(request_id)
+        if request is not None:
+            request.cancelled = True
+
+    def is_idle(self) -> bool:
+        return not self.pending_requests and not self.active_request_ids
+
+    @staticmethod
+    def logits_to_logprobs(logits: np.ndarray) -> np.ndarray:
+        logits = logits.astype(np.float32, copy=False)
+        max_logit = float(np.max(logits))
+        shifted = logits - max_logit
+        return shifted - math.log(float(np.sum(np.exp(shifted, dtype=np.float64))))
+
+    def step(self) -> bool:
+        step_started_at = time.perf_counter()
+        if self.closed:
+            return False
+        try:
+            self.admit_waiting()
+            batch_items = self.build_batch()
+            if not batch_items:
+                if self.maybe_fill_batched_draft_tokens():
+                    self.finalize_cancelled()
+                    return True
+                return self.finalize_cancelled()
+            draft_processing_enabled = self.batch_needs_draft_processing(batch_items)
+            self.defer_sampled_draft_processing = (
+                draft_processing_enabled
+                and self.supports_sampled_draft_processing
+                and all(item.kind == "decode" for item in batch_items)
+            )
+            self.model.set_draft_processing_enabled(draft_processing_enabled)
+            self.model.clear_batch()
+            for item in batch_items:
+                prefill = item.prefill_state
+                if prefill is not None and prefill.embeddings is not None:
+                    if prefill.positions is None:
+                        raise RuntimeError("embedding batch item is missing positions")
+                    self.model.add_batch_embeddings(
+                        seq_id=item.seq_id,
+                        embeddings=prefill.embeddings,
+                        positions=prefill.positions,
+                        output_indices=item.output_indices,
+                    )
+                else:
+                    self.model.add_batch_tokens(
+                        seq_id=item.seq_id,
+                        start_pos=item.llama_start_pos,
+                        tokens=item.tokens,
+                        output_indices=item.output_indices,
+                    )
+            decode_started_at = time.perf_counter()
+            try:
+                non_causal_decode = any(
+                    item.prefill_state is not None and item.prefill_state.non_causal
+                    for item in batch_items
+                )
+                try:
+                    if non_causal_decode:
+                        llama_cpp.llama_set_causal_attn(self.model.ctx, False)
+                    self.model.decode()
+                finally:
+                    if non_causal_decode:
+                        llama_cpp.llama_set_causal_attn(self.model.ctx, True)
+                decode_elapsed = time.perf_counter() - decode_started_at
+                if draft_processing_enabled and not self.defer_sampled_draft_processing:
+                    draft_process_started_at = time.perf_counter()
+                    self.model.process_draft_batch()
+                    draft_process_elapsed = time.perf_counter() - draft_process_started_at
+                    self.metrics.draft_process_seconds_total += draft_process_elapsed
+                    self.metrics.draft_process_calls_total += 1
+                    self.observe_draft_process(batch_items, draft_process_elapsed)
+            except BaseException as exc:  # noqa: BLE001
+                for request_id in list(self.active_request_ids):
+                    self.fail_request(self.requests[request_id], exc)
+                for request in list(self.pending_requests):
+                    self.fail_request(request, exc)
+                return True
+            self.metrics.observe_decode(
+                batch_items,
+                decode_elapsed,
+            )
+            process_batch_started_at = time.perf_counter()
+            self.process_batch(batch_items)
+            self.metrics.process_batch_seconds_total += (
+                time.perf_counter() - process_batch_started_at
+            )
+            self.metrics.process_batch_calls_total += 1
+            if self.defer_sampled_draft_processing:
+                try:
+                    self.process_sampled_draft_batch(batch_items)
+                finally:
+                    self.apply_deferred_draft_rollbacks(batch_items)
+                    self.defer_sampled_draft_processing = False
+            self.finalize_cancelled()
+            return True
+        finally:
+            self.metrics.scheduler_step_seconds_total += (
+                time.perf_counter() - step_started_at
+            )
+            self.metrics.scheduler_step_calls_total += 1
+
+    def observe_draft_process(
+        self,
+        items: Sequence["CompletionScheduler.BatchItem"],
+        elapsed_seconds: float,
+    ) -> None:
+        if self.model.draft_provider is None or elapsed_seconds <= 0.0:
+            return
+        self.metrics.draft_seconds_total += elapsed_seconds
+
+    def item_allows_mtp_processing(self, item: "CompletionScheduler.BatchItem") -> bool:
+        request = self.requests.get(item.request_id)
+        if request is None:
+            return False
+        if item.kind == "prefill":
+            return not any(segment.kind != "text" for segment in request.prompt_plan.segments)
+        decode = item.decode_state
+        if decode is None:
+            return False
+        return not request.completions[decode.completion_index].multimodal_prompt
+
+    def batch_needs_draft_processing(
+        self,
+        batch_items: Sequence["CompletionScheduler.BatchItem"],
+    ) -> bool:
+        if self.model.draft_provider is None:
+            return False
+        if not self.draft_batch_size_allowed(batch_items):
+            return False
+        if isinstance(self.model.draft_provider, MTPDraftProvider) and not all(
+            self.item_allows_mtp_processing(item) for item in batch_items
+        ):
+            return False
+        for item in batch_items:
+            request = self.requests.get(item.request_id)
+            if request is None:
+                continue
+            if item.kind == "prefill":
+                if self.request_needs_prompt_logits(request):
+                    return True
+                continue
+            decode = item.decode_state
+            if decode is None:
+                continue
+            completion = request.completions[decode.completion_index]
+            if not completion.finished:
+                return True
+        return False
+
+    def draft_batch_size_allowed(
+        self,
+        batch_items: Sequence["CompletionScheduler.BatchItem"],
+    ) -> bool:
+        max_batch_size = getattr(self.model, "draft_model_max_batch_size", None)
+        if max_batch_size is None:
+            return True
+        draft_batch_size = sum(
+            1
+            for item in batch_items
+            if item.kind == "decode" and item.decode_state is not None
+        )
+        return draft_batch_size <= max_batch_size
+
+    def active_draft_batch_size_allowed(self, draft_batch_size: int) -> bool:
+        max_batch_size = getattr(self.model, "draft_model_max_batch_size", None)
+        return max_batch_size is None or draft_batch_size <= max_batch_size
+
+    def admit_waiting(self) -> None:
+        while self.pending_requests:
+            request = self.pending_requests[0]
+            if request.cancelled:
+                self.fail_request(request, CompletionRequestCancelledError("request cancelled"))
+                continue
+            if not self.can_admit(request):
+                break
+            self.pending_requests.popleft()
+            self.admit_request(request)
+
+    def can_admit(self, request: CompletionRequest) -> bool:
+        return self.memory_policy.can_admit(request)
+
+    def admit_request(self, request: CompletionRequest) -> None:
+        self.memory_policy.admit_request(request)
+        if request.admitted:
+            self.metrics.requests_admitted_total += 1
+
+    def build_batch(self) -> List[CompletionScheduler.BatchItem]:
+        prompt_requests = [
+            self.requests[request_id]
+            for request_id in self.active_request_ids
+            if self.requests[request_id].admitted
+            and not self.requests[request_id].prompt_done
+            and not self.requests[request_id].cancelled
+        ]
+        completions = [
+            completion
+            for request_id in self.active_request_ids
+            for completion in self.requests[request_id].completions
+            if (completion.pending_input_tokens or completion.draft_tokens)
+            and not completion.finished
+        ]
+        if not prompt_requests and not completions:
+            return []
+
+        for request in prompt_requests:
+            if request.prompt_cursor < len(request.prompt_tokens):
+                segment = self._current_prompt_segment(request)
+                if segment.kind != "text":
+                    token_count = min(
+                        segment.batch_rows
+                        if segment.media is not None and segment.media.non_causal
+                        else self._pending_tokens_length(request),
+                        self.model.n_batch,
+                    )
+                    if segment.batch_rows > self.model.n_batch:
+                        if segment.media is not None and segment.media.non_causal:
+                            raise RuntimeError(
+                                "non-causal media prompt segment exceeds model.n_batch; "
+                                "increase n_batch"
+                            )
+                    item, _ = self._build_pending_batch_item(
+                        request,
+                        token_count,
+                        0,
+                    )
+                    return [item]
+
+        ordered_sequences = self._ordered_pending_sequences(prompt_requests, completions)
+        allocations = self._allocate_pending_tokens_for_model(ordered_sequences)
+        if ordered_sequences:
+            self.sequence_round_robin += 1
+
+        items: List[CompletionScheduler.BatchItem] = []
+        output_index = 0
+        for source in ordered_sequences:
+            token_count = allocations.get(self._pending_sequence_id(source), 0)
+            if token_count <= 0:
+                continue
+            item, output_index = self._build_pending_batch_item(
+                source,
+                token_count,
+                output_index,
+            )
+            items.append(item)
+        return self._split_mixed_mtp_batch(items)
+
+    def _split_mixed_mtp_batch(
+        self,
+        items: List["CompletionScheduler.BatchItem"],
+    ) -> List["CompletionScheduler.BatchItem"]:
+        if not isinstance(self.model.draft_provider, MTPDraftProvider) or len(items) <= 1:
+            return items
+        eligibility = [self.item_allows_mtp_processing(item) for item in items]
+        if all(eligibility) or not any(eligibility):
+            return items
+        keep_eligible = eligibility[0]
+        split_items = [
+            item
+            for item, item_eligible in zip(items, eligibility)
+            if item_eligible == keep_eligible
+        ]
+        self._renumber_output_indices(split_items)
+        return split_items
+
+    @staticmethod
+    def _renumber_output_indices(
+        items: Sequence["CompletionScheduler.BatchItem"],
+    ) -> None:
+        next_output_index = 0
+        for item in items:
+            for index, output_index in enumerate(item.output_indices):
+                if output_index is None:
+                    continue
+                item.output_indices[index] = next_output_index
+                next_output_index += 1
+
+    def _allocate_pending_tokens_for_model(
+        self,
+        ordered_sequences: Sequence[Union[CompletionRequest, Completion]],
+    ) -> Dict[int, int]:
+        if not self._needs_homogeneous_recurrent_draft_batching():
+            return self._allocate_pending_tokens(ordered_sequences, self.model.n_batch)
+
+        active = [
+            source
+            for source in ordered_sequences
+            if self._pending_tokens_length(source) > 0
+        ]
+        if not active:
+            return {}
+
+        first_draft_count = self._recurrent_draft_batch_token_count(active[0])
+        if first_draft_count > 0:
+            return self._allocate_homogeneous_recurrent_draft_tokens(
+                active,
+                first_draft_count,
+            )
+
+        non_draft_sources = [
+            source
+            for source in active
+            if self._recurrent_draft_batch_token_count(source) == 0
+        ]
+        return self._allocate_pending_tokens(non_draft_sources, self.model.n_batch)
+
+    def _needs_homogeneous_recurrent_draft_batching(self) -> bool:
+        return bool(
+            self.model.draft_provider is not None
+            and getattr(self.model, "draft_target_batching", False)
+            and self.model.exact_checkpoints_only
+            and self.model.n_rs_seq > 0
+        )
+
+    def _recurrent_draft_batch_token_count(
+        self,
+        source: Union[CompletionRequest, Completion],
+    ) -> int:
+        if isinstance(source, CompletionRequest):
+            return 0
+        if (
+            source.finished
+            or source.pending_finish_reason is not None
+            or not source.pending_input_tokens
+            or not source.draft_tokens
+        ):
+            return 0
+        token_count = min(
+            len(source.pending_input_tokens) + len(source.draft_tokens),
+            len(source.pending_input_tokens) + self.model.n_rs_seq,
+        )
+        return token_count if token_count > len(source.pending_input_tokens) else 0
+
+    def _allocate_homogeneous_recurrent_draft_tokens(
+        self,
+        sources: Sequence[Union[CompletionRequest, Completion]],
+        token_count: int,
+    ) -> Dict[int, int]:
+        # Recurrent rollback snapshots are only valid if the whole per-sequence
+        # pending+draft group fits in a single llama.cpp ubatch.
+        safe_capacity = min(self.model.n_batch, self.model.n_ubatch)
+        if token_count > safe_capacity:
+            return self._allocate_recurrent_pending_only_tokens(sources, safe_capacity)
+
+        allocations: Dict[int, int] = {}
+        remaining_capacity = safe_capacity
+        for source in sources:
+            if remaining_capacity < token_count:
+                break
+            if self._recurrent_draft_batch_token_count(source) != token_count:
+                continue
+            seq_id = self._pending_sequence_id(source)
+            allocations[seq_id] = token_count
+            remaining_capacity -= token_count
+        return allocations
+
+    def _allocate_recurrent_pending_only_tokens(
+        self,
+        sources: Sequence[Union[CompletionRequest, Completion]],
+        capacity: int,
+    ) -> Dict[int, int]:
+        allocations: Dict[int, int] = {}
+        remaining_capacity = capacity
+        for source in sources:
+            if remaining_capacity <= 0:
+                break
+            if self._recurrent_draft_batch_token_count(source) == 0:
+                continue
+            if isinstance(source, CompletionRequest):
+                continue
+            pending_count = min(len(source.pending_input_tokens), remaining_capacity)
+            if pending_count <= 0:
+                continue
+            allocations[source.seq_id] = pending_count
+            remaining_capacity -= pending_count
+        return allocations
+
+    def _ordered_pending_sequences(
+        self,
+        prompt_requests: Sequence[CompletionRequest],
+        completions: Sequence[Completion],
+    ) -> List[Union[CompletionRequest, Completion]]:
+        if not prompt_requests and not completions:
+            return []
+        prompt_by_request_id = {request.id: request for request in prompt_requests}
+        completions_by_request_id: Dict[str, List[Completion]] = {}
+        for completion in completions:
+            completions_by_request_id.setdefault(completion.request_id, []).append(completion)
+        sequence_list: List[Union[CompletionRequest, Completion]] = []
+        for request_id, request in self.requests.items():
+            if request_id not in self.active_request_ids or request.cancelled:
+                continue
+            prompt_request = prompt_by_request_id.get(request_id)
+            if prompt_request is not None:
+                sequence_list.append(prompt_request)
+            sequence_list.extend(completions_by_request_id.get(request_id, []))
+        if not sequence_list:
+            return []
+        start = self.sequence_round_robin % len(sequence_list)
+        return sequence_list[start:] + sequence_list[:start]
+
+    def _pending_sequence_id(
+        self,
+        source: Union[CompletionRequest, Completion],
+    ) -> int:
+        if isinstance(source, CompletionRequest):
+            if source.base_seq_id is None:
+                raise RuntimeError("prompt sequence is missing base seq id")
+            return source.base_seq_id
+        return source.seq_id
+
+    def _current_prompt_segment(self, request: CompletionRequest) -> PromptSegment:
+        return request.prompt_plan.segment_at(request.prompt_cursor)
+
+    def _pending_tokens_length(
+        self,
+        source: Union[CompletionRequest, Completion],
+    ) -> int:
+        if isinstance(source, CompletionRequest):
+            if source.prompt_cursor >= len(source.prompt_tokens):
+                return 0
+            segment = self._current_prompt_segment(source)
+            if segment.kind != "text":
+                segment_offset = source.prompt_cursor - segment.start_pos
+                if segment.media is not None and segment.media.non_causal:
+                    if segment_offset != 0:
+                        raise RuntimeError("non-causal media segment was partially scheduled")
+                    return segment.batch_rows
+                return segment.end_pos - source.prompt_cursor
+            return segment.end_pos - source.prompt_cursor
+        draft_count = (
+            len(source.draft_tokens)
+            if source.pending_finish_reason is None
+            and getattr(self.model, "draft_target_batching", True)
+            else 0
+        )
+        return len(source.pending_input_tokens) + draft_count
+
+    def _pending_allocation_for_capacity(
+        self,
+        source: Union[CompletionRequest, Completion],
+        already_allocated: int,
+        capacity: int,
+    ) -> int:
+        if capacity <= 0:
+            return 0
+        if not isinstance(source, CompletionRequest):
+            remaining = self._pending_tokens_length(source) - already_allocated
+            return min(remaining, capacity)
+        segment = self._current_prompt_segment(source)
+        if segment.kind == "text":
+            remaining = self._pending_tokens_length(source) - already_allocated
+            return min(remaining, capacity)
+        if segment.media is not None and segment.media.non_causal:
+            if already_allocated == 0 and segment.batch_rows <= capacity:
+                return segment.batch_rows
+            return 0
+        segment_offset = source.prompt_cursor - segment.start_pos + already_allocated
+        return segment.rows_for_capacity(segment_offset, capacity)
+
+    def _allocate_pending_tokens(
+        self,
+        sources: Sequence[Union[CompletionRequest, Completion]],
+        capacity: int,
+    ) -> Dict[int, int]:
+        allocations: Dict[int, int] = {}
+        active = [source for source in sources if self._pending_tokens_length(source) > 0]
+        remaining_capacity = capacity
+
+        for source in active:
+            if remaining_capacity <= 0:
+                break
+            seq_id = self._pending_sequence_id(source)
+            allocation = self._pending_allocation_for_capacity(
+                source,
+                allocations.get(seq_id, 0),
+                remaining_capacity,
+            )
+            if allocation <= 0:
+                continue
+            allocations[seq_id] = allocations.get(seq_id, 0) + allocation
+            remaining_capacity -= allocation
+
+        while remaining_capacity > 0 and active:
+            share = max(1, remaining_capacity // len(active))
+            progress = False
+            next_active: List[Union[CompletionRequest, Completion]] = []
+            for source in active:
+                seq_id = self._pending_sequence_id(source)
+                remaining_tokens = self._pending_tokens_length(source) - allocations.get(seq_id, 0)
+                if remaining_tokens <= 0:
+                    continue
+                allocation = self._pending_allocation_for_capacity(
+                    source,
+                    allocations.get(seq_id, 0),
+                    min(share, remaining_capacity),
+                )
+                if allocation <= 0:
+                    next_active.append(source)
+                    continue
+                allocations[seq_id] = allocations.get(seq_id, 0) + allocation
+                remaining_capacity -= allocation
+                progress = True
+                if remaining_tokens > allocation and remaining_capacity > 0:
+                    next_active.append(source)
+            if not progress:
+                break
+            active = next_active
+        return allocations
+
+    def _build_pending_batch_item(
+        self,
+        source: Union[CompletionRequest, Completion],
+        token_count: int,
+        output_index: int,
+    ) -> Tuple["CompletionScheduler.BatchItem", int]:
+        if isinstance(source, CompletionRequest):
+            if source.base_seq_id is None:
+                raise RuntimeError("prompt sequence is missing base seq id")
+            segment = self._current_prompt_segment(source)
+            if segment.kind != "text":
+                segment_offset = source.prompt_cursor - segment.start_pos
+                non_causal = segment.media is not None and segment.media.non_causal
+                if non_causal:
+                    if segment_offset != 0 or token_count < segment.batch_rows:
+                        raise RuntimeError("non-causal media segment must be scheduled atomically")
+                    row_count = segment.batch_rows
+                    embeddings, positions, position_increments = segment.media_slice(
+                        0,
+                        row_count,
+                    )
+                else:
+                    row_count = segment.rows_for_capacity(segment_offset, token_count)
+                    if row_count <= 0:
+                        raise RuntimeError("media prompt allocation is too small")
+                    embeddings, positions, position_increments = segment.media_slice(
+                        segment_offset,
+                        row_count,
+                    )
+                logical_start = source.prompt_cursor
+                logical_end = logical_start + row_count
+                output_indices = [None] * row_count
+                output_positions = [logical_start] * row_count
+                if output_positions:
+                    output_positions[-1] = logical_end - 1
+                needs_last_output = (
+                    (source.payload.echo and source.payload.logprobs is not None
+                     and source.prompt_plan.has_text_token_at(logical_end))
+                    or self.model.exact_checkpoints_only
+                    or logical_end == len(source.prompt_tokens)
+                )
+                if needs_last_output and output_indices:
+                    output_indices[-1] = output_index
+                    output_index += 1
+                return (
+                    CompletionScheduler.BatchItem.prefill(
+                        request_id=source.id,
+                        seq_id=source.base_seq_id,
+                        start_pos=logical_start,
+                        llama_start_pos=segment.decode_start_pos + sum(
+                            segment.decoder_position_increments[:segment_offset]
+                        ),
+                        tokens=[],
+                        identity_tokens=list(
+                            segment.identity_tokens[
+                                segment_offset : segment_offset + row_count
+                            ]
+                        ),
+                        output_indices=output_indices,
+                        output_positions=output_positions,
+                        position_increments=position_increments,
+                        embeddings=embeddings,
+                        positions=positions,
+                        non_causal=non_causal,
+                        prompt_advance_to=logical_end,
+                    ),
+                    output_index,
+                )
+            segment_offset = source.prompt_cursor - segment.start_pos
+            max_count = min(token_count, segment.end_pos - source.prompt_cursor)
+            chunk = list(segment.text_tokens[segment_offset : segment_offset + max_count])
+            identity_chunk = list(
+                segment.identity_tokens[segment_offset : segment_offset + max_count]
+            )
+            ends_prompt = source.prompt_cursor + len(identity_chunk) == len(source.prompt_tokens)
+            output_indices: List[Optional[int]] = [None] * len(chunk)
+            output_positions = [source.prompt_cursor + index for index in range(len(chunk))]
+            for index, output_position in enumerate(output_positions):
+                if (
+                    source.payload.echo
+                    and source.payload.logprobs is not None
+                    and source.prompt_plan.has_text_token_at(output_position + 1)
+                ):
+                    output_indices[index] = output_index
+                    output_index += 1
+            if self.model.exact_checkpoints_only and chunk and output_indices[-1] is None:
+                output_indices[-1] = output_index
+                output_index += 1
+            elif ends_prompt and chunk and output_indices[-1] is None:
+                output_indices[-1] = output_index
+                output_index += 1
+            return (
+                CompletionScheduler.BatchItem.prefill(
+                    request_id=source.id,
+                    seq_id=source.base_seq_id,
+                    start_pos=source.prompt_cursor,
+                    llama_start_pos=segment.decode_start_pos + segment_offset,
+                    tokens=chunk,
+                    identity_tokens=identity_chunk,
+                    output_indices=output_indices,
+                    output_positions=output_positions,
+                    position_increments=segment.decoder_position_increments[
+                        segment_offset : segment_offset + len(identity_chunk)
+                    ],
+                ),
+                output_index,
+            )
+
+        request = self.requests[source.request_id]
+        pending_count = min(token_count, len(source.pending_input_tokens))
+        draft_count = (
+            max(0, token_count - pending_count)
+            if getattr(self.model, "draft_target_batching", True)
+            else 0
+        )
+        scheduled_tokens = [
+            *source.pending_input_tokens[:pending_count],
+            *(
+                source.draft_tokens[:draft_count]
+                if source.pending_finish_reason is None
+                else []
+            ),
+        ]
+        output_indices = list(range(output_index, output_index + len(scheduled_tokens)))
+        start_pos = self.radix_trie.length(source.seq_id)
+        return (
+            CompletionScheduler.BatchItem.decode(
+                request_id=request.id,
+                seq_id=source.seq_id,
+                start_pos=start_pos,
+                llama_start_pos=self.sequence_history.position_length(source.seq_id),
+                tokens=list(scheduled_tokens),
+                identity_tokens=list(scheduled_tokens),
+                output_indices=output_indices,
+                output_positions=[
+                    start_pos + index
+                    for index in range(len(scheduled_tokens))
+                ],
+                position_increments=[1] * len(scheduled_tokens),
+                completion_index=source.index,
+                pending_count=pending_count,
+            ),
+            output_index + len(scheduled_tokens),
+        )
+
+    def process_batch(self, items: List[CompletionScheduler.BatchItem]) -> None:
+        output_count = sum(
+            output_index is not None
+            for item in items
+            for output_index in item.output_indices
+        )
+        for item in items:
+            request = self.requests[item.request_id]
+            if request.cancelled:
+                continue
+            self.radix_trie.extend(item.seq_id, item.identity_tokens)
+            self.sequence_history.extend(
+                item.seq_id,
+                item.identity_tokens,
+                item.position_increments,
+            )
+            if item.kind == "prefill":
+                request.capture_prompt_logprobs(
+                    model=self.model,
+                    formatter=self.formatter,
+                    output_indices=item.output_indices,
+                    output_positions=item.output_positions,
+                    output_count=output_count,
+                    output_index_to_logits_index=self.output_index_to_logits_index,
+                )
+                prompt_output_index = self.output_index_to_logits_index(
+                    self.last_output_index(item.output_indices),
+                    output_count,
+                )
+                prompt_logits = (
+                    self.model.logits(prompt_output_index)
+                    if prompt_output_index is not None
+                    else None
+                )
+                prefill = item.require_prefill()
+                if prefill.prompt_advance_to is not None:
+                    request.prompt_cursor = prefill.prompt_advance_to
+                else:
+                    request.prompt_cursor += len(item.identity_tokens)
+                if request.prompt_cursor == len(request.prompt_tokens):
+                    request.prompt_done = True
+                    request.prompt_logits = prompt_logits
+                    self.maybe_save_prompt_checkpoint(request)
+                    self.maybe_save_sequence_cache(request)
+                    self.start_completions(
+                        request,
+                        prompt_output_index=prompt_output_index,
+                        prompt_logits=request.prompt_logits,
+                    )
+            else:
+                decode = item.require_decode()
+                completion = request.completions[decode.completion_index]
+                self.process_generation_item(
+                    completion,
+                    item,
+                    output_count,
+                )
+                self.finalize_request_if_ready(request)
+        if not self.defer_sampled_draft_processing:
+            self.maybe_fill_batched_draft_tokens()
+
+    @property
+    def should_defer_draft_fill(self) -> bool:
+        return bool(
+            self.model.draft_provider is not None
+            and getattr(self.model.draft_provider, "batched_draft", False)
+        )
+
+    @property
+    def supports_sampled_draft_processing(self) -> bool:
+        return bool(
+            self.model.draft_provider is not None
+            and getattr(self.model.draft_provider, "sampled_batch_draft", False)
+        )
+
+    def maybe_fill_draft_tokens(self, completion: Completion) -> None:
+        if (
+            self.model.draft_provider is None
+            or completion.finished
+            or completion.pending_finish_reason is not None
+            or completion.draft_tokens
+        ):
+            return
+        remaining_tokens = completion.max_total_tokens - completion.total_tokens
+        if not getattr(self.model, "draft_target_batching", True):
+            remaining_tokens = min(remaining_tokens, 1)
+        if remaining_tokens <= 0:
+            return
+        input_ids = self.draft_input_ids(completion)
+        if isinstance(self.model.draft_provider, MTPDraftProvider) and completion.multimodal_prompt:
+            return
+        can_draft = getattr(self.model.draft_provider, "can_draft", None)
+        if can_draft is not None and not can_draft(
+            int(input_ids.shape[0]),
+            seq_id=completion.seq_id,
+        ):
+            return
+        draft_started_at = time.perf_counter()
+        try:
+            proposed = self.model.draft_provider.draft(
+                input_ids,
+                seq_id=completion.seq_id,
+                max_tokens=remaining_tokens,
+            )
+        finally:
+            draft_elapsed = time.perf_counter() - draft_started_at
+            self.metrics.draft_seconds_total += draft_elapsed
+            self.metrics.draft_generate_seconds_total += draft_elapsed
+            self.metrics.draft_generate_calls_total += 1
+        if proposed.size == 0:
+            return
+        limited = [int(token) for token in proposed[:remaining_tokens]]
+        if not limited:
+            return
+        completion.draft_tokens = limited
+        self.speculative_stats["draft_proposals"] += 1
+        self.speculative_stats["draft_tokens_proposed"] += len(limited)
+
+    def maybe_fill_batched_draft_tokens(self) -> bool:
+        if not self.should_defer_draft_fill or self.model.draft_provider is None:
+            return False
+        draft_many = getattr(self.model.draft_provider, "draft_many", None)
+        if draft_many is None:
+            return False
+
+        completions: List[Completion] = []
+        draft_requests: List[Tuple[np.ndarray, int, Optional[int]]] = []
+        remaining_by_completion: List[int] = []
+        for request_id in self.active_request_ids:
+            request = self.requests.get(request_id)
+            if request is None or not request.admitted:
+                continue
+            for completion in request.completions:
+                if (
+                    completion.finished
+                    or completion.pending_finish_reason is not None
+                    or completion.draft_tokens
+                ):
+                    continue
+                remaining_tokens = completion.max_total_tokens - completion.total_tokens
+                if not getattr(self.model, "draft_target_batching", True):
+                    remaining_tokens = min(remaining_tokens, 1)
+                if remaining_tokens <= 0:
+                    continue
+                input_ids = self.draft_input_ids(completion)
+                if (
+                    isinstance(self.model.draft_provider, MTPDraftProvider)
+                    and completion.multimodal_prompt
+                ):
+                    continue
+                can_draft = getattr(self.model.draft_provider, "can_draft", None)
+                if can_draft is not None and not can_draft(
+                    int(input_ids.shape[0]),
+                    seq_id=completion.seq_id,
+                ):
+                    continue
+                completions.append(completion)
+                draft_requests.append((input_ids, completion.seq_id, remaining_tokens))
+                remaining_by_completion.append(remaining_tokens)
+
+        if not draft_requests:
+            return False
+        if not self.active_draft_batch_size_allowed(len(draft_requests)):
+            return False
+
+        draft_started_at = time.perf_counter()
+        proposed_many = draft_many(draft_requests)
+        draft_elapsed = time.perf_counter() - draft_started_at
+        self.metrics.draft_seconds_total += draft_elapsed
+        self.metrics.draft_generate_seconds_total += draft_elapsed
+        self.metrics.draft_generate_calls_total += 1
+        made_progress = False
+
+        for completion, proposed, remaining_tokens in zip(
+            completions,
+            proposed_many,
+            remaining_by_completion,
+        ):
+            if proposed.size == 0:
+                continue
+            limited = [int(token) for token in proposed[:remaining_tokens]]
+            if not limited:
+                continue
+            self.speculative_stats["draft_proposals"] += 1
+            self.speculative_stats["draft_tokens_proposed"] += len(limited)
+            completion.draft_tokens = limited
+            made_progress = True
+        return made_progress
+
+    def draft_input_ids(self, completion: Completion) -> np.ndarray:
+        return np.array(
+            [*completion.prompt_tokens, *completion.completion_tokens],
+            dtype=np.intc,
+        )
+
+    def process_sampled_draft_batch(
+        self,
+        items: Sequence["CompletionScheduler.BatchItem"],
+    ) -> None:
+        if self.model.draft_provider is None:
+            return
+        process_sampled_batch = getattr(
+            self.model.draft_provider,
+            "process_sampled_batch",
+            None,
+        )
+        if process_sampled_batch is None:
+            return
+        if not self.draft_batch_size_allowed(items):
+            return
+
+        output_count = sum(
+            output_index is not None
+            for item in items
+            for output_index in item.output_indices
+        )
+        completions: List[Completion] = []
+        update_items: List["CompletionScheduler.BatchItem"] = []
+        updates: List["MTPDraftProvider.SampledBatchUpdate"] = []
+        remaining_by_completion: List[int] = []
+        batch_row_offset = 0
+        for item in items:
+            item_batch_rows = list(
+                range(batch_row_offset, batch_row_offset + len(item.tokens))
+            )
+            batch_row_offset += len(item.tokens)
+            decode = item.decode_state
+            if item.kind != "decode" or decode is None:
+                continue
+            request = self.requests.get(item.request_id)
+            if request is None:
+                continue
+            completion = request.completions[decode.completion_index]
+            if completion.finished or completion.pending_finish_reason is not None:
+                continue
+            remaining_tokens = completion.max_total_tokens - completion.total_tokens
+            if remaining_tokens <= 0:
+                continue
+            resolved_output_indices = [
+                self.output_index_to_logits_index(output_index, output_count)
+                for output_index in item.output_indices
+            ]
+            if any(output_index is None for output_index in resolved_output_indices):
+                continue
+            sample_index = decode.pending_count + decode.accepted_draft_count - 1
+            target_count = len(item.tokens)
+            if sample_index < 0 or sample_index >= target_count:
+                continue
+            if decode.sampled_pending_token is None:
+                continue
+            completions.append(completion)
+            update_items.append(item)
+            updates.append(
+                MTPDraftProvider.SampledBatchUpdate(
+                    seq_id=item.seq_id,
+                    start_pos=item.llama_start_pos,
+                    tokens=item.tokens,
+                    row_indices=item_batch_rows,
+                    target_count=target_count,
+                    sample_index=sample_index,
+                    pending_token=decode.sampled_pending_token,
+                    max_tokens=remaining_tokens,
+                )
+            )
+            remaining_by_completion.append(remaining_tokens)
+
+        if not updates:
+            return
+
+        draft_started_at = time.perf_counter()
+        proposed_many = process_sampled_batch(updates)
+        for item in update_items:
+            item.require_decode().rollback_draft_processed = True
+        draft_elapsed = time.perf_counter() - draft_started_at
+        self.metrics.draft_seconds_total += draft_elapsed
+        self.metrics.draft_sampled_batch_seconds_total += draft_elapsed
+        self.metrics.draft_sampled_batch_calls_total += 1
+        for completion, proposed, remaining_tokens in zip(
+            completions,
+            proposed_many,
+            remaining_by_completion,
+        ):
+            if proposed.size == 0:
+                continue
+            limited = [int(token) for token in proposed[:remaining_tokens]]
+            if not limited:
+                continue
+            completion.draft_tokens = limited
+            self.speculative_stats["draft_proposals"] += 1
+            self.speculative_stats["draft_tokens_proposed"] += len(limited)
+
+    def rollback_draft_verification(
+        self,
+        item: "CompletionScheduler.BatchItem",
+        keep_len: int,
+        accepted_draft_count: int,
+        *,
+        defer_draft_state: bool,
+    ) -> None:
+        decode = item.require_decode()
+        if defer_draft_state:
+            decode.rollback_keep_len = keep_len
+            decode.rollback_accepted_draft_count = accepted_draft_count
+            decode.rollback_draft_processed = False
+            return
+        self.model.accept_draft_tokens(item.seq_id, accepted_draft_count)
+        self.truncate_sequence(item.seq_id, keep_len)
+
+    def apply_deferred_draft_rollbacks(
+        self,
+        items: Sequence["CompletionScheduler.BatchItem"],
+    ) -> None:
+        for item in items:
+            decode = item.require_decode()
+            rollback_keep_len = decode.rollback_keep_len
+            if rollback_keep_len is None:
+                deferred_accept_draft_count = decode.deferred_accept_draft_count
+                if (
+                    not decode.rollback_draft_processed
+                    and deferred_accept_draft_count is not None
+                ):
+                    self.model.accept_draft_tokens(
+                        item.seq_id,
+                        deferred_accept_draft_count,
+                    )
+                    deferred_truncate_draft_len = decode.deferred_truncate_draft_len
+                    if deferred_truncate_draft_len is not None:
+                        self.model.truncate_draft_sequence(
+                            item.seq_id,
+                            deferred_truncate_draft_len,
+                        )
+                decode.deferred_accept_draft_count = None
+                decode.deferred_truncate_draft_len = None
+                decode.rollback_draft_processed = False
+                continue
+
+            draft_state_needs_fallback = not decode.rollback_draft_processed
+            if draft_state_needs_fallback:
+                self.model.accept_draft_tokens(
+                    item.seq_id,
+                    decode.rollback_accepted_draft_count,
+                )
+            self.truncate_sequence(
+                item.seq_id,
+                rollback_keep_len,
+                truncate_draft=draft_state_needs_fallback,
+            )
+            decode.rollback_keep_len = None
+            decode.rollback_accepted_draft_count = 0
+            decode.rollback_draft_processed = False
+            decode.deferred_accept_draft_count = None
+            decode.deferred_truncate_draft_len = None
+
+    def sample_completion_token(self, completion: Completion, output_index: int) -> int:
+        sample_started_at = time.perf_counter()
+        try:
+            return completion.sampler.sample(self.model.ctx, output_index)
+        finally:
+            self.metrics.sample_seconds_total += time.perf_counter() - sample_started_at
+            self.metrics.sample_calls_total += 1
+
+    def process_generation_item(
+        self,
+        completion: Completion,
+        item: CompletionScheduler.BatchItem,
+        output_count: int,
+    ) -> None:
+        if completion.finished:
+            return
+        decode = item.require_decode()
+        pending_count = decode.pending_count
+        resolved_output_indices = [
+            self.output_index_to_logits_index(output_index, output_count)
+            for output_index in item.output_indices
+        ]
+        if any(output_index is None for output_index in resolved_output_indices):
+            raise RuntimeError("generation outputs are required")
+        logits_indices: List[int] = []
+        for output_index in resolved_output_indices:
+            assert output_index is not None
+            logits_indices.append(int(output_index))
+        if completion.pending_finish_reason is not None:
+            if self.model.store_logits:
+                self.checkpoint_logits[completion.seq_id] = self.model.logits(
+                    logits_indices[-1]
+                )
+            completion.pending_input_tokens = completion.pending_input_tokens[pending_count:]
+            finish_reason: str = completion.pending_finish_reason
+            completion.pending_finish_reason = None
+            self.finish_completion(completion, finish_reason)
+            return
+
+        if pending_count:
+            completion.pending_input_tokens = completion.pending_input_tokens[pending_count:]
+
+        decoded_draft_tokens = item.tokens[pending_count:]
+        accepted_draft_count = 0
+        defer_draft_state = self.defer_sampled_draft_processing
+        if decoded_draft_tokens and pending_count <= 0:
+            raise RuntimeError("draft verification requires at least one pending token")
+        if decoded_draft_tokens:
+            self.metrics.draft_batches_verified_total += 1
+            self.metrics.draft_target_tokens_verified_total += len(decoded_draft_tokens)
+
+        for draft_index, draft_token in enumerate(decoded_draft_tokens):
+            verify_output_index = pending_count + draft_index - 1
+            if verify_output_index >= len(logits_indices):
+                raise RuntimeError("missing target output for draft verification")
+            logits_index = logits_indices[verify_output_index]
+            sampled_token = self.sample_completion_token(completion, logits_index)
+            need_logits = self.model.store_logits or completion.needs_token_logprob
+            logits = self.model.logits(logits_index) if need_logits else None
+            if self.model.store_logits and logits is not None:
+                self.checkpoint_logits[completion.seq_id] = logits
+            if sampled_token != draft_token:
+                self.record_draft_acceptance_length(accepted_draft_count)
+                rejected = max(0, len(completion.draft_tokens) - accepted_draft_count)
+                if rejected > 0:
+                    self.speculative_stats["draft_tokens_rejected"] += rejected
+                self.metrics.draft_target_tokens_wasted_total += (
+                    max(0, len(decoded_draft_tokens) - accepted_draft_count - 1)
+                )
+                keep_len = item.start_pos + pending_count + accepted_draft_count
+                self.rollback_draft_verification(
+                    item,
+                    keep_len,
+                    accepted_draft_count,
+                    defer_draft_state=defer_draft_state,
+                )
+                completion.draft_tokens.clear()
+                prev_tokens = [*completion.prompt_tokens, *completion.completion_tokens]
+                if logits is not None:
+                    record = Token.from_logits(
+                        model=self.model,
+                        formatter=self.formatter,
+                        prev_tokens=prev_tokens,
+                        prev_text_bytes=completion.detokenized_prefix_bytes,
+                        token=sampled_token,
+                        logits=logits,
+                        logprobs_count=completion.logprobs,
+                        need_token_logprob=completion.needs_token_logprob,
+                    )
+                else:
+                    record = Token.from_token(
+                        model=self.model,
+                        prev_tokens=prev_tokens,
+                        prev_text_bytes=completion.detokenized_prefix_bytes,
+                        token=sampled_token,
+                    )
+                mismatch_finish_reason: Optional[str] = self.handle_completion_token(
+                    completion,
+                    sampled_token,
+                    record,
+                    decoded=False,
+                )
+                if mismatch_finish_reason is not None:
+                    completion.pending_finish_reason = mismatch_finish_reason
+                else:
+                    decode.sampled_pending_token = sampled_token
+                decode.accepted_draft_count = accepted_draft_count
+                return
+            prev_tokens = [*completion.prompt_tokens, *completion.completion_tokens]
+            if logits is not None:
+                record = Token.from_logits(
+                    model=self.model,
+                    formatter=self.formatter,
+                    prev_tokens=prev_tokens,
+                    prev_text_bytes=completion.detokenized_prefix_bytes,
+                    token=draft_token,
+                    logits=logits,
+                    logprobs_count=completion.logprobs,
+                    need_token_logprob=completion.needs_token_logprob,
+                )
+            else:
+                record = Token.from_token(
+                    model=self.model,
+                    prev_tokens=prev_tokens,
+                    prev_text_bytes=completion.detokenized_prefix_bytes,
+                    token=draft_token,
+                )
+            accepted_finish_reason: Optional[str] = self.handle_completion_token(
+                completion,
+                draft_token,
+                record,
+                decoded=True,
+            )
+            accepted_draft_count += 1
+            self.speculative_stats["draft_tokens_accepted"] += 1
+            if accepted_finish_reason is not None:
+                self.record_draft_acceptance_length(accepted_draft_count)
+                rejected = max(0, len(completion.draft_tokens) - accepted_draft_count)
+                if rejected > 0:
+                    self.speculative_stats["draft_tokens_rejected"] += rejected
+                self.metrics.draft_target_tokens_wasted_total += (
+                    len(decoded_draft_tokens) - accepted_draft_count
+                )
+                keep_len = item.start_pos + pending_count + accepted_draft_count
+                self.rollback_draft_verification(
+                    item,
+                    keep_len,
+                    accepted_draft_count,
+                    defer_draft_state=defer_draft_state,
+                )
+                completion.draft_tokens.clear()
+                decode.accepted_draft_count = accepted_draft_count
+                self.finish_completion(completion, accepted_finish_reason)
+                return
+
+        decode.accepted_draft_count = accepted_draft_count
+        if decoded_draft_tokens:
+            self.record_draft_acceptance_length(accepted_draft_count)
+        if not defer_draft_state:
+            self.model.accept_draft_tokens(completion.seq_id, accepted_draft_count)
+        elif decoded_draft_tokens:
+            decode.deferred_accept_draft_count = accepted_draft_count
+        if accepted_draft_count:
+            completion.draft_tokens = completion.draft_tokens[accepted_draft_count:]
+
+        final_logits_index = logits_indices[-1]
+        next_token = self.sample_completion_token(completion, final_logits_index)
+        need_final_logits = self.model.store_logits or completion.needs_token_logprob
+        final_logits = (
+            self.model.logits(final_logits_index) if need_final_logits else None
+        )
+        if self.model.store_logits and final_logits is not None:
+            self.checkpoint_logits[completion.seq_id] = final_logits
+        if completion.draft_tokens and next_token != completion.draft_tokens[0]:
+            self.speculative_stats["draft_tokens_rejected"] += len(completion.draft_tokens)
+            if not defer_draft_state:
+                self.model.truncate_draft_sequence(
+                    completion.seq_id,
+                    item.start_pos + len(item.tokens),
+                )
+            else:
+                decode.deferred_truncate_draft_len = item.start_pos + len(item.tokens)
+            completion.draft_tokens.clear()
+        elif completion.draft_tokens and next_token == completion.draft_tokens[0]:
+            if getattr(self.model, "draft_target_batching", True):
+                completion.draft_tokens = completion.draft_tokens[1:]
+            else:
+                completion.draft_tokens.clear()
+            self.speculative_stats["draft_tokens_accepted"] += 1
+            self.metrics.draft_tokens_reused_as_pending_total += 1
+
+        prev_tokens = [*completion.prompt_tokens, *completion.completion_tokens]
+        if final_logits is not None:
+            record = Token.from_logits(
+                model=self.model,
+                formatter=self.formatter,
+                prev_tokens=prev_tokens,
+                prev_text_bytes=completion.detokenized_prefix_bytes,
+                token=next_token,
+                logits=final_logits,
+                logprobs_count=completion.logprobs,
+                need_token_logprob=completion.needs_token_logprob,
+            )
+        else:
+            record = Token.from_token(
+                model=self.model,
+                prev_tokens=prev_tokens,
+                prev_text_bytes=completion.detokenized_prefix_bytes,
+                token=next_token,
+            )
+        final_finish_reason: Optional[str] = self.handle_completion_token(
+            completion,
+            next_token,
+            record,
+            decoded=False,
+        )
+        if final_finish_reason is not None:
+            completion.pending_finish_reason = final_finish_reason
+        else:
+            decode.sampled_pending_token = next_token
+
+    def maybe_save_prompt_checkpoint(self, request: CompletionRequest) -> None:
+        if (
+            not self.model.exact_checkpoints_only
+            or request.prompt_checkpoint_saved
+            or request.base_seq_id is None
+            or not request.prompt_tokens
+            or request.prompt_logits is None
+            or not self.unused_sequences
+        ):
+            return
+        checkpoint_seq_id = self.unused_sequences.pop()
+        self.copy_sequence_state(
+            request.base_seq_id,
+            checkpoint_seq_id,
+            len(request.prompt_tokens),
+        )
+        self.checkpoint_logits[checkpoint_seq_id] = request.prompt_logits
+        self.free_sequences[checkpoint_seq_id] = None
+        self.free_sequences.move_to_end(checkpoint_seq_id)
+        request.prompt_checkpoint_saved = True
+        self.metrics.checkpoint_saves_total += 1
+
+    @staticmethod
+    def output_index_to_logits_index(
+        output_index: Optional[int],
+        output_count: int,
+    ) -> Optional[int]:
+        if output_index is None:
+            return None
+        return output_index - output_count
+
+    @staticmethod
+    def last_output_index(output_indices: Sequence[Optional[int]]) -> Optional[int]:
+        for output_index in reversed(output_indices):
+            if output_index is not None:
+                return output_index
+        return None
+
+    def start_completions(
+        self,
+        request: CompletionRequest,
+        prompt_output_index: Optional[int],
+        prompt_logits: Optional[np.ndarray] = None,
+    ) -> None:
+        if request.completions:
+            return
+        assert request.base_seq_id is not None
+        prompt_tokens = list(request.prompt_plan.text_tokens)
+        prompt_length = len(request.prompt_tokens)
+        multimodal_prompt = any(
+            segment.kind != "text" for segment in request.prompt_plan.segments
+        )
+        prompt_text = request.prompt_text
+        if request.payload.stop is None:
+            stop_sequences: List[bytes] = []
+        elif isinstance(request.payload.stop, str):
+            stop_sequences = [request.payload.stop.encode("utf-8")]
+        else:
+            stop_sequences = [item.encode("utf-8") for item in request.payload.stop]
+        logit_bias = (
+            {int(token): float(bias) for token, bias in request.payload.logit_bias.items()}
+            if request.payload.logit_bias
+            else None
+        )
+        prompt_text_bytes = self.model.detokenize(prompt_tokens) if prompt_tokens else b""
+        for offset, seq_id in enumerate(request.completion_seq_ids):
+            if offset > 0:
+                if prompt_length:
+                    self.memory_policy.copy_prompt_state(
+                        request.base_seq_id,
+                        seq_id,
+                        prompt_length,
+                    )
+            sampler = Sampler(
+                seed=(request.payload.seed or llama_cpp.LLAMA_DEFAULT_SEED) + offset,
+                vocab=self.model.vocab,
+                n_vocab=self.model.n_vocab,
+                top_p=request.payload.top_p,
+                temperature=request.payload.temperature,
+                frequency_penalty=request.payload.frequency_penalty or 0.0,
+                presence_penalty=request.payload.presence_penalty or 0.0,
+                logit_bias=logit_bias,
+                grammar_text=request.grammar_text,
+                grammar_root=request.grammar_root,
+            )
+            request.completions.append(
+                Completion(
+                    request_id=request.id,
+                    index=offset,
+                    seq_id=seq_id,
+                    sampler=sampler,
+                    prompt_tokens=prompt_tokens,
+                    prompt_length=prompt_length,
+                    prompt_text=prompt_text,
+                    multimodal_prompt=multimodal_prompt,
+                    max_total_tokens=request.effective_max_len,
+                    stop_sequences=stop_sequences,
+                    logprobs=request.payload.logprobs,
+                    detokenized_prefix_bytes=bytearray(prompt_text_bytes),
+                    rank_by_score=(
+                        request.payload.best_of is not None
+                        and request.payload.best_of > request.payload.n
+                    ),
+                )
+            )
+        if request.payload.max_tokens == 0 or request.effective_max_len == prompt_length:
+            for completion in request.completions:
+                self.finish_completion(completion, "length")
+            self.finalize_request_if_ready(request)
+            return
+        if prompt_output_index is None:
+            if prompt_logits is not None:
+                for completion in request.completions:
+                    self.sample_completion_from_logits(completion, prompt_logits)
+                return
+            raise RuntimeError("prompt output is required to start generation")
+        for completion in request.completions:
+            self.sample_completion(completion, prompt_output_index)
+
+    def sample_completion(
+        self,
+        completion: Completion,
+        output_index: Optional[int],
+    ) -> None:
+        if completion.finished:
+            return
+        if output_index is None:
+            raise RuntimeError("missing logits output")
+        token = self.sample_completion_token(completion, output_index)
+        prev_tokens = [*completion.prompt_tokens, *completion.completion_tokens]
+        if self.model.store_logits or completion.needs_token_logprob:
+            logits = self.model.logits(output_index)
+            if self.model.store_logits:
+                self.checkpoint_logits[completion.seq_id] = logits
+            record = Token.from_logits(
+                model=self.model,
+                formatter=self.formatter,
+                prev_tokens=prev_tokens,
+                prev_text_bytes=completion.detokenized_prefix_bytes,
+                token=token,
+                logits=logits,
+                logprobs_count=completion.logprobs,
+                need_token_logprob=completion.needs_token_logprob,
+            )
+        else:
+            record = Token.from_token(
+                model=self.model,
+                prev_tokens=prev_tokens,
+                prev_text_bytes=completion.detokenized_prefix_bytes,
+                token=token,
+            )
+        finish_reason = self.handle_completion_token(
+            completion,
+            token,
+            record,
+            decoded=False,
+        )
+        if finish_reason is not None:
+            completion.pending_finish_reason = finish_reason
+
+    def sample_completion_from_logits(
+        self,
+        completion: Completion,
+        logits: np.ndarray,
+    ) -> None:
+        if completion.finished:
+            return
+        token = completion.sampler.sample_logits(logits)
+        prev_tokens = [*completion.prompt_tokens, *completion.completion_tokens]
+        if self.model.store_logits or completion.needs_token_logprob:
+            if self.model.store_logits:
+                self.checkpoint_logits[completion.seq_id] = logits.copy()
+            record = Token.from_logits(
+                model=self.model,
+                formatter=self.formatter,
+                prev_tokens=prev_tokens,
+                prev_text_bytes=completion.detokenized_prefix_bytes,
+                token=token,
+                logits=logits,
+                logprobs_count=completion.logprobs,
+                need_token_logprob=completion.needs_token_logprob,
+            )
+        else:
+            record = Token.from_token(
+                model=self.model,
+                prev_tokens=prev_tokens,
+                prev_text_bytes=completion.detokenized_prefix_bytes,
+                token=token,
+            )
+        finish_reason = self.handle_completion_token(
+            completion,
+            token,
+            record,
+            decoded=False,
+        )
+        if finish_reason is not None:
+            completion.pending_finish_reason = finish_reason
+
+    def handle_completion_token(
+        self,
+        completion: Completion,
+        token: int,
+        record: Token,
+        *,
+        decoded: bool,
+    ) -> Optional[str]:
+        if record.token_logprob is not None:
+            completion.score_sum += record.token_logprob
+        rendered_start = len(completion.rendered_bytes)
+        completion.completion_tokens.append(token)
+        self.metrics.observe_predicted_token()
+        completion.token_records.append(record)
+        completion.rendered_bytes.extend(record.text_bytes)
+        completion.detokenized_prefix_bytes.extend(record.text_bytes)
+        finish_reason: Optional[str] = None
+        if llama_cpp.llama_vocab_is_eog(self.model.vocab, token):
+            finish_reason = "stop"
+        elif completion.total_tokens >= completion.max_total_tokens:
+            finish_reason = "length"
+        else:
+            max_stop_length = completion.max_stop_sequence_length
+            search_start = max(0, rendered_start - max_stop_length + 1)
+            if any(
+                completion.rendered_bytes.find(stop, search_start) != -1
+                for stop in completion.stop_sequences
+            ):
+                finish_reason = "stop"
+        if not decoded:
+            completion.pending_input_tokens.append(token)
+        if (
+            completion.request_id in self.requests
+            and self.requests[completion.request_id].payload.stream
+            and finish_reason is None
+        ):
+            self.flush_stream_updates(completion, finish_reason=None)
+        if not decoded and finish_reason is None and not self.should_defer_draft_fill:
+            self.maybe_fill_draft_tokens(completion)
+        return finish_reason
+
+    def flush_stream_updates(
+        self,
+        completion: Completion,
+        finish_reason: Optional[str],
+    ) -> None:
+        request = self.requests[completion.request_id]
+        for payload in self.formatter.stream_completion_chunks(
+            request,
+            completion,
+            finish_reason,
+        ):
+            if request.on_stream_chunk is not None:
+                request.on_stream_chunk(payload)
+
+    def finish_completion(self, completion: Completion, finish_reason: str) -> None:
+        if completion.finished:
+            return
+        completion.finished = True
+        completion.finish_reason = finish_reason
+        completion.pending_input_tokens.clear()
+        completion.draft_tokens.clear()
+        request = self.requests[completion.request_id]
+        if request.payload.stream:
+            self.flush_stream_updates(completion, finish_reason=finish_reason)
+            if request.on_stream_chunk is not None:
+                request.on_stream_chunk(
+                    self.formatter.completion_finish_chunk(
+                        request,
+                        completion,
+                        finish_reason,
+                    )
+                )
+
+    def finalize_request_if_ready(self, request: CompletionRequest) -> None:
+        if not request.completions or not all(completion.finished for completion in request.completions):
+            return
+        selected = request.selected_completions()
+        result = self.formatter.build_completion_response(request, selected)
+        self.metrics.requests_completed_total += 1
+        self.release_request(request)
+        if request.on_done is not None:
+            request.on_done(result)
+
+    def truncate_sequence(
+        self,
+        seq_id: int,
+        keep_len: int,
+        *,
+        truncate_draft: bool = True,
+    ) -> None:
+        current_len = self.radix_trie.length(seq_id)
+        if current_len <= keep_len:
+            return
+        keep_pos = self.sequence_history.position_length_for_prefix(seq_id, keep_len)
+        if not llama_cpp.llama_memory_seq_rm(self.model.mem, seq_id, keep_pos, -1):
+            raise RuntimeError(
+                f"failed to truncate model sequence {seq_id} at position {keep_pos}"
+            )
+        if truncate_draft:
+            self.model.truncate_draft_sequence(seq_id, keep_pos)
+        self.truncate_sequence_metadata(seq_id, current_len, keep_len)
+
+    def copy_sequence_state(
+        self,
+        source_sequence_id: int,
+        dest_sequence_id: int,
+        keep_len: int,
+        *,
+        copy_all_state: bool = False,
+    ) -> None:
+        if keep_len <= 0:
+            return
+        source_length = self.radix_trie.length(source_sequence_id)
+        keep_pos = self.sequence_history.position_length_for_prefix(
+            source_sequence_id,
+            keep_len,
+        )
+        copy_p0 = 0
+        copy_p1 = keep_pos
+        if copy_all_state or not self.model.kv_unified:
+            copy_p0 = -1
+            copy_p1 = -1
+        llama_cpp.llama_memory_seq_cp(
+            self.model.mem,
+            source_sequence_id,
+            dest_sequence_id,
+            copy_p0,
+            copy_p1,
+        )
+        self.model.copy_draft_sequence(
+            source_sequence_id,
+            dest_sequence_id,
+            copy_p0,
+            copy_p1,
+        )
+        if copy_all_state and source_length > keep_len:
+            if not llama_cpp.llama_memory_seq_rm(
+                self.model.mem,
+                dest_sequence_id,
+                keep_pos,
+                -1,
+            ):
+                raise RuntimeError(
+                    f"failed to truncate copied model sequence {dest_sequence_id} "
+                    f"at position {keep_pos}"
+                )
+            self.model.truncate_draft_sequence(dest_sequence_id, keep_pos)
+        self.radix_trie.copy(source_sequence_id, dest_sequence_id, keep_len)
+        self.sequence_history.copy(
+            source_sequence_id,
+            dest_sequence_id,
+            source_length,
+            keep_len,
+        )
+
+    def truncate_sequence_metadata(
+        self,
+        seq_id: int,
+        current_len: int,
+        keep_len: int,
+    ) -> None:
+        self.radix_trie.truncate(seq_id, keep_len)
+        self.sequence_history.truncate(seq_id, current_len, keep_len)
+        self.checkpoint_logits.pop(seq_id, None)
+
+    def truncate_free_sequence(self, seq_id: int, keep_len: int) -> None:
+        if seq_id not in self.free_sequences:
+            return
+        self.truncate_sequence(seq_id, keep_len)
+        self.free_sequences.move_to_end(seq_id)
+
+    def delete_free_sequence(self, seq_id: int) -> None:
+        if seq_id not in self.free_sequences:
+            return
+        self.truncate_sequence(seq_id, 0)
+        del self.free_sequences[seq_id]
+        self.checkpoint_logits.pop(seq_id, None)
+        self.unused_sequences.append(seq_id)
+        self.metrics.checkpoint_evictions_total += 1
+
+    def claim_unused_sequence(self) -> int:
+        seq_id = self.unused_sequences.pop()
+        self.claimed_sequences.add(seq_id)
+        return seq_id
+
+    def claim_free_sequence(self, seq_id: int) -> int:
+        del self.free_sequences[seq_id]
+        self.claimed_sequences.add(seq_id)
+        return seq_id
+
+    def activate_request(
+        self,
+        request: CompletionRequest,
+        *,
+        base_seq_id: int,
+        sibling_seq_ids: List[int],
+    ) -> None:
+        request.base_seq_id = base_seq_id
+        request.sibling_seq_ids = sibling_seq_ids
+        request.completion_seq_ids = [base_seq_id, *sibling_seq_ids]
+        request.admitted = True
+        self.active_request_ids.add(request.id)
+
+    def release_request(self, request: CompletionRequest) -> None:
+        for completion in request.completions:
+            completion.sampler.close()
+        for seq_id in request.completion_seq_ids:
+            if seq_id in self.claimed_sequences:
+                self.claimed_sequences.remove(seq_id)
+                self.free_sequences[seq_id] = None
+                self.free_sequences.move_to_end(seq_id)
+        self.active_request_ids.discard(request.id)
+        self.requests.pop(request.id, None)
+
+    def remove_pending_request(self, request: CompletionRequest) -> None:
+        try:
+            self.pending_requests.remove(request)
+        except ValueError:
+            pass
+
+    def fail_request(self, request: CompletionRequest, exc: BaseException) -> None:
+        self.remove_pending_request(request)
+        if request.id in self.active_request_ids or request.admitted:
+            self.release_request(request)
+        else:
+            self.requests.pop(request.id, None)
+        if isinstance(exc, CompletionRequestCancelledError):
+            self.metrics.requests_cancelled_total += 1
+        else:
+            self.metrics.requests_failed_total += 1
+        if request.on_error is not None:
+            request.on_error(exc)
+
+    @staticmethod
+    def _format_prometheus_value(value: Union[int, float]) -> str:
+        if isinstance(value, int):
+            return str(value)
+        if not math.isfinite(value):
+            raise ValueError(f"invalid Prometheus metric value: {value}")
+        return format(value, ".16g")
+
+    def sequence_cache_metric_definitions(
+        self,
+    ) -> List[Tuple[str, str, str, Union[int, float]]]:
+        return [
+            (
+                "counter",
+                "batch_server:sequence_cache_hits_total",
+                "Number of external sequence cache entries hydrated.",
+                self.metrics.sequence_cache_hits_total,
+            ),
+            (
+                "counter",
+                "batch_server:sequence_cache_save_requests_total",
+                "Number of external sequence cache save requests.",
+                self.metrics.sequence_cache_save_requests_total,
+            ),
+            (
+                "counter",
+                "batch_server:sequence_cache_load_failures_total",
+                "Number of external sequence cache load failures.",
+                self.metrics.sequence_cache_load_failures_total,
+            ),
+            (
+                "counter",
+                "batch_server:sequence_cache_lookup_failures_total",
+                "Number of external sequence cache lookup failures.",
+                self.metrics.sequence_cache_lookup_failures_total,
+            ),
+            (
+                "counter",
+                "batch_server:sequence_cache_save_failures_total",
+                "Number of external sequence cache save failures.",
+                self.metrics.sequence_cache_save_failures_total,
+            ),
+            (
+                "counter",
+                "batch_server:sequence_cache_tokens_loaded_total",
+                "Number of prompt tokens hydrated from external sequence cache.",
+                self.metrics.sequence_cache_tokens_loaded_total,
+            ),
+        ]
+
+    def speculative_metric_definitions(
+        self,
+    ) -> List[Tuple[str, str, str, Union[int, float]]]:
+        proposed = self.speculative_stats["draft_tokens_proposed"]
+        accepted = self.speculative_stats["draft_tokens_accepted"]
+        proposals = self.speculative_stats["draft_proposals"]
+        verified_proposals = sum(self.draft_acceptance_length_counts.values())
+        verified_accepted = sum(
+            accepted_tokens * count
+            for accepted_tokens, count in self.draft_acceptance_length_counts.items()
+        )
+        acceptance_rate = accepted / proposed if proposed > 0 else 0.0
+        average_proposed_tokens = proposed / proposals if proposals > 0 else 0.0
+        average_verified_acceptance_length = (
+            verified_accepted / verified_proposals if verified_proposals > 0 else 0.0
+        )
+        return [
+            (
+                "counter",
+                "batch_server:draft_proposals_total",
+                "Number of speculative draft proposal batches generated.",
+                proposals,
+            ),
+            (
+                "counter",
+                "batch_server:draft_tokens_proposed_total",
+                "Number of speculative draft tokens proposed.",
+                proposed,
+            ),
+            (
+                "counter",
+                "batch_server:draft_tokens_accepted_total",
+                "Number of speculative draft tokens accepted.",
+                accepted,
+            ),
+            (
+                "counter",
+                "batch_server:draft_tokens_rejected_total",
+                "Number of speculative draft tokens rejected.",
+                self.speculative_stats["draft_tokens_rejected"],
+            ),
+            (
+                "counter",
+                "batch_server:draft_batches_verified_total",
+                "Number of completion items that target-verified draft tokens.",
+                self.metrics.draft_batches_verified_total,
+            ),
+            (
+                "counter",
+                "batch_server:draft_target_tokens_verified_total",
+                "Number of draft tokens decoded by the target for verification.",
+                self.metrics.draft_target_tokens_verified_total,
+            ),
+            (
+                "counter",
+                "batch_server:draft_target_tokens_wasted_total",
+                "Number of target-verified draft tokens rejected before emission.",
+                self.metrics.draft_target_tokens_wasted_total,
+            ),
+            (
+                "counter",
+                "batch_server:draft_tokens_reused_as_pending_total",
+                "Number of draft tokens accepted by the next pending-token sample.",
+                self.metrics.draft_tokens_reused_as_pending_total,
+            ),
+            (
+                "gauge",
+                "batch_server:draft_acceptance_rate",
+                "Fraction of proposed draft tokens accepted, including reused pending matches.",
+                acceptance_rate,
+            ),
+            (
+                "gauge",
+                "batch_server:draft_average_proposed_tokens",
+                "Average number of tokens in generated draft proposals.",
+                average_proposed_tokens,
+            ),
+            (
+                "gauge",
+                "batch_server:draft_average_verified_acceptance_length",
+                "Average accepted draft-token prefix length for target-verified proposals only.",
+                average_verified_acceptance_length,
+            ),
+        ]
+
+    def record_draft_acceptance_length(self, accepted_tokens: int) -> None:
+        accepted_tokens = max(0, int(accepted_tokens))
+        self.draft_acceptance_length_counts[accepted_tokens] = (
+            self.draft_acceptance_length_counts.get(accepted_tokens, 0) + 1
+        )
+
+    def draft_acceptance_length_metric_lines(self) -> List[str]:
+        lines = [
+            "# HELP batch_server:draft_acceptance_length_total Number of verified draft proposals by accepted-token prefix length.",
+            "# TYPE batch_server:draft_acceptance_length_total counter",
+        ]
+        for accepted_tokens, count in sorted(self.draft_acceptance_length_counts.items()):
+            lines.append(
+                f'batch_server:draft_acceptance_length_total{{accepted_tokens="{accepted_tokens}"}} {count}'
+            )
+        return lines
+
+    def render_prometheus_metrics(self) -> str:
+        active_completions = sum(
+            1
+            for request_id in self.active_request_ids
+            for completion in self.requests[request_id].completions
+            if not completion.finished
+        )
+        checkpoint_entries = sum(
+            1 for seq_id in self.free_sequences if seq_id in self.checkpoint_logits
+        )
+        prompt_tokens_seconds = (
+            self.metrics.prompt_tokens_total / self.metrics.prompt_seconds_total
+            if self.metrics.prompt_seconds_total > 0
+            else 0.0
+        )
+        predicted_tokens_seconds = (
+            self.metrics.tokens_predicted_total
+            / self.metrics.tokens_predicted_seconds_total
+            if self.metrics.tokens_predicted_seconds_total > 0
+            else 0.0
+        )
+        n_busy_slots_per_decode = (
+            self.metrics.n_busy_slots_total / self.metrics.n_decode_total
+            if self.metrics.n_decode_total > 0
+            else 0.0
+        )
+        draft_provider_metrics: List[Tuple[str, str, str, Union[int, float]]] = []
+        if self.model.draft_provider is not None:
+            draft_metric_definitions = getattr(
+                self.model.draft_provider,
+                "metric_definitions",
+                None,
+            )
+            if draft_metric_definitions is not None:
+                draft_provider_metrics = cast(
+                    List[Tuple[str, str, str, Union[int, float]]],
+                    draft_metric_definitions(),
+                )
+        metrics_def: List[Tuple[str, str, str, Union[int, float]]] = [
+            (
+                "counter",
+                "llamacpp:prompt_tokens_total",
+                "Number of prompt tokens processed.",
+                self.metrics.prompt_tokens_total,
+            ),
+            (
+                "counter",
+                "llamacpp:prompt_seconds_total",
+                "Estimated prompt processing time in seconds.",
+                self.metrics.prompt_seconds_total,
+            ),
+            (
+                "counter",
+                "llamacpp:tokens_predicted_total",
+                "Number of generated tokens processed.",
+                self.metrics.tokens_predicted_total,
+            ),
+            (
+                "counter",
+                "llamacpp:tokens_predicted_seconds_total",
+                "Estimated generation processing time in seconds.",
+                self.metrics.tokens_predicted_seconds_total,
+            ),
+            (
+                "counter",
+                "batch_server:scheduler_step_seconds_total",
+                "Total scheduler step wall time.",
+                self.metrics.scheduler_step_seconds_total,
+            ),
+            (
+                "counter",
+                "batch_server:process_batch_seconds_total",
+                "Time spent processing decoded batches after llama_decode().",
+                self.metrics.process_batch_seconds_total,
+            ),
+            (
+                "counter",
+                "batch_server:sample_seconds_total",
+                "Time spent sampling target logits.",
+                self.metrics.sample_seconds_total,
+            ),
+            (
+                "counter",
+                "batch_server:draft_seconds_total",
+                "Speculative draft processing time in seconds.",
+                self.metrics.draft_seconds_total,
+            ),
+            (
+                "counter",
+                "batch_server:draft_process_seconds_total",
+                "Time spent feeding target batches into the draft context.",
+                self.metrics.draft_process_seconds_total,
+            ),
+            (
+                "counter",
+                "batch_server:draft_generate_seconds_total",
+                "Time spent generating speculative draft tokens.",
+                self.metrics.draft_generate_seconds_total,
+            ),
+            (
+                "counter",
+                "batch_server:draft_sampled_batch_seconds_total",
+                "Time spent generating speculative tokens from sampled target batches.",
+                self.metrics.draft_sampled_batch_seconds_total,
+            ),
+            (
+                "counter",
+                "batch_server:draft_process_calls_total",
+                "Number of target-to-draft context processing phases.",
+                self.metrics.draft_process_calls_total,
+            ),
+            (
+                "counter",
+                "batch_server:draft_generate_calls_total",
+                "Number of speculative draft generation phases.",
+                self.metrics.draft_generate_calls_total,
+            ),
+            (
+                "counter",
+                "batch_server:draft_sampled_batch_calls_total",
+                "Number of sampled target batch draft phases.",
+                self.metrics.draft_sampled_batch_calls_total,
+            ),
+            (
+                "counter",
+                "llamacpp:n_decode_total",
+                "Total number of llama_decode() calls.",
+                self.metrics.n_decode_total,
+            ),
+            (
+                "counter",
+                "batch_server:scheduler_step_calls_total",
+                "Number of scheduler steps.",
+                self.metrics.scheduler_step_calls_total,
+            ),
+            (
+                "counter",
+                "batch_server:process_batch_calls_total",
+                "Number of decoded batch processing phases.",
+                self.metrics.process_batch_calls_total,
+            ),
+            (
+                "counter",
+                "batch_server:sample_calls_total",
+                "Number of target logit sampling calls.",
+                self.metrics.sample_calls_total,
+            ),
+            (
+                "counter",
+                "llamacpp:n_tokens_max",
+                "Largest observed n_tokens.",
+                self.metrics.n_tokens_max,
+            ),
+            (
+                "gauge",
+                "llamacpp:n_busy_slots_per_decode",
+                "Average number of busy sequences per llama_decode() call.",
+                n_busy_slots_per_decode,
+            ),
+            (
+                "gauge",
+                "llamacpp:prompt_tokens_seconds",
+                "Average prompt throughput in tokens/s.",
+                prompt_tokens_seconds,
+            ),
+            (
+                "gauge",
+                "llamacpp:predicted_tokens_seconds",
+                "Average generation throughput in tokens/s.",
+                predicted_tokens_seconds,
+            ),
+            (
+                "gauge",
+                "llamacpp:requests_processing",
+                "Number of requests processing.",
+                len(self.active_request_ids),
+            ),
+            (
+                "gauge",
+                "llamacpp:requests_deferred",
+                "Number of requests deferred.",
+                len(self.pending_requests),
+            ),
+            (
+                "counter",
+                "batch_server:requests_submitted_total",
+                "Number of requests submitted.",
+                self.metrics.requests_submitted_total,
+            ),
+            (
+                "counter",
+                "batch_server:requests_admitted_total",
+                "Number of requests admitted.",
+                self.metrics.requests_admitted_total,
+            ),
+            (
+                "counter",
+                "batch_server:requests_completed_total",
+                "Number of requests completed successfully.",
+                self.metrics.requests_completed_total,
+            ),
+            (
+                "counter",
+                "batch_server:requests_cancelled_total",
+                "Number of requests cancelled.",
+                self.metrics.requests_cancelled_total,
+            ),
+            (
+                "counter",
+                "batch_server:requests_failed_total",
+                "Number of requests failed.",
+                self.metrics.requests_failed_total,
+            ),
+            (
+                "gauge",
+                "batch_server:active_completions",
+                "Number of active unfinished completions.",
+                active_completions,
+            ),
+            (
+                "gauge",
+                "batch_server:claimed_sequences",
+                "Number of claimed sequence ids.",
+                len(self.claimed_sequences),
+            ),
+            (
+                "gauge",
+                "batch_server:free_sequences",
+                "Number of reusable free sequence ids.",
+                len(self.free_sequences),
+            ),
+            (
+                "gauge",
+                "batch_server:unused_sequences",
+                "Number of unused sequence ids.",
+                len(self.unused_sequences),
+            ),
+            (
+                "gauge",
+                "batch_server:checkpoint_entries",
+                "Number of free sequence checkpoints with stored logits.",
+                checkpoint_entries,
+            ),
+            (
+                "counter",
+                "batch_server:checkpoint_hits_total",
+                "Number of exact checkpoint hits.",
+                self.metrics.checkpoint_hits_total,
+            ),
+            (
+                "counter",
+                "batch_server:checkpoint_saves_total",
+                "Number of prompt checkpoints saved.",
+                self.metrics.checkpoint_saves_total,
+            ),
+            (
+                "counter",
+                "batch_server:checkpoint_evictions_total",
+                "Number of free checkpoints evicted.",
+                self.metrics.checkpoint_evictions_total,
+            ),
+            *self.sequence_cache_metric_definitions(),
+            *self.speculative_metric_definitions(),
+            *draft_provider_metrics,
+            (
+                "gauge",
+                "batch_server:radix_trie_sequences",
+                "Number of sequences tracked in the radix trie.",
+                len(self.radix_trie.sequence_lengths),
+            ),
+            (
+                "gauge",
+                "batch_server:radix_trie_tokens",
+                "Total tokens tracked in the radix trie.",
+                self.sequence_history.size,
+            ),
+        ]
+        lines: List[str] = []
+        for metric_type, name, help_text, value in metrics_def:
+            lines.append(f"# HELP {name} {help_text}")
+            lines.append(f"# TYPE {name} {metric_type}")
+            lines.append(f"{name} {self._format_prometheus_value(value)}")
+        lines.extend(self.draft_acceptance_length_metric_lines())
+        lines.append("")
+        return "\n".join(lines)
+
+    def finalize_cancelled(self) -> bool:
+        finalized = False
+        for request in list(self.pending_requests):
+            if request.cancelled:
+                self.fail_request(request, CompletionRequestCancelledError("request cancelled"))
+                finalized = True
+        for request_id in list(self.active_request_ids):
+            request = self.requests[request_id]
+            if request.cancelled:
+                self.fail_request(request, CompletionRequestCancelledError("request cancelled"))
+                finalized = True
+        return finalized
+
+
+class CompletionService:
+    def __init__(self, scheduler: CompletionScheduler) -> None:
+        self.scheduler = scheduler
+        self.formatter = scheduler.formatter
+        self.condition = threading.Condition()
+        self.commands: Deque[Callable[[], None]] = deque()
+        self.closed = False
+        self.worker = threading.Thread(
+            target=self.run_loop,
+            name="completion-service",
+            daemon=True,
+        )
+        self.worker.start()
+
+    def close(self) -> None:
+        with self.condition:
+            self.closed = True
+            self.condition.notify_all()
+        self.worker.join()
+        self.scheduler.close()
+
+    def enqueue(self, command: Callable[[], None]) -> None:
+        with self.condition:
+            if self.closed:
+                raise RuntimeError("completion service closed")
+            self.commands.append(command)
+            self.condition.notify_all()
+
+    def call_on_scheduler(self, callback: Callable[[], Any]) -> Any:
+        result_box: Dict[str, Any] = {}
+        error_box: Dict[str, BaseException] = {}
+        done = threading.Event()
+
+        def run_callback() -> None:
+            try:
+                result_box["result"] = callback()
+            except BaseException as exc:  # noqa: BLE001
+                error_box["error"] = exc
+            finally:
+                done.set()
+
+        self.enqueue(run_callback)
+        done.wait()
+        if "error" in error_box:
+            raise error_box["error"]
+        return result_box.get("result")
+
+    def call_on_idle_scheduler(self, callback: Callable[[], Any]) -> Any:
+        result_box: Dict[str, Any] = {}
+        error_box: Dict[str, BaseException] = {}
+        done = threading.Event()
+
+        def run_callback() -> None:
+            if not self.scheduler.is_idle():
+                with self.condition:
+                    self.commands.appendleft(run_callback)
+                    self.condition.notify_all()
+                return
+            try:
+                result_box["result"] = callback()
+            except BaseException as exc:  # noqa: BLE001
+                error_box["error"] = exc
+            finally:
+                done.set()
+
+        self.enqueue(run_callback)
+        done.wait()
+        if "error" in error_box:
+            raise error_box["error"]
+        return result_box.get("result")
+
+    def create_embedding(
+        self,
+        payload: CreateEmbeddingRequest,
+    ) -> CreateEmbeddingResponse:
+        embedding = self.call_on_idle_scheduler(
+            lambda: self.scheduler.create_embedding(payload)
+        )
+        return cast(CreateEmbeddingResponse, embedding)
+
+    def render_prometheus_metrics(self) -> str:
+        metrics = self.call_on_scheduler(self.scheduler.render_prometheus_metrics)
+        return cast(str, metrics)
+
+    def run_loop(self) -> None:
+        while True:
+            with self.condition:
+                while not self.closed and not self.commands and self.scheduler.is_idle():
+                    self.condition.wait()
+                if self.closed and not self.commands and self.scheduler.is_idle():
+                    return
+                commands = list(self.commands)
+                self.commands.clear()
+            for command in commands:
+                command()
+            progressed = self.scheduler.step()
+            if progressed:
+                continue
+            with self.condition:
+                if self.closed and not self.commands and self.scheduler.is_idle():
+                    return
+                if not self.commands:
+                    self.condition.wait(timeout=0.01)
+
+    def submit_request(
+        self,
+        request: CompletionRequest,
+    ) -> Tuple[CompletionStream, Callable[[], None]]:
+        mailbox: "queue.Queue[object]" = queue.Queue()
+        done = threading.Event()
+        cancelled = threading.Event()
+        sentinel = object()
+        result_box: Dict[str, OpenAICompletion] = {}
+        error_box: Dict[str, BaseException] = {}
+
+        def on_stream_chunk(chunk: CompletionChunk) -> None:
+            mailbox.put(chunk)
+
+        def on_done(result: OpenAICompletion) -> None:
+            result_box["result"] = result
+            done.set()
+            mailbox.put(sentinel)
+
+        def on_error(exc: BaseException) -> None:
+            error_box["error"] = exc
+            done.set()
+            mailbox.put(sentinel)
+
+        request.on_stream_chunk = on_stream_chunk
+        request.on_done = on_done
+        request.on_error = on_error
+
+        def cancel() -> None:
+            if cancelled.is_set():
+                return
+            cancelled.set()
+            try:
+                def cancel_request() -> None:
+                    self.scheduler.cancel(request.id)
+
+                self.enqueue(cancel_request)
+            except RuntimeError:
+                pass
+
+        def stream() -> CompletionStream:
+            try:
+                while True:
+                    item = mailbox.get()
+                    if item is sentinel:
+                        break
+                    yield cast(CompletionChunk, item)
+            finally:
+                if not done.is_set():
+                    cancel()
+            if "error" in error_box:
+                raise error_box["error"]
+            result = result_box.get("result")
+            if result is None:
+                raise RuntimeError("missing completion result")
+            return result
+
+        def submit_request() -> None:
+            self.scheduler.submit_request(request)
+
+        self.enqueue(submit_request)
+        return stream(), cancel
+
+    def submit(
+        self,
+        payload: CreateCompletionRequest,
+    ) -> Tuple[CompletionStream, Callable[[], None]]:
+        request = self.request_from_payload(payload)
+        return self.submit_request(request)
+
+    def request_from_payload(
+        self,
+        payload: CreateCompletionRequest,
+    ) -> CompletionRequest:
+        prompt_text, prompt_plan = self.prepare_completion_prompt(payload)
+        return self.request_from_prepared(
+            payload=payload,
+            prompt_text=prompt_text,
+            prompt_plan=prompt_plan,
+        )
+
+    def request_from_prepared(
+        self,
+        *,
+        payload: CreateCompletionRequest,
+        prompt_text: str,
+        prompt_plan: PromptPlan,
+        grammar_text: Optional[str] = None,
+        chat_tool_name: Optional[str] = None,
+        on_stream_chunk: Optional[Callable[[CompletionChunk], None]] = None,
+        on_done: Optional[Callable[[OpenAICompletion], None]] = None,
+        on_error: Optional[Callable[[BaseException], None]] = None,
+    ) -> CompletionRequest:
+        model = self.scheduler.model
+        prompt_visible_start = self.prompt_visible_start(prompt_plan)
+        return CompletionRequest.from_prepared(
+            payload=payload,
+            prompt_text=prompt_text,
+            prompt_plan=prompt_plan,
+            max_seq_len=model.max_seq_len,
+            max_output_tokens=model.max_output_tokens,
+            prompt_visible_start=prompt_visible_start,
+            prompt_records=self.initial_prompt_records(
+                payload,
+                prompt_plan,
+                prompt_visible_start,
+            ),
+            grammar_text=grammar_text,
+            chat_tool_name=chat_tool_name,
+            on_stream_chunk=on_stream_chunk,
+            on_done=on_done,
+            on_error=on_error,
+        )
+
+    def prepare_completion_prompt(
+        self,
+        payload: CreateCompletionRequest,
+    ) -> Tuple[str, PromptPlan]:
+        model = self.scheduler.model
+        prompts = payload.normalized_prompt()
+        if len(prompts) != 1:
+            raise CompletionRequestValidationError("multiple prompts are not supported")
+        prompt_item = prompts[0]
+        if isinstance(prompt_item, str):
+            prompt_text = prompt_item
+            try:
+                prompt_tokens = model.build_prompt_tokens(prompt_text, payload.suffix)
+            except ValueError as exc:
+                raise CompletionRequestValidationError(str(exc)) from exc
+        else:
+            if payload.suffix is not None:
+                raise CompletionRequestValidationError(
+                    "suffix is not supported with token id prompts"
+                )
+            prompt_tokens = list(prompt_item)
+            prompt_text = model.detokenize(prompt_tokens).decode("utf-8", errors="ignore")
+        return prompt_text, PromptPlan.from_tokens(prompt_text, prompt_tokens)
+
+    def prompt_visible_start(self, prompt_plan: PromptPlan) -> int:
+        model = self.scheduler.model
+        if prompt_plan.text_tokens and prompt_plan.text_tokens[0] == model.bos_token:
+            return 1
+        return 0
+
+    def initial_prompt_records(
+        self,
+        payload: CreateCompletionRequest,
+        prompt_plan: PromptPlan,
+        prompt_visible_start: int,
+    ) -> List[Token]:
+        if not (payload.echo and payload.logprobs is not None):
+            return []
+        if prompt_visible_start >= len(prompt_plan.text_tokens):
+            return []
+        model = self.scheduler.model
+        first_pos = prompt_visible_start
+        first_token = prompt_plan.text_tokens[first_pos]
+        return [
+            Token(
+                token=first_token,
+                text_bytes=model.token_bytes_with_prev(
+                    prompt_plan.text_tokens[:first_pos],
+                    first_token,
+                ),
+                token_logprob=None,
+                top_logprobs=None,
+            )
+        ]
+
+
+def create_app() -> FastAPI:
+    app = FastAPI()
+    app.state.service = None
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=["*"],
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+
+    async def watch_http_disconnect(
+        http_request: Request,
+        cancel: Callable[[], None],
+    ) -> None:
+        while True:
+            message = await http_request.receive()
+            if message["type"] == "http.disconnect":
+                cancel()
+                return
+
+    async def disconnected_cancelled_response_or_raise(
+        http_request: Request,
+        exc: BaseException,
+    ) -> Response:
+        if isinstance(exc, CompletionRequestCancelledError):
+            if await http_request.is_disconnected():
+                return Response(status_code=204)
+        raise exc
+
+    def bad_request(exc: CompletionRequestValidationError) -> HTTPException:
+        return HTTPException(status_code=400, detail=str(exc))
+
+    async def collect_completion_result(
+        formatter: OpenAIFormatter,
+        http_request: Request,
+        stream: CompletionStream,
+        cancel: Callable[[], None],
+    ) -> OpenAICompletion | Response:
+        disconnect_task = asyncio.create_task(
+            watch_http_disconnect(http_request, cancel)
+        )
+        try:
+            return await asyncio.to_thread(formatter.collect_completion, stream)
+        except asyncio.CancelledError:
+            cancel()
+            raise
+        except BaseException as exc:
+            return await disconnected_cancelled_response_or_raise(http_request, exc)
+        finally:
+            disconnect_task.cancel()
+            stream.close()
+
+    async def collect_completion_results(
+        formatter: OpenAIFormatter,
+        http_request: Request,
+        submissions: Sequence[
+            Tuple[
+                CompletionStream,
+                Callable[[], None],
+            ]
+        ],
+    ) -> List[OpenAICompletion] | Response:
+        streams = [stream for stream, _ in submissions]
+        cancel_all = [cancel for _, cancel in submissions]
+
+        def cancel_all_requests() -> None:
+            for cancel in cancel_all:
+                cancel()
+
+        disconnect_task = asyncio.create_task(
+            watch_http_disconnect(
+                http_request,
+                cancel_all_requests,
+            )
+        )
+        try:
+            return await asyncio.gather(
+                *(
+                    asyncio.to_thread(formatter.collect_completion, stream)
+                    for stream in streams
+                )
+            )
+        except asyncio.CancelledError:
+            cancel_all_requests()
+            raise
+        except BaseException as exc:
+            return await disconnected_cancelled_response_or_raise(http_request, exc)
+        finally:
+            disconnect_task.cancel()
+            for stream in streams:
+                stream.close()
+
+    async def stream_sse_chunks(
+        formatter: OpenAIFormatter,
+        http_request: Request,
+        stream: CompletionStream,
+        cancel: Callable[[], None],
+        chunk_payloads: Callable[[CompletionChunk], Iterable[Any]],
+    ) -> AsyncIterator[bytes]:
+        disconnect_task = asyncio.create_task(
+            watch_http_disconnect(http_request, cancel)
+        )
+        try:
+            while True:
+                done, chunk = await asyncio.to_thread(
+                    formatter.next_stream_chunk,
+                    stream,
+                )
+                if done:
+                    break
+                assert chunk is not None
+                for payload in chunk_payloads(chunk):
+                    yield formatter.encode_sse_payload(payload)
+            yield b"data: [DONE]\n\n"
+        except asyncio.CancelledError:
+            cancel()
+            raise
+        except BaseException as exc:
+            cancel()
+            if (
+                isinstance(exc, CompletionRequestCancelledError)
+                and await http_request.is_disconnected()
+            ):
+                return
+            raise
+        finally:
+            disconnect_task.cancel()
+
+    async def stream_sse_outputs(
+        formatter: OpenAIFormatter,
+        http_request: Request,
+        stream: CompletionStream,
+        cancel: Callable[[], None],
+        *,
+        initial_payloads: Iterable[BaseModel | Dict[str, Any]],
+        chunk_payloads: Callable[[CompletionChunk], Iterable[Any]],
+        done_payloads: Callable[
+            [Optional[OpenAICompletion]], Iterable[BaseModel | Dict[str, Any]]
+        ],
+    ) -> AsyncIterator[bytes]:
+        disconnect_task = asyncio.create_task(
+            watch_http_disconnect(http_request, cancel)
+        )
+        try:
+            for payload in initial_payloads:
+                yield formatter.encode_sse_payload(payload)
+            while True:
+                done, chunk, result = await asyncio.to_thread(
+                    formatter.next_stream_output,
+                    stream,
+                )
+                if done:
+                    for payload in done_payloads(result):
+                        yield formatter.encode_sse_payload(payload)
+                    break
+                assert chunk is not None
+                for payload in chunk_payloads(chunk):
+                    yield formatter.encode_sse_payload(payload)
+            yield b"data: [DONE]\n\n"
+        except asyncio.CancelledError:
+            cancel()
+            raise
+        except BaseException as exc:
+            cancel()
+            if (
+                isinstance(exc, CompletionRequestCancelledError)
+                and await http_request.is_disconnected()
+            ):
+                return
+            raise
+        finally:
+            disconnect_task.cancel()
+
+    async def stream_websocket_responses(
+        websocket: WebSocket,
+        formatter: OpenAIFormatter,
+        stream: CompletionStream,
+        _: Callable[[], None],
+        *,
+        initial_payloads: Iterable[BaseModel | Dict[str, Any]],
+        chunk_payloads: Callable[[CompletionChunk], Iterable[Any]],
+        done_payloads: Callable[
+            [Optional[OpenAICompletion]], Iterable[BaseModel | Dict[str, Any]]
+        ],
+    ) -> None:
+        try:
+            for payload in initial_payloads:
+                await websocket.send_json(payload)
+            while True:
+                done, chunk, result = await asyncio.to_thread(
+                    formatter.next_stream_output,
+                    stream,
+                )
+                if done:
+                    for payload in done_payloads(result):
+                        await websocket.send_json(payload)
+                    break
+                assert chunk is not None
+                for payload in chunk_payloads(chunk):
+                    await websocket.send_json(payload)
+        finally:
+            stream.close()
+
+    @app.post("/v1/completions")
+    async def create_completion(  # pyright: ignore[reportUnusedFunction]
+        http_request: Request,
+        body: CreateCompletionRequest,
+    ):
+        service: CompletionService = app.state.service
+        formatter = service.formatter
+        prompts = body.normalized_prompt()
+        if len(prompts) > 1:
+            if body.stream:
+                raise HTTPException(
+                    status_code=400,
+                    detail="streaming does not support multiple prompts",
+                )
+            try:
+                submissions = [
+                    service.submit(body.model_copy(update={"prompt": prompt}))
+                    for prompt in prompts
+                ]
+            except CompletionRequestValidationError as exc:
+                raise bad_request(exc) from exc
+            results = await collect_completion_results(
+                formatter, http_request, submissions
+            )
+            if isinstance(results, Response):
+                return results
+            return JSONResponse(
+                formatter.aggregate_completion_results(results).model_dump(
+                    mode="json",
+                    exclude_none=True,
+                )
+            )
+        try:
+            stream, cancel = service.submit(
+                body.model_copy(update={"prompt": prompts[0]})
+            )
+        except CompletionRequestValidationError as exc:
+            raise bad_request(exc) from exc
+        if body.stream:
+            return StreamingResponse(
+                stream_sse_chunks(
+                    formatter,
+                    http_request,
+                    stream,
+                    cancel,
+                    lambda chunk: [chunk],
+                ),
+                media_type="text/event-stream",
+            )
+        result = await collect_completion_result(
+            formatter, http_request, stream, cancel
+        )
+        if isinstance(result, Response):
+            return result
+        return JSONResponse(result.model_dump(mode="json", exclude_none=True))
+
+    @app.post("/v1/embeddings", response_model=CreateEmbeddingResponse)
+    async def create_embedding(  # pyright: ignore[reportUnusedFunction]
+        body: CreateEmbeddingRequest,
+    ) -> JSONResponse:
+        service: CompletionService = app.state.service
+        try:
+            embedding = await asyncio.to_thread(service.create_embedding, body)
+        except CompletionRequestValidationError as exc:
+            raise bad_request(exc) from exc
+        return JSONResponse(embedding.model_dump(mode="json", exclude_none=True))
+
+    @app.post("/v1/chat/completions")
+    async def create_chat_completion(  # pyright: ignore[reportUnusedFunction]
+        http_request: Request, body: CreateChatCompletionRequest
+    ):
+        service: CompletionService = app.state.service
+        formatter = service.formatter
+        try:
+            parts = formatter.completion_request_from_chat_request(
+                body,
+            )
+        except CompletionRequestValidationError as exc:
+            raise bad_request(exc) from exc
+        template_functions = (
+            [function.to_template_function() for function in body.functions]
+            if body.functions is not None
+            else None
+        )
+        template_tools = (
+            [tool.to_template_tool() for tool in body.tools]
+            if body.tools is not None
+            else None
+        )
+        try:
+            request = service.request_from_prepared(
+                payload=parts.payload,
+                prompt_text=parts.prompt_text,
+                prompt_plan=parts.prompt_plan,
+                grammar_text=parts.grammar_text,
+                chat_tool_name=parts.tool_name,
+            )
+            stream, cancel = service.submit_request(request)
+        except CompletionRequestValidationError as exc:
+            raise bad_request(exc) from exc
+        if body.stream:
+            started_indices: set[int] = set()
+
+            def chat_chunk_payloads(
+                completion_chunk: CompletionChunk,
+            ) -> Iterable[BaseModel | Dict[str, Any]]:
+                return formatter.convert_completion_chunk_to_chat_chunks(
+                    completion_chunk,
+                    started_indices,
+                    parts.tool_name,
+                    functions=template_functions,
+                    tools=template_tools,
+                    parsed_states=parsed_states,
+                    generation_prompt=parts.generation_prompt,
+                )
+
+            parsed_states: Dict[int, Dict[str, Any]] = {}
+            return StreamingResponse(
+                stream_sse_chunks(
+                    formatter,
+                    http_request,
+                    stream,
+                    cancel,
+                    chat_chunk_payloads,
+                ),
+                media_type="text/event-stream",
+            )
+        completion = await collect_completion_result(
+            formatter, http_request, stream, cancel
+        )
+        if isinstance(completion, Response):
+            return completion
+        chat_response = formatter.convert_completion_response_to_chat(
+            completion,
+            parts.tool_name,
+            functions=template_functions,
+            tools=template_tools,
+            generation_prompt=parts.generation_prompt,
+        )
+        if isinstance(chat_response, BaseModel):
+            return JSONResponse(
+                chat_response.model_dump(mode="json", exclude_none=True)
+            )
+        return JSONResponse(chat_response)
+
+    @app.post("/v1/responses")
+    async def create_response(  # pyright: ignore[reportUnusedFunction]
+        http_request: Request,
+        body: CreateResponseRequest,
+    ):
+        service: CompletionService = app.state.service
+        formatter = service.formatter
+        try:
+            chat_parts = formatter.response_request_to_chat_parts(body)
+            parts = formatter.completion_request_from_response_chat_parts(chat_parts)
+        except CompletionRequestValidationError as exc:
+            raise bad_request(exc) from exc
+        response_tools = chat_parts.tools
+        try:
+            request = service.request_from_prepared(
+                payload=parts.payload,
+                prompt_text=parts.prompt_text,
+                prompt_plan=parts.prompt_plan,
+                grammar_text=parts.grammar_text,
+                chat_tool_name=parts.tool_name,
+            )
+            stream, cancel = service.submit_request(request)
+        except CompletionRequestValidationError as exc:
+            raise bad_request(exc) from exc
+        if body.stream:
+            started_indices: set[int] = set()
+            parsed_states: Dict[int, ResponseParser] = {}
+            stream_state = OpenAIFormatter.ResponsesStream(
+                body=body,
+                response_id="resp_" + request.id,
+                created_at=float(request.created),
+                model=service.scheduler.model.model_path,
+            )
+
+            def response_chunk_payloads(
+                completion_chunk: CompletionChunk,
+            ) -> Iterable[BaseModel | Dict[str, Any]]:
+                chat_chunks = formatter.convert_completion_chunk_to_chat_chunks(
+                    completion_chunk,
+                    started_indices,
+                    parts.tool_name,
+                    tools=response_tools,
+                    parsed_states=parsed_states,
+                    generation_prompt=parts.generation_prompt,
+                )
+                payloads: List[BaseModel | Dict[str, Any]] = []
+                for chat_chunk in chat_chunks:
+                    payloads.extend(
+                        formatter.convert_chat_chunk_to_response_events(
+                            chat_chunk,
+                            stream_state,
+                        )
+                    )
+                return payloads
+
+            return StreamingResponse(
+                stream_sse_outputs(
+                    formatter,
+                    http_request,
+                    stream,
+                    cancel,
+                    initial_payloads=formatter.start_response_stream(stream_state),
+                    chunk_payloads=response_chunk_payloads,
+                    done_payloads=lambda completion: (
+                        formatter.response_stream_terminal_events(
+                            stream_state,
+                            completion,
+                        )
+                    ),
+                ),
+                media_type="text/event-stream",
+            )
+        completion = await collect_completion_result(
+            formatter, http_request, stream, cancel
+        )
+        if isinstance(completion, Response):
+            return completion
+        return JSONResponse(
+            formatter.convert_completion_response_to_response(
+                completion,
+                body,
+                parts.tool_name,
+                tools=response_tools,
+                generation_prompt=parts.generation_prompt,
+            )
+        )
+
+    @app.websocket("/v1/responses")
+    async def responses_websocket(  # pyright: ignore[reportUnusedFunction]
+        websocket: WebSocket,
+    ):
+        await websocket.accept()
+        service: CompletionService = app.state.service
+        formatter = service.formatter
+        # HTTP /v1/responses remains stateless. The websocket transport keeps
+        # per-connection response history so Codex can use previous_response_id
+        # within a single live session.
+        websocket_response_history: Dict[str, ResponsesWebSocketState] = {}
+
+        def websocket_request_with_ephemeral_history(
+            ws_body: ResponseCreateWebSocketRequest,
+        ) -> CreateResponseRequest:
+            body = ws_body.to_create_response_request()
+            previous_response_id = body.previous_response_id
+            if previous_response_id is None:
+                return body
+            prior_state = websocket_response_history.get(previous_response_id)
+            if prior_state is None:
+                raise CompletionRequestValidationError(
+                    f"unknown previous_response_id: {previous_response_id}"
+                )
+            current_items = formatter._clone_response_input_items(body.input)
+            replay_items = formatter._clone_response_input_items(
+                prior_state.input_items
+            )
+            for item in prior_state.output_items:
+                normalized = formatter._normalize_response_output_item_for_input(item)
+                if normalized is not None:
+                    replay_items.append(normalized)
+            replay_items.extend(current_items)
+            return body.model_copy(
+                update={
+                    "input": replay_items,
+                    "previous_response_id": None,
+                }
+            )
+
+        async def send_error(message: str) -> None:
+            await websocket.send_json(
+                {
+                    "type": "error",
+                    "error": {
+                        "message": message,
+                    },
+                }
+            )
+
+        try:
+            while True:
+                try:
+                    payload = await websocket.receive_json()
+                except WebSocketDisconnect:
+                    break
+                except Exception:
+                    await send_error("invalid websocket request payload")
+                    continue
+
+                try:
+                    ws_body = ResponseCreateWebSocketRequest.model_validate(payload)
+                    body = websocket_request_with_ephemeral_history(ws_body)
+                    if ws_body.generate is False:
+                        body = body.model_copy(update={"max_output_tokens": 0})
+                    chat_parts = formatter.response_request_to_chat_parts(body)
+                    parts = formatter.completion_request_from_response_chat_parts(chat_parts)
+                    response_tools = chat_parts.tools
+                    request = service.request_from_prepared(
+                        payload=parts.payload,
+                        prompt_text=parts.prompt_text,
+                        prompt_plan=parts.prompt_plan,
+                        grammar_text=parts.grammar_text,
+                        chat_tool_name=parts.tool_name,
+                    )
+                    stream, cancel = service.submit_request(request)
+                except CompletionRequestValidationError as exc:
+                    await send_error(str(exc))
+                    continue
+
+                started_indices: set[int] = set()
+                parsed_states: Dict[int, ResponseParser] = {}
+                stream_state = OpenAIFormatter.ResponsesStream(
+                    body=body,
+                    response_id="resp_" + request.id,
+                    created_at=float(request.created),
+                    model=service.scheduler.model.model_path,
+                )
+
+                def response_chunk_payloads(
+                    completion_chunk: CompletionChunk,
+                ) -> Iterable[BaseModel | Dict[str, Any]]:
+                    chat_chunks = formatter.convert_completion_chunk_to_chat_chunks(
+                        completion_chunk,
+                        started_indices,
+                        parts.tool_name,
+                        tools=response_tools,
+                        parsed_states=parsed_states,
+                        generation_prompt=parts.generation_prompt,
+                    )
+                    payloads: List[BaseModel | Dict[str, Any]] = []
+                    for chat_chunk in chat_chunks:
+                        payloads.extend(
+                            formatter.convert_chat_chunk_to_response_events(
+                                chat_chunk,
+                                stream_state,
+                            )
+                        )
+                    return payloads
+
+                try:
+                    await stream_websocket_responses(
+                        websocket,
+                        formatter,
+                        stream,
+                        cancel,
+                        initial_payloads=formatter.start_response_stream(stream_state),
+                        chunk_payloads=response_chunk_payloads,
+                        done_payloads=lambda completion: (
+                            formatter.response_stream_terminal_events(
+                                stream_state,
+                                completion,
+                            )
+                        ),
+                    )
+                    websocket_response_history[stream_state.response_id] = (
+                        ResponsesWebSocketState(
+                            input_items=formatter._clone_response_input_items(
+                                body.input
+                            ),
+                            output_items=copy.deepcopy(stream_state.output),
+                        )
+                    )
+                except WebSocketDisconnect:
+                    cancel()
+                    break
+                except BaseException as exc:
+                    cancel()
+                    await send_error(str(exc))
+        except WebSocketDisconnect:
+            pass
+
+    @app.get("/v1/models", response_model=ModelListResponse)
+    async def list_models() -> ModelListResponse:  # pyright: ignore[reportUnusedFunction]
+        service = app.state.service
+        model = service.scheduler.model
+        model_id = getattr(model, "model_alias", None) or model.model_path
+        created = int(time.time())
+        return ModelListResponse(
+            data=[
+                ModelCardResponse(
+                    id=model_id,
+                    created=created,
+                    owned_by="llama-cpp-python",
+                )
+            ]
+        )
+
+    @app.get("/healthz", response_model=HealthzResponse)
+    async def healthz() -> HealthzResponse:  # pyright: ignore[reportUnusedFunction]
+        return HealthzResponse()
+
+    @app.get("/metrics")
+    async def metrics() -> Response:  # pyright: ignore[reportUnusedFunction]
+        service = cast(Optional[CompletionService], getattr(app.state, "service", None))
+        if service is None:
+            raise HTTPException(status_code=503, detail="completion service unavailable")
+        payload = await asyncio.to_thread(service.render_prometheus_metrics)
+        return Response(
+            payload,
+            media_type="text/plain; version=0.0.4; charset=utf-8",
+        )
+
+    return app
+
+
+APP = create_app()
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-C", "--config-file", required=True)
+    args = parser.parse_args()
+    config = ConfigFile.load(args.config_file)
+    model_path = config.model.resolve_model_path()
+    loras = [
+        Model.LoraAdapter(path=lora.resolve_path(), scale=lora.scale)
+        for lora in config.model.loras
+    ]
+    model = Model(
+        model_path=model_path,
+        model_alias=config.model.alias,
+        chat_template=config.model.chat_template,
+        loras=loras,
+        n_gpu_layers=config.model.n_gpu_layers,
+        split_mode=config.model.split_mode,
+        main_gpu=config.model.main_gpu,
+        tensor_split=config.model.tensor_split,
+        vocab_only=config.model.vocab_only,
+        use_mmap=config.model.use_mmap,
+        use_mlock=config.model.use_mlock,
+        kv_overrides=config.model.kv_overrides,
+        n_ctx=config.model.n_ctx,
+        n_batch=config.model.n_batch,
+        n_ubatch=config.model.n_ubatch,
+        n_seq_max=config.model.n_seq_max,
+        n_threads=config.model.threads,
+        n_threads_batch=config.model.threads_batch,
+        rope_scaling_type=config.model.rope_scaling_type,
+        pooling_type=config.model.pooling_type,
+        attention_type=config.model.attention_type,
+        embedding=config.model.embedding,
+        rope_freq_base=config.model.rope_freq_base,
+        rope_freq_scale=config.model.rope_freq_scale,
+        yarn_ext_factor=config.model.yarn_ext_factor,
+        yarn_attn_factor=config.model.yarn_attn_factor,
+        yarn_beta_fast=config.model.yarn_beta_fast,
+        yarn_beta_slow=config.model.yarn_beta_slow,
+        yarn_orig_ctx=config.model.yarn_orig_ctx,
+        offload_kqv=config.model.offload_kqv,
+        flash_attn=config.model.flash_attn,
+        op_offload=config.model.op_offload,
+        swa_full=config.model.swa_full,
+        no_perf=config.model.no_perf,
+        type_k=config.model.type_k,
+        type_v=config.model.type_v,
+        kv_unified=config.model.kv_unified,
+        max_seq_len=config.model.max_seq_len,
+        max_output_tokens=config.model.max_output_tokens,
+        draft_model=config.model.draft_model,
+        draft_model_path=config.model.resolve_draft_model_path(),
+        draft_model_num_pred_tokens=config.model.draft_model_num_pred_tokens,
+        draft_model_max_ngram_size=config.model.draft_model_max_ngram_size,
+        draft_model_top_k=config.model.draft_model_top_k,
+        draft_model_p_min=config.model.draft_model_p_min,
+        draft_model_max_batch_size=config.model.draft_model_max_batch_size,
+        draft_model_threads=config.model.draft_model_threads,
+        draft_model_threads_batch=config.model.draft_model_threads_batch,
+        response_schema=config.model.response_schema,
+        store_logits=config.model.store_logits,
+    )
+    if config.model.mtmd is not None:
+        if model.chat_formatter is None:
+            raise RuntimeError("MTMD requires a GGUF chat template")
+        mmproj_path = config.model.mtmd.resolve_mmproj_path()
+        embedding_cache: Optional[MTMDEmbeddingCache] = None
+        if config.model.mtmd.embedding_cache is not None:
+            embedding_cache = MTMDEmbeddingCache(
+                path=config.model.mtmd.embedding_cache.path,
+                max_bytes=config.model.mtmd.embedding_cache.max_bytes,
+                model_fingerprint=MTMDEmbeddingCache.fingerprint_file(model_path),
+                mmproj_fingerprint=MTMDEmbeddingCache.fingerprint_file(mmproj_path),
+            )
+        model.mtmd_processor = MTMDProcessor(
+            model_path=model.model_path,
+            llama_model=model.llama_model,
+            chat_formatter=model.chat_formatter,
+            tokenize=model.tokenize,
+            n_embd_inp=model.n_embd_inp,
+            n_batch=model.n_batch,
+            n_ubatch=model.n_ubatch,
+            n_threads_batch=model.n_threads_batch,
+            mmproj_path=mmproj_path,
+            batch_max_tokens=config.model.mtmd.batch_max_tokens,
+            embedding_cache=embedding_cache,
+            allowed_media_domains=config.model.mtmd.allowed_media_domains,
+            allowed_local_media_path=config.model.mtmd.allowed_local_media_path,
+            image_max_bytes=config.model.mtmd.image_max_bytes,
+            audio_max_bytes=config.model.mtmd.audio_max_bytes,
+            video_max_bytes=config.model.mtmd.video_max_bytes,
+            image_timeout_seconds=config.model.mtmd.image_timeout_seconds,
+        )
+    sequence_cache: Optional[SequenceCache] = None
+    if config.disk_cache is not None:
+        sequence_cache = SequenceDiskCache(
+            path=config.disk_cache.path,
+            max_bytes=config.disk_cache.max_bytes,
+            min_tokens=config.disk_cache.min_tokens,
+            compatibility_key=SequenceDiskCache.compatibility_key_for_model(model),
+        )
+    scheduler = CompletionScheduler(model, sequence_cache=sequence_cache)
+    APP.state.service = CompletionService(scheduler)
+    try:
+        uvicorn.run(
+            APP, host=config.server.host, port=config.server.port, log_level="info"
+        )
+    finally:
+        APP.state.service.close()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/llama_cpp/__init__.py b/llama_cpp/__init__.py
index cda38a841f..ed3c342f20 100644
--- a/llama_cpp/__init__.py
+++ b/llama_cpp/__init__.py
@@ -1,4 +1,4 @@
 from .llama_cpp import *
 from .llama import *
 
-__version__ = "0.3.2"
+__version__ = "0.3.31"
diff --git a/llama_cpp/_ctypes_extensions.py b/llama_cpp/_ctypes_extensions.py
index e88ed387df..02cee8a88f 100644
--- a/llama_cpp/_ctypes_extensions.py
+++ b/llama_cpp/_ctypes_extensions.py
@@ -19,6 +19,9 @@
 from typing_extensions import TypeAlias
 
 
+_EMSCRIPTEN_SIDE_MODULE_SUFFIX = ".cpython-00-wasm32-emscripten.so"
+
+
 # Load the library
 def load_shared_library(lib_base_name: str, base_path: pathlib.Path):
     """Platform independent shared library loader"""
@@ -26,7 +29,12 @@ def load_shared_library(lib_base_name: str, base_path: pathlib.Path):
     # for llamacpp) and "llama" (default name for this repo)
     lib_paths: List[pathlib.Path] = []
     # Determine the file extension based on the platform
-    if sys.platform.startswith("linux") or sys.platform.startswith("freebsd"):
+    if sys.platform == "emscripten":
+        # Use a CPython-style tag that Pyodide skips during package auto-load.
+        lib_paths += [
+            base_path / f"lib{lib_base_name}{_EMSCRIPTEN_SIDE_MODULE_SUFFIX}",
+        ]
+    elif sys.platform.startswith("linux") or sys.platform.startswith("freebsd"):
         lib_paths += [
             base_path / f"lib{lib_base_name}.so",
         ]
@@ -60,6 +68,33 @@ def load_shared_library(lib_base_name: str, base_path: pathlib.Path):
             os.add_dll_directory(os.path.join(os.environ["HIP_PATH"], "lib"))
         cdll_args["winmode"] = ctypes.RTLD_GLOBAL
 
+    if sys.platform == "emscripten":
+        cdll_args["mode"] = ctypes.RTLD_GLOBAL
+        lib_dir = str(base_path)
+        ld_library_path = os.environ.get("LD_LIBRARY_PATH", "")
+        if lib_dir not in ld_library_path.split(os.pathsep):
+            os.environ["LD_LIBRARY_PATH"] = (
+                lib_dir
+                if not ld_library_path
+                else f"{lib_dir}{os.pathsep}{ld_library_path}"
+            )
+
+        emscripten_dependencies = {
+            "llama": ("ggml-base", "ggml-cpu", "ggml"),
+            "mtmd": ("ggml-base", "ggml-cpu", "ggml", "llama"),
+        }
+        for dependency in emscripten_dependencies.get(lib_base_name, ()):
+            dependency_path = (
+                base_path / f"lib{dependency}{_EMSCRIPTEN_SIDE_MODULE_SUFFIX}"
+            )
+            if dependency_path.exists():
+                try:
+                    ctypes.CDLL(str(dependency_path), **cdll_args)  # type: ignore
+                except Exception as e:
+                    raise RuntimeError(
+                        f"Failed to load shared library '{dependency_path}': {e}"
+                    )
+
     # Try to load the shared library, handling potential errors
     for lib_path in lib_paths:
         if lib_path.exists():
diff --git a/llama_cpp/_ggml.py b/llama_cpp/_ggml.py
index 5bee8a93b5..5ece01e032 100644
--- a/llama_cpp/_ggml.py
+++ b/llama_cpp/_ggml.py
@@ -2,6 +2,7 @@
 
 This module provides a minimal interface for working with ggml tensors from llama-cpp-python
 """
+
 import os
 import pathlib
 
@@ -9,4 +10,3 @@
 
 libggml_base_path = pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib"
 libggml = ctypes_ext.load_shared_library("ggml", libggml_base_path)
-
diff --git a/llama_cpp/_internals.py b/llama_cpp/_internals.py
index 994d5f1497..b0fe94d01f 100644
--- a/llama_cpp/_internals.py
+++ b/llama_cpp/_internals.py
@@ -2,6 +2,7 @@
 
 import os
 import ctypes
+import warnings
 
 from typing import (
     Dict,
@@ -9,6 +10,8 @@
     Tuple,
     Optional,
     Sequence,
+    Callable,
+    Union,
 )
 from dataclasses import dataclass, field
 from contextlib import ExitStack
@@ -41,6 +44,9 @@ def __init__(
         self.params = params
         self.verbose = verbose
         self._exit_stack = ExitStack()
+        # LlamaModel does not use samplers, but close() can run after partial init.
+        self.sampler = None
+        self.custom_samplers = []
 
         model = None
 
@@ -48,43 +54,54 @@ def __init__(
             raise ValueError(f"Model path does not exist: {path_model}")
 
         with suppress_stdout_stderr(disable=verbose):
-            model = llama_cpp.llama_load_model_from_file(
+            model = llama_cpp.llama_model_load_from_file(
                 self.path_model.encode("utf-8"), self.params
             )
 
         if model is None:
             raise ValueError(f"Failed to load model from file: {path_model}")
 
+        vocab = llama_cpp.llama_model_get_vocab(model)
+
+        if vocab is None:
+            raise ValueError(f"Failed to get vocab from model: {path_model}")
+
         self.model = model
+        self.vocab = vocab
 
         def free_model():
             if self.model is None:
                 return
-            llama_cpp.llama_free_model(self.model)
+            llama_cpp.llama_model_free(self.model)
             self.model = None
 
         self._exit_stack.callback(free_model)
 
     def close(self):
+        if self.sampler is not None:
+            # NOTE: Must remove custom samplers before free or llama.cpp will try to free them
+            for i, _ in reversed(self.custom_samplers):
+                llama_cpp.llama_sampler_chain_remove(self.sampler, i)
+            self.custom_samplers.clear()
         self._exit_stack.close()
 
     def __del__(self):
         self.close()
 
     def vocab_type(self) -> int:
-        return llama_cpp.llama_vocab_type(self.model)
+        return llama_cpp.llama_vocab_type(self.vocab)
 
     def n_vocab(self) -> int:
-        return llama_cpp.llama_n_vocab(self.model)
+        return llama_cpp.llama_vocab_n_tokens(self.vocab)
 
     def n_ctx_train(self) -> int:
-        return llama_cpp.llama_n_ctx_train(self.model)
+        return llama_cpp.llama_model_n_ctx_train(self.model)
 
     def n_embd(self) -> int:
-        return llama_cpp.llama_n_embd(self.model)
+        return llama_cpp.llama_model_n_embd(self.model)
 
     def rope_freq_scale_train(self) -> float:
-        return llama_cpp.llama_rope_freq_scale_train(self.model)
+        return llama_cpp.llama_model_rope_freq_scale_train(self.model)
 
     def desc(self) -> str:
         buf = ctypes.create_string_buffer(1024)
@@ -98,53 +115,53 @@ def n_params(self) -> int:
         return llama_cpp.llama_model_n_params(self.model)
 
     def get_tensor(self, name: str) -> ctypes.c_void_p:
-        return llama_cpp.llama_get_model_tensor(self.model, name.encode("utf-8"))
+        raise NotImplementedError("get_tensor is not implemented in llama.cpp")
 
     # Vocab
 
     def token_get_text(self, token: int) -> str:
-        return llama_cpp.llama_token_get_text(self.model, token).decode("utf-8")
+        return llama_cpp.llama_vocab_get_text(self.vocab, token).decode("utf-8")
 
     def token_get_score(self, token: int) -> float:
-        return llama_cpp.llama_token_get_score(self.model, token)
+        return llama_cpp.llama_vocab_get_score(self.vocab, token)
 
     def token_get_attr(self, token: int) -> int:
-        return llama_cpp.llama_token_get_attr(self.model, token)
+        return llama_cpp.llama_vocab_get_attr(self.vocab, token)
 
     # Special tokens
 
     def token_bos(self) -> int:
-        return llama_cpp.llama_token_bos(self.model)
+        return llama_cpp.llama_vocab_bos(self.vocab)
 
     def token_eos(self) -> int:
-        return llama_cpp.llama_token_eos(self.model)
+        return llama_cpp.llama_vocab_eos(self.vocab)
 
     def token_cls(self) -> int:
-        return llama_cpp.llama_token_cls(self.model)
+        return llama_cpp.llama_vocab_bos(self.vocab)
 
     def token_sep(self) -> int:
-        return llama_cpp.llama_token_sep(self.model)
+        return llama_cpp.llama_vocab_sep(self.vocab)
 
     def token_nl(self) -> int:
-        return llama_cpp.llama_token_nl(self.model)
+        return llama_cpp.llama_vocab_nl(self.vocab)
 
     def token_prefix(self) -> int:
-        return llama_cpp.llama_token_prefix(self.model)
+        return llama_cpp.llama_vocab_fim_pre(self.vocab)
 
     def token_middle(self) -> int:
-        return llama_cpp.llama_token_middle(self.model)
+        return llama_cpp.llama_vocab_fim_mid(self.vocab)
 
     def token_suffix(self) -> int:
-        return llama_cpp.llama_token_suffix(self.model)
+        return llama_cpp.llama_vocab_fim_suf(self.vocab)
 
     def token_eot(self) -> int:
-        return llama_cpp.llama_token_eot(self.model)
+        return llama_cpp.llama_vocab_eot(self.vocab)
 
     def add_bos_token(self) -> bool:
-        return llama_cpp.llama_add_bos_token(self.model)
+        return llama_cpp.llama_vocab_get_add_bos(self.vocab)
 
     def add_eos_token(self) -> bool:
-        return llama_cpp.llama_add_eos_token(self.model)
+        return llama_cpp.llama_vocab_get_add_eos(self.vocab)
 
     # Tokenization
 
@@ -152,13 +169,13 @@ def tokenize(self, text: bytes, add_bos: bool, special: bool):
         n_ctx = self.n_ctx_train()
         tokens = (llama_cpp.llama_token * n_ctx)()
         n_tokens = llama_cpp.llama_tokenize(
-            self.model, text, len(text), tokens, n_ctx, add_bos, special
+            self.vocab, text, len(text), tokens, n_ctx, add_bos, special
         )
         if n_tokens < 0:
             n_tokens = abs(n_tokens)
             tokens = (llama_cpp.llama_token * n_tokens)()
             n_tokens = llama_cpp.llama_tokenize(
-                self.model, text, len(text), tokens, n_tokens, add_bos, special
+                self.vocab, text, len(text), tokens, n_tokens, add_bos, special
             )
             if n_tokens < 0:
                 raise RuntimeError(
@@ -168,7 +185,7 @@ def tokenize(self, text: bytes, add_bos: bool, special: bool):
 
     def token_to_piece(self, token: int, special: bool = False) -> bytes:
         buf = ctypes.create_string_buffer(32)
-        llama_cpp.llama_token_to_piece(self.model, token, buf, 32, 0, special)
+        llama_cpp.llama_token_to_piece(self.vocab, token, buf, 32, 0, special)
         return bytes(buf)
 
     def detokenize(self, tokens: List[int], special: bool = False) -> bytes:
@@ -177,7 +194,7 @@ def detokenize(self, tokens: List[int], special: bool = False) -> bytes:
         buffer = (ctypes.c_char * size)()
         for token in tokens:
             n = llama_cpp.llama_token_to_piece(
-                self.model, llama_cpp.llama_token(token), buffer, size, 0, special
+                self.vocab, llama_cpp.llama_token(token), buffer, size, 0, special
             )
             assert n <= size
             output += bytes(buffer[:n])
@@ -243,12 +260,14 @@ def __init__(
         self.verbose = verbose
         self._exit_stack = ExitStack()
 
-        ctx = llama_cpp.llama_new_context_with_model(self.model.model, self.params)
+        ctx = llama_cpp.llama_init_from_model(self.model.model, self.params)
 
         if ctx is None:
             raise ValueError("Failed to create llama_context")
 
         self.ctx = ctx
+        self.memory = llama_cpp.llama_get_memory(self.ctx)
+        self.sampler = None  # LlamaContext doesn't manage samplers directly, but some cleanup code expects this attribute
 
         def free_ctx():
             if self.ctx is None:
@@ -271,30 +290,38 @@ def pooling_type(self) -> int:
         return llama_cpp.llama_pooling_type(self.ctx)
 
     def kv_cache_clear(self):
-        llama_cpp.llama_kv_cache_clear(self.ctx)
+        # Embedding models with non-causal attention may not allocate memory.
+        if self.memory is None:
+            return
+        llama_cpp.llama_memory_clear(self.memory, True)
 
-    def kv_cache_seq_rm(self, seq_id: int, p0: int, p1: int):
-        llama_cpp.llama_kv_cache_seq_rm(self.ctx, seq_id, p0, p1)
+    def kv_cache_seq_rm(self, seq_id: int, p0: int, p1: int) -> bool:
+        assert self.memory is not None, "Memory is not initialized"
+        seq_id = seq_id if seq_id >= 0 else 0
+        return llama_cpp.llama_memory_seq_rm(self.memory, seq_id, p0, p1)
 
     def kv_cache_seq_cp(self, seq_id_src: int, seq_id_dst: int, p0: int, p1: int):
-        llama_cpp.llama_kv_cache_seq_cp(self.ctx, seq_id_src, seq_id_dst, p0, p1)
+        assert self.memory is not None, "Memory is not initialized"
+        llama_cpp.llama_memory_seq_cp(self.memory, seq_id_src, seq_id_dst, p0, p1)
 
     def kv_cache_seq_keep(self, seq_id: int):
-        llama_cpp.llama_kv_cache_seq_keep(self.ctx, seq_id)
+        assert self.memory is not None, "Memory is not initialized"
+        llama_cpp.llama_memory_seq_keep(self.memory, seq_id)
 
     def kv_cache_seq_shift(self, seq_id: int, p0: int, p1: int, shift: int):
-        llama_cpp.llama_kv_cache_seq_add(self.ctx, seq_id, p0, p1, shift)
+        assert self.memory is not None, "Memory is not initialized"
+        llama_cpp.llama_memory_seq_add(self.memory, seq_id, p0, p1, shift)
 
     def get_state_size(self) -> int:
-        return llama_cpp.llama_get_state_size(self.ctx)
+        return llama_cpp.llama_state_get_size(self.ctx)
 
     # TODO: copy_state_data
 
     # TODO: set_state_data
 
-    # TODO: llama_load_session_file
+    # TODO: llama_state_load_file
 
-    # TODO: llama_save_session_file
+    # TODO: llama_state_save_file
 
     def decode(self, batch: LlamaBatch):
         return_code = llama_cpp.llama_decode(
@@ -304,6 +331,14 @@ def decode(self, batch: LlamaBatch):
         if return_code != 0:
             raise RuntimeError(f"llama_decode returned {return_code}")
 
+    def encode(self, batch: LlamaBatch):
+        return_code = llama_cpp.llama_encode(
+            self.ctx,
+            batch.batch,
+        )
+        if return_code != 0:
+            raise RuntimeError(f"llama_encode returned {return_code}")
+
     def set_n_threads(self, n_threads: int, n_threads_batch: int):
         llama_cpp.llama_set_n_threads(self.ctx, n_threads, n_threads_batch)
 
@@ -316,11 +351,18 @@ def get_logits_ith(self, i: int):
     def get_embeddings(self):
         return llama_cpp.llama_get_embeddings(self.ctx)
 
-    # Sampling functions
+    def get_embeddings_ith(self, i: int):
+        return llama_cpp.llama_get_embeddings_ith(self.ctx, i)
+
+    def get_embeddings_seq(self, seq_id: int):
+        return llama_cpp.llama_get_embeddings_seq(self.ctx, seq_id)
+
+    # Sampling functions - deprecated, use LlamaSampler instead
 
     def set_rng_seed(self, seed: int):
-        # TODO: Fix
-        llama_cpp.llama_set_rng_seed(self.ctx, seed)
+        raise NotImplementedError(
+            "set_rng_seed is deprecated, use LlamaSampler instead"
+        )
 
     def sample_repetition_penalties(
         self,
@@ -331,54 +373,43 @@ def sample_repetition_penalties(
         penalty_freq: float,
         penalty_present: float,
     ):
-        llama_cpp.llama_sample_repetition_penalties(
-            self.ctx,
-            llama_cpp.byref(candidates.candidates),
-            last_tokens_data,
-            penalty_last_n,
-            penalty_repeat,
-            penalty_freq,
-            penalty_present,
+        raise NotImplementedError(
+            "sample_repetition_penalties is deprecated, use LlamaSampler instead"
         )
 
     def sample_softmax(self, candidates: "_LlamaTokenDataArray"):
-        llama_cpp.llama_sample_softmax(
-            self.ctx,
-            llama_cpp.byref(candidates.candidates),
+        raise NotImplementedError(
+            "sample_softmax is deprecated, use LlamaSampler instead"
         )
 
     def sample_top_k(self, candidates: "_LlamaTokenDataArray", k: int, min_keep: int):
-        llama_cpp.llama_sample_top_k(
-            self.ctx, llama_cpp.byref(candidates.candidates), k, min_keep
+        raise NotImplementedError(
+            "sample_top_k is deprecated, use LlamaSampler instead"
         )
 
     def sample_top_p(self, candidates: "_LlamaTokenDataArray", p: float, min_keep: int):
-        llama_cpp.llama_sample_top_p(
-            self.ctx, llama_cpp.byref(candidates.candidates), p, min_keep
+        raise NotImplementedError(
+            "sample_top_p is deprecated, use LlamaSampler instead"
         )
 
     def sample_min_p(self, candidates: "_LlamaTokenDataArray", p: float, min_keep: int):
-        llama_cpp.llama_sample_min_p(
-            self.ctx, llama_cpp.byref(candidates.candidates), p, min_keep
+        raise NotImplementedError(
+            "sample_min_p is deprecated, use LlamaSampler instead"
         )
 
     def sample_typical(
         self, candidates: "_LlamaTokenDataArray", p: float, min_keep: int
     ):
-        llama_cpp.llama_sample_typical(
-            self.ctx, llama_cpp.byref(candidates.candidates), p, min_keep
+        raise NotImplementedError(
+            "sample_typical is deprecated, use LlamaSampler instead"
         )
 
     def sample_temp(self, candidates: "_LlamaTokenDataArray", temp: float):
-        llama_cpp.llama_sample_temp(
-            self.ctx, llama_cpp.byref(candidates.candidates), temp
-        )
+        raise NotImplementedError("sample_temp is deprecated, use LlamaSampler instead")
 
     def sample_grammar(self, candidates: "_LlamaTokenDataArray", grammar: LlamaGrammar):
-        llama_cpp.llama_sample_grammar(
-            self.ctx,
-            llama_cpp.byref(candidates.candidates),
-            grammar.grammar,
+        raise NotImplementedError(
+            "sample_grammar is deprecated, use LlamaSampler instead"
         )
 
     def sample_token_mirostat(
@@ -389,13 +420,8 @@ def sample_token_mirostat(
         m: int,
         mu: llama_cpp.CtypesPointerOrRef[ctypes.c_float],
     ) -> int:
-        return llama_cpp.llama_sample_token_mirostat(
-            self.ctx,
-            llama_cpp.byref(candidates.candidates),
-            tau,
-            eta,
-            m,
-            mu,
+        raise NotImplementedError(
+            "sample_token_mirostat is deprecated, use LlamaSampler instead"
         )
 
     def sample_token_mirostat_v2(
@@ -405,29 +431,25 @@ def sample_token_mirostat_v2(
         eta: float,
         mu: llama_cpp.CtypesPointerOrRef[ctypes.c_float],
     ) -> int:
-        return llama_cpp.llama_sample_token_mirostat_v2(
-            self.ctx,
-            llama_cpp.byref(candidates.candidates),
-            tau,
-            eta,
-            mu,
+        raise NotImplementedError(
+            "sample_token_mirostat_v2 is deprecated, use LlamaSampler instead"
         )
 
     def sample_token_greedy(self, candidates: "_LlamaTokenDataArray") -> int:
-        return llama_cpp.llama_sample_token_greedy(
-            self.ctx,
-            llama_cpp.byref(candidates.candidates),
+        raise NotImplementedError(
+            "sample_token_greedy is deprecated, use LlamaSampler instead"
         )
 
     def sample_token(self, candidates: "_LlamaTokenDataArray") -> int:
-        return llama_cpp.llama_sample_token(
-            self.ctx,
-            llama_cpp.byref(candidates.candidates),
+        raise NotImplementedError(
+            "sample_token is deprecated, use LlamaSampler instead"
         )
 
     # Grammar
     def grammar_accept_token(self, grammar: LlamaGrammar, token: int):
-        llama_cpp.llama_grammar_accept_token(grammar.grammar, self.ctx, token)
+        raise NotImplementedError(
+            "grammar_accept_token is deprecated, use LlamaSampler instead"
+        )
 
     def reset_timings(self):
         llama_cpp.llama_perf_context_reset(self.ctx)
@@ -458,6 +480,7 @@ def __init__(
             raise ValueError("Failed to create llama_batch")
 
         self.batch = batch
+        self.sampler = None  # LlamaBatch doesn't use samplers, but some cleanup code expects this attribute
 
         def free_batch():
             if self.batch is None:
@@ -501,7 +524,7 @@ def add_sequence(self, batch: Sequence[int], seq_id: int, logits_all: bool):
             self.batch.seq_id[j][0] = seq_id
             self.batch.n_seq_id[j] = 1
             self.batch.logits[j] = logits_all
-        self.batch.logits[n_tokens - 1] = True
+        self.batch.logits[n_tokens0 + n_tokens - 1] = True
 
 
 class LlamaTokenDataArray:
@@ -520,6 +543,7 @@ def __init__(self, *, n_vocab: int):
         )
         self.default_candidates_data_id = np.arange(self.n_vocab, dtype=np.intc)  # type: ignore
         self.default_candidates_data_p = np.zeros(self.n_vocab, dtype=np.single)
+        self.sampler = None  # LlamaTokenDataArray doesn't use samplers, but some cleanup code expects this attribute
 
     def copy_logits(self, logits: npt.NDArray[np.single]):
         self.candidates_data.id[:] = self.default_candidates_data_id
@@ -608,104 +632,17 @@ def sample(
         idx: int = 0,
         logits_array: Optional[npt.NDArray[np.single]] = None,
     ):
-        n_vocab = ctx_main.model.n_vocab()
-        id: int = 0
-
-        if logits_array is None:
-            logits = ctx_main.get_logits_ith(idx)
-            logits_array = np.array(
-                ctypes.cast(logits, ctypes.POINTER(ctypes.c_float * n_vocab)).contents,
-                dtype=np.single,
-            )
-
-        # apply logit_bias
-        for token, logit_bias in self.params.logit_bias.items():
-            logits_array[token] += logit_bias
-
-        token_data_array = LlamaTokenDataArray(
-            n_vocab=n_vocab
-        )  # TODO: Only create this once
-        token_data_array.copy_logits(logits_array)
-
-        # apply penalties
-        if len(self.prev) > 0:
-            nl_token = ctx_main.model.token_nl()
-            nl_logit = logits_array[nl_token]
-            last_tokens = self.prev[-self.params.penalty_last_n :]
-            last_tokens_size = min(len(last_tokens), self.params.penalty_last_n)
-            if last_tokens_size > 0:
-                last_tokens_p = (llama_cpp.llama_token * len(last_tokens))(*last_tokens)
-                ctx_main.sample_repetition_penalties(
-                    token_data_array,
-                    last_tokens_p,
-                    last_tokens_size,
-                    self.params.penalty_repeat,
-                    self.params.penalty_freq,
-                    self.params.penalty_present,
-                )
-            if not self.params.penalize_nl:
-                token_data_array.candidates_data.logit[nl_token] = nl_logit
-
-        if self.grammar is not None:
-            ctx_main.sample_grammar(token_data_array, self.grammar)
-
-        if self.params.temp < 0:
-            ctx_main.sample_softmax(token_data_array)
-            id = token_data_array.candidates_data.id[0]
-        elif self.params.temp == 0:
-            id = ctx_main.sample_token_greedy(token_data_array)
-        else:
-            if self.params.mirostat == 1:
-                mirostat_m = 100
-                ctx_main.sample_temp(token_data_array, self.params.temp)
-                id = ctx_main.sample_token_mirostat(
-                    token_data_array,
-                    self.params.mirostat_tau,
-                    self.params.mirostat_eta,
-                    mirostat_m,
-                    ctypes.pointer(self.mirostat_mu),
-                )
-            elif self.params.mirostat == 2:
-                ctx_main.sample_temp(token_data_array, self.params.temp)
-                id = ctx_main.sample_token_mirostat_v2(
-                    token_data_array,
-                    self.params.mirostat_tau,
-                    self.params.mirostat_eta,
-                    ctypes.pointer(self.mirostat_mu),
-                )
-            else:
-                min_keep = max(1, self.params.n_probs)
-                ctx_main.sample_top_k(
-                    token_data_array, self.params.top_k, min_keep=min_keep
-                )
-                ctx_main.sample_typical(
-                    token_data_array, self.params.typical_p, min_keep=min_keep
-                )
-                ctx_main.sample_top_p(
-                    token_data_array, self.params.top_p, min_keep=min_keep
-                )
-                ctx_main.sample_min_p(
-                    token_data_array, self.params.min_p, min_keep=min_keep
-                )
-                ctx_main.sample_temp(token_data_array, self.params.temp)
-                id = ctx_main.sample_token(token_data_array)
-        return id
+        # This method is deprecated in favor of using LlamaSampler directly
+        raise NotImplementedError(
+            "LlamaSamplingContext.sample is deprecated, use LlamaSampler instead"
+        )
 
     def accept(self, ctx_main: LlamaContext, id: int, apply_grammar: bool):
-        if apply_grammar and self.grammar is not None:
-            ctx_main.grammar_accept_token(self.grammar, id)
         self.prev.append(id)
 
 
-from typing import List, Callable, Optional, Union
-import ctypes
-import llama_cpp
-
-
 class CustomSampler:
-    def __init__(
-        self, apply_func: typing.Callable[[llama_cpp.llama_token_data_array], None]
-    ):
+    def __init__(self, apply_func: Callable[[llama_cpp.llama_token_data_array], None]):
         self.apply_func = apply_func
 
         def apply_wrapper(
@@ -737,127 +674,213 @@ def get_sampler(self) -> llama_cpp.llama_sampler_p:
 
 class LlamaSampler:
     def __init__(self):
-        params = llama_cpp.llama_sampler_chain_params()
+        params = llama_cpp.llama_sampler_chain_default_params()
         self.sampler = llama_cpp.llama_sampler_chain_init(params)
-        self.samplers: List[llama_cpp.llama_sampler_p] = []
         self.custom_samplers: List[Tuple[int, CustomSampler]] = []
+        self._exit_stack = ExitStack()
+
+        def free_sampler():
+            if self.sampler is not None:
+                # NOTE: Must remove custom samplers before free or llama.cpp will try to free them
+                for i, _ in reversed(self.custom_samplers):
+                    llama_cpp.llama_sampler_chain_remove(self.sampler, i)
+                llama_cpp.llama_sampler_free(self.sampler)
+                self.sampler = None
+
+        self._exit_stack.callback(free_sampler)
+
+    def close(self):
+        self._exit_stack.close()
+
+    def __del__(self):
+        self.close()
 
     def add_greedy(self):
         sampler = llama_cpp.llama_sampler_init_greedy()
-        self._add_sampler(sampler)
+        llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
 
     def add_dist(self, seed: int):
         sampler = llama_cpp.llama_sampler_init_dist(seed)
-        self._add_sampler(sampler)
+        llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
 
     def add_softmax(self):
-        sampler = llama_cpp.llama_sampler_init_softmax()
-        self._add_sampler(sampler)
+        warnings.warn(
+            "add_softmax is deprecated; llama_sampler_init_dist now samples directly from logits",
+            DeprecationWarning,
+            stacklevel=2,
+        )
 
     def add_top_k(self, k: int):
         sampler = llama_cpp.llama_sampler_init_top_k(k)
-        self._add_sampler(sampler)
+        llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
 
-    def add_top_p(self, p: float, min_keep: int):
+    def add_top_p(self, p: float, min_keep: int = 1):
         sampler = llama_cpp.llama_sampler_init_top_p(p, min_keep)
-        self._add_sampler(sampler)
+        llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
 
-    def add_min_p(self, p: float, min_keep: int):
+    def add_min_p(self, p: float, min_keep: int = 1):
         sampler = llama_cpp.llama_sampler_init_min_p(p, min_keep)
-        self._add_sampler(sampler)
+        llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
 
-    def add_typical(self, p: float, min_keep: int):
+    def add_typical(self, p: float, min_keep: int = 1):
         sampler = llama_cpp.llama_sampler_init_typical(p, min_keep)
-        self._add_sampler(sampler)
+        llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
 
     def add_temp(self, temp: float):
         sampler = llama_cpp.llama_sampler_init_temp(temp)
-        self._add_sampler(sampler)
+        llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
 
     def add_temp_ext(self, t: float, delta: float, exponent: float):
         sampler = llama_cpp.llama_sampler_init_temp_ext(t, delta, exponent)
-        self._add_sampler(sampler)
+        llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
+
+    def add_xtc(self, p: float, t: float, min_keep: int, seed: int):
+        sampler = llama_cpp.llama_sampler_init_xtc(p, t, min_keep, seed)
+        llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
+
+    def add_top_n_sigma(self, n: float):
+        sampler = llama_cpp.llama_sampler_init_top_n_sigma(n)
+        llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
 
     def add_mirostat(self, n_vocab: int, seed: int, tau: float, eta: float, m: int):
         sampler = llama_cpp.llama_sampler_init_mirostat(n_vocab, seed, tau, eta, m)
-        self._add_sampler(sampler)
+        llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
 
     def add_mirostat_v2(self, seed: int, tau: float, eta: float):
         sampler = llama_cpp.llama_sampler_init_mirostat_v2(seed, tau, eta)
-        self._add_sampler(sampler)
+        llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
 
     def add_grammar(self, model: LlamaModel, grammar: LlamaGrammar):
         sampler = llama_cpp.llama_sampler_init_grammar(
-            model.model, grammar._grammar.encode("utf-8"), grammar._root.encode("utf-8")
+            model.vocab, grammar._grammar.encode("utf-8"), grammar._root.encode("utf-8")
+        )
+        llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
+
+    def add_grammar_lazy_patterns(
+        self,
+        model: LlamaModel,
+        grammar: LlamaGrammar,
+        trigger_patterns: List[str],
+        trigger_tokens: List[int],
+    ):
+        # Convert patterns to C array
+        pattern_ptrs = (ctypes.c_char_p * len(trigger_patterns))()
+        for i, pattern in enumerate(trigger_patterns):
+            pattern_ptrs[i] = pattern.encode("utf-8")
+
+        # Convert tokens to C array
+        token_array = (llama_cpp.llama_token * len(trigger_tokens))(*trigger_tokens)
+
+        sampler = llama_cpp.llama_sampler_init_grammar_lazy_patterns(
+            model.vocab,
+            grammar._grammar.encode("utf-8"),
+            grammar._root.encode("utf-8"),
+            pattern_ptrs,
+            len(trigger_patterns),
+            token_array,
+            len(trigger_tokens),
         )
-        self._add_sampler(sampler)
+        llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
 
     def add_penalties(
         self,
-        n_vocab: int,
-        special_eos_id: int,
-        linefeed_id: int,
         penalty_last_n: int,
         penalty_repeat: float,
         penalty_freq: float,
         penalty_present: float,
-        penalize_nl: bool,
-        ignore_eos: bool,
     ):
         sampler = llama_cpp.llama_sampler_init_penalties(
-            n_vocab,
-            special_eos_id,
-            linefeed_id,
             penalty_last_n,
             penalty_repeat,
             penalty_freq,
             penalty_present,
-            penalize_nl,
-            ignore_eos,
         )
-        self._add_sampler(sampler)
+        llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
 
-    def init_logit_bias(
-        self, n_vocab: int, n_logit_bias, logit_bias: llama_cpp.llama_logit_bias_p
+    def add_dry(
+        self,
+        model: LlamaModel,
+        n_ctx_train: int,
+        dry_multiplier: float,
+        dry_base: float,
+        dry_allowed_length: int,
+        dry_penalty_last_n: int,
+        seq_breakers: List[str],
     ):
+        # Convert seq_breakers to C array
+        breaker_ptrs = (ctypes.c_char_p * len(seq_breakers))()
+        for i, breaker in enumerate(seq_breakers):
+            breaker_ptrs[i] = breaker.encode("utf-8")
+
+        sampler = llama_cpp.llama_sampler_init_dry(
+            model.vocab,
+            n_ctx_train,
+            dry_multiplier,
+            dry_base,
+            dry_allowed_length,
+            dry_penalty_last_n,
+            breaker_ptrs,
+            len(seq_breakers),
+        )
+        llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
+
+    def add_logit_bias(self, n_vocab: int, logit_bias: Dict[int, float]):
+        # Convert logit_bias dict to C array
+        bias_array = (llama_cpp.llama_logit_bias * len(logit_bias))()
+        for i, (token, bias) in enumerate(logit_bias.items()):
+            bias_array[i].token = token
+            bias_array[i].bias = bias
+
         sampler = llama_cpp.llama_sampler_init_logit_bias(
-            n_vocab, n_logit_bias, logit_bias
+            n_vocab, len(logit_bias), bias_array
         )
-        self._add_sampler(sampler)
+        llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
+
+    def add_infill(self, model: LlamaModel):
+        sampler = llama_cpp.llama_sampler_init_infill(model.vocab)
+        llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
 
     def add_custom(
         self, apply_func: Callable[[llama_cpp.llama_token_data_array], None]
     ):
         custom_sampler = CustomSampler(apply_func)
         sampler = custom_sampler.get_sampler()
-        self._add_sampler(sampler)
+        llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
         # NOTE: Must remove custom samplers before free or llama.cpp will try to free them
         self.custom_samplers.append(
             (llama_cpp.llama_sampler_chain_n(self.sampler) - 1, custom_sampler)
         )
 
-    def _add_sampler(self, sampler: llama_cpp.llama_sampler_p):
-        assert self.sampler is not None
-        llama_cpp.llama_sampler_chain_add(self.sampler, sampler)
-        self.samplers.append(sampler)
-
     def get_seed(self) -> int:
-        assert self.sampler is not None
         return llama_cpp.llama_sampler_get_seed(self.sampler)
 
-    def sample(self, ctx: LlamaContext, idx: int) -> int:
-        assert self.sampler is not None
+    def sample(self, ctx: LlamaContext, idx: int = -1) -> int:
         return llama_cpp.llama_sampler_sample(self.sampler, ctx.ctx, idx)
 
-    def close(self):
-        if self.sampler:
-            # NOTE: Must remove custom samplers before free or llama.cpp will try to free them
-            for i, _ in reversed(self.custom_samplers):
-                llama_cpp.llama_sampler_chain_remove(self.sampler, i)
-            llama_cpp.llama_sampler_free(self.sampler)
-            self.sampler = None
-        self.samplers.clear()
-        self.custom_samplers.clear()
+    def accept(self, token: int):
+        llama_cpp.llama_sampler_accept(self.sampler, token)
 
-    def __del__(self):
-        self.close()
+    def reset(self):
+        llama_cpp.llama_sampler_reset(self.sampler)
+
+    def clone(self):
+        # NOTE: Custom samplers cannot be cloned due to Python callback limitations
+        if self.custom_samplers:
+            raise NotImplementedError(
+                "Cannot clone LlamaSampler that contains custom samplers"
+            )
+
+        cloned_sampler = llama_cpp.llama_sampler_clone(self.sampler)
+        # Create a new wrapper around the cloned sampler
+        new_sampler = LlamaSampler.__new__(LlamaSampler)
+        new_sampler.sampler = cloned_sampler
+        new_sampler.custom_samplers = []
+        new_sampler._exit_stack = ExitStack()
+
+        def free_sampler():
+            if new_sampler.sampler is not None:
+                llama_cpp.llama_sampler_free(new_sampler.sampler)
+                new_sampler.sampler = None
+
+        new_sampler._exit_stack.callback(free_sampler)
+        return new_sampler
diff --git a/llama_cpp/_logger.py b/llama_cpp/_logger.py
index 787b3f108e..31d89d0995 100644
--- a/llama_cpp/_logger.py
+++ b/llama_cpp/_logger.py
@@ -25,6 +25,7 @@
 
 _last_log_level = GGML_LOG_LEVEL_TO_LOGGING_LEVEL[0]
 
+
 # typedef void (*ggml_log_callback)(enum ggml_log_level level, const char * text, void * user_data);
 @llama_cpp.llama_log_callback
 def llama_log_callback(
@@ -34,7 +35,9 @@ def llama_log_callback(
 ):
     # TODO: Correctly implement continue previous log
     global _last_log_level
-    log_level = GGML_LOG_LEVEL_TO_LOGGING_LEVEL[level] if level != 5 else _last_log_level
+    log_level = (
+        GGML_LOG_LEVEL_TO_LOGGING_LEVEL[level] if level != 5 else _last_log_level
+    )
     if logger.level <= GGML_LOG_LEVEL_TO_LOGGING_LEVEL[level]:
         print(text.decode("utf-8"), end="", flush=True, file=sys.stderr)
     _last_log_level = log_level
diff --git a/llama_cpp/_utils.py b/llama_cpp/_utils.py
index 29628193bb..08ede64205 100644
--- a/llama_cpp/_utils.py
+++ b/llama_cpp/_utils.py
@@ -34,6 +34,22 @@ def __enter__(self):
         self.old_stdout = self.sys.stdout
         self.old_stderr = self.sys.stderr
 
+        # In Jupyter notebooks, ipykernel replaces sys.stdout/stderr with
+        # OutStream objects that hold their own copy of the original fd in
+        # _original_stdstream_copy. This bypasses our dup2 redirect, so we
+        # need to point that copy at the real fd temporarily.
+        # https://github.com/ipython/ipykernel/blob/912923542d55e6c80c0e7c1f94c648b52a225011/ipykernel/iostream.py#L618-L622
+        self._saved_stdout_copy = getattr(
+            self.sys.stdout, "_original_stdstream_copy", None
+        )
+        self._saved_stderr_copy = getattr(
+            self.sys.stderr, "_original_stdstream_copy", None
+        )
+        if self._saved_stdout_copy is not None:
+            self.sys.stdout._original_stdstream_copy = self.old_stdout_fileno_undup
+        if self._saved_stderr_copy is not None:
+            self.sys.stderr._original_stdstream_copy = self.old_stderr_fileno_undup
+
         self.os.dup2(outnull_file.fileno(), self.old_stdout_fileno_undup)
         self.os.dup2(errnull_file.fileno(), self.old_stderr_fileno_undup)
 
@@ -45,7 +61,6 @@ def __exit__(self, *_):
         if self.disable:
             return
 
-        # Check if sys.stdout and sys.stderr have fileno method
         self.sys.stdout = self.old_stdout
         self.sys.stderr = self.old_stderr
 
@@ -55,6 +70,12 @@ def __exit__(self, *_):
         self.os.close(self.old_stdout_fileno)
         self.os.close(self.old_stderr_fileno)
 
+        # Restore ipykernel's OutStream fd copies
+        if self._saved_stdout_copy is not None:
+            self.sys.stdout._original_stdstream_copy = self._saved_stdout_copy
+        if self._saved_stderr_copy is not None:
+            self.sys.stderr._original_stdstream_copy = self._saved_stderr_copy
+
 
 class MetaSingleton(type):
     """
diff --git a/llama_cpp/llama.py b/llama_cpp/llama.py
index d15a88b00e..b5bffd46b5 100644
--- a/llama_cpp/llama.py
+++ b/llama_cpp/llama.py
@@ -66,7 +66,6 @@ def __init__(
         split_mode: int = llama_cpp.LLAMA_SPLIT_MODE_LAYER,
         main_gpu: int = 0,
         tensor_split: Optional[List[float]] = None,
-        rpc_servers: Optional[str] = None,
         vocab_only: bool = False,
         use_mmap: bool = True,
         use_mlock: bool = False,
@@ -82,6 +81,7 @@ def __init__(
             int
         ] = llama_cpp.LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED,
         pooling_type: int = llama_cpp.LLAMA_POOLING_TYPE_UNSPECIFIED,
+        attention_type: int = llama_cpp.LLAMA_ATTENTION_TYPE_UNSPECIFIED,
         rope_freq_base: float = 0.0,
         rope_freq_scale: float = 0.0,
         yarn_ext_factor: float = -1.0,
@@ -93,7 +93,10 @@ def __init__(
         embedding: bool = False,
         offload_kqv: bool = True,
         flash_attn: bool = False,
+        op_offload: Optional[bool] = None,
+        swa_full: Optional[bool] = None,
         # Sampling Params
+        no_perf: bool = False,
         last_n_tokens_size: int = 64,
         # LoRA Params
         lora_base: Optional[str] = None,
@@ -149,7 +152,6 @@ def __init__(
             split_mode: How to split the model across GPUs. See llama_cpp.LLAMA_SPLIT_* for options.
             main_gpu: main_gpu interpretation depends on split_mode: LLAMA_SPLIT_MODE_NONE: the GPU that is used for the entire model. LLAMA_SPLIT_MODE_ROW: the GPU that is used for small tensors and intermediate results. LLAMA_SPLIT_MODE_LAYER: ignored
             tensor_split: How split tensors should be distributed across GPUs. If None, the model is not split.
-            rpc_servers: Comma separated list of RPC servers to use for offloading
             vocab_only: Only load the vocabulary no weights.
             use_mmap: Use mmap if possible.
             use_mlock: Force the system to keep the model in RAM.
@@ -162,6 +164,7 @@ def __init__(
             n_threads_batch: Number of threads to use for batch processing
             rope_scaling_type: RoPE scaling type, from `enum llama_rope_scaling_type`. ref: https://github.com/ggerganov/llama.cpp/pull/2054
             pooling_type: Pooling type, from `enum llama_pooling_type`.
+            attention_type: Attention type, from `enum llama_attention_type`.
             rope_freq_base: RoPE base frequency, 0 = from model
             rope_freq_scale: RoPE frequency scaling factor, 0 = from model
             yarn_ext_factor: YaRN extrapolation mix factor, negative = from model
@@ -173,6 +176,9 @@ def __init__(
             embedding: Embedding mode only.
             offload_kqv: Offload K, Q, V to GPU.
             flash_attn: Use flash attention.
+            op_offload: offload host tensor operations to device
+            swa_full: use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
+            no_perf: Measure performance timings.
             last_n_tokens_size: Maximum number of tokens to keep in the last_n_tokens deque.
             lora_base: Optional path to base model, useful if using a quantized base model and you want to apply LoRA to an f16 model.
             lora_path: Path to a LoRA file to apply to the model.
@@ -224,11 +230,6 @@ def __init__(
         )  # 0x7FFFFFFF is INT32 max, will be auto set to all layers
         self.model_params.split_mode = split_mode
         self.model_params.main_gpu = main_gpu
-        if rpc_servers is not None:
-            self.model_params.rpc_servers = rpc_servers.encode("utf-8")
-            self._rpc_servers = rpc_servers
-        else:
-            self._rpc_servers = None
         self.tensor_split = tensor_split
         self._c_tensor_split = None
         if self.tensor_split is not None:
@@ -320,6 +321,7 @@ def __init__(
             else llama_cpp.LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED
         )
         self.context_params.pooling_type = pooling_type
+        self.context_params.attention_type = attention_type
         self.context_params.rope_freq_base = (
             rope_freq_base if rope_freq_base != 0.0 else 0
         )
@@ -339,18 +341,28 @@ def __init__(
             yarn_beta_slow if yarn_beta_slow != 0.0 else 0
         )
         self.context_params.yarn_orig_ctx = yarn_orig_ctx if yarn_orig_ctx != 0 else 0
-        self.context_params.logits_all = (
-            logits_all if draft_model is None else True
-        )  # Must be set to True for speculative decoding
+        self._logits_all = logits_all if draft_model is None else True
         self.context_params.embeddings = embedding  # TODO: Rename to embeddings
         self.context_params.offload_kqv = offload_kqv
-        self.context_params.flash_attn = flash_attn
+        self.context_params.flash_attn_type = (
+            llama_cpp.LLAMA_FLASH_ATTN_TYPE_ENABLED
+            if flash_attn
+            else llama_cpp.LLAMA_FLASH_ATTN_TYPE_DISABLED
+        )
+
+        if op_offload is not None:
+            self.context_params.op_offload = op_offload
+
+        if swa_full is not None:
+            self.context_params.swa_full = swa_full
+
         #  KV cache quantization
         if type_k is not None:
             self.context_params.type_k = type_k
         if type_v is not None:
             self.context_params.type_v = type_v
         # Sampling Params
+        self.context_params.no_perf = no_perf
         self.last_n_tokens_size = last_n_tokens_size
 
         self.cache: Optional[BaseLlamaCache] = None
@@ -385,6 +397,12 @@ def __init__(
             self.context_params.n_batch = self.n_batch
             self.context_params.n_ubatch = min(self.n_batch, n_ubatch)
 
+        if embedding:
+            self.context_params.n_seq_max = min(
+                self.n_batch,
+                llama_cpp.llama_max_parallel_sequences(),
+            )
+            self.context_params.kv_unified = True
         self._ctx = self._stack.enter_context(
             contextlib.closing(
                 internals.LlamaContext(
@@ -406,10 +424,10 @@ def __init__(
             )
         )
 
-        self._lora_adapter: Optional[llama_cpp.llama_lora_adapter_p] = None
+        self._lora_adapter: Optional[llama_cpp.llama_adapter_lora_p] = None
 
         if self.lora_path:
-            self._lora_adapter = llama_cpp.llama_lora_adapter_init(
+            self._lora_adapter = llama_cpp.llama_adapter_lora_init(
                 self._model.model,
                 self.lora_path.encode("utf-8"),
             )
@@ -421,14 +439,14 @@ def __init__(
             def free_lora_adapter():
                 if self._lora_adapter is None:
                     return
-                llama_cpp.llama_lora_adapter_free(self._lora_adapter)
+                llama_cpp.llama_adapter_lora_free(self._lora_adapter)
                 self._lora_adapter = None
 
             self._stack.callback(free_lora_adapter)
 
-            if llama_cpp.llama_lora_adapter_set(
-                self._ctx.ctx, self._lora_adapter, self.lora_scale
-            ):
+            adapters = (llama_cpp.llama_adapter_lora_p_ctypes * 1)(self._lora_adapter)
+            scales = (ctypes.c_float * 1)(self.lora_scale)
+            if llama_cpp.llama_set_adapters_lora(self._ctx.ctx, adapters, 1, scales):
                 raise RuntimeError(
                     f"Failed to set LoRA adapter from lora path: {self.lora_path}"
                 )
@@ -453,6 +471,8 @@ def free_lora_adapter():
         self._candidates = internals.LlamaTokenDataArray(n_vocab=self._n_vocab)
 
         self.n_tokens = 0
+        # Restored or truncated state must decode before sampling.
+        self._requires_eval = True
         self.input_ids: npt.NDArray[np.intc] = np.ndarray((n_ctx,), dtype=np.intc)
         self.scores: npt.NDArray[np.single] = np.ndarray(
             (n_ctx if logits_all == True else n_batch, self._n_vocab), dtype=np.single
@@ -541,6 +561,10 @@ def free_lora_adapter():
 
         self._sampler = None
 
+        # Cache recurrent/hybrid model detection to avoid repeated FFI calls
+        self._is_recurrent = llama_cpp.llama_model_is_recurrent(self._model.model)
+        self._is_hybrid = llama_cpp.llama_model_is_hybrid(self._model.model)
+
     @property
     def ctx(self) -> llama_cpp.llama_context_p:
         return self._ctx.ctx
@@ -565,7 +589,7 @@ def eval_tokens(self) -> Deque[int]:
     def eval_logits(self) -> Deque[List[float]]:
         return deque(
             self.scores[: self.n_tokens, :].tolist(),
-            maxlen=self._n_ctx if self.context_params.logits_all else 1,
+            maxlen=self._n_ctx if self._logits_all else 1,
         )
 
     def tokenize(
@@ -625,6 +649,12 @@ def set_seed(self, seed: int):
     def reset(self):
         """Reset the model state."""
         self.n_tokens = 0
+        self._requires_eval = True
+
+        if self._is_recurrent or self._is_hybrid:
+            mem = llama_cpp.llama_get_memory(self._ctx.ctx)
+            if mem is not None:
+                llama_cpp.llama_memory_clear(mem, True)
 
     def eval(self, tokens: Sequence[int]):
         """Evaluate a list of tokens.
@@ -638,13 +668,13 @@ def eval(self, tokens: Sequence[int]):
             n_past = self.n_tokens
             n_tokens = len(batch)
             self._batch.set_batch(
-                batch=batch, n_past=n_past, logits_all=self.context_params.logits_all
+                batch=batch, n_past=n_past, logits_all=self._logits_all
             )
             self._ctx.decode(self._batch)
             # Save tokens
             self.input_ids[n_past : n_past + n_tokens] = batch
             # Save logits
-            if self.context_params.logits_all:
+            if self._logits_all:
                 rows = n_tokens
                 cols = self._n_vocab
                 logits = np.ctypeslib.as_array(
@@ -662,6 +692,7 @@ def eval(self, tokens: Sequence[int]):
                 pass
             # Update n_tokens
             self.n_tokens += n_tokens
+            self._requires_eval = False
 
     def _init_sampler(
         self,
@@ -706,22 +737,21 @@ def apply_func(token_data_array: llama_cpp.llama_token_data_array_p):
             sampler.add_custom(apply_func)
 
         sampler.add_penalties(
-            n_vocab=self._n_vocab,
-            special_eos_id=self._token_eos,
-            linefeed_id=self._token_nl,
+            # n_vocab=self._n_vocab,
+            # special_eos_id=self._token_eos,
+            # linefeed_id=self._token_nl,
             penalty_last_n=self.last_n_tokens_size,
             penalty_repeat=repeat_penalty,
             penalty_freq=frequency_penalty,
             penalty_present=presence_penalty,
-            penalize_nl=penalize_nl,
-            ignore_eos=False,
+            # penalize_nl=penalize_nl,
+            # ignore_eos=False,
         )
 
         if grammar is not None:
             sampler.add_grammar(self._model, grammar)
 
         if temp < 0.0:
-            sampler.add_softmax()
             sampler.add_dist(self._seed)
         elif temp == 0.0:
             sampler.add_greedy()
@@ -874,22 +904,54 @@ def generate(
             grammar=grammar,
         )
 
+        tokens = list(tokens)
+
         # Check for kv cache prefix match
         if reset and self.n_tokens > 0:
             longest_prefix = 0
-            for a, b in zip(self._input_ids, tokens[:-1]):
+            for a, b in zip(self._input_ids, tokens):
                 if a == b:
                     longest_prefix += 1
                 else:
                     break
-            if longest_prefix > 0:
+
+            prompt_consumed = longest_prefix == len(tokens)
+            exact_prompt_cached = self.n_tokens == len(tokens) and prompt_consumed
+
+            # Exact cache hits can sample immediately only when the current
+            # logits were produced by a live decode, not restored state.
+            if exact_prompt_cached and not self._requires_eval:
                 reset = False
-                tokens = tokens[longest_prefix:]
-                self.n_tokens = longest_prefix
+                tokens = []
+                reuse_prefix = 0
                 if self.verbose:
                     print(
-                        f"Llama.generate: {longest_prefix} prefix-match hit, "
-                        f"remaining {len(tokens)} prompt tokens to eval",
+                        "Llama.generate: full prompt already cached, skipping reset",
+                        file=sys.stderr,
+                    )
+            else:
+                # If there is no suffix to decode, replay one token to refresh
+                # logits after truncating to a valid prefix.
+                reuse_prefix = longest_prefix - 1 if prompt_consumed else longest_prefix
+
+            # Prefix hits can reuse memory because the suffix decode refreshes
+            # logits before sampling.
+            if reuse_prefix > 0:
+                if self._ctx.kv_cache_seq_rm(-1, reuse_prefix, -1):
+                    reset = False
+                    tokens = tokens[reuse_prefix:]
+                    self.n_tokens = reuse_prefix
+                    self._requires_eval = True
+                    if self.verbose:
+                        print(
+                            f"Llama.generate: {reuse_prefix} prefix-match hit, "
+                            f"remaining {len(tokens)} prompt tokens to eval",
+                            file=sys.stderr,
+                        )
+                elif self.verbose:
+                    print(
+                        f"Llama.generate: {reuse_prefix} prefix-match found "
+                        f"but partial kv removal not supported, re-evaluating full prompt",
                         file=sys.stderr,
                     )
 
@@ -902,7 +964,6 @@ def generate(
         #     grammar.reset()
 
         sample_idx = self.n_tokens + len(tokens) - 1
-        tokens = list(tokens)
 
         # Eval and sample
         while True:
@@ -929,7 +990,8 @@ def generate(
 
                 sample_idx += 1
                 if stopping_criteria is not None and stopping_criteria(
-                    self._input_ids[: sample_idx], self._scores[sample_idx - self.n_tokens, :]
+                    self._input_ids[:sample_idx],
+                    self._scores[sample_idx - self.n_tokens, :],
                 ):
                     return
                 tokens_or_none = yield token
@@ -941,6 +1003,7 @@ def generate(
                 if sample_idx < self.n_tokens and token != self._input_ids[sample_idx]:
                     self.n_tokens = sample_idx
                     self._ctx.kv_cache_seq_rm(-1, self.n_tokens, -1)
+                    self._requires_eval = True
                     break
 
             if self.draft_model is not None:
@@ -1011,10 +1074,17 @@ def embed(
         """
         n_embd = self.n_embd()
         n_batch = self.n_batch
+        n_seq_max = self.context_params.n_seq_max
 
         # get pooling information
         pooling_type = self.pooling_type()
-        logits_all = pooling_type == llama_cpp.LLAMA_POOLING_TYPE_NONE
+        # In embedding mode every input token must be marked as an output, regardless of
+        # pooling type. llama.cpp would otherwise override per-token `logits[i]` and emit
+        # "embeddings required but some input tokens were not marked as outputs ->
+        # overriding" once per input. Pooling NONE vs MEAN/CLS only changes how the
+        # per-token outputs are read back (see decode_batch below), not whether they are
+        # produced. See abetlen/llama-cpp-python#2208.
+        logits_all = True
 
         if self.context_params.embeddings is False:
             raise RuntimeError(
@@ -1036,7 +1106,7 @@ def embed(
         data: Union[List[List[float]], List[List[List[float]]]] = []
 
         def decode_batch(seq_sizes: List[int]):
-            llama_cpp.llama_kv_cache_clear(self._ctx.ctx)
+            self._ctx.kv_cache_clear()
             self._ctx.decode(self._batch)
             self._batch.reset()
 
@@ -1085,7 +1155,7 @@ def decode_batch(seq_sizes: List[int]):
                 )
 
             # time to eval batch
-            if t_batch + n_tokens > n_batch:
+            if t_batch + n_tokens > n_batch or p_batch >= n_seq_max:
                 decode_batch(s_batch)
                 s_batch = []
                 t_batch = 0
@@ -1107,7 +1177,7 @@ def decode_batch(seq_sizes: List[int]):
 
         output = data[0] if isinstance(input, str) else data
 
-        llama_cpp.llama_kv_cache_clear(self._ctx.ctx)
+        self._ctx.kv_cache_clear()
         self.reset()
 
         if return_count:
@@ -1141,7 +1211,7 @@ def _create_completion(
         stopping_criteria: Optional[StoppingCriteriaList] = None,
         logits_processor: Optional[LogitsProcessorList] = None,
         grammar: Optional[LlamaGrammar] = None,
-        logit_bias: Optional[Dict[str, float]] = None,
+        logit_bias: Optional[Dict[int, float]] = None,
     ) -> Union[
         Iterator[CreateCompletionResponse], Iterator[CreateCompletionStreamResponse]
     ]:
@@ -1152,9 +1222,9 @@ def _create_completion(
         bos_token_id: int = self.token_bos()
         cls_token_id: int = self._model.token_cls()
         sep_token_id: int = self._model.token_sep()
-        prefix_token_id: int = self._model.token_prefix()
-        middle_token_id: int = self._model.token_middle()
-        suffix_token_id: int = self._model.token_suffix()
+        prefix_token_id: int = 0  # self._model.token_prefix() # TODO: Fix
+        middle_token_id: int = 0  # self._model.token_middle() # TODO: Fix
+        suffix_token_id: int = 0  # self._model.token_suffix() # TODO: Fix
         add_space_prefix: bool = (
             self.metadata.get("tokenizer.ggml.add_space_prefix", "true") == "true"
         )
@@ -1285,7 +1355,7 @@ def logit_bias_processor(
         else:
             stop_sequences = []
 
-        if logprobs is not None and self.context_params.logits_all is False:
+        if logprobs is not None and self._logits_all is False:
             raise ValueError(
                 "logprobs is not supported for models created with logits_all=False"
             )
@@ -1310,7 +1380,7 @@ def logit_bias_processor(
         if seed is not None:
             self.set_seed(seed)
         else:
-            self.set_seed(random.Random(self._seed).randint(0, 2 ** 32))
+            self.set_seed(random.Random(self._seed).randint(0, 2**32))
 
         finish_reason = "length"
         multibyte_fix = 0
@@ -1332,7 +1402,7 @@ def logit_bias_processor(
             logits_processor=logits_processor,
             grammar=grammar,
         ):
-            if llama_cpp.llama_token_is_eog(self._model.model, token):
+            if llama_cpp.llama_vocab_is_eog(self._model.vocab, token):
                 text = self.detokenize(completion_tokens, prev_tokens=prompt_tokens)
                 finish_reason = "stop"
                 break
@@ -1761,7 +1831,7 @@ def create_completion(
         stopping_criteria: Optional[StoppingCriteriaList] = None,
         logits_processor: Optional[LogitsProcessorList] = None,
         grammar: Optional[LlamaGrammar] = None,
-        logit_bias: Optional[Dict[str, float]] = None,
+        logit_bias: Optional[Dict[int, float]] = None,
     ) -> Union[CreateCompletionResponse, Iterator[CreateCompletionStreamResponse]]:
         """Generate text from a prompt.
 
@@ -1858,7 +1928,7 @@ def __call__(
         stopping_criteria: Optional[StoppingCriteriaList] = None,
         logits_processor: Optional[LogitsProcessorList] = None,
         grammar: Optional[LlamaGrammar] = None,
-        logit_bias: Optional[Dict[str, float]] = None,
+        logit_bias: Optional[Dict[int, float]] = None,
     ) -> Union[CreateCompletionResponse, Iterator[CreateCompletionStreamResponse]]:
         """Generate text from a prompt.
 
@@ -1951,7 +2021,7 @@ def create_chat_completion(
         model: Optional[str] = None,
         logits_processor: Optional[LogitsProcessorList] = None,
         grammar: Optional[LlamaGrammar] = None,
-        logit_bias: Optional[Dict[str, float]] = None,
+        logit_bias: Optional[Dict[int, float]] = None,
         logprobs: Optional[bool] = None,
         top_logprobs: Optional[int] = None,
     ) -> Union[
@@ -2051,7 +2121,10 @@ def create_chat_completion_openai_v1(
             stream = kwargs.get("stream", False)  # type: ignore
             assert isinstance(stream, bool)
             if stream:
-                return (ChatCompletionChunk(**chunk) for chunk in self.create_chat_completion(*args, **kwargs))  # type: ignore
+                return (
+                    ChatCompletionChunk(**chunk)
+                    for chunk in self.create_chat_completion(*args, **kwargs)
+                )  # type: ignore
             else:
                 return ChatCompletion(**self.create_chat_completion(*args, **kwargs))  # type: ignore
         except ImportError:
@@ -2073,7 +2146,7 @@ def __getstate__(self):
             use_mlock=self.model_params.use_mlock,
             kv_overrides=self.kv_overrides,
             # Context Params
-            seed=self.context_params.seed,
+            seed=self._seed,
             n_ctx=self.context_params.n_ctx,
             n_batch=self.n_batch,
             n_ubatch=self.context_params.n_ubatch,
@@ -2081,6 +2154,7 @@ def __getstate__(self):
             n_threads_batch=self.context_params.n_threads_batch,
             rope_scaling_type=self.context_params.rope_scaling_type,
             pooling_type=self.context_params.pooling_type,
+            attention_type=self.context_params.attention_type,
             rope_freq_base=self.context_params.rope_freq_base,
             rope_freq_scale=self.context_params.rope_freq_scale,
             yarn_ext_factor=self.context_params.yarn_ext_factor,
@@ -2088,11 +2162,17 @@ def __getstate__(self):
             yarn_beta_fast=self.context_params.yarn_beta_fast,
             yarn_beta_slow=self.context_params.yarn_beta_slow,
             yarn_orig_ctx=self.context_params.yarn_orig_ctx,
-            logits_all=self.context_params.logits_all,
+            logits_all=self._logits_all,
             embedding=self.context_params.embeddings,
             offload_kqv=self.context_params.offload_kqv,
-            flash_attn=self.context_params.flash_attn,
+            flash_attn=(
+                self.context_params.flash_attn_type
+                == llama_cpp.LLAMA_FLASH_ATTN_TYPE_ENABLED
+            ),
+            op_offload=self.context_params.op_offload,
+            swa_full=self.context_params.swa_full,
             # Sampling Params
+            no_perf=self.context_params.no_perf,
             last_n_tokens_size=self.last_n_tokens_size,
             # LoRA Params
             lora_base=self.lora_base,
@@ -2119,13 +2199,13 @@ def __setstate__(self, state):
     def save_state(self) -> LlamaState:
         if self.verbose:
             print("Llama.save_state: saving llama state", file=sys.stderr)
-        state_size = llama_cpp.llama_get_state_size(self._ctx.ctx)
+        state_size = llama_cpp.llama_state_get_size(self._ctx.ctx)
         if self.verbose:
             print(f"Llama.save_state: got state size: {state_size}", file=sys.stderr)
         llama_state = (ctypes.c_uint8 * int(state_size))()
         if self.verbose:
             print("Llama.save_state: allocated state", file=sys.stderr)
-        n_bytes = llama_cpp.llama_copy_state_data(self._ctx.ctx, llama_state)
+        n_bytes = llama_cpp.llama_state_get_data(self._ctx.ctx, llama_state, state_size)
         if self.verbose:
             print(f"Llama.save_state: copied llama state: {n_bytes}", file=sys.stderr)
         if int(n_bytes) > int(state_size):
@@ -2153,12 +2233,16 @@ def load_state(self, state: LlamaState) -> None:
         rest[rest > 0] = 0.0
         self.input_ids = state.input_ids.copy()
         self.n_tokens = state.n_tokens
+        self._requires_eval = True
         self._seed = state.seed
         state_size = state.llama_state_size
         LLamaStateArrayType = ctypes.c_uint8 * state_size
         llama_state = LLamaStateArrayType.from_buffer_copy(state.llama_state)
 
-        if llama_cpp.llama_set_state_data(self._ctx.ctx, llama_state) != state_size:
+        if (
+            llama_cpp.llama_state_set_data(self._ctx.ctx, llama_state, state_size)
+            != state_size
+        ):
             raise RuntimeError("Failed to set llama state data")
 
     def n_ctx(self) -> int:
@@ -2310,7 +2394,11 @@ def from_pretrained(
         if additional_files:
             for additonal_file_name in additional_files:
                 # find the additional shard file:
-                matching_additional_files = [file for file in file_list if fnmatch.fnmatch(file, additonal_file_name)]
+                matching_additional_files = [
+                    file
+                    for file in file_list
+                    if fnmatch.fnmatch(file, additonal_file_name)
+                ]
 
                 if len(matching_additional_files) == 0:
                     raise ValueError(
@@ -2325,12 +2413,15 @@ def from_pretrained(
                     )
 
                 (matching_additional_file,) = matching_additional_files
+                additional_subfolder = str(Path(matching_additional_file).parent)
+                additional_file_name = Path(matching_additional_file).name
 
-                # download the additional file
+                # Split additional file paths independently to avoid duplicating
+                # the main model subfolder in Hugging Face download URLs.
                 hf_hub_download(
                     repo_id=repo_id,
-                    filename=matching_additional_file,
-                    subfolder=subfolder,
+                    filename=additional_file_name,
+                    subfolder=additional_subfolder,
                     local_dir=local_dir,
                     local_dir_use_symlinks=local_dir_use_symlinks,
                     cache_dir=cache_dir,
diff --git a/llama_cpp/llama_cache.py b/llama_cpp/llama_cache.py
index e059e98e1b..5220c79337 100644
--- a/llama_cpp/llama_cache.py
+++ b/llama_cpp/llama_cache.py
@@ -52,9 +52,9 @@ class LlamaRAMCache(BaseLlamaCache):
     def __init__(self, capacity_bytes: int = (2 << 30)):
         super().__init__(capacity_bytes)
         self.capacity_bytes = capacity_bytes
-        self.cache_state: OrderedDict[
-            Tuple[int, ...], "llama_cpp.llama.LlamaState"
-        ] = OrderedDict()
+        self.cache_state: OrderedDict[Tuple[int, ...], "llama_cpp.llama.LlamaState"] = (
+            OrderedDict()
+        )
 
     @property
     def cache_size(self):
diff --git a/llama_cpp/llama_chat_format.py b/llama_cpp/llama_chat_format.py
index dfb0af65e6..0034bdae98 100644
--- a/llama_cpp/llama_chat_format.py
+++ b/llama_cpp/llama_chat_format.py
@@ -8,6 +8,7 @@
 import random
 import string
 
+from datetime import datetime
 from contextlib import ExitStack
 from typing import (
     Any,
@@ -23,11 +24,13 @@
 )
 
 import jinja2
+from jinja2.ext import Extension
 from jinja2.sandbox import ImmutableSandboxedEnvironment
 
 import numpy as np
 import numpy.typing as npt
 
+import llama_cpp.llama_cpp as llama_cpp
 import llama_cpp.llama as llama
 import llama_cpp.llama_types as llama_types
 import llama_cpp.llama_grammar as llama_grammar
@@ -190,6 +193,15 @@ def __call__(
 
 
 class Jinja2ChatFormatter(ChatFormatter):
+    class IgnoreGenerationTags(Extension):
+        """Pass-through for HuggingFace's ``{% generation %}`` chat-template tag."""
+
+        tags = {"generation"}
+
+        def parse(self, parser: jinja2.parser.Parser):
+            parser.stream.skip(1)
+            return parser.parse_statements(("name:endgeneration",), drop_needle=True)
+
     def __init__(
         self,
         template: str,
@@ -207,11 +219,41 @@ def __init__(
             set(stop_token_ids) if stop_token_ids is not None else None
         )
 
-        self._environment = ImmutableSandboxedEnvironment(
+        environment = ImmutableSandboxedEnvironment(
             loader=jinja2.BaseLoader(),
             trim_blocks=True,
             lstrip_blocks=True,
-        ).from_string(self.template)
+            # Keep this aligned with Transformers' chat-template Jinja extensions.
+            # https://github.com/huggingface/transformers/blob/39603d0e5cdb6f00e8d473d7fcbb01032d709181/src/transformers/utils/chat_template_utils.py#L489-L490
+            extensions=[
+                Jinja2ChatFormatter.IgnoreGenerationTags,
+                jinja2.ext.loopcontrols,
+            ],
+        )
+        # Match Transformers' chat-template JSON rendering behavior.
+        # https://github.com/huggingface/transformers/blob/39603d0e5cdb6f00e8d473d7fcbb01032d709181/src/transformers/utils/chat_template_utils.py#L481-L484
+        environment.filters["tojson"] = self.tojson
+        self._environment = environment.from_string(self.template)
+
+    @staticmethod
+    def strftime_now(f: str) -> str:
+        return datetime.now().strftime(f)
+
+    @staticmethod
+    def tojson(
+        x: Any,
+        ensure_ascii: bool = False,
+        indent: Optional[int] = None,
+        separators: Optional[Tuple[str, str]] = None,
+        sort_keys: bool = False,
+    ) -> str:
+        return json.dumps(
+            x,
+            ensure_ascii=ensure_ascii,
+            indent=indent,
+            separators=separators,
+            sort_keys=sort_keys,
+        )
 
     def __call__(
         self,
@@ -236,6 +278,8 @@ def raise_exception(message: str):
             function_call=function_call,
             tools=tools,
             tool_choice=tool_choice,
+            strftime_now=self.strftime_now,
+            **kwargs,
         )
 
         stopping_criteria = None
@@ -259,6 +303,35 @@ def to_chat_handler(self) -> LlamaChatCompletionHandler:
         return chat_formatter_to_chat_completion_handler(self)
 
 
+def _convert_text_completion_logprobs_to_chat(
+    logprobs: Optional[llama_types.CompletionLogprobs],
+) -> llama_types.ChatCompletionLogprobs:
+    if logprobs is None:
+        return None
+
+    return {
+        "content": [
+            {
+                "token": token,
+                "bytes": None,
+                "logprob": logprob,
+                "top_logprobs": [
+                    {
+                        "token": top_token,
+                        "logprob": top_logprob,
+                        "bytes": None,
+                    }
+                    for top_token, top_logprob in top_logprobs.items()
+                ],
+            }
+            for (token, logprob, top_logprobs) in zip(
+                logprobs["tokens"], logprobs["token_logprobs"], logprobs["top_logprobs"]
+            )
+        ],
+        "refusal": None,
+    }
+
+
 def _convert_text_completion_to_chat(
     completion: llama_types.Completion,
 ) -> llama_types.ChatCompletion:
@@ -275,7 +348,9 @@ def _convert_text_completion_to_chat(
                     "role": "assistant",
                     "content": completion["choices"][0]["text"],
                 },
-                "logprobs": completion["choices"][0]["logprobs"],
+                "logprobs": _convert_text_completion_logprobs_to_chat(
+                    completion["choices"][0]["logprobs"]
+                ),
                 "finish_reason": completion["choices"][0]["finish_reason"],
             }
         ],
@@ -319,7 +394,9 @@ def _convert_text_completion_chunks_to_chat(
                         if chunk["choices"][0]["finish_reason"] is None
                         else {}
                     ),
-                    "logprobs": chunk["choices"][0]["logprobs"],
+                    "logprobs": _convert_text_completion_logprobs_to_chat(
+                        chunk["choices"][0]["logprobs"]
+                    ),
                     "finish_reason": chunk["choices"][0]["finish_reason"],
                 }
             ],
@@ -336,7 +413,9 @@ def _convert_completion_to_chat(
     llama_types.CreateChatCompletionResponse, Iterator[llama_types.ChatCompletionChunk]
 ]:
     if stream:
-        chunks: Iterator[llama_types.CreateCompletionStreamResponse] = completion_or_chunks  # type: ignore
+        chunks: Iterator[llama_types.CreateCompletionStreamResponse] = (
+            completion_or_chunks  # type: ignore
+        )
         return _convert_text_completion_chunks_to_chat(chunks)
     else:
         completion: llama_types.Completion = completion_or_chunks  # type: ignore
@@ -382,7 +461,9 @@ def _convert_completion_to_chat_function(
                             }
                         ],
                     },
-                    "logprobs": completion["choices"][0]["logprobs"],
+                    "logprobs": _convert_text_completion_logprobs_to_chat(
+                        completion["choices"][0]["logprobs"]
+                    ),
                     "finish_reason": "tool_calls",
                 }
             ],
@@ -390,7 +471,9 @@ def _convert_completion_to_chat_function(
         }
         return chat_completion
     else:
-        chunks: Iterator[llama_types.CreateCompletionStreamResponse] = completion_or_chunks  # type: ignore
+        chunks: Iterator[llama_types.CreateCompletionStreamResponse] = (
+            completion_or_chunks  # type: ignore
+        )
 
         def _stream_response_to_function_stream(
             chunks: Iterator[llama_types.CreateCompletionStreamResponse],
@@ -435,7 +518,9 @@ def _stream_response_to_function_stream(
                             {
                                 "index": 0,
                                 "finish_reason": None,
-                                "logprobs": chunk["choices"][0]["logprobs"],
+                                "logprobs": _convert_text_completion_logprobs_to_chat(
+                                    chunk["choices"][0]["logprobs"]
+                                ),
                                 "delta": {
                                     "role": None,
                                     "content": None,
@@ -472,7 +557,9 @@ def _stream_response_to_function_stream(
                         {
                             "index": 0,
                             "finish_reason": None,
-                            "logprobs": chunk["choices"][0]["logprobs"],
+                            "logprobs": _convert_text_completion_logprobs_to_chat(
+                                chunk["choices"][0]["logprobs"]
+                            ),
                             "delta": {
                                 "role": None,
                                 "content": None,
@@ -567,6 +654,7 @@ def chat_completion_handler(
             function_call=function_call,
             tools=tools,
             tool_choice=tool_choice,
+            **kwargs,
         )
         prompt = llama.tokenize(
             result.prompt.encode("utf-8"),
@@ -670,7 +758,7 @@ def chat_completion_handler(
 
 
 def hf_autotokenizer_to_chat_formatter(
-    pretrained_model_name_or_path: Union[str, os.PathLike[str]]
+    pretrained_model_name_or_path: Union[str, os.PathLike[str]],
 ) -> ChatFormatter:
     # https://huggingface.co/docs/transformers/main/chat_templating
     # https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1#instruction-format
@@ -684,7 +772,9 @@ def format_autotokenizer(
         **kwargs: Any,
     ) -> ChatFormatterResponse:
         tokenizer.use_default_system_prompt = False  # type: ignore
-        prompt: str = tokenizer.apply_chat_template(messages, tokenize=False)  # type: ignore
+        prompt: str = tokenizer.apply_chat_template(  # type: ignore
+            messages, tokenize=False, **kwargs
+        )
         assert isinstance(prompt, str)
         # Return formatted prompt and eos token by default
         return ChatFormatterResponse(
@@ -695,7 +785,7 @@ def format_autotokenizer(
 
 
 def hf_autotokenizer_to_chat_completion_handler(
-    pretrained_model_name_or_path: Union[str, os.PathLike[str]]
+    pretrained_model_name_or_path: Union[str, os.PathLike[str]],
 ) -> LlamaChatCompletionHandler:
     chat_formatter = hf_autotokenizer_to_chat_formatter(pretrained_model_name_or_path)
     return chat_formatter_to_chat_completion_handler(chat_formatter)
@@ -741,6 +831,7 @@ def format_tokenizer_config(
             messages=messages,
             bos_token=bos_token,
             eos_token=eos_token,
+            **kwargs,
         )
         return ChatFormatterResponse(
             prompt=prompt, stop=[eos_token, bos_token], added_special=True
@@ -1520,9 +1611,9 @@ def prepare_messages_for_inference(
                 message["name"] = f"functions.{message['name']}"
             # Function call requests by assistant
             if "function_call" in message:
-                message["function_call"][
-                    "name"
-                ] = f"functions.{message['function_call']['name']}"
+                message["function_call"]["name"] = (
+                    f"functions.{message['function_call']['name']}"
+                )
             all_messages.append(message)
 
         all_messages.append(
@@ -1600,7 +1691,9 @@ def message_to_str(msg: llama_types.ChatCompletionRequestMessage):
             logits_processor=logits_processor,
             grammar=grammar,
         )
-        return _convert_completion_to_chat(completion_or_completion_chunks, stream=stream)  # type: ignore
+        return _convert_completion_to_chat(
+            completion_or_completion_chunks, stream=stream
+        )  # type: ignore
 
     if function_call is None or (
         isinstance(function_call, str) and function_call == "auto"
@@ -1716,7 +1809,9 @@ def message_to_str(msg: llama_types.ChatCompletionRequestMessage):
                         }
                     ],
                 },
-                "logprobs": completion["choices"][0]["logprobs"],
+                "logprobs": _convert_text_completion_logprobs_to_chat(
+                    completion["choices"][0]["logprobs"]
+                ),
                 "finish_reason": "tool_calls",
             }
         ],
@@ -1757,9 +1852,9 @@ def functionary_v1_v2_chat_handler(
     SYSTEM_MESSAGE = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. The assistant calls functions with appropriate input when necessary"""
 
     tokenizer = llama.tokenizer_
-    assert hasattr(
-        tokenizer, "hf_tokenizer"
-    ), "Please provide a valid hf_tokenizer_path from https://huggingface.co/meetkai when initializing the Llama class"
+    assert hasattr(tokenizer, "hf_tokenizer"), (
+        "Please provide a valid hf_tokenizer_path from https://huggingface.co/meetkai when initializing the Llama class"
+    )
     from transformers import AutoTokenizer
 
     if "<|START_OF_FUNCTION_CALL|>" in tokenizer.hf_tokenizer.additional_special_tokens:
@@ -1909,9 +2004,9 @@ def prepare_messages_for_inference(
                 message["name"] = f"functions.{message['name']}"
             # Function call requests by assistant
             if "function_call" in message:
-                message["function_call"][
-                    "name"
-                ] = f"functions.{message['function_call']['name']}"
+                message["function_call"]["name"] = (
+                    f"functions.{message['function_call']['name']}"
+                )
             all_messages.append(message)
 
         if version == "v1":
@@ -1973,7 +2068,9 @@ def prepare_messages_for_inference(
             completion_or_completion_chunks["choices"][0]["text"] = (
                 completion_or_completion_chunks["choices"][0]["text"].lstrip()
             )
-        return _convert_completion_to_chat(completion_or_completion_chunks, stream=stream)  # type: ignore
+        return _convert_completion_to_chat(
+            completion_or_completion_chunks, stream=stream
+        )  # type: ignore
 
     def get_grammar(function_call):
         function_body = None
@@ -2128,7 +2225,9 @@ def generate_streaming(tools, functions, function_call, prompt):
                         choices=[
                             {
                                 "index": 0,
-                                "logprobs": chunk["choices"][0]["logprobs"],
+                                "logprobs": _convert_text_completion_logprobs_to_chat(
+                                    chunk["choices"][0]["logprobs"]
+                                ),
                                 "delta": {
                                     "role": None,
                                     "content": None,
@@ -2230,7 +2329,9 @@ def generate_streaming(tools, functions, function_call, prompt):
                         choices=[
                             {
                                 "index": 0,
-                                "logprobs": chunk["choices"][0]["logprobs"],
+                                "logprobs": _convert_text_completion_logprobs_to_chat(
+                                    chunk["choices"][0]["logprobs"]
+                                ),
                                 "delta": {
                                     "role": "assistant",
                                     "content": None,
@@ -2268,9 +2369,9 @@ def generate_streaming(tools, functions, function_call, prompt):
                                         choices=[
                                             {
                                                 "index": 0,
-                                                "logprobs": chunk["choices"][0][
-                                                    "logprobs"
-                                                ],
+                                                "logprobs": _convert_text_completion_logprobs_to_chat(
+                                                    chunk["choices"][0]["logprobs"]
+                                                ),
                                                 "delta": {
                                                     "role": "assistant",
                                                     "content": buffer.pop(0),
@@ -2293,7 +2394,9 @@ def generate_streaming(tools, functions, function_call, prompt):
                                 choices=[
                                     {
                                         "index": 0,
-                                        "logprobs": chunk["choices"][0]["logprobs"],
+                                        "logprobs": _convert_text_completion_logprobs_to_chat(
+                                            chunk["choices"][0]["logprobs"]
+                                        ),
                                         "delta": {
                                             "role": "assistant",
                                             "content": (
@@ -2379,7 +2482,9 @@ def generate_streaming(tools, functions, function_call, prompt):
                                 choices=[
                                     {
                                         "index": 0,
-                                        "logprobs": chunk["choices"][0]["logprobs"],
+                                        "logprobs": _convert_text_completion_logprobs_to_chat(
+                                            chunk["choices"][0]["logprobs"]
+                                        ),
                                         "delta": {
                                             "role": None,
                                             "content": None,
@@ -2613,7 +2718,9 @@ def generate_streaming(tools, functions, function_call, prompt):
             choices=[
                 {
                     "index": 0,
-                    "logprobs": completion["choices"][0]["logprobs"],
+                    "logprobs": _convert_text_completion_logprobs_to_chat(
+                        completion["choices"][0]["logprobs"]
+                    ),
                     "message": {
                         "role": "assistant",
                         "content": None if content == "" else content,
@@ -2628,7 +2735,7 @@ def generate_streaming(tools, functions, function_call, prompt):
 
 class Llava15ChatHandler:
     DEFAULT_SYSTEM_MESSAGE: Optional[str] = (
-        "A chat between a curious human and an artificial intelligence assistant.  The assistant gives helpful, detailed, and polite answers to the human's questions."
+        "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions."
     )
 
     CHAT_FORMAT = (
@@ -2667,70 +2774,80 @@ class Llava15ChatHandler:
     )
 
     def __init__(self, clip_model_path: str, verbose: bool = True):
-        import llama_cpp.llava_cpp as llava_cpp
+        import llama_cpp.mtmd_cpp as mtmd_cpp
 
         self.clip_model_path = clip_model_path
         self.verbose = verbose
-
-        self._llava_cpp = llava_cpp  # TODO: Fix
+        self._mtmd_cpp = mtmd_cpp
         self._exit_stack = ExitStack()
-        self._last_image_embed: Optional[
-            llava_cpp.CtypesPointer[llava_cpp.llava_image_embed]
-        ] = None
-        self._last_image_hash: Optional[int] = None
+        self.mtmd_ctx: Optional[mtmd_cpp.mtmd_context_p] = None
 
         if not os.path.exists(clip_model_path):
             raise ValueError(f"Clip model path does not exist: {clip_model_path}")
 
-        with suppress_stdout_stderr(disable=self.verbose):
-            clip_ctx = self._llava_cpp.clip_model_load(self.clip_model_path.encode(), 0)
+    def _init_mtmd_context(self, llama_model: llama.Llama):
+        """Initialize mtmd context with the llama model."""
+        if self.mtmd_ctx is not None:
+            return  # Already initialized
 
-            if clip_ctx is None:
-                raise ValueError(f"Failed to load clip model: {clip_model_path}")
+        with suppress_stdout_stderr(disable=self.verbose):
+            # Get default parameters
+            ctx_params = self._mtmd_cpp.mtmd_context_params_default()
+            ctx_params.use_gpu = True  # TODO: Make this configurable
+            ctx_params.print_timings = self.verbose
+            ctx_params.n_threads = llama_model.n_threads
+            ctx_params.flash_attn_type = (
+                llama_cpp.LLAMA_FLASH_ATTN_TYPE_ENABLED
+                if (
+                    llama_model.context_params.flash_attn_type
+                    == llama_cpp.LLAMA_FLASH_ATTN_TYPE_ENABLED
+                )
+                else llama_cpp.LLAMA_FLASH_ATTN_TYPE_DISABLED
+            )
 
-            self.clip_ctx = clip_ctx
+            # Initialize mtmd context
+            self.mtmd_ctx = self._mtmd_cpp.mtmd_init_from_file(
+                self.clip_model_path.encode(), llama_model.model, ctx_params
+            )
 
-            def clip_free():
-                with suppress_stdout_stderr(disable=self.verbose):
-                    self._llava_cpp.clip_free(self.clip_ctx)
+            if self.mtmd_ctx is None:
+                raise ValueError(
+                    f"Failed to load mtmd context from: {self.clip_model_path}"
+                )
 
-            self._exit_stack.callback(clip_free)
+            # Check if vision is supported
+            if not self._mtmd_cpp.mtmd_support_vision(self.mtmd_ctx):
+                raise ValueError("Vision is not supported by this model")
 
-        def last_image_embed_free():
-            with suppress_stdout_stderr(disable=self.verbose):
-                if self._last_image_embed is not None:
-                    self._llava_cpp.llava_image_embed_free(self._last_image_embed)
-                    self._last_image_embed = None
+            def mtmd_free():
+                with suppress_stdout_stderr(disable=self.verbose):
+                    if self.mtmd_ctx is not None:
+                        self._mtmd_cpp.mtmd_free(self.mtmd_ctx)
+                        self.mtmd_ctx = None
 
-        self._exit_stack.callback(last_image_embed_free)
+            self._exit_stack.callback(mtmd_free)
 
     def load_image(self, image_url: str) -> bytes:
         return self._load_image(image_url)
 
-    def _embed_image_bytes(self, image_bytes: bytes, n_threads_batch: int = 1):
-        if (
-            self._last_image_embed is not None
-            and self._last_image_hash is not None
-            and hash(image_bytes) == self._last_image_hash
-        ):
-            return self._last_image_embed
+    def _create_bitmap_from_bytes(self, image_bytes: bytes):
+        """Create mtmd_bitmap from image bytes."""
+        if self.mtmd_ctx is None:
+            raise ValueError("mtmd context not initialized")
+
         with suppress_stdout_stderr(disable=self.verbose):
-            # Free the previous image embed
-            if self._last_image_embed is not None:
-                self._llava_cpp.llava_image_embed_free(self._last_image_embed)
-                self._last_image_embed = None
-                self._last_image_hash = None
-            embed = self._llava_cpp.llava_image_embed_make_with_bytes(
-                self.clip_ctx,
-                n_threads_batch,
-                (ctypes.c_uint8 * len(image_bytes)).from_buffer(
-                    bytearray(image_bytes)
-                ),
+            # Create bitmap from buffer using helper function
+            bitmap = self._mtmd_cpp.mtmd_helper_bitmap_init_from_buf(
+                self.mtmd_ctx,
+                (ctypes.c_uint8 * len(image_bytes)).from_buffer(bytearray(image_bytes)),
                 len(image_bytes),
+                False,
             )
-            self._last_image_embed = embed
-            self._last_image_hash = hash(image_bytes)
-            return embed
+
+            if bitmap is None:
+                raise ValueError("Failed to create bitmap from image bytes")
+
+            return bitmap
 
     def __call__(
         self,
@@ -2771,7 +2888,9 @@ def __call__(
         llama_types.CreateChatCompletionResponse,
         Iterator[llama_types.CreateChatCompletionStreamResponse],
     ]:
-        assert self.clip_ctx is not None
+        # Initialize mtmd context
+        self._init_mtmd_context(llama)
+        assert self.mtmd_ctx is not None
 
         system_prompt = _get_system_message(messages)
         if system_prompt == "" and self.DEFAULT_SYSTEM_MESSAGE is not None:
@@ -2786,54 +2905,140 @@ def __call__(
             trim_blocks=True,
             lstrip_blocks=True,
         ).from_string(self.CHAT_FORMAT)
+
+        # Get the default media marker
+        media_marker = self._mtmd_cpp.mtmd_default_marker().decode("utf-8")
+
+        # Replace image URLs with media markers in the template
         text = template.render(
             messages=messages,
             add_generation_prompt=True,
             eos_token=llama.detokenize([llama.token_eos()]),
             bos_token=llama.detokenize([llama.token_bos()]),
         )
-        split_text = self.split_text_on_image_urls(text, image_urls)
+
+        # Replace image URLs in text with media markers
+        for image_url in image_urls:
+            text = text.replace(image_url, media_marker)
 
         if self.verbose:
             print(text, file=sys.stderr)
 
+        # Create bitmaps from images
+        bitmaps = []
+        bitmap_cleanup = []
+        try:
+            for image_url in image_urls:
+                image_bytes = self.load_image(image_url)
+                bitmap = self._create_bitmap_from_bytes(image_bytes)
+                bitmaps.append(bitmap)
+                bitmap_cleanup.append(bitmap)
+
+            # Create input text structure
+            input_text = self._mtmd_cpp.mtmd_input_text()
+            input_text.text = text.encode("utf-8")
+            input_text.add_special = True
+            input_text.parse_special = True
+
+            # Create input chunks
+            chunks = self._mtmd_cpp.mtmd_input_chunks_init()
+            if chunks is None:
+                raise ValueError("Failed to create input chunks")
 
-        # Evaluate prompt
-        llama.reset()
-        llama._ctx.kv_cache_clear()
-        for type_, value in split_text:
-            if type_ == "text":
-                tokens = llama.tokenize(
-                    value.encode("utf8"), add_bos=False, special=True
+            try:
+                # Tokenize text and images together
+                bitmap_array = (self._mtmd_cpp.mtmd_bitmap_p_ctypes * len(bitmaps))(
+                    *bitmaps
                 )
-                if llama.n_tokens + len(tokens) > llama.n_ctx():
-                    raise ValueError(
-                        f"Prompt exceeds n_ctx: {llama.n_tokens + len(tokens)} > {llama.n_ctx()}"
-                    )
-                llama.eval(tokens)
-            else:
-                image_bytes = self.load_image(value)
-                embed = self._embed_image_bytes(image_bytes, llama.context_params.n_threads_batch)
-                if llama.n_tokens + embed.contents.n_image_pos > llama.n_ctx():
-                    raise ValueError(
-                        f"Prompt exceeds n_ctx: {llama.n_tokens + embed.contents.n_image_pos} > {llama.n_ctx()}"
-                    )
-                n_past = ctypes.c_int(llama.n_tokens)
-                n_past_p = ctypes.pointer(n_past)
-                with suppress_stdout_stderr(disable=self.verbose):
-                    self._llava_cpp.llava_eval_image_embed(
-                        llama.ctx,
-                        embed,
-                        llama.n_batch,
-                        n_past_p,
-                    )
-                # Required to avoid issues with hf tokenizer
-                llama.input_ids[llama.n_tokens : n_past.value] = -1
-                llama.n_tokens = n_past.value
+                result = self._mtmd_cpp.mtmd_tokenize(
+                    self.mtmd_ctx,
+                    chunks,
+                    ctypes.byref(input_text),
+                    bitmap_array,
+                    len(bitmaps),
+                )
+
+                if result != 0:
+                    raise ValueError(f"Failed to tokenize input: error code {result}")
+
+                # Reset llama context
+                llama.reset()
+                llama._ctx.kv_cache_clear()
+
+                # Process each chunk
+                n_past = llama_cpp.llama_pos(0)
+                n_chunks = self._mtmd_cpp.mtmd_input_chunks_size(chunks)
+
+                for i in range(n_chunks):
+                    chunk = self._mtmd_cpp.mtmd_input_chunks_get(chunks, i)
+                    if chunk is None:
+                        continue
+
+                    chunk_type = self._mtmd_cpp.mtmd_input_chunk_get_type(chunk)
+
+                    if chunk_type == self._mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_TEXT:
+                        # Handle text chunk
+                        n_tokens_out = ctypes.c_size_t()
+                        tokens_ptr = self._mtmd_cpp.mtmd_input_chunk_get_tokens_text(
+                            chunk, ctypes.byref(n_tokens_out)
+                        )
+
+                        if tokens_ptr and n_tokens_out.value > 0:
+                            # Convert ctypes array to Python list
+                            tokens = [tokens_ptr[j] for j in range(n_tokens_out.value)]
+
+                            if llama.n_tokens + len(tokens) > llama.n_ctx():
+                                raise ValueError(
+                                    f"Prompt exceeds n_ctx: {llama.n_tokens + len(tokens)} > {llama.n_ctx()}"
+                                )
+                            llama.eval(tokens)
+
+                    elif chunk_type in [
+                        self._mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_IMAGE,
+                        self._mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_AUDIO,
+                    ]:
+                        # Handle image/audio chunk using helper
+                        chunk_n_tokens = self._mtmd_cpp.mtmd_input_chunk_get_n_tokens(
+                            chunk
+                        )
+
+                        if llama.n_tokens + chunk_n_tokens > llama.n_ctx():
+                            raise ValueError(
+                                f"Prompt exceeds n_ctx: {llama.n_tokens + chunk_n_tokens} > {llama.n_ctx()}"
+                            )
+
+                        new_n_past = llama_cpp.llama_pos(0)
+                        result = self._mtmd_cpp.mtmd_helper_eval_chunk_single(
+                            self.mtmd_ctx,
+                            llama._ctx.ctx,
+                            chunk,
+                            llama_cpp.llama_pos(llama.n_tokens),
+                            llama_cpp.llama_seq_id(0),
+                            llama.n_batch,
+                            False,  # logits_last
+                            ctypes.byref(new_n_past),
+                        )
+
+                        if result != 0:
+                            raise ValueError(
+                                f"Failed to evaluate chunk: error code {result}"
+                            )
+
+                        # Update llama's token count
+                        llama.n_tokens = new_n_past.value
 
-        # Get prompt tokens to avoid a cache miss
-        prompt = llama.input_ids[: llama.n_tokens].tolist()
+                # Get prompt tokens to avoid a cache miss
+                prompt = llama.input_ids[: llama.n_tokens].tolist()
 
+            finally:
+                self._mtmd_cpp.mtmd_input_chunks_free(chunks)
+
+        finally:
+            # Cleanup bitmaps
+            for bitmap in bitmap_cleanup:
+                self._mtmd_cpp.mtmd_bitmap_free(bitmap)
+
+        # Handle response format and tools (same as before)
         if response_format is not None and response_format["type"] == "json_object":
             grammar = _grammar_for_response_format(response_format)
 
@@ -2908,6 +3113,7 @@ def __call__(
             grammar=grammar,
             logit_bias=logit_bias,
         )
+
         if tool is not None:
             tool_name = tool["function"]["name"]
             return _convert_completion_to_chat_function(
@@ -2951,6 +3157,8 @@ def get_image_urls(messages: List[llama_types.ChatCompletionRequestMessage]):
 
     @staticmethod
     def split_text_on_image_urls(text: str, image_urls: List[str]):
+        """This method is no longer used in the new implementation."""
+
         def find_first(s: str, substrs: List[str]):
             for i, substr in enumerate(substrs):
                 pos = s.find(substr)
@@ -3058,9 +3266,518 @@ def from_pretrained(
         )
 
 
+class MTMDChatHandler:
+    def __init__(
+        self, clip_model_path: str, verbose: bool = True, use_gpu: bool = True
+    ):
+        import llama_cpp.mtmd_cpp as mtmd_cpp
+
+        self.clip_model_path = clip_model_path
+        self.verbose = verbose
+        self.use_gpu = use_gpu
+        self._mtmd_cpp = mtmd_cpp
+        self._exit_stack = ExitStack()
+        self.mtmd_ctx: Optional[mtmd_cpp.mtmd_context_p] = None
+
+        if not os.path.exists(clip_model_path):
+            raise ValueError(f"Clip model path does not exist: {clip_model_path}")
+
+    def _init_mtmd_context(self, llama_model: llama.Llama):
+        self.verbose = llama_model.verbose
+        if self.mtmd_ctx is not None:
+            return
+
+        with suppress_stdout_stderr(disable=self.verbose):
+            ctx_params = self._mtmd_cpp.mtmd_context_params_default()
+            ctx_params.use_gpu = self.use_gpu
+            ctx_params.print_timings = self.verbose
+            ctx_params.n_threads = llama_model.n_threads
+            ctx_params.flash_attn_type = (
+                llama_cpp.LLAMA_FLASH_ATTN_TYPE_ENABLED
+                if (
+                    llama_model.context_params.flash_attn_type
+                    == llama_cpp.LLAMA_FLASH_ATTN_TYPE_ENABLED
+                )
+                else llama_cpp.LLAMA_FLASH_ATTN_TYPE_DISABLED
+            )
+
+            self.mtmd_ctx = self._mtmd_cpp.mtmd_init_from_file(
+                self.clip_model_path.encode(), llama_model.model, ctx_params
+            )
+
+            if self.mtmd_ctx is None:
+                raise ValueError(
+                    f"Failed to load mtmd context from: {self.clip_model_path}"
+                )
+
+            if not self._mtmd_cpp.mtmd_support_vision(self.mtmd_ctx):
+                raise ValueError("Vision is not supported by this model")
+
+            def mtmd_free():
+                with suppress_stdout_stderr(disable=self.verbose):
+                    if self.mtmd_ctx is not None:
+                        self._mtmd_cpp.mtmd_free(self.mtmd_ctx)
+                        self.mtmd_ctx = None
+
+            self._exit_stack.callback(mtmd_free)
+
+    def load_image(self, image_url: str) -> bytes:
+        return self._load_image(image_url)
+
+    def _create_bitmap_from_bytes(self, image_bytes: bytes):
+        if self.mtmd_ctx is None:
+            raise ValueError("mtmd context not initialized")
+
+        with suppress_stdout_stderr(disable=self.verbose):
+            bitmap = self._mtmd_cpp.mtmd_helper_bitmap_init_from_buf(
+                self.mtmd_ctx,
+                (ctypes.c_uint8 * len(image_bytes)).from_buffer(bytearray(image_bytes)),
+                len(image_bytes),
+                False,
+            )
+
+            if bitmap is None:
+                raise ValueError("Failed to create bitmap from image bytes")
+
+            return bitmap
+
+    def _get_chat_template(self, llama_model: llama.Llama) -> str:
+        chat_template = llama_model.metadata.get("tokenizer.chat_template")
+        if not isinstance(chat_template, str) or chat_template == "":
+            raise ValueError(
+                f"{self.__class__.__name__} requires tokenizer.chat_template metadata"
+            )
+        return chat_template
+
+    def _get_template_messages(
+        self,
+        messages: List[llama_types.ChatCompletionRequestMessage],
+        media_marker: str,
+    ) -> List[Any]:
+        return [
+            self._convert_message_for_template(message, media_marker)
+            for message in messages
+        ]
+
+    @classmethod
+    def _convert_message_for_template(
+        cls,
+        message: llama_types.ChatCompletionRequestMessage,
+        media_marker: str,
+    ) -> Dict[str, Any]:
+        message_dict = dict(message)
+        content = message_dict.get("content")
+        if isinstance(content, list):
+            message_dict["content"] = [
+                cls._convert_content_part_for_template(part, media_marker)
+                for part in content
+            ]
+        return message_dict
+
+    @staticmethod
+    def _convert_content_part_for_template(
+        part: Any,
+        media_marker: str,
+    ) -> Any:
+        if isinstance(part, dict) and part.get("type") == "image_url":
+            return {"type": "text", "text": media_marker}
+        return part
+
+    @staticmethod
+    def _decode_token_piece(piece: Any) -> str:
+        if isinstance(piece, bytes):
+            return piece.decode("utf-8", errors="ignore")
+        return str(piece)
+
+    def _postprocess_template_text(
+        self,
+        text: str,
+        image_urls: List[str],
+        media_marker: str,
+    ) -> str:
+        for image_url in image_urls:
+            text = text.replace(image_url, media_marker)
+        return text
+
+    def __call__(
+        self,
+        *,
+        llama: llama.Llama,
+        messages: List[llama_types.ChatCompletionRequestMessage],
+        functions: Optional[List[llama_types.ChatCompletionFunction]] = None,
+        function_call: Optional[llama_types.ChatCompletionRequestFunctionCall] = None,
+        tools: Optional[List[llama_types.ChatCompletionTool]] = None,
+        tool_choice: Optional[llama_types.ChatCompletionToolChoiceOption] = None,
+        temperature: float = 0.2,
+        top_p: float = 0.95,
+        top_k: int = 40,
+        min_p: float = 0.05,
+        typical_p: float = 1.0,
+        stream: bool = False,
+        stop: Optional[Union[str, List[str]]] = [],
+        seed: Optional[int] = None,
+        response_format: Optional[
+            llama_types.ChatCompletionRequestResponseFormat
+        ] = None,
+        max_tokens: Optional[int] = None,
+        presence_penalty: float = 0.0,
+        frequency_penalty: float = 0.0,
+        repeat_penalty: float = 1.1,
+        tfs_z: float = 1.0,
+        mirostat_mode: int = 0,
+        mirostat_tau: float = 5.0,
+        mirostat_eta: float = 0.1,
+        model: Optional[str] = None,
+        logits_processor: Optional[llama.LogitsProcessorList] = None,
+        grammar: Optional[llama.LlamaGrammar] = None,
+        logit_bias: Optional[Dict[str, float]] = None,
+        logprobs: Optional[bool] = None,
+        top_logprobs: Optional[int] = None,
+        **kwargs,  # type: ignore
+    ) -> Union[
+        llama_types.CreateChatCompletionResponse,
+        Iterator[llama_types.CreateChatCompletionStreamResponse],
+    ]:
+        self._init_mtmd_context(llama)
+        assert self.mtmd_ctx is not None
+
+        image_urls = self.get_image_urls(messages)
+        media_marker = self._mtmd_cpp.mtmd_default_marker().decode("utf-8")
+        template_env = ImmutableSandboxedEnvironment(
+            trim_blocks=True,
+            lstrip_blocks=True,
+            extensions=[
+                Jinja2ChatFormatter.IgnoreGenerationTags,
+                jinja2.ext.loopcontrols,
+            ],
+        )
+        template_env.filters["tojson"] = Jinja2ChatFormatter.tojson
+        template = template_env.from_string(self._get_chat_template(llama))
+
+        def raise_exception(message: str):
+            raise ValueError(message)
+
+        text = template.render(
+            messages=self._get_template_messages(messages, media_marker),
+            add_generation_prompt=True,
+            eos_token=self._decode_token_piece(llama.detokenize([llama.token_eos()])),
+            bos_token=self._decode_token_piece(llama.detokenize([llama.token_bos()])),
+            raise_exception=raise_exception,
+            functions=functions,
+            function_call=function_call,
+            tools=tools,
+            tool_choice=tool_choice,
+            strftime_now=Jinja2ChatFormatter.strftime_now,
+            **kwargs,
+        )
+        text = self._postprocess_template_text(text, image_urls, media_marker)
+
+        if self.verbose:
+            print(text, file=sys.stderr)
+
+        bitmaps = []
+        bitmap_cleanup = []
+        try:
+            for image_url in image_urls:
+                image_bytes = self.load_image(image_url)
+                bitmap = self._create_bitmap_from_bytes(image_bytes)
+                bitmaps.append(bitmap)
+                bitmap_cleanup.append(bitmap)
+
+            input_text = self._mtmd_cpp.mtmd_input_text()
+            input_text.text = text.encode("utf-8")
+            input_text.add_special = True
+            input_text.parse_special = True
+
+            chunks = self._mtmd_cpp.mtmd_input_chunks_init()
+            if chunks is None:
+                raise ValueError("Failed to create input chunks")
+
+            try:
+                bitmap_array = (self._mtmd_cpp.mtmd_bitmap_p_ctypes * len(bitmaps))(
+                    *bitmaps
+                )
+                result = self._mtmd_cpp.mtmd_tokenize(
+                    self.mtmd_ctx,
+                    chunks,
+                    ctypes.byref(input_text),
+                    bitmap_array,
+                    len(bitmaps),
+                )
+
+                if result != 0:
+                    raise ValueError(f"Failed to tokenize input: error code {result}")
+
+                llama.reset()
+                llama._ctx.kv_cache_clear()
+
+                n_chunks = self._mtmd_cpp.mtmd_input_chunks_size(chunks)
+
+                for i in range(n_chunks):
+                    chunk = self._mtmd_cpp.mtmd_input_chunks_get(chunks, i)
+                    if chunk is None:
+                        continue
+
+                    chunk_type = self._mtmd_cpp.mtmd_input_chunk_get_type(chunk)
+
+                    if chunk_type == self._mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_TEXT:
+                        n_tokens_out = ctypes.c_size_t()
+                        tokens_ptr = self._mtmd_cpp.mtmd_input_chunk_get_tokens_text(
+                            chunk, ctypes.byref(n_tokens_out)
+                        )
+
+                        if tokens_ptr and n_tokens_out.value > 0:
+                            tokens = [tokens_ptr[j] for j in range(n_tokens_out.value)]
+
+                            if llama.n_tokens + len(tokens) > llama.n_ctx():
+                                raise ValueError(
+                                    f"Prompt exceeds n_ctx: {llama.n_tokens + len(tokens)} > {llama.n_ctx()}"
+                                )
+                            llama.eval(tokens)
+
+                    elif chunk_type in [
+                        self._mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_IMAGE,
+                        self._mtmd_cpp.MTMD_INPUT_CHUNK_TYPE_AUDIO,
+                    ]:
+                        chunk_n_tokens = self._mtmd_cpp.mtmd_input_chunk_get_n_tokens(
+                            chunk
+                        )
+
+                        if llama.n_tokens + chunk_n_tokens > llama.n_ctx():
+                            raise ValueError(
+                                f"Prompt exceeds n_ctx: {llama.n_tokens + chunk_n_tokens} > {llama.n_ctx()}"
+                            )
+
+                        new_n_past = llama_cpp.llama_pos(0)
+                        result = self._mtmd_cpp.mtmd_helper_eval_chunk_single(
+                            self.mtmd_ctx,
+                            llama._ctx.ctx,
+                            chunk,
+                            llama_cpp.llama_pos(llama.n_tokens),
+                            llama_cpp.llama_seq_id(0),
+                            llama.n_batch,
+                            False,  # logits_last
+                            ctypes.byref(new_n_past),
+                        )
+
+                        if result != 0:
+                            raise ValueError(
+                                f"Failed to evaluate chunk: error code {result}"
+                            )
+
+                        llama.n_tokens = new_n_past.value
+
+                prompt = llama.input_ids[: llama.n_tokens].tolist()
+
+            finally:
+                self._mtmd_cpp.mtmd_input_chunks_free(chunks)
+
+        finally:
+            for bitmap in bitmap_cleanup:
+                self._mtmd_cpp.mtmd_bitmap_free(bitmap)
+
+        if response_format is not None and response_format["type"] == "json_object":
+            grammar = _grammar_for_response_format(response_format)
+
+        if functions is not None:
+            tools = [
+                {
+                    "type": "function",
+                    "function": function,
+                }
+                for function in functions
+            ]
+
+        if function_call is not None:
+            if isinstance(function_call, str) and (
+                function_call == "none" or function_call == "auto"
+            ):
+                tool_choice = function_call
+            if isinstance(function_call, dict) and "name" in function_call:
+                tool_choice = {
+                    "type": "function",
+                    "function": {
+                        "name": function_call["name"],
+                    },
+                }
+
+        tool = None
+        if (
+            tool_choice is not None
+            and isinstance(tool_choice, dict)
+            and tools is not None
+        ):
+            name = tool_choice["function"]["name"]
+            tool = next((t for t in tools if t["function"]["name"] == name), None)
+            if tool is None:
+                raise ValueError(f"Tool choice '{name}' not found in tools.")
+            schema = tool["function"]["parameters"]
+            try:
+                grammar = llama_grammar.LlamaGrammar.from_json_schema(
+                    json.dumps(schema), verbose=llama.verbose
+                )
+            except Exception as e:
+                if llama.verbose:
+                    print(str(e), file=sys.stderr)
+                grammar = llama_grammar.LlamaGrammar.from_string(
+                    llama_grammar.JSON_GBNF, verbose=llama.verbose
+                )
+
+        completion_or_chunks = llama.create_completion(
+            prompt=prompt,
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+            min_p=min_p,
+            typical_p=typical_p,
+            logprobs=top_logprobs if logprobs else None,
+            stream=stream,
+            stop=stop,
+            seed=seed,
+            max_tokens=max_tokens,
+            presence_penalty=presence_penalty,
+            frequency_penalty=frequency_penalty,
+            repeat_penalty=repeat_penalty,
+            tfs_z=tfs_z,
+            mirostat_mode=mirostat_mode,
+            mirostat_tau=mirostat_tau,
+            mirostat_eta=mirostat_eta,
+            model=model,
+            logits_processor=logits_processor,
+            grammar=grammar,
+            logit_bias=logit_bias,
+        )
+
+        if tool is not None:
+            tool_name = tool["function"]["name"]
+            return _convert_completion_to_chat_function(
+                tool_name, completion_or_chunks, stream
+            )
+        return _convert_completion_to_chat(completion_or_chunks, stream=stream)
+
+    @staticmethod
+    def _load_image(image_url: str) -> bytes:
+        if image_url.startswith("data:"):
+            import base64
+
+            image_bytes = base64.b64decode(image_url.split(",")[1])
+            return image_bytes
+        else:
+            import urllib.request
+
+            with urllib.request.urlopen(image_url) as f:
+                image_bytes = f.read()
+                return image_bytes
+
+    @staticmethod
+    def get_image_urls(messages: List[llama_types.ChatCompletionRequestMessage]):
+        image_urls: List[str] = []
+        for message in messages:
+            if message["role"] == "user":
+                if message["content"] is None:
+                    continue
+                for content in message["content"]:
+                    if isinstance(content, dict) and "type" in content:
+                        if content["type"] == "image_url":
+                            if (
+                                isinstance(content["image_url"], dict)
+                                and "url" in content["image_url"]
+                            ):
+                                image_urls.append(content["image_url"]["url"])
+                            else:
+                                image_urls.append(content["image_url"])
+        return image_urls
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        repo_id: str,
+        filename: Optional[str],
+        local_dir: Optional[Union[str, os.PathLike[str]]] = None,
+        local_dir_use_symlinks: Union[bool, Literal["auto"]] = "auto",
+        cache_dir: Optional[Union[str, os.PathLike[str]]] = None,
+        **kwargs: Any,
+    ) -> "MTMDChatHandler":
+        import fnmatch
+        from pathlib import Path
+
+        try:
+            from huggingface_hub import hf_hub_download, HfFileSystem  # type: ignore
+            from huggingface_hub.utils import validate_repo_id  # type: ignore
+        except ImportError:
+            raise ImportError(
+                "Llama.from_pretrained requires the huggingface-hub package. "
+                "You can install it with `pip install huggingface-hub`."
+            )
+
+        validate_repo_id(repo_id)
+
+        hffs = HfFileSystem()
+
+        files = [
+            file["name"] if isinstance(file, dict) else file
+            for file in hffs.ls(repo_id)  # type: ignore
+        ]
+
+        file_list: List[str] = []
+        for file in files:
+            rel_path = Path(file).relative_to(repo_id)
+            file_list.append(str(rel_path))
+
+        matching_files = [file for file in file_list if fnmatch.fnmatch(file, filename)]  # type: ignore
+
+        if len(matching_files) == 0:
+            raise ValueError(
+                f"No file found in {repo_id} that match {filename}\n\n"
+                f"Available Files:\n{json.dumps(file_list)}"
+            )
+
+        if len(matching_files) > 1:
+            raise ValueError(
+                f"Multiple files found in {repo_id} matching {filename}\n\n"
+                f"Available Files:\n{json.dumps(files)}"
+            )
+
+        (matching_file,) = matching_files
+
+        subfolder = str(Path(matching_file).parent)
+        filename = Path(matching_file).name
+
+        hf_hub_download(
+            repo_id=repo_id,
+            filename=filename,
+            subfolder=subfolder,
+            local_dir=cast(Union[str, Path, None], local_dir),
+            local_dir_use_symlinks=local_dir_use_symlinks,
+            cache_dir=cast(Union[str, Path, None], cache_dir),
+        )
+
+        if local_dir is None:
+            model_path = hf_hub_download(
+                repo_id=repo_id,
+                filename=filename,
+                subfolder=subfolder,
+                local_dir=local_dir,
+                local_dir_use_symlinks=local_dir_use_symlinks,
+                cache_dir=cast(Union[str, Path, None], cache_dir),
+                local_files_only=True,
+            )
+        else:
+            model_path = os.path.join(local_dir, filename)
+
+        return cls(
+            clip_model_path=model_path,
+            **kwargs,
+        )
+
+
+class Gemma4ChatHandler(MTMDChatHandler):
+    pass
+
+
 class ObsidianChatHandler(Llava15ChatHandler):
     # Prompt Format
-    # The model followed ChatML format. However, with ### as the seperator
+    # The model followed ChatML format. However, with ### as the separator
 
     # <|im_start|>user
     # What is this sign about?\n<image>
@@ -3332,7 +4049,6 @@ class MiniCPMv26ChatHandler(Llava15ChatHandler):
         "{% endif %}"
         "{% endif %}"
         "{% endfor %}"
-
         "{% for content in message['content'] %}"
         "{% if content.type == 'text' %}"
         "{{ content.text }}"
@@ -3350,6 +4066,67 @@ class MiniCPMv26ChatHandler(Llava15ChatHandler):
     )
 
 
+class Qwen25VLChatHandler(Llava15ChatHandler):
+    DEFAULT_SYSTEM_MESSAGE = "You are a helpful assistant."
+
+    CHAT_FORMAT = (
+        # "{% set image_count = namespace(value=0) %}"
+        # "{% set video_count = namespace(value=0) %}"
+        "{% for message in messages %}"
+        "{% if loop.first and message['role'] != 'system' %}"
+        "<|im_start|>system\n"
+        "{{ self.DEFAULT_SYSTEM_MESSAGE }}<|im_end|>\n"
+        "{% endif %}"
+        "<|im_start|>{{ message['role'] }}\n"
+        "{% if message['content'] is string %}"
+        "{{ message['content'] }}<|im_end|>\n"
+        "{% else %}"
+        "{% for content in message['content'] %}"
+        "{% if content['type'] == 'image_url' %}"
+        "{% if content.image_url is string %}"
+        "{{ content.image_url }}"
+        "{% else %}"
+        "{{ content.image_url.url }}"
+        "{% endif %}"
+        # "{% set image_count.value = image_count.value + 1 %}"
+        "{% elif content['type'] == 'text' %}"
+        "{{ content['text'] }}"
+        "{% endif %}"
+        "{% endfor %}"
+        "<|im_end|>\n"
+        "{% endif %}"
+        "{% endfor %}"
+        "<|im_start|>assistant\n"
+    )
+
+    def __call__(self, **kwargs):
+        llama = kwargs["llama"]
+
+        # Clear state for multiple runs
+        llama.reset()
+        llama._ctx.kv_cache_clear()
+        llama.n_tokens = 0
+
+        if hasattr(llama, "input_ids"):
+            llama.input_ids.fill(0)
+
+        # Clear any handler state
+        if hasattr(self, "_last_image_embed"):
+            self._last_image_embed = None
+            self._last_image_hash = None
+
+        if self.verbose:
+            messages = kwargs.get("messages", [])
+            image_count = len(self.get_image_urls(messages))
+            print(
+                f"Minimal - Cleared state, processing {image_count} images",
+                file=sys.stderr,
+            )
+
+        # Use parent implementation
+        return super().__call__(**kwargs)
+
+
 @register_chat_completion_handler("chatml-function-calling")
 def chatml_function_calling(
     llama: llama.Llama,
@@ -3467,7 +4244,9 @@ def chatml_function_calling(
     stop = (
         [stop, "<|im_end|>"]
         if isinstance(stop, str)
-        else stop + ["<|im_end|>"] if stop else ["<|im_end|>"]
+        else stop + ["<|im_end|>"]
+        if stop
+        else ["<|im_end|>"]
     )
 
     # Case 1: No tool choice by user
@@ -3569,7 +4348,7 @@ def chatml_function_calling(
     # Case 3: Automatic tool choice
     assert isinstance(tool_choice, str) and tool_choice == "auto"
     function_names = " | ".join(
-        [f'''"functions.{tool['function']['name']}:"''' for tool in tools]
+        [f'''"functions.{tool["function"]["name"]}:"''' for tool in tools]
     )
     initial_gbnf_tool_grammar = (
         """root   ::= functions | "message:"\n"""
@@ -3745,7 +4524,9 @@ def chatml_function_calling(
                 {
                     "finish_reason": "tool_calls",
                     "index": 0,
-                    "logprobs": completion["choices"][0]["logprobs"],
+                    "logprobs": _convert_text_completion_logprobs_to_chat(
+                        completion["choices"][0]["logprobs"]
+                    ),
                     "message": {
                         "role": "assistant",
                         "content": None,
diff --git a/llama_cpp/llama_cpp.py b/llama_cpp/llama_cpp.py
index bb2ba1993c..176709d96e 100644
--- a/llama_cpp/llama_cpp.py
+++ b/llama_cpp/llama_cpp.py
@@ -3,6 +3,7 @@
 import os
 import ctypes
 import pathlib
+import warnings
 
 from typing import (
     Callable,
@@ -33,13 +34,25 @@
 # Specify the base name of the shared library to load
 _lib_base_name = "llama"
 _override_base_path = os.environ.get("LLAMA_CPP_LIB_PATH")
-_base_path = pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib" if _override_base_path is None else pathlib.Path(_override_base_path)
+_base_path = (
+    pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib"
+    if _override_base_path is None
+    else pathlib.Path(_override_base_path)
+)
 # Load the library
 _lib = load_shared_library(_lib_base_name, _base_path)
 
 ctypes_function = ctypes_function_for_shared_library(_lib)
 
 
+def _warn_deprecated(symbol: str, hint: str) -> None:
+    warnings.warn(
+        f"{symbol} is deprecated; {hint}",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+
+
 # from ggml.h
 # // NOTE: always add types at the end of the enum to keep backward compatibility
 # enum ggml_type {
@@ -73,7 +86,10 @@
 #     GGML_TYPE_I64     = 27,
 #     GGML_TYPE_F64     = 28,
 #     GGML_TYPE_IQ1_M   = 29,
-#     GGML_TYPE_COUNT,
+#     GGML_TYPE_MXFP4   = 39,
+#     GGML_TYPE_NVFP4   = 40,
+#     GGML_TYPE_Q1_0    = 41,
+#     GGML_TYPE_COUNT   = 42,
 # };
 GGML_TYPE_F32 = 0
 GGML_TYPE_F16 = 1
@@ -103,7 +119,10 @@
 GGML_TYPE_I64 = 27
 GGML_TYPE_F64 = 28
 GGML_TYPE_IQ1_M = 29
-GGML_TYPE_COUNT = 30
+GGML_TYPE_MXFP4 = 39
+GGML_TYPE_NVFP4 = 40
+GGML_TYPE_Q1_0 = 41
+GGML_TYPE_COUNT = 42
 
 # from ggml-backend.h
 # typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data);
@@ -149,6 +168,10 @@
 # define LLAMA_STATE_SEQ_VERSION 2
 LLAMA_STATE_SEQ_VERSION = 2
 
+# struct llama_vocab;
+llama_vocab_p = NewType("llama_vocab_p", int)
+llama_vocab_p_ctypes = ctypes.c_void_p
+
 # struct llama_model;
 llama_model_p = NewType("llama_model_p", int)
 llama_model_p_ctypes = ctypes.c_void_p
@@ -157,9 +180,17 @@
 llama_context_p = NewType("llama_context_p", int)
 llama_context_p_ctypes = ctypes.c_void_p
 
-# # struct llama_sampler;
-# llama_sampler_p = NewType("llama_sampler_p", int)
-# llama_sampler_p_ctypes = ctypes.c_void_p
+# typedef struct llama_memory_i * llama_memory_t;
+llama_memory_t = NewType("llama_memory_t", int)
+llama_memory_t_ctypes = ctypes.c_void_p
+
+# struct llama_kv_cache; (DEPRECATED)
+llama_kv_cache_p = NewType("llama_kv_cache_p", int)
+llama_kv_cache_p_ctypes = ctypes.c_void_p
+
+# struct gguf_context;
+gguf_context_p = NewType("gguf_context_p", int)
+gguf_context_p_ctypes = ctypes.c_void_p
 
 # typedef int32_t llama_pos;
 llama_pos = ctypes.c_int32
@@ -168,15 +199,18 @@
 llama_token_p = ctypes.POINTER(llama_token)
 # typedef int32_t llama_seq_id;
 llama_seq_id = ctypes.c_int32
+# typedef uint32_t llama_state_seq_flags;
+llama_state_seq_flags = ctypes.c_uint32
 
 
 # enum llama_vocab_type {
-#     LLAMA_VOCAB_TYPE_NONE = 0, // For models without vocab
-#     LLAMA_VOCAB_TYPE_SPM  = 1, // LLaMA tokenizer based on byte-level BPE with byte fallback
-#     LLAMA_VOCAB_TYPE_BPE  = 2, // GPT-2 tokenizer based on byte-level BPE
-#     LLAMA_VOCAB_TYPE_WPM  = 3, // BERT tokenizer based on WordPiece
-#     LLAMA_VOCAB_TYPE_UGM  = 4, // T5 tokenizer based on Unigram
-#     LLAMA_VOCAB_TYPE_RWKV = 5, // RWKV tokenizer based on greedy tokenization
+#     LLAMA_VOCAB_TYPE_NONE   = 0, // For models without vocab
+#     LLAMA_VOCAB_TYPE_SPM    = 1, // LLaMA tokenizer based on byte-level BPE with byte fallback
+#     LLAMA_VOCAB_TYPE_BPE    = 2, // GPT-2 tokenizer based on byte-level BPE
+#     LLAMA_VOCAB_TYPE_WPM    = 3, // BERT tokenizer based on WordPiece
+#     LLAMA_VOCAB_TYPE_UGM    = 4, // T5 tokenizer based on Unigram
+#     LLAMA_VOCAB_TYPE_RWKV   = 5, // RWKV tokenizer based on greedy tokenization
+#     LLAMA_VOCAB_TYPE_PLAMO2 = 6, // PLaMo-2 tokenizer based on Aho-Corasick with dynamic programming
 # };
 LLAMA_VOCAB_TYPE_NONE = 0
 """For models without vocab"""
@@ -190,8 +224,11 @@
 """T5 tokenizer based on Unigram"""
 LLAMA_VOCAB_TYPE_RWKV = 5
 """RWKV tokenizer based on greedy tokenization"""
+LLAMA_VOCAB_TYPE_PLAMO2 = 6
+"""PLaMo-2 tokenizer based on Aho-Corasick with dynamic programming"""
 
 
+# NOTE: Deprecated and will be removed in the future. (already gone in llama.cpp)
 # // pre-tokenization types
 # enum llama_vocab_pre_type {
 #     LLAMA_VOCAB_PRE_TYPE_DEFAULT        = 0,
@@ -221,6 +258,15 @@
 #     LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH   = 24,
 #     LLAMA_VOCAB_PRE_TYPE_EXAONE         = 25,
 #     LLAMA_VOCAB_PRE_TYPE_CHAMELEON      = 26,
+#     LLAMA_VOCAB_PRE_TYPE_MINERVA        = 27,
+#     LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM  = 28,
+#     LLAMA_VOCAB_PRE_TYPE_GPT4O          = 29,
+#     LLAMA_VOCAB_PRE_TYPE_SUPERBPE       = 30,
+#     LLAMA_VOCAB_PRE_TYPE_TRILLION       = 31,
+#     LLAMA_VOCAB_PRE_TYPE_BAILINGMOE     = 32,
+#     LLAMA_VOCAB_PRE_TYPE_LLAMA4         = 33,
+#     LLAMA_VOCAB_PRE_TYPE_PIXTRAL        = 34,
+#     LLAMA_VOCAB_PRE_TYPE_SEED_CODER     = 35,
 # };
 LLAMA_VOCAB_PRE_TYPE_DEFAULT = 0
 LLAMA_VOCAB_PRE_TYPE_LLAMA3 = 1
@@ -238,7 +284,7 @@
 LLAMA_VOCAB_PRE_TYPE_DBRX = 13
 LLAMA_VOCAB_PRE_TYPE_SMAUG = 14
 LLAMA_VOCAB_PRE_TYPE_PORO = 15
-LLAMA_VOCAV_PRE_TYPE_CHATGLM3 = 16
+LLAMA_VOCAB_PRE_TYPE_CHATGLM3 = 16
 LLAMA_VOCAB_PRE_TYPE_CHATGLM4 = 17
 LLAMA_VOCAB_PRE_TYPE_VIKING = 18
 LLAMA_VOCAB_PRE_TYPE_JAIS = 19
@@ -249,18 +295,33 @@
 LLAMA_VOCAB_PRE_TYPE_GPT3_FINNISH = 24
 LLAMA_VOCAB_PRE_TYPE_EXAONE = 25
 LLAMA_VOCAB_PRE_TYPE_CHAMELEON = 26
+LLAMA_VOCAB_PRE_TYPE_MINERVA = 27
+LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM = 28
+LLAMA_VOCAB_PRE_TYPE_GPT4O = 29
+LLAMA_VOCAB_PRE_TYPE_SUPERBPE = 30
+LLAMA_VOCAB_PRE_TYPE_TRILLION = 31
+LLAMA_VOCAB_PRE_TYPE_BAILINGMOE = 32
+LLAMA_VOCAB_PRE_TYPE_LLAMA4 = 33
+LLAMA_VOCAB_PRE_TYPE_PIXTRAL = 34
+LLAMA_VOCAB_PRE_TYPE_SEED_CODER = 35
 
 
 # // note: these values should be synchronized with ggml_rope
 # // TODO: maybe move this enum to ggml.h (ggml_rope_type)
 # enum llama_rope_type {
-#     LLAMA_ROPE_TYPE_NONE = -1,
-#     LLAMA_ROPE_TYPE_NORM =  0,
-#     LLAMA_ROPE_TYPE_NEOX = GGML_ROPE_TYPE_NEOX,
+#     LLAMA_ROPE_TYPE_NONE   = -1,
+#     LLAMA_ROPE_TYPE_NORM   = 0,
+#     LLAMA_ROPE_TYPE_NEOX   = GGML_ROPE_TYPE_NEOX,
+#     LLAMA_ROPE_TYPE_MROPE  = GGML_ROPE_TYPE_MROPE,
+#     LLAMA_ROPE_TYPE_IMROPE = GGML_ROPE_TYPE_IMROPE,
+#     LLAMA_ROPE_TYPE_VISION = GGML_ROPE_TYPE_VISION,
 # };
 LLAMA_ROPE_TYPE_NONE = -1
 LLAMA_ROPE_TYPE_NORM = 0
 LLAMA_ROPE_TYPE_NEOX = GGML_ROPE_TYPE_NEOX = 2
+LLAMA_ROPE_TYPE_MROPE = GGML_ROPE_TYPE_MROPE = 8
+LLAMA_ROPE_TYPE_IMROPE = GGML_ROPE_TYPE_IMROPE = 40
+LLAMA_ROPE_TYPE_VISION = GGML_ROPE_TYPE_VISION = 24
 
 
 # enum llama_token_type { //TODO: remove, required until per token attributes are available from GGUF file
@@ -342,11 +403,14 @@
 #     LLAMA_FTYPE_MOSTLY_IQ4_XS        = 30, // except 1d tensors
 #     LLAMA_FTYPE_MOSTLY_IQ1_M         = 31, // except 1d tensors
 #     LLAMA_FTYPE_MOSTLY_BF16          = 32, // except 1d tensors
-#     LLAMA_FTYPE_MOSTLY_Q4_0_4_4      = 33, // except 1d tensors
-#     LLAMA_FTYPE_MOSTLY_Q4_0_4_8      = 34, // except 1d tensors
-#     LLAMA_FTYPE_MOSTLY_Q4_0_8_8      = 35, // except 1d tensors
+#     //LLAMA_FTYPE_MOSTLY_Q4_0_4_4      = 33, // removed from gguf files, use Q4_0 and runtime repack
+#     //LLAMA_FTYPE_MOSTLY_Q4_0_4_8      = 34, // removed from gguf files, use Q4_0 and runtime repack
+#     //LLAMA_FTYPE_MOSTLY_Q4_0_8_8      = 35, // removed from gguf files, use Q4_0 and runtime repack
 #     LLAMA_FTYPE_MOSTLY_TQ1_0         = 36, // except 1d tensors
 #     LLAMA_FTYPE_MOSTLY_TQ2_0         = 37, // except 1d tensors
+#     LLAMA_FTYPE_MOSTLY_MXFP4_MOE     = 38, // except 1d tensors
+#     LLAMA_FTYPE_MOSTLY_NVFP4         = 39, // except 1d tensors
+#     LLAMA_FTYPE_MOSTLY_Q1_0          = 40, // except 1d tensors
 #
 #     LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
 # };
@@ -380,11 +444,14 @@
 LLAMA_FTYPE_MOSTLY_IQ4_XS = 30
 LLAMA_FTYPE_MOSTLY_IQ1_M = 31
 LLAMA_FTYPE_MOSTLY_BF16 = 32
-LLAMA_FTYPE_MOSTLY_Q4_0_4_4 = 33
-LLAMA_FTYPE_MOSTLY_Q4_0_4_8 = 34
-LLAMA_FTYPE_MOSTLY_Q4_0_8_8 = 35
+# LLAMA_FTYPE_MOSTLY_Q4_0_4_4 = 33
+# LLAMA_FTYPE_MOSTLY_Q4_0_4_8 = 34
+# LLAMA_FTYPE_MOSTLY_Q4_0_8_8 = 35
 LLAMA_FTYPE_MOSTLY_TQ1_0 = 36
 LLAMA_FTYPE_MOSTLY_TQ2_0 = 37
+LLAMA_FTYPE_MOSTLY_MXFP4_MOE = 38
+LLAMA_FTYPE_MOSTLY_NVFP4 = 39
+LLAMA_FTYPE_MOSTLY_Q1_0 = 40
 LLAMA_FTYPE_GUESSED = 1024
 
 # enum llama_rope_scaling_type {
@@ -392,13 +459,15 @@
 #     LLAMA_ROPE_SCALING_TYPE_NONE        = 0,
 #     LLAMA_ROPE_SCALING_TYPE_LINEAR      = 1,
 #     LLAMA_ROPE_SCALING_TYPE_YARN        = 2,
-#     LLAMA_ROPE_SCALING_TYPE_MAX_VALUE   = LLAMA_ROPE_SCALING_TYPE_YARN,
+#     LLAMA_ROPE_SCALING_TYPE_LONGROPE    = 3,
+#     LLAMA_ROPE_SCALING_TYPE_MAX_VALUE   = LLAMA_ROPE_SCALING_TYPE_LONGROPE,
 # };
 LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED = -1
 LLAMA_ROPE_SCALING_TYPE_NONE = 0
 LLAMA_ROPE_SCALING_TYPE_LINEAR = 1
 LLAMA_ROPE_SCALING_TYPE_YARN = 2
-LLAMA_ROPE_SCALING_TYPE_MAX_VALUE = LLAMA_ROPE_SCALING_TYPE_YARN
+LLAMA_ROPE_SCALING_TYPE_LONGROPE = 3
+LLAMA_ROPE_SCALING_TYPE_MAX_VALUE = LLAMA_ROPE_SCALING_TYPE_LONGROPE
 
 # enum llama_pooling_type {
 #     LLAMA_POOLING_TYPE_UNSPECIFIED = -1,
@@ -425,14 +494,34 @@
 LLAMA_ATTENTION_TYPE_NON_CAUSAL = 1
 
 
+# enum llama_flash_attn_type {
+#     LLAMA_FLASH_ATTN_TYPE_AUTO     = -1,
+#     LLAMA_FLASH_ATTN_TYPE_DISABLED = 0,
+#     LLAMA_FLASH_ATTN_TYPE_ENABLED  = 1,
+# };
+LLAMA_FLASH_ATTN_TYPE_AUTO = -1
+LLAMA_FLASH_ATTN_TYPE_DISABLED = 0
+LLAMA_FLASH_ATTN_TYPE_ENABLED = 1
+
+
 # enum llama_split_mode {
-#     LLAMA_SPLIT_MODE_NONE  = 0, // single GPU
-#     LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs
-#     LLAMA_SPLIT_MODE_ROW   = 2, // split rows across GPUs
+#     LLAMA_SPLIT_MODE_NONE   = 0, // single GPU
+#     LLAMA_SPLIT_MODE_LAYER  = 1, // split layers and KV across GPUs
+#     LLAMA_SPLIT_MODE_ROW    = 2, // split layers and KV across GPUs, use tensor parallelism if supported
+#     LLAMA_SPLIT_MODE_TENSOR = 3,
 # };
 LLAMA_SPLIT_MODE_NONE = 0
 LLAMA_SPLIT_MODE_LAYER = 1
 LLAMA_SPLIT_MODE_ROW = 2
+LLAMA_SPLIT_MODE_TENSOR = 3
+
+
+# enum llama_context_type {
+#     LLAMA_CONTEXT_TYPE_DEFAULT = 0,
+#     LLAMA_CONTEXT_TYPE_MTP     = 1,
+# };
+LLAMA_CONTEXT_TYPE_DEFAULT = 0
+LLAMA_CONTEXT_TYPE_MTP = 1
 
 
 # typedef struct llama_token_data {
@@ -502,31 +591,35 @@ class llama_token_data_array(ctypes.Structure):
 )
 
 
-# // Input data for llama_decode
+# // Input data for llama_encode/llama_decode
 # // A llama_batch object can contain input about one or many sequences
 # // The provided arrays (i.e. token, embd, pos, etc.) must have size of n_tokens
 # //
 # // - token  : the token ids of the input (used when embd is NULL)
 # // - embd   : token embeddings (i.e. float vector of size n_embd) (used when token is NULL)
 # // - pos    : the positions of the respective token in the sequence
-# //            (if set to NULL, the token position will be tracked automatically by llama_decode)
+# //            (if set to NULL, the token position will be tracked automatically by llama_encode/llama_decode)
 # // - seq_id : the sequence to which the respective token belongs
 # //            (if set to NULL, the sequence ID will be assumed to be 0)
 # // - logits : if zero, the logits (and/or the embeddings) for the respective token will not be output
-# //            (if set to NULL, only the logits for last token will be returned)
+# //            (if set to NULL:
+# //               - if embeddings: all tokens are output
+# //               - if not:        only the last token is output
+# //            )
 # //
 # typedef struct llama_batch {
 #     int32_t n_tokens;
 
+
 #     llama_token  *  token;
 #     float        *  embd;
 #     llama_pos    *  pos;
 #     int32_t      *  n_seq_id;
 #     llama_seq_id ** seq_id;
-#     int8_t       *  logits; // TODO: rename this to "output"
+#     int8_t       *  logits;   // TODO: rename this to "output"
 # } llama_batch;
 class llama_batch(ctypes.Structure):
-    """Input data for llama_decode
+    """Input data for llama_encode/llama_decode
 
     A llama_batch object can contain input about one or many sequences
 
@@ -573,6 +666,34 @@ class llama_batch(ctypes.Structure):
 LLAMA_KV_OVERRIDE_TYPE_STR = 3
 
 
+# enum llama_model_meta_key {
+#     LLAMA_MODEL_META_KEY_SAMPLING_SEQUENCE,
+#     LLAMA_MODEL_META_KEY_SAMPLING_TOP_K,
+#     LLAMA_MODEL_META_KEY_SAMPLING_TOP_P,
+#     LLAMA_MODEL_META_KEY_SAMPLING_MIN_P,
+#     LLAMA_MODEL_META_KEY_SAMPLING_XTC_PROBABILITY,
+#     LLAMA_MODEL_META_KEY_SAMPLING_XTC_THRESHOLD,
+#     LLAMA_MODEL_META_KEY_SAMPLING_TEMP,
+#     LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_LAST_N,
+#     LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_REPEAT,
+#     LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT,
+#     LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_TAU,
+#     LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_ETA,
+# };
+LLAMA_MODEL_META_KEY_SAMPLING_SEQUENCE = 0
+LLAMA_MODEL_META_KEY_SAMPLING_TOP_K = 1
+LLAMA_MODEL_META_KEY_SAMPLING_TOP_P = 2
+LLAMA_MODEL_META_KEY_SAMPLING_MIN_P = 3
+LLAMA_MODEL_META_KEY_SAMPLING_XTC_PROBABILITY = 4
+LLAMA_MODEL_META_KEY_SAMPLING_XTC_THRESHOLD = 5
+LLAMA_MODEL_META_KEY_SAMPLING_TEMP = 6
+LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_LAST_N = 7
+LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_REPEAT = 8
+LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT = 9
+LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_TAU = 10
+LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_ETA = 11
+
+
 # struct llama_model_kv_override {
 #     enum llama_model_kv_override_type tag;
 
@@ -614,25 +735,65 @@ class llama_model_kv_override(ctypes.Structure):
         value: Union[int, float, bool, bytes]
 
 
+# struct llama_model_tensor_override {
+#     const char * pattern;
+#     enum ggml_type type;
+# };
+class llama_model_tensor_override(ctypes.Structure):
+    """Override the quantization type for tensors matching a pattern."""
+
+    _fields_ = [
+        ("pattern", ctypes.c_char_p),
+        ("type", ctypes.c_int),
+    ]
+
+    if TYPE_CHECKING:
+        pattern: Optional[bytes]
+        type: int
+
+
+# struct llama_model_imatrix_data {
+#     const char * name;
+#     const float * data;
+#     size_t size;
+# };
+class llama_model_imatrix_data(ctypes.Structure):
+    """Importance matrix data for a tensor used during quantization."""
+
+    _fields_ = [
+        ("name", ctypes.c_char_p),
+        ("data", ctypes.POINTER(ctypes.c_float)),
+        ("size", ctypes.c_size_t),
+    ]
+
+    if TYPE_CHECKING:
+        name: Optional[bytes]
+        data: CtypesPointer[ctypes.c_float]
+        size: int
+
+
+# struct llama_model_tensor_buft_override {
+#     const char * pattern;
+#     ggml_backend_buffer_type_t buft;
+# };
+
+
 # struct llama_model_params {
 #     // NULL-terminated list of devices to use for offloading (if NULL, all available devices are used)
 #     ggml_backend_dev_t * devices;
 
+#     // NULL-terminated list of buffer types to use for tensors that match a pattern
+#     const struct llama_model_tensor_buft_override * tensor_buft_overrides;
+
 #     int32_t n_gpu_layers; // number of layers to store in VRAM
 #     enum llama_split_mode split_mode; // how to split the model across multiple GPUs
 
-#     // main_gpu interpretation depends on split_mode:
-#     // LLAMA_SPLIT_MODE_NONE: the GPU that is used for the entire model
-#     // LLAMA_SPLIT_MODE_ROW: the GPU that is used for small tensors and intermediate results
-#     // LLAMA_SPLIT_MODE_LAYER: ignored
+#     // the GPU that is used for the entire model when split_mode is LLAMA_SPLIT_MODE_NONE
 #     int32_t main_gpu;
 
 #     // proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices()
 #     const float * tensor_split;
 
-#     // comma separated list of RPC servers to use for offloading
-#     const char * rpc_servers;
-
 #     // Called with a progress value between 0.0 and 1.0. Pass NULL to disable.
 #     // If the provided progress_callback returns true, model loading continues.
 #     // If it returns false, model loading is immediately aborted.
@@ -646,74 +807,113 @@ class llama_model_kv_override(ctypes.Structure):
 
 
 #     // Keep the booleans together to avoid misalignment during copy-by-value.
-#     bool vocab_only;    // only load the vocabulary, no weights
-#     bool use_mmap;      // use mmap if possible
-#     bool use_mlock;     // force system to keep model in RAM
-#     bool check_tensors; // validate model tensor data
+#     bool vocab_only;      // only load the vocabulary, no weights
+#     bool use_mmap;        // use mmap if possible
+#     bool use_direct_io;   // use direct io, takes precedence over use_mmap when supported
+#     bool use_mlock;       // force system to keep model in RAM
+#     bool check_tensors;   // validate model tensor data
+#     bool use_extra_bufts; // use extra buffer types (used for weight repacking)
+#     bool no_host;         // bypass host buffer allowing extra buffers to be used
+#     bool no_alloc;        // only load metadata and simulate memory allocations
 # };
 class llama_model_params(ctypes.Structure):
     """Parameters for llama_model
 
     Attributes:
+        devices (ctypes.Array[ggml_backend_dev_t]): NULL-terminated list of devices to use for offloading (if NULL, all available devices are used)
+        tensor_buft_overrides (ctypes.Array[llama_model_tensor_buft_override]): NULL-terminated list of buffer types to use for tensors that match a pattern
         n_gpu_layers (int): number of layers to store in VRAM
         split_mode (int): how to split the model across multiple GPUs
-        main_gpu (int): the GPU that is used for the entire model. main_gpu interpretation depends on split_mode: LLAMA_SPLIT_NONE: the GPU that is used for the entire model LLAMA_SPLIT_ROW: the GPU that is used for small tensors and intermediate results LLAMA_SPLIT_LAYER: ignored
+        main_gpu (int): the GPU that is used for the entire model when split_mode is LLAMA_SPLIT_MODE_NONE
         tensor_split (ctypes.Array[ctypes.ctypes.c_float]): proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices()
-        rpc_servers (ctypes.c_char_p): comma separated list of RPC servers to use for offloading
         progress_callback (llama_progress_callback): called with a progress value between 0.0 and 1.0. Pass NULL to disable. If the provided progress_callback returns true, model loading continues. If it returns false, model loading is immediately aborted.
         progress_callback_user_data (ctypes.ctypes.c_void_p): context pointer passed to the progress callback
         kv_overrides (ctypes.Array[llama_model_kv_override]): override key-value pairs of the model meta data
         vocab_only (bool): only load the vocabulary, no weights
         use_mmap (bool): use mmap if possible
+        use_direct_io (bool): use direct io, takes precedence over use_mmap when supported
         use_mlock (bool): force system to keep model in RAM
-        check_tensors (bool): validate model tensor data"""
+        check_tensors (bool): validate model tensor data
+        use_extra_bufts (bool): use extra buffer types (used for weight repacking)
+        no_host (bool): bypass host buffer allowing extra buffers to be used
+        no_alloc (bool): only load metadata and simulate memory allocations"""
 
     if TYPE_CHECKING:
+        devices: CtypesArray[ctypes.c_void_p]  # NOTE: unused
+        tensor_buft_overrides: CtypesArray[
+            llama_model_tensor_buft_override
+        ]  # NOTE: unused
         n_gpu_layers: int
         split_mode: int
         main_gpu: int
         tensor_split: CtypesArray[ctypes.c_float]
-        rpc_servers: ctypes.c_char_p
         progress_callback: Callable[[float, ctypes.c_void_p], bool]
         progress_callback_user_data: ctypes.c_void_p
         kv_overrides: CtypesArray[llama_model_kv_override]
         vocab_only: bool
         use_mmap: bool
+        use_direct_io: bool
         use_mlock: bool
         check_tensors: bool
+        use_extra_bufts: bool
+        no_host: bool
+        no_alloc: bool
 
     _fields_ = [
-        ("devices", ctypes.c_void_p), # NOTE: unnused
+        ("devices", ctypes.c_void_p),  # NOTE: unnused
+        ("tensor_buft_overrides", ctypes.c_void_p),  # NOTE: unused
         ("n_gpu_layers", ctypes.c_int32),
         ("split_mode", ctypes.c_int),
         ("main_gpu", ctypes.c_int32),
         ("tensor_split", ctypes.POINTER(ctypes.c_float)),
-        ("rpc_servers", ctypes.c_char_p),
         ("progress_callback", llama_progress_callback),
         ("progress_callback_user_data", ctypes.c_void_p),
         ("kv_overrides", ctypes.POINTER(llama_model_kv_override)),
         ("vocab_only", ctypes.c_bool),
         ("use_mmap", ctypes.c_bool),
+        ("use_direct_io", ctypes.c_bool),
         ("use_mlock", ctypes.c_bool),
         ("check_tensors", ctypes.c_bool),
+        ("use_extra_bufts", ctypes.c_bool),
+        ("no_host", ctypes.c_bool),
+        ("no_alloc", ctypes.c_bool),
+    ]
+
+
+# struct llama_sampler_seq_config {
+#     llama_seq_id           seq_id;
+#     struct llama_sampler * sampler;
+# };
+class llama_sampler_seq_config(ctypes.Structure):
+    if TYPE_CHECKING:
+        seq_id: int
+        sampler: ctypes.c_void_p
+
+    _fields_ = [
+        ("seq_id", llama_seq_id),
+        ("sampler", ctypes.c_void_p),
     ]
 
 
 # // NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations
-# //       https://github.com/ggerganov/llama.cpp/pull/7544
+# //       https://github.com/ggml-org/llama.cpp/pull/7544
 # struct llama_context_params {
 #     uint32_t n_ctx;             // text context, 0 = from model
 #     uint32_t n_batch;           // logical maximum batch size that can be submitted to llama_decode
 #     uint32_t n_ubatch;          // physical maximum batch size
 #     uint32_t n_seq_max;         // max number of sequences (i.e. distinct states for recurrent models)
+#     uint32_t n_rs_seq;          // number of recurrent-state snapshots per seq for rollback (0 = no rollback) [EXPERIMENTAL]
+#     uint32_t n_outputs_max;     // max outputs in a ubatch (0 = n_batch)
 #     int32_t  n_threads;         // number of threads to use for generation
 #     int32_t  n_threads_batch;   // number of threads to use for batch processing
 
+#     enum llama_context_type      ctx_type;          // set the context type (e.g. MTP)
 #     enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
 #     enum llama_pooling_type      pooling_type;      // whether to pool (sum) embedding results by sequence id
 #     enum llama_attention_type    attention_type;    // attention type to use for embeddings
+#     enum llama_flash_attn_type   flash_attn_type;   // when to enable Flash Attention
 
-#     // ref: https://github.com/ggerganov/llama.cpp/pull/2054
+#     // ref: https://github.com/ggml-org/llama.cpp/pull/2054
 #     float    rope_freq_base;   // RoPE base frequency, 0 = from model
 #     float    rope_freq_scale;  // RoPE frequency scaling factor, 0 = from model
 #     float    yarn_ext_factor;  // YaRN extrapolation mix factor, negative = from model
@@ -721,7 +921,7 @@ class llama_model_params(ctypes.Structure):
 #     float    yarn_beta_fast;   // YaRN low correction dim
 #     float    yarn_beta_slow;   // YaRN high correction dim
 #     uint32_t yarn_orig_ctx;    // YaRN original context size
-#     float    defrag_thold;     // defragment the KV cache if holes/size > thold, < 0 disabled (default)
+#     float    defrag_thold;     // defragment the KV cache if holes/size > thold, <= 0 disabled (default)
 
 #     ggml_backend_sched_eval_callback cb_eval;
 #     void * cb_eval_user_data;
@@ -729,19 +929,30 @@ class llama_model_params(ctypes.Structure):
 #     enum ggml_type type_k; // data type for K cache [EXPERIMENTAL]
 #     enum ggml_type type_v; // data type for V cache [EXPERIMENTAL]
 
-#     // Keep the booleans together to avoid misalignment during copy-by-value.
-#     bool logits_all;  // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
-#     bool embeddings;  // if true, extract embeddings (together with logits)
-#     bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
-#     bool flash_attn;  // whether to use flash attention [EXPERIMENTAL]
-#     bool no_perf;     // whether to measure performance timings
-
-
 #     // Abort callback
 #     // if it returns true, execution of llama_decode() will be aborted
 #     // currently works only with CPU execution
 #     ggml_abort_callback abort_callback;
 #     void *              abort_callback_data;
+
+
+#     // Keep the booleans together and at the end of the struct to avoid misalignment during copy-by-value.
+#     bool embeddings;  // if true, extract embeddings (together with logits)
+#     bool offload_kqv; // offload the KQV ops (including the KV cache) to GPU
+#     bool no_perf;     // measure performance timings
+#     bool op_offload;  // offload host tensor operations to device
+#     bool swa_full;    // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
+#                       // NOTE: setting to false when n_seq_max > 1 can cause bad performance in some cases
+#                       //       ref: https://github.com/ggml-org/llama.cpp/pull/13845#issuecomment-2924800573
+#     bool kv_unified;  // use a unified buffer across the input sequences when computing the attention
+#                       // try to disable when n_seq_max > 1 for improved performance when the sequences do not share a large prefix
+#                       // ref: https://github.com/ggml-org/llama.cpp/pull/14363
+#     struct llama_sampler_seq_config * samplers;
+#     size_t                            n_samplers;
+#
+#     // a source/target/parent context
+#     // can be utilized in various ways, for example by sharing results or llama_memory between 2 contexts
+#     struct llama_context * ctx_other;
 # };
 class llama_context_params(ctypes.Structure):
     """Parameters for llama_context
@@ -751,11 +962,15 @@ class llama_context_params(ctypes.Structure):
         n_batch (int): logical maximum batch size that can be submitted to llama_decode
         n_ubatch (int): physical maximum batch size
         n_seq_max (int): max number of sequences (i.e. distinct states for recurrent models)
+        n_rs_seq (int): number of recurrent-state snapshots per sequence for rollback
+        n_outputs_max (int): max outputs in a ubatch, 0 = n_batch
         n_threads (int): number of threads to use for generation
         n_threads_batch (int): number of threads to use for batch processing
+        ctx_type (int): context type, from `enum llama_context_type`
         rope_scaling_type (int): RoPE scaling type, from `enum llama_rope_scaling_type`
         pooling_type (int): whether to pool (sum) embedding results by sequence id (ignored if no pooling layer)
         attention_type (int): attention type to use for embeddings
+        flash_attn_type (int): when to enable flash attention
         rope_freq_base (float): RoPE base frequency, 0 = from model
         rope_freq_scale (float): RoPE frequency scaling factor, 0 = from model
         yarn_ext_factor (float): YaRN extrapolation mix factor, negative = from model
@@ -763,17 +978,22 @@ class llama_context_params(ctypes.Structure):
         yarn_beta_fast (float): YaRN low correction dim
         yarn_beta_slow (float): YaRN high correction dim
         yarn_orig_ctx (int): YaRN original context size
-        defrag_thold (float): defragment the KV cache if holes/size > thold, < 0 disabled (default)
+        defrag_thold (float): defragment the KV cache if holes/size > thold, <= 0 disabled (default)
         cb_eval (ggml_backend_sched_eval_callback): callback for scheduling eval
         cb_eval_user_data (ctypes.ctypes.c_void_p): user data for cb_eval
         type_k (int): data type for K cache
         type_v (int): data type for V cache
-        logits_all (bool): the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
-        embeddings (bool): if true, extract embeddings (together with logits)
-        offload_kqv (bool): whether to offload the KQV ops (including the KV cache) to GPU
-        flash_attn (bool): whether to use flash attention
         abort_callback (ggml_abort_callback): abort callback if it returns true, execution of llama_decode() will be aborted
         abort_callback_data (ctypes.ctypes.c_void_p): data for abort_callback
+        embeddings (bool): if true, extract embeddings (together with logits)
+        offload_kqv (bool): whether to offload the KQV ops (including the KV cache) to GPU
+        no_perf (bool): whether to measure performance timings
+        op_offload (bool): offload host tensor operations to device
+        swa_full (bool): use full-size SWA cache
+        kv_unified (bool): use a unified buffer across the input sequences when computing the attention
+        samplers (ctypes.POINTER(llama_sampler_seq_config)): backend sampler chain configuration
+        n_samplers (int): number of backend sampler chain configurations
+        ctx_other (llama_context_p): source, target, or parent context
     """
 
     if TYPE_CHECKING:
@@ -781,11 +1001,15 @@ class llama_context_params(ctypes.Structure):
         n_batch: int
         n_ubatch: int
         n_seq_max: int
+        n_rs_seq: int
+        n_outputs_max: int
         n_threads: int
         n_threads_batch: int
+        ctx_type: int
         rope_scaling_type: int
         pooling_type: int
         attention_type: int
+        flash_attn_type: int
         rope_freq_base: float
         rope_freq_scale: float
         yarn_ext_factor: float
@@ -798,23 +1022,32 @@ class llama_context_params(ctypes.Structure):
         cb_eval_user_data: ctypes.c_void_p
         type_k: int
         type_v: int
-        logits_all: bool
-        embeddings: bool
-        offload_kqv: bool
-        flash_attn: bool
         abort_callback: Callable[[ctypes.c_void_p], bool]
         abort_callback_data: ctypes.c_void_p
+        embeddings: bool
+        offload_kqv: bool
+        no_perf: bool
+        op_offload: bool
+        swa_full: bool
+        kv_unified: bool
+        samplers: ctypes.POINTER(llama_sampler_seq_config)
+        n_samplers: int
+        ctx_other: llama_context_p
 
     _fields_ = [
         ("n_ctx", ctypes.c_uint32),
         ("n_batch", ctypes.c_uint32),
         ("n_ubatch", ctypes.c_uint32),
         ("n_seq_max", ctypes.c_uint32),
+        ("n_rs_seq", ctypes.c_uint32),
+        ("n_outputs_max", ctypes.c_uint32),
         ("n_threads", ctypes.c_int32),
         ("n_threads_batch", ctypes.c_int32),
+        ("ctx_type", ctypes.c_int),
         ("rope_scaling_type", ctypes.c_int),
         ("pooling_type", ctypes.c_int),
         ("attention_type", ctypes.c_int),
+        ("flash_attn_type", ctypes.c_int),
         ("rope_freq_base", ctypes.c_float),
         ("rope_freq_scale", ctypes.c_float),
         ("yarn_ext_factor", ctypes.c_float),
@@ -827,12 +1060,17 @@ class llama_context_params(ctypes.Structure):
         ("cb_eval_user_data", ctypes.c_void_p),
         ("type_k", ctypes.c_int),
         ("type_v", ctypes.c_int),
-        ("logits_all", ctypes.c_bool),
-        ("embeddings", ctypes.c_bool),
-        ("offload_kqv", ctypes.c_bool),
-        ("flash_attn", ctypes.c_bool),
         ("abort_callback", ggml_abort_callback),
         ("abort_callback_data", ctypes.c_void_p),
+        ("embeddings", ctypes.c_bool),
+        ("offload_kqv", ctypes.c_bool),
+        ("no_perf", ctypes.c_bool),
+        ("op_offload", ctypes.c_bool),
+        ("swa_full", ctypes.c_bool),
+        ("kv_unified", ctypes.c_bool),
+        ("samplers", ctypes.POINTER(llama_sampler_seq_config)),
+        ("n_samplers", ctypes.c_size_t),
+        ("ctx_other", llama_context_p_ctypes),
     ]
 
 
@@ -854,17 +1092,20 @@ class llama_context_params(ctypes.Structure):
 
 # // model quantization parameters
 # typedef struct llama_model_quantize_params {
-#     int32_t nthread;                     // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
-#     enum llama_ftype ftype;              // quantize to this llama_ftype
-#     enum ggml_type output_tensor_type;   // output tensor type
-#     enum ggml_type token_embedding_type; // token embeddings tensor type
-#     bool allow_requantize;               // allow quantizing non-f32/f16 tensors
-#     bool quantize_output_tensor;         // quantize output.weight
-#     bool only_copy;                      // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
-#     bool pure;                           // quantize all tensors to the default type
-#     bool keep_split;                     // quantize to the same number of shards
-#     void * imatrix;                      // pointer to importance matrix data
-#     void * kv_overrides;                 // pointer to vector containing overrides
+#     int32_t nthread;                      // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
+#     enum llama_ftype ftype;               // quantize to this llama_ftype
+#     enum ggml_type output_tensor_type;    // output tensor type
+#     enum ggml_type token_embedding_type;  // token embeddings tensor type
+#     bool allow_requantize;                // allow quantizing non-f32/f16 tensors
+#     bool quantize_output_tensor;          // quantize output.weight
+#     bool only_copy;                       // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
+#     bool pure;                            // quantize all tensors to the default type
+#     bool keep_split;                      // quantize to the same number of shards
+#     bool dry_run;                         // calculate and show the final quantization size without performing quantization
+#     const struct llama_model_imatrix_data * imatrix;         // pointer to importance matrix data
+#     const struct llama_model_kv_override * kv_overrides;     // pointer to kv overrides
+#     const struct llama_model_tensor_override * tt_overrides; // pointer to tensor overrides
+#     const int32_t * prune_layers;                            // pointer to layer indices to prune
 # } llama_model_quantize_params;
 class llama_model_quantize_params(ctypes.Structure):
     """Parameters for llama_model_quantize
@@ -879,8 +1120,11 @@ class llama_model_quantize_params(ctypes.Structure):
         only_copy (bool): only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
         pure (bool): quantize all tensors to the default type
         keep_split (bool): quantize to the same number of shards
-        imatrix (ctypes.c_void_p): pointer to importance matrix data
-        kv_overrides (ctypes.c_void_p): pointer to vector containing overrides
+        dry_run (bool): calculate and show the final quantization size without performing quantization
+        imatrix (ctypes.Array[llama_model_imatrix_data]): pointer to importance matrix data
+        kv_overrides (ctypes.Array[llama_model_kv_override]): pointer to kv overrides
+        tt_overrides (ctypes.Array[llama_model_tensor_override]): pointer to tensor overrides
+        prune_layers (ctypes.Array[ctypes.c_int32]): pointer to layer indices to prune
     """
 
     if TYPE_CHECKING:
@@ -893,8 +1137,11 @@ class llama_model_quantize_params(ctypes.Structure):
         only_copy: bool
         pure: bool
         keep_split: bool
-        imatrix: ctypes.c_void_p
-        kv_overrides: ctypes.c_void_p
+        dry_run: bool
+        imatrix: CtypesPointer[llama_model_imatrix_data]
+        kv_overrides: CtypesPointer[llama_model_kv_override]
+        tt_overrides: CtypesPointer[llama_model_tensor_override]
+        prune_layers: CtypesPointer[ctypes.c_int32]
 
     _fields_ = [
         ("nthread", ctypes.c_int32),
@@ -906,8 +1153,11 @@ class llama_model_quantize_params(ctypes.Structure):
         ("only_copy", ctypes.c_bool),
         ("pure", ctypes.c_bool),
         ("keep_split", ctypes.c_bool),
-        ("imatrix", ctypes.c_void_p),
-        ("kv_overrides", ctypes.c_void_p),
+        ("dry_run", ctypes.c_bool),
+        ("imatrix", ctypes.POINTER(llama_model_imatrix_data)),
+        ("kv_overrides", ctypes.POINTER(llama_model_kv_override)),
+        ("tt_overrides", ctypes.POINTER(llama_model_tensor_override)),
+        ("prune_layers", ctypes.POINTER(ctypes.c_int32)),
     ]
 
 
@@ -965,9 +1215,9 @@ class llama_chat_message(ctypes.Structure):
 
 
 # // lora adapter
-# struct llama_lora_adapter;
-llama_lora_adapter_p = ctypes.c_void_p
-llama_lora_adapter_p_ctypes = ctypes.POINTER(ctypes.c_void_p)
+# struct llama_adapter_lora;
+llama_adapter_lora_p = ctypes.c_void_p
+llama_adapter_lora_p_ctypes = ctypes.POINTER(ctypes.c_void_p)
 
 
 # // Helpers for getting default parameters
@@ -1015,10 +1265,16 @@ def llama_model_quantize_default_params() -> llama_model_quantize_params:
     ...
 
 
+# LLAMA_API const char * llama_flash_attn_type_name(enum llama_flash_attn_type flash_attn_type);
+@ctypes_function("llama_flash_attn_type_name", [ctypes.c_int], ctypes.c_char_p)
+def llama_flash_attn_type_name(flash_attn_type: int, /) -> Optional[bytes]:
+    """Get the flash attention type name."""
+    ...
+
+
 # // Initialize the llama + ggml backend
 # // If numa is true, use NUMA optimizations
 # // Call once at the start of the program
-# LLAMA_API void llama_backend_init(bool numa);
 # LLAMA_API void llama_backend_init(void);
 @ctypes_function(
     "llama_backend_init",
@@ -1027,7 +1283,6 @@ def llama_model_quantize_default_params() -> llama_model_quantize_params:
 )
 def llama_backend_init():
     """Initialize the llama + ggml backend
-    If numa is true, use NUMA optimizations
     Call once at the start of the program"""
     ...
 
@@ -1049,6 +1304,18 @@ def llama_backend_init():
 GGML_NUMA_STRATEGY_COUNT = 5
 
 
+# // Call once at the end of the program - currently only used for MPI
+# LLAMA_API void llama_backend_free(void);
+@ctypes_function(
+    "llama_backend_free",
+    [],
+    None,
+)
+def llama_backend_free():
+    """Call once at the end of the program - currently only used for MPI"""
+    ...
+
+
 # //optional:
 # LLAMA_API void llama_numa_init(enum ggml_numa_strategy numa);
 @ctypes_function(
@@ -1056,68 +1323,214 @@ def llama_backend_init():
     [ctypes.c_int],
     None,
 )
-def llama_numa_init(numa: int, /):
-    ...
+def llama_numa_init(numa: int, /): ...
 
 
 # // Optional: an auto threadpool gets created in ggml if not passed explicitly
 # LLAMA_API void llama_attach_threadpool(
-#            struct   llama_context * ctx,
-#         ggml_threadpool_t   threadpool,
-#         ggml_threadpool_t   threadpool_batch);
+#         struct llama_context * ctx,
+#            ggml_threadpool_t   threadpool,
+#            ggml_threadpool_t   threadpool_batch);
+# TODO: Add llama_attach_threadpool
 
 
 # LLAMA_API void llama_detach_threadpool(struct llama_context * ctx);
+# TODO: Add llama_detach_threadpool
 
 
-# // Call once at the end of the program - currently only used for MPI
-# LLAMA_API void llama_backend_free(void);
+# DEPRECATED(LLAMA_API struct llama_model * llama_load_model_from_file(
+#                          const char * path_model,
+#           struct llama_model_params   params),
+#         "use llama_model_load_from_file instead");
 @ctypes_function(
-    "llama_backend_free",
-    [],
-    None,
+    "llama_load_model_from_file",
+    [ctypes.c_char_p, llama_model_params],
+    llama_model_p_ctypes,
 )
-def llama_backend_free():
-    """Call once at the end of the program - currently only used for MPI"""
-    ...
+def llama_load_model_from_file(
+    path_model: bytes, params: llama_model_params, /
+) -> Optional[llama_model_p]: ...
+
+
+_llama_load_model_from_file = llama_load_model_from_file
 
 
-# LLAMA_API struct llama_model * llama_load_model_from_file(
+def llama_load_model_from_file(
+    path_model: bytes, params: llama_model_params, /
+) -> Optional[llama_model_p]:
+    _warn_deprecated(
+        "llama_load_model_from_file",
+        "use llama_model_load_from_file instead",
+    )
+    return _llama_load_model_from_file(path_model, params)
+
+
+# // Load the model from a file
+# // If the file is split into multiple parts, the file name must follow this pattern: <name>-%05d-of-%05d.gguf
+# // If the split file name does not follow this pattern, use llama_model_load_from_splits
+# LLAMA_API struct llama_model * llama_model_load_from_file(
 #                          const char * path_model,
 #           struct llama_model_params   params);
 @ctypes_function(
-    "llama_load_model_from_file",
+    "llama_model_load_from_file",
     [ctypes.c_char_p, llama_model_params],
     llama_model_p_ctypes,
 )
-def llama_load_model_from_file(
+def llama_model_load_from_file(
     path_model: bytes, params: llama_model_params, /
 ) -> Optional[llama_model_p]:
+    """Load the model from a file
+
+    If the file is split into multiple parts, the file name must follow this pattern: <name>-%05d-of-%05d.gguf
+
+    If the split file name does not follow this pattern, use llama_model_load_from_splits"""
+    ...
+
+
+# // Load the model from multiple splits (support custom naming scheme)
+# // The paths must be in the correct order
+# LLAMA_API struct llama_model * llama_model_load_from_splits(
+#                          const char ** paths,
+#                              size_t    n_paths,
+#           struct llama_model_params    params);
+@ctypes_function(
+    "llama_model_load_from_splits",
+    [ctypes.POINTER(ctypes.c_char_p), ctypes.c_size_t, llama_model_params],
+    llama_model_p_ctypes,
+)
+def llama_model_load_from_splits(
+    paths: List[bytes], n_paths: int, params: llama_model_params, /
+) -> Optional[llama_model_p]:
+    """Load the model from multiple splits (support custom naming scheme)
+
+    The paths must be in the correct order"""
+    ...
+
+
+# // Load a model from an open FILE pointer
+# LLAMA_API struct llama_model * llama_model_load_from_file_ptr(
+#                                FILE * file,
+#           struct llama_model_params   params);
+@ctypes_function(
+    "llama_model_load_from_file_ptr",
+    [ctypes.c_void_p, llama_model_params],
+    llama_model_p_ctypes,
+)
+def llama_model_load_from_file_ptr(
+    file: ctypes.c_void_p, params: llama_model_params, /
+) -> Optional[llama_model_p]:
+    """Load a model from an open FILE pointer."""
+    ...
+
+
+# LLAMA_API void llama_model_save_to_file(
+#         const struct llama_model * model,
+#                     const char * path_model);
+@ctypes_function(
+    "llama_model_save_to_file",
+    [llama_model_p_ctypes, ctypes.c_char_p],
+    None,
+)
+def llama_model_save_to_file(model: llama_model_p, path_model: bytes, /):
+    """Save the model to a file"""
     ...
 
 
-# LLAMA_API void llama_free_model(struct llama_model * model);
+# DEPRECATED(LLAMA_API void llama_free_model(struct llama_model * model),
+#         "use llama_model_free instead");
 @ctypes_function(
     "llama_free_model",
     [llama_model_p_ctypes],
     None,
 )
+def llama_free_model(model: llama_model_p, /): ...
+
+
+_llama_free_model = llama_free_model
+
+
 def llama_free_model(model: llama_model_p, /):
+    _warn_deprecated("llama_free_model", "use llama_model_free instead")
+    return _llama_free_model(model)
+
+
+# LLAMA_API void llama_model_free(struct llama_model * model);
+@ctypes_function(
+    "llama_model_free",
+    [llama_model_p_ctypes],
+    None,
+)
+def llama_model_free(model: llama_model_p, /): ...
+
+
+# typedef void (*llama_model_set_tensor_data_t)(struct ggml_tensor * tensor, void * userdata);
+llama_model_set_tensor_data_t = ctypes.CFUNCTYPE(None, ctypes.c_void_p, ctypes.c_void_p)
+
+
+# LLAMA_API struct llama_model * llama_model_init_from_user(
+#                 struct gguf_context * metadata,
+#       llama_model_set_tensor_data_t   set_tensor_data,
+#                                void * set_tensor_data_ud,
+#           struct llama_model_params   params);
+@ctypes_function(
+    "llama_model_init_from_user",
+    [
+        gguf_context_p_ctypes,
+        llama_model_set_tensor_data_t,
+        ctypes.c_void_p,
+        llama_model_params,
+    ],
+    llama_model_p_ctypes,
+)
+def llama_model_init_from_user(
+    metadata: gguf_context_p,
+    set_tensor_data: llama_model_set_tensor_data_t,
+    set_tensor_data_ud: ctypes.c_void_p,
+    params: llama_model_params,
+    /,
+) -> Optional[llama_model_p]:
+    """Initialize a model from user-provided metadata and tensor data."""
     ...
 
 
-# LLAMA_API struct llama_context * llama_new_context_with_model(
+# LLAMA_API struct llama_context * llama_init_from_model(
 #                  struct llama_model * model,
 #         struct llama_context_params   params);
+@ctypes_function(
+    "llama_init_from_model",
+    [llama_model_p_ctypes, llama_context_params],
+    llama_context_p_ctypes,
+)
+def llama_init_from_model(
+    model: llama_model_p, params: llama_context_params, /
+) -> Optional[llama_context_p]: ...
+
+
+# DEPRECATED(LLAMA_API struct llama_context * llama_new_context_with_model(
+#                  struct llama_model * model,
+#         struct llama_context_params   params),
+#         "use llama_init_from_model instead");
 @ctypes_function(
     "llama_new_context_with_model",
     [llama_model_p_ctypes, llama_context_params],
     llama_context_p_ctypes,
 )
+def llama_new_context_with_model(
+    model: llama_model_p, params: llama_context_params, /
+) -> Optional[llama_context_p]: ...
+
+
+_llama_new_context_with_model = llama_new_context_with_model
+
+
 def llama_new_context_with_model(
     model: llama_model_p, params: llama_context_params, /
 ) -> Optional[llama_context_p]:
-    ...
+    _warn_deprecated(
+        "llama_new_context_with_model",
+        "use llama_init_from_model instead",
+    )
+    return _llama_new_context_with_model(model, params)
 
 
 # // Frees all allocated memory
@@ -1138,129 +1551,260 @@ def llama_free(ctx: llama_context_p, /):
     [],
     ctypes.c_int64,
 )
-def llama_time_us() -> int:
-    ...
+def llama_time_us() -> int: ...
 
 
 # LLAMA_API size_t llama_max_devices(void);
 @ctypes_function("llama_max_devices", [], ctypes.c_size_t)
-def llama_max_devices() -> int:
+def llama_max_devices() -> int: ...
+
+
+# LLAMA_API size_t llama_max_parallel_sequences(void);
+@ctypes_function("llama_max_parallel_sequences", [], ctypes.c_size_t)
+def llama_max_parallel_sequences() -> int: ...
+
+
+# LLAMA_API size_t llama_max_tensor_buft_overrides(void);
+@ctypes_function("llama_max_tensor_buft_overrides", [], ctypes.c_size_t)
+def llama_max_tensor_buft_overrides() -> int:
+    """Get the maximum number of tensor buffer type overrides."""
     ...
 
 
 # LLAMA_API bool llama_supports_mmap       (void);
 @ctypes_function("llama_supports_mmap", [], ctypes.c_bool)
-def llama_supports_mmap() -> bool:
-    ...
+def llama_supports_mmap() -> bool: ...
 
 
 # LLAMA_API bool llama_supports_mlock      (void);
 @ctypes_function("llama_supports_mlock", [], ctypes.c_bool)
-def llama_supports_mlock() -> bool:
-    ...
+def llama_supports_mlock() -> bool: ...
 
 
 # LLAMA_API bool llama_supports_gpu_offload(void);
 @ctypes_function("llama_supports_gpu_offload", [], ctypes.c_bool)
-def llama_supports_gpu_offload() -> bool:
-    ...
+def llama_supports_gpu_offload() -> bool: ...
 
 
 # LLAMA_API bool llama_supports_rpc        (void);
 @ctypes_function("llama_supports_rpc", [], ctypes.c_bool)
-def llama_supports_rpc() -> bool:
-    ...
+def llama_supports_rpc() -> bool: ...
 
 
 # LLAMA_API uint32_t llama_n_ctx      (const struct llama_context * ctx);
 @ctypes_function("llama_n_ctx", [llama_context_p_ctypes], ctypes.c_uint32)
-def llama_n_ctx(ctx: llama_context_p, /) -> int:
+def llama_n_ctx(ctx: llama_context_p, /) -> int: ...
+
+
+# LLAMA_API uint32_t llama_n_ctx_seq  (const struct llama_context * ctx);
+@ctypes_function("llama_n_ctx_seq", [llama_context_p_ctypes], ctypes.c_uint32)
+def llama_n_ctx_seq(ctx: llama_context_p, /) -> int:
+    """Get the context size per sequence."""
     ...
 
 
 # LLAMA_API uint32_t llama_n_batch    (const struct llama_context * ctx);
 @ctypes_function("llama_n_batch", [llama_context_p_ctypes], ctypes.c_uint32)
-def llama_n_batch(ctx: llama_context_p, /) -> int:
-    ...
+def llama_n_batch(ctx: llama_context_p, /) -> int: ...
 
 
 # LLAMA_API uint32_t llama_n_ubatch   (const struct llama_context * ctx);
 @ctypes_function("llama_n_ubatch", [llama_context_p_ctypes], ctypes.c_uint32)
-def llama_n_ubatch(ctx: llama_context_p, /) -> int:
-    ...
+def llama_n_ubatch(ctx: llama_context_p, /) -> int: ...
 
 
 # LLAMA_API uint32_t llama_n_seq_max  (const struct llama_context * ctx);
 @ctypes_function("llama_n_seq_max", [llama_context_p_ctypes], ctypes.c_uint32)
-def llama_n_seq_max(ctx: llama_context_p, /) -> int:
-    ...
+def llama_n_seq_max(ctx: llama_context_p, /) -> int: ...
 
 
-# LLAMA_API int32_t llama_n_vocab    (const struct llama_model * model);
-@ctypes_function("llama_n_vocab", [llama_model_p_ctypes], ctypes.c_int32)
-def llama_n_vocab(model: llama_model_p, /) -> int:
-    ...
+# LLAMA_API uint32_t llama_n_rs_seq   (const struct llama_context * ctx);
+@ctypes_function("llama_n_rs_seq", [llama_context_p_ctypes], ctypes.c_uint32)
+def llama_n_rs_seq(ctx: llama_context_p, /) -> int: ...
 
 
-# LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
+# DEPRECATED(LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model), "use llama_model_n_ctx_train instead");
 @ctypes_function("llama_n_ctx_train", [llama_model_p_ctypes], ctypes.c_int32)
+def llama_n_ctx_train(model: llama_model_p, /) -> int: ...
+
+
+_llama_n_ctx_train = llama_n_ctx_train
+
+
 def llama_n_ctx_train(model: llama_model_p, /) -> int:
-    ...
+    _warn_deprecated("llama_n_ctx_train", "use llama_model_n_ctx_train instead")
+    return _llama_n_ctx_train(model)
 
 
-# LLAMA_API int32_t llama_n_embd     (const struct llama_model * model);
+# DEPRECATED(LLAMA_API int32_t llama_n_embd     (const struct llama_model * model), "use llama_model_n_embd instead");
 @ctypes_function("llama_n_embd", [llama_model_p_ctypes], ctypes.c_int32)
+def llama_n_embd(model: llama_model_p, /) -> int: ...
+
+
+_llama_n_embd = llama_n_embd
+
+
 def llama_n_embd(model: llama_model_p, /) -> int:
-    ...
+    _warn_deprecated("llama_n_embd", "use llama_model_n_embd instead")
+    return _llama_n_embd(model)
 
 
-# LLAMA_API int32_t llama_n_layer    (const struct llama_model * model);
+# DEPRECATED(LLAMA_API int32_t llama_n_layer    (const struct llama_model * model), "use llama_model_n_layer instead");
 @ctypes_function("llama_n_layer", [llama_model_p_ctypes], ctypes.c_int32)
+def llama_n_layer(model: llama_model_p, /) -> int: ...
+
+
+_llama_n_layer = llama_n_layer
+
+
 def llama_n_layer(model: llama_model_p, /) -> int:
-    ...
+    _warn_deprecated("llama_n_layer", "use llama_model_n_layer instead")
+    return _llama_n_layer(model)
 
 
-# LLAMA_API int32_t llama_n_head     (const struct llama_model * model);
+# DEPRECATED(LLAMA_API int32_t llama_n_head     (const struct llama_model * model), "use llama_model_n_head instead");
 @ctypes_function("llama_n_head", [llama_model_p_ctypes], ctypes.c_int32)
+def llama_n_head(model: llama_model_p, /) -> int: ...
+
+
+_llama_n_head = llama_n_head
+
+
 def llama_n_head(model: llama_model_p, /) -> int:
-    ...
+    _warn_deprecated("llama_n_head", "use llama_model_n_head instead")
+    return _llama_n_head(model)
+
+
+# DEPRECATED(LLAMA_API int32_t llama_n_vocab    (const struct llama_vocab * vocab), "use llama_vocab_n_tokens instead");
+@ctypes_function("llama_n_vocab", [llama_vocab_p_ctypes], ctypes.c_int32)
+def llama_n_vocab(model: llama_vocab_p, /) -> int: ...
+
+
+_llama_n_vocab = llama_n_vocab
 
 
-# LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx);
+def llama_n_vocab(model: llama_vocab_p, /) -> int:
+    _warn_deprecated("llama_n_vocab", "use llama_vocab_n_tokens instead")
+    return _llama_n_vocab(model)
+
+
+# LLAMA_API const struct llama_model * llama_get_model   (const struct llama_context * ctx);
 @ctypes_function("llama_get_model", [llama_context_p_ctypes], llama_model_p_ctypes)
-def llama_get_model(ctx: llama_context_p, /) -> Optional[llama_model_p]:
+def llama_get_model(ctx: llama_context_p, /) -> Optional[llama_model_p]: ...
+
+
+# LLAMA_API           llama_memory_t   llama_get_memory  (const struct llama_context * ctx);
+@ctypes_function("llama_get_memory", [llama_context_p_ctypes], llama_memory_t_ctypes)
+def llama_get_memory(ctx: llama_context_p, /) -> Optional[llama_memory_t]:
+    """Get the memory for the context"""
     ...
 
 
-# LLAMA_API enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx);
+# LLAMA_API  enum llama_pooling_type    llama_pooling_type(const struct llama_context * ctx);
 @ctypes_function("llama_pooling_type", [llama_context_p_ctypes], ctypes.c_int)
-def llama_pooling_type(ctx: llama_context_p, /) -> int:
-    ...
+def llama_pooling_type(ctx: llama_context_p, /) -> int: ...
 
 
-# LLAMA_API enum llama_vocab_type   llama_vocab_type  (const struct llama_model * model);
-@ctypes_function("llama_vocab_type", [llama_model_p_ctypes], ctypes.c_int)
-def llama_vocab_type(model: llama_model_p, /) -> int:
+# LLAMA_API const struct llama_vocab * llama_model_get_vocab(const struct llama_model * model);
+@ctypes_function("llama_model_get_vocab", [llama_model_p_ctypes], llama_vocab_p_ctypes)
+def llama_model_get_vocab(model: llama_model_p, /) -> Optional[llama_vocab_p]: ...
+
+
+# LLAMA_API enum llama_rope_type       llama_model_rope_type(const struct llama_model * model);
+@ctypes_function("llama_model_rope_type", [llama_model_p_ctypes], ctypes.c_int)
+def llama_model_rope_type(model: llama_model_p, /) -> int: ...
+
+
+# LLAMA_API int32_t llama_model_n_ctx_train(const struct llama_model * model);
+@ctypes_function("llama_model_n_ctx_train", [llama_model_p_ctypes], ctypes.c_int32)
+def llama_model_n_ctx_train(model: llama_model_p, /) -> int: ...
+
+
+# LLAMA_API int32_t llama_model_n_embd     (const struct llama_model * model);
+@ctypes_function("llama_model_n_embd", [llama_model_p_ctypes], ctypes.c_int32)
+def llama_model_n_embd(model: llama_model_p, /) -> int: ...
+
+
+# LLAMA_API int32_t llama_model_n_embd_inp (const struct llama_model * model);
+@ctypes_function("llama_model_n_embd_inp", [llama_model_p_ctypes], ctypes.c_int32)
+def llama_model_n_embd_inp(model: llama_model_p, /) -> int:
+    """Get the model input embedding size."""
     ...
 
 
-# LLAMA_API enum llama_rope_type    llama_rope_type   (const struct llama_model * model);
-@ctypes_function("llama_rope_type", [llama_model_p_ctypes], ctypes.c_int)
-def llama_rope_type(model: llama_model_p, /) -> int:
+# LLAMA_API int32_t llama_model_n_embd_out (const struct llama_model * model);
+@ctypes_function("llama_model_n_embd_out", [llama_model_p_ctypes], ctypes.c_int32)
+def llama_model_n_embd_out(model: llama_model_p, /) -> int:
+    """Get the model output embedding size."""
     ...
 
 
+# LLAMA_API int32_t llama_model_n_layer    (const struct llama_model * model);
+@ctypes_function("llama_model_n_layer", [llama_model_p_ctypes], ctypes.c_int32)
+def llama_model_n_layer(model: llama_model_p, /) -> int: ...
+
+
+# LLAMA_API int32_t llama_model_n_layer_nextn(const struct llama_model * model);
+@ctypes_function("llama_model_n_layer_nextn", [llama_model_p_ctypes], ctypes.c_int32)
+def llama_model_n_layer_nextn(model: llama_model_p, /) -> int: ...
+
+
+# LLAMA_API int32_t llama_model_n_head     (const struct llama_model * model);
+@ctypes_function("llama_model_n_head", [llama_model_p_ctypes], ctypes.c_int32)
+def llama_model_n_head(model: llama_model_p, /) -> int: ...
+
+
+# LLAMA_API int32_t llama_model_n_head_kv  (const struct llama_model * model);
+@ctypes_function("llama_model_n_head_kv", [llama_model_p_ctypes], ctypes.c_int32)
+def llama_model_n_head_kv(model: llama_model_p, /) -> int: ...
+
+
+# LLAMA_API int32_t llama_model_n_swa      (const struct llama_model * model);
+@ctypes_function("llama_model_n_swa", [llama_model_p_ctypes], ctypes.c_int32)
+def llama_model_n_swa(model: llama_model_p, /) -> int: ...
+
+
 # // Get the model's RoPE frequency scaling factor
-# LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model);
-@ctypes_function("llama_rope_freq_scale_train", [llama_model_p_ctypes], ctypes.c_float)
-def llama_rope_freq_scale_train(model: llama_model_p, /) -> float:
-    """Get the model's RoPE frequency scaling factor"""
+# LLAMA_API float llama_model_rope_freq_scale_train(const struct llama_model * model);
+@ctypes_function(
+    "llama_model_rope_freq_scale_train", [llama_model_p_ctypes], ctypes.c_float
+)
+def llama_model_rope_freq_scale_train(model: llama_model_p, /) -> float: ...
+
+
+# // Returns the number of classifier outputs (only valid for classifier models)
+# // Undefined behavior for non-classifier models
+# LLAMA_API uint32_t llama_model_n_cls_out(const struct llama_model * model);
+@ctypes_function("llama_model_n_cls_out", [llama_model_p_ctypes], ctypes.c_uint32)
+def llama_model_n_cls_out(model: llama_model_p, /) -> int:
+    """Returns the number of classifier outputs (only valid for classifier models)"""
+    ...
+
+
+# // Returns label of classifier output by index (<n_cls_out). Returns nullptr if no label provided
+# LLAMA_API const char * llama_model_cls_label(const struct llama_model * model, uint32_t i);
+@ctypes_function(
+    "llama_model_cls_label", [llama_model_p_ctypes, ctypes.c_uint32], ctypes.c_char_p
+)
+def llama_model_cls_label(model: llama_model_p, i: int, /) -> Optional[bytes]:
+    """Returns label of classifier output by index. Returns None if no label provided"""
     ...
 
 
+# LLAMA_API enum llama_vocab_type   llama_vocab_type  (const struct llama_model * model);
+@ctypes_function("llama_vocab_type", [llama_vocab_p_ctypes], ctypes.c_int)
+def llama_vocab_type(vocab: llama_vocab_p, /) -> int: ...
+
+
+# LLAMA_API int32_t llama_vocab_n_tokens(const struct llama_vocab * vocab);
+@ctypes_function("llama_vocab_n_tokens", [llama_vocab_p_ctypes], ctypes.c_int32)
+def llama_vocab_n_tokens(vocab: llama_vocab_p, /) -> int: ...
+
+
 # // Functions to access the model's GGUF metadata scalar values
 # // - The functions return the length of the string on success, or -1 on failure
 # // - The output string is always null-terminated and cleared on failure
+# // - When retrieving a string, an extra byte must be allocated to account for the null terminator
 # // - GGUF array values are not supported by these functions
 
 
@@ -1295,8 +1839,16 @@ def llama_model_meta_count(model: llama_model_p, /) -> int:
     ...
 
 
-# // Get metadata key name by index
-# LLAMA_API int32_t llama_model_meta_key_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size);
+# // Get sampling metadata key name. Returns nullptr if the key is invalid
+# LLAMA_API const char * llama_model_meta_key_str(enum llama_model_meta_key key);
+@ctypes_function("llama_model_meta_key_str", [ctypes.c_int], ctypes.c_char_p)
+def llama_model_meta_key_str(key: int, /) -> Optional[bytes]:
+    """Get sampling metadata key name. Returns None if the key is invalid."""
+    ...
+
+
+# // Get metadata key name by index
+# LLAMA_API int32_t llama_model_meta_key_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size);
 @ctypes_function(
     "llama_model_meta_key_by_index",
     [
@@ -1366,6 +1918,22 @@ def llama_model_size(model: llama_model_p, /) -> int:
     ...
 
 
+# // Get the default chat template. Returns nullptr if not available
+# // If name is NULL, returns the default chat template
+# LLAMA_API const char * llama_model_chat_template(const struct llama_model * model, const char * name);
+@ctypes_function(
+    "llama_model_chat_template",
+    [llama_model_p_ctypes, ctypes.c_char_p],
+    ctypes.c_char_p,
+)
+def llama_model_chat_template(
+    model: llama_model_p, name: Optional[bytes], /
+) -> Optional[bytes]:
+    """Get the default chat template. Returns None if not available
+    If name is None, returns the default chat template"""
+    ...
+
+
 # // Returns the total number of parameters in the model
 # LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model);
 @ctypes_function("llama_model_n_params", [llama_model_p_ctypes], ctypes.c_uint64)
@@ -1374,18 +1942,6 @@ def llama_model_n_params(model: llama_model_p, /) -> int:
     ...
 
 
-# // Get a llama model tensor
-# LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name);
-@ctypes_function(
-    "llama_get_model_tensor", [llama_model_p_ctypes, ctypes.c_char_p], ctypes.c_void_p
-)
-def llama_get_model_tensor(
-    model: llama_model_p, name: Union[ctypes.c_char_p, bytes], /
-) -> ctypes.c_void_p:
-    """Get a llama model tensor"""
-    ...
-
-
 # // Returns true if the model contains an encoder that requires llama_encode() call
 # LLAMA_API bool llama_model_has_encoder(const struct llama_model * model);
 @ctypes_function("llama_model_has_encoder", [llama_model_p_ctypes], ctypes.c_bool)
@@ -1423,6 +1979,22 @@ def llama_model_is_recurrent(model: llama_model_p, /) -> bool:
     ...
 
 
+# // Returns true if the model is hybrid (like Jamba, Granite, etc.)
+# LLAMA_API bool llama_model_is_hybrid(const struct llama_model * model);
+@ctypes_function("llama_model_is_hybrid", [llama_model_p_ctypes], ctypes.c_bool)
+def llama_model_is_hybrid(model: llama_model_p, /) -> bool:
+    """Returns true if the model is hybrid (like Jamba, Granite, etc.)"""
+    ...
+
+
+# // Returns true if the model is diffusion-based (like LLaDA, Dream, etc.)
+# LLAMA_API bool llama_model_is_diffusion(const struct llama_model * model);
+@ctypes_function("llama_model_is_diffusion", [llama_model_p_ctypes], ctypes.c_bool)
+def llama_model_is_diffusion(model: llama_model_p, /) -> bool:
+    """Returns true if the model is diffusion-based (like LLaDA, Dream, etc.)"""
+    ...
+
+
 # // Returns 0 on success
 # LLAMA_API uint32_t llama_model_quantize(
 #         const char * fname_inp,
@@ -1447,104 +2019,201 @@ def llama_model_quantize(
     ...
 
 
+# //
+# // Adapters
+# //
+
+
 # // Load a LoRA adapter from file
-# // The loaded adapter will be associated to the given model, and will be free when the model is deleted
-# LLAMA_API struct llama_lora_adapter * llama_lora_adapter_init(
+# LLAMA_API struct llama_adapter_lora * llama_adapter_lora_init(
 #         struct llama_model * model,
 #         const char * path_lora);
 @ctypes_function(
-    "llama_lora_adapter_init",
+    "llama_adapter_lora_init",
     [llama_model_p_ctypes, ctypes.c_char_p],
-    llama_lora_adapter_p_ctypes,
+    llama_adapter_lora_p_ctypes,
 )
-def llama_lora_adapter_init(
+def llama_adapter_lora_init(
     model: llama_model_p, path_lora: bytes, /
-) -> Optional[llama_lora_adapter_p]:
-    """Load a LoRA adapter from file
-    The loaded adapter will be associated to the given model, and will be free when the model is deleted
-    """
-    ...
+) -> Optional[llama_adapter_lora_p]: ...
 
 
-# // Add a loaded LoRA adapter to given context
-# // This will not modify model's weight
-# LLAMA_API int32_t llama_lora_adapter_set(
-#         struct llama_context * ctx,
-#         struct llama_lora_adapter * adapter,
-#         float scale);
+# // Get metadata value as a string by key name
+# LLAMA_API int32_t llama_adapter_meta_val_str(const struct llama_adapter_lora * adapter, const char * key, char * buf, size_t buf_size);
 @ctypes_function(
-    "llama_lora_adapter_set",
-    [llama_context_p_ctypes, llama_lora_adapter_p_ctypes, ctypes.c_float],
+    "llama_adapter_meta_val_str",
+    [
+        llama_adapter_lora_p_ctypes,
+        ctypes.c_char_p,
+        ctypes.c_char_p,
+        ctypes.c_size_t,
+    ],
     ctypes.c_int32,
 )
-def llama_lora_adapter_set(
-    ctx: llama_context_p, adapter: llama_lora_adapter_p, scale: float, /
+def llama_adapter_meta_val_str(
+    adapter: llama_adapter_lora_p,
+    key: bytes,
+    buf: Union[bytes, CtypesArray[ctypes.c_char]],
+    buf_size: int,
+    /,
 ) -> int:
-    """Add a loaded LoRA adapter to given context
-    This will not modify model's weight"""
+    """Get adapter metadata value as a string by key name."""
     ...
 
 
-# // Remove a specific LoRA adapter from given context
-# // Return -1 if the adapter is not present in the context
-# LLAMA_API int32_t llama_lora_adapter_remove(
-#         struct llama_context * ctx,
-#         struct llama_lora_adapter * adapter);
+# // Get the number of metadata key/value pairs
+# LLAMA_API int32_t llama_adapter_meta_count(const struct llama_adapter_lora * adapter);
+@ctypes_function(
+    "llama_adapter_meta_count", [llama_adapter_lora_p_ctypes], ctypes.c_int32
+)
+def llama_adapter_meta_count(adapter: llama_adapter_lora_p, /) -> int:
+    """Get the number of adapter metadata key/value pairs."""
+    ...
+
+
+# // Get metadata key name by index
+# LLAMA_API int32_t llama_adapter_meta_key_by_index(const struct llama_adapter_lora * adapter, int32_t i, char * buf, size_t buf_size);
 @ctypes_function(
-    "llama_lora_adapter_remove",
-    [llama_context_p_ctypes, llama_lora_adapter_p_ctypes],
+    "llama_adapter_meta_key_by_index",
+    [
+        llama_adapter_lora_p_ctypes,
+        ctypes.c_int32,
+        ctypes.c_char_p,
+        ctypes.c_size_t,
+    ],
     ctypes.c_int32,
 )
-def llama_lora_adapter_remove(
-    ctx: llama_context_p, adapter: llama_lora_adapter_p, /
+def llama_adapter_meta_key_by_index(
+    adapter: llama_adapter_lora_p,
+    i: int,
+    buf: Union[bytes, CtypesArray[ctypes.c_char]],
+    buf_size: int,
+    /,
 ) -> int:
-    """Remove a LoRA adapter from given context
-    Return -1 if the adapter is not present in the context"""
+    """Get adapter metadata key name by index."""
     ...
 
 
-# // Remove all LoRA adapters from given context
-# LLAMA_API void llama_lora_adapter_clear(
-#         struct llama_context * ctx);
+# // Get metadata value as a string by index
+# LLAMA_API int32_t llama_adapter_meta_val_str_by_index(const struct llama_adapter_lora * adapter, int32_t i, char * buf, size_t buf_size);
 @ctypes_function(
-    "llama_lora_adapter_clear",
-    [llama_context_p_ctypes],
-    None,
+    "llama_adapter_meta_val_str_by_index",
+    [
+        llama_adapter_lora_p_ctypes,
+        ctypes.c_int32,
+        ctypes.c_char_p,
+        ctypes.c_size_t,
+    ],
+    ctypes.c_int32,
 )
-def llama_lora_adapter_clear(ctx: llama_context_p, /):
-    """Remove all LoRA adapters from given context"""
+def llama_adapter_meta_val_str_by_index(
+    adapter: llama_adapter_lora_p,
+    i: int,
+    buf: Union[bytes, CtypesArray[ctypes.c_char]],
+    buf_size: int,
+    /,
+) -> int:
+    """Get adapter metadata value as a string by index."""
     ...
 
 
 # // Manually free a LoRA adapter
 # // Note: loaded adapters will be free when the associated model is deleted
-# LLAMA_API void llama_lora_adapter_free(struct llama_lora_adapter * adapter);
+# LLAMA_API void llama_adapter_lora_free(struct llama_adapter_lora * adapter);
 @ctypes_function(
-    "llama_lora_adapter_free",
-    [llama_lora_adapter_p_ctypes],
+    "llama_adapter_lora_free",
+    [llama_adapter_lora_p_ctypes],
     None,
 )
-def llama_lora_adapter_free(adapter: llama_lora_adapter_p, /):
-    """Manually free a LoRA adapter
-    Note: loaded adapters will be free when the associated model is deleted"""
+def llama_adapter_lora_free(adapter: llama_adapter_lora_p, /): ...
+
+
+# // Get the invocation tokens if the current lora is an alora
+# LLAMA_API uint64_t            llama_adapter_get_alora_n_invocation_tokens(const struct llama_adapter_lora * adapter);
+@ctypes_function(
+    "llama_adapter_get_alora_n_invocation_tokens",
+    [llama_adapter_lora_p_ctypes],
+    ctypes.c_uint64,
+)
+def llama_adapter_get_alora_n_invocation_tokens(
+    adapter: llama_adapter_lora_p, /
+) -> int:
+    """Get the invocation token count if the current LoRA is an aLoRA."""
+    ...
+
+
+# LLAMA_API const llama_token * llama_adapter_get_alora_invocation_tokens  (const struct llama_adapter_lora * adapter);
+@ctypes_function(
+    "llama_adapter_get_alora_invocation_tokens",
+    [llama_adapter_lora_p_ctypes],
+    ctypes.POINTER(llama_token),
+)
+def llama_adapter_get_alora_invocation_tokens(
+    adapter: llama_adapter_lora_p, /
+) -> Optional[CtypesPointer[llama_token]]:
+    """Get the invocation tokens if the current LoRA is an aLoRA."""
+    ...
+
+
+# // The following functions operate on a llama_context, hence the naming: llama_verb_...
+
+
+# // Set LoRa adapters on the context. Will only modify if the adapters currently in context are different.
+# LLAMA_API int32_t llama_set_adapters_lora(
+#         struct llama_context * ctx,
+#         struct llama_adapter_lora ** adapters,
+#         size_t n_adapters,
+#         float * scales);
+@ctypes_function(
+    "llama_set_adapters_lora",
+    [
+        llama_context_p_ctypes,
+        ctypes.POINTER(llama_adapter_lora_p_ctypes),
+        ctypes.c_size_t,
+        ctypes.POINTER(ctypes.c_float),
+    ],
+    ctypes.c_int32,
+)
+def llama_set_adapters_lora(
+    ctx: llama_context_p,
+    adapters: Optional[CtypesArray[llama_adapter_lora_p_ctypes]],
+    n_adapters: int,
+    scales: Optional[CtypesArray[ctypes.c_float]],
+    /,
+) -> int:
+    """Set LoRA adapters on the context if they differ from the current adapters."""
     ...
 
 
+# Deprecated compatibility wrapper for the renamed llama_set_adapters_lora().
+def llama_set_adapter_lora(
+    ctx: llama_context_p, adapter: llama_adapter_lora_p, scale: float, /
+) -> int:
+    warnings.warn(
+        "llama_set_adapter_lora is deprecated; use llama_set_adapters_lora instead",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+    adapters = (llama_adapter_lora_p_ctypes * 1)(adapter)
+    scales = (ctypes.c_float * 1)(scale)
+    return llama_set_adapters_lora(ctx, adapters, 1, scales)
+
+
 # // Apply a loaded control vector to a llama_context, or if data is NULL, clear
 # // the currently loaded vector.
 # // n_embd should be the size of a single layer's control, and data should point
 # // to an n_embd x n_layers buffer starting from layer 1.
 # // il_start and il_end are the layer range the vector should apply to (both inclusive)
 # // See llama_control_vector_load in common to load a control vector.
-# LLAMA_API int32_t llama_control_vector_apply(
-#         struct llama_context * lctx,
+# LLAMA_API int32_t llama_set_adapter_cvec(
+#         struct llama_context * ctx,
 #                  const float * data,
 #                       size_t   len,
 #                      int32_t   n_embd,
 #                      int32_t   il_start,
 #                      int32_t   il_end);
 @ctypes_function(
-    "llama_control_vector_apply",
+    "llama_set_adapter_cvec",
     [
         llama_context_p_ctypes,
         ctypes.POINTER(ctypes.c_float),
@@ -1555,8 +2224,8 @@ def llama_lora_adapter_free(adapter: llama_lora_adapter_p, /):
     ],
     ctypes.c_int32,
 )
-def llama_control_vector_apply(
-    lctx: llama_context_p,
+def llama_set_adapter_cvec(
+    ctx: llama_context_p,
     data: CtypesPointerOrRef[ctypes.c_float],
     len: int,
     n_embd: int,
@@ -1573,149 +2242,42 @@ def llama_control_vector_apply(
     ...
 
 
-# //
-# // KV cache
-# //
-
-
-# // Information associated with an individual cell in the KV cache view.
-# struct llama_kv_cache_view_cell {
-#     // The position for this cell. Takes KV cache shifts into account.
-#     // May be negative if the cell is not populated.
-#     llama_pos pos;
-# };
-class llama_kv_cache_view_cell(ctypes.Structure):
-    """Information associated with an individual cell in the KV cache view.
-
-    Attributes:
-        pos (llama_pos): The position for this cell. Takes KV cache shifts into account.
-            May be negative if the cell is not populated."""
-
-    if TYPE_CHECKING:
-        pos: llama_pos
-
-    _fields_ = [("pos", llama_pos)]
-
-
-# // An updateable view of the KV cache.
-# struct llama_kv_cache_view {
-#     // Number of KV cache cells. This will be the same as the context size.
-#     int32_t n_cells;
-
-#     // Maximum number of sequences that can exist in a cell. It's not an error
-#     // if there are more sequences in a cell than this value, however they will
-#     // not be visible in the view cells_sequences.
-#     int32_t n_seq_max;
-
-#     // Number of tokens in the cache. For example, if there are two populated
-#     // cells, the first with 1 sequence id in it and the second with 2 sequence
-#     // ids then you'll have 3 tokens.
-#     int32_t token_count;
-
-#     // Number of populated cache cells.
-#     int32_t used_cells;
-
-#     // Maximum contiguous empty slots in the cache.
-#     int32_t max_contiguous;
-
-#     // Index to the start of the max_contiguous slot range. Can be negative
-#     // when cache is full.
-#     int32_t max_contiguous_idx;
-
-#     // Information for an individual cell.
-#     struct llama_kv_cache_view_cell * cells;
-
-
-#     // The sequences for each cell. There will be n_seq_max items per cell.
-#     llama_seq_id * cells_sequences;
-# };
-class llama_kv_cache_view(ctypes.Structure):
-    if TYPE_CHECKING:
-        n_cells: int
-        n_max_seq: int
-        token_count: int
-        used_cells: int
-        max_contiguous: int
-        max_contiguous_idx: int
-        cells: CtypesArray[llama_kv_cache_view_cell]
-        cells_sequences: CtypesArray[llama_seq_id]
-
-    _fields_ = [
-        ("n_cells", ctypes.c_int32),
-        ("n_max_seq", ctypes.c_int32),
-        ("token_count", ctypes.c_int32),
-        ("used_cells", ctypes.c_int32),
-        ("max_contiguous", ctypes.c_int32),
-        ("max_contiguous_idx", ctypes.c_int32),
-        ("cells", ctypes.POINTER(llama_kv_cache_view_cell)),
-        ("cells_sequences", ctypes.POINTER(llama_seq_id)),
-    ]
-
-
-llama_kv_cache_view_p = ctypes.POINTER(llama_kv_cache_view)
-
-
-# // Create an empty KV cache view. (use only for debugging purposes)
-# LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max);
-@ctypes_function(
-    "llama_kv_cache_view_init",
-    [llama_context_p_ctypes, ctypes.c_int32],
-    llama_kv_cache_view,
-)
-def llama_kv_cache_view_init(
-    ctx: llama_context_p, n_seq_max: Union[ctypes.c_int32, int], /
-) -> llama_kv_cache_view:
-    """Create an empty KV cache view. (use only for debugging purposes)"""
-    ...
-
-
-# // Free a KV cache view. (use only for debugging purposes)
-# LLAMA_API void llama_kv_cache_view_free(struct llama_kv_cache_view * view);
-@ctypes_function("llama_kv_cache_view_free", [llama_kv_cache_view_p], None)
-def llama_kv_cache_view_free(view: "ctypes.pointer[llama_kv_cache_view]", /):  # type: ignore
-    """Free a KV cache view. (use only for debugging purposes)"""
-    ...
-
-
-# // Update the KV cache view structure with the current state of the KV cache. (use only for debugging purposes)
-# LLAMA_API void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_kv_cache_view * view);
-@ctypes_function(
-    "llama_kv_cache_view_update", [llama_context_p_ctypes, llama_kv_cache_view_p], None
-)
-def llama_kv_cache_view_update(ctx: llama_context_p, view: CtypesPointerOrRef[llama_kv_cache_view], /):  # type: ignore
-    """Update the KV cache view structure with the current state of the KV cache. (use only for debugging purposes)"""
-    ...
+# Deprecated compatibility wrapper for the renamed llama_set_adapter_cvec().
+def llama_apply_adapter_cvec(
+    ctx: llama_context_p,
+    data: CtypesPointerOrRef[ctypes.c_float],
+    len: int,
+    n_embd: int,
+    il_start: int,
+    il_end: int,
+    /,
+) -> int:
+    warnings.warn(
+        "llama_apply_adapter_cvec is deprecated; use llama_set_adapter_cvec instead",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+    return llama_set_adapter_cvec(ctx, data, len, n_embd, il_start, il_end)
 
 
-# // Returns the number of tokens in the KV cache (slow, use only for debug)
-# // If a KV cell has multiple sequences assigned to it, it will be counted multiple times
-# LLAMA_API int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx);
-@ctypes_function(
-    "llama_get_kv_cache_token_count", [llama_context_p_ctypes], ctypes.c_int32
-)
-def llama_get_kv_cache_token_count(ctx: llama_context_p, /) -> int:
-    """Returns the number of tokens in the KV cache (slow, use only for debug)
-    If a KV cell has multiple sequences assigned to it, it will be counted multiple times
-    """
-    ...
+# //
+# // Memory
+# //
 
 
-# // Returns the number of used KV cells (i.e. have at least one sequence assigned to them)
-# LLAMA_API int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx);
+# // Clear the memory contents
+# // If data == true, the data buffers will also be cleared together with the metadata
+# LLAMA_API void llama_memory_clear(
+#         llama_memory_t mem,
+#                   bool data);
 @ctypes_function(
-    "llama_get_kv_cache_used_cells", [llama_context_p_ctypes], ctypes.c_int32
+    "llama_memory_clear",
+    [llama_memory_t_ctypes, ctypes.c_bool],
+    None,
 )
-def llama_get_kv_cache_used_cells(ctx: llama_context_p, /) -> int:
-    """Returns the number of used KV cells (i.e. have at least one sequence assigned to them)"""
-    ...
-
-
-# // Clear the KV cache - both cell info is erased and KV data is zeroed
-# LLAMA_API void llama_kv_cache_clear(
-#         struct llama_context * ctx);
-@ctypes_function("llama_kv_cache_clear", [llama_context_p_ctypes], None)
-def llama_kv_cache_clear(ctx: llama_context_p, /):
-    """Clear the KV cache"""
+def llama_memory_clear(mem: llama_memory_t, data: bool, /):
+    """Clear the memory contents
+    If data == true, the data buffers will also be cleared together with the metadata"""
     ...
 
 
@@ -1724,23 +2286,23 @@ def llama_kv_cache_clear(ctx: llama_context_p, /):
 # // seq_id < 0 : match any sequence
 # // p0 < 0     : [0,  p1]
 # // p1 < 0     : [p0, inf)
-# LLAMA_API bool llama_kv_cache_seq_rm(
-#         struct llama_context * ctx,
-#                 llama_seq_id   seq_id,
-#                    llama_pos   p0,
-#                    llama_pos   p1);
+# LLAMA_API bool llama_memory_seq_rm(
+#         llama_memory_t mem,
+#           llama_seq_id seq_id,
+#              llama_pos p0,
+#              llama_pos p1);
 @ctypes_function(
-    "llama_kv_cache_seq_rm",
+    "llama_memory_seq_rm",
     [
-        llama_context_p_ctypes,
+        llama_memory_t_ctypes,
         llama_seq_id,
         llama_pos,
         llama_pos,
     ],
     ctypes.c_bool,
 )
-def llama_kv_cache_seq_rm(
-    ctx: llama_context_p,
+def llama_memory_seq_rm(
+    mem: llama_memory_t,
     seq_id: Union[llama_seq_id, int],
     p0: Union[llama_pos, int],
     p1: Union[llama_pos, int],
@@ -1757,19 +2319,18 @@ def llama_kv_cache_seq_rm(
 
 
 # // Copy all tokens that belong to the specified sequence to another sequence
-# // Note that this does not allocate extra KV cache memory - it simply assigns the tokens to the new sequence
 # // p0 < 0 : [0,  p1]
 # // p1 < 0 : [p0, inf)
-# LLAMA_API void llama_kv_cache_seq_cp(
-#         struct llama_context * ctx,
-#                 llama_seq_id   seq_id_src,
-#                 llama_seq_id   seq_id_dst,
-#                    llama_pos   p0,
-#                    llama_pos   p1);
+# LLAMA_API void llama_memory_seq_cp(
+#         llama_memory_t mem,
+#           llama_seq_id seq_id_src,
+#           llama_seq_id seq_id_dst,
+#              llama_pos p0,
+#              llama_pos p1);
 @ctypes_function(
-    "llama_kv_cache_seq_cp",
+    "llama_memory_seq_cp",
     [
-        llama_context_p_ctypes,
+        llama_memory_t_ctypes,
         llama_seq_id,
         llama_seq_id,
         llama_pos,
@@ -1777,8 +2338,8 @@ def llama_kv_cache_seq_rm(
     ],
     None,
 )
-def llama_kv_cache_seq_cp(
-    ctx: llama_context_p,
+def llama_memory_seq_cp(
+    mem: llama_memory_t,
     seq_id_src: Union[llama_seq_id, int],
     seq_id_dst: Union[llama_seq_id, int],
     p0: Union[llama_pos, int],
@@ -1786,40 +2347,34 @@ def llama_kv_cache_seq_cp(
     /,
 ):
     """Copy all tokens that belong to the specified sequence to another sequence
-    Note that this does not allocate extra KV cache memory - it simply assigns the tokens to the new sequence
     p0 < 0 : [0,  p1]
     p1 < 0 : [p0, inf)"""
     ...
 
 
 # // Removes all tokens that do not belong to the specified sequence
-# LLAMA_API void llama_kv_cache_seq_keep(
-#         struct llama_context * ctx,
-#                 llama_seq_id   seq_id);
-@ctypes_function(
-    "llama_kv_cache_seq_keep", [llama_context_p_ctypes, llama_seq_id], None
-)
-def llama_kv_cache_seq_keep(ctx: llama_context_p, seq_id: Union[llama_seq_id, int], /):
+# LLAMA_API void llama_memory_seq_keep(
+#         llama_memory_t mem,
+#           llama_seq_id seq_id);
+@ctypes_function("llama_memory_seq_keep", [llama_memory_t_ctypes, llama_seq_id], None)
+def llama_memory_seq_keep(mem: llama_memory_t, seq_id: Union[llama_seq_id, int], /):
     """Removes all tokens that do not belong to the specified sequence"""
     ...
 
 
 # // Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1)
-# // If the KV cache is RoPEd, the KV data is updated accordingly:
-# //   - lazily on next llama_decode()
-# //   - explicitly with llama_kv_cache_update()
 # // p0 < 0 : [0,  p1]
 # // p1 < 0 : [p0, inf)
-# LLAMA_API void llama_kv_cache_seq_add(
-#         struct llama_context * ctx,
-#                 llama_seq_id   seq_id,
-#                    llama_pos   p0,
-#                    llama_pos   p1,
-#                    llama_pos   delta);
+# LLAMA_API void llama_memory_seq_add(
+#         llama_memory_t mem,
+#           llama_seq_id seq_id,
+#              llama_pos p0,
+#              llama_pos p1,
+#              llama_pos delta);
 @ctypes_function(
-    "llama_kv_cache_seq_add",
+    "llama_memory_seq_add",
     [
-        llama_context_p_ctypes,
+        llama_memory_t_ctypes,
         llama_seq_id,
         llama_pos,
         llama_pos,
@@ -1827,8 +2382,8 @@ def llama_kv_cache_seq_keep(ctx: llama_context_p, seq_id: Union[llama_seq_id, in
     ],
     None,
 )
-def llama_kv_cache_seq_add(
-    ctx: llama_context_p,
+def llama_memory_seq_add(
+    mem: llama_memory_t,
     seq_id: Union[llama_seq_id, int],
     p0: Union[llama_pos, int],
     p1: Union[llama_pos, int],
@@ -1836,28 +2391,24 @@ def llama_kv_cache_seq_add(
     /,
 ):
     """Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1)
-    If the KV cache is RoPEd, the KV data is updated accordingly:
-    - lazily on next llama_decode()
-    - explicitly with llama_kv_cache_update()
     p0 < 0 : [0,  p1]
     p1 < 0 : [p0, inf)"""
     ...
 
 
 # // Integer division of the positions by factor of `d > 1`
-# // If the KV cache is RoPEd, the KV data is updated accordingly
 # // p0 < 0 : [0,  p1]
 # // p1 < 0 : [p0, inf)
-# LLAMA_API void llama_kv_cache_seq_div(
-#         struct llama_context * ctx,
-#                 llama_seq_id   seq_id,
-#                    llama_pos   p0,
-#                    llama_pos   p1,
-#                          int   d);
+# LLAMA_API void llama_memory_seq_div(
+#         llama_memory_t mem,
+#           llama_seq_id seq_id,
+#              llama_pos p0,
+#              llama_pos p1,
+#                    int d);
 @ctypes_function(
-    "llama_kv_cache_seq_div",
+    "llama_memory_seq_div",
     [
-        llama_context_p_ctypes,
+        llama_memory_t_ctypes,
         llama_seq_id,
         llama_pos,
         llama_pos,
@@ -1865,8 +2416,8 @@ def llama_kv_cache_seq_add(
     ],
     None,
 )
-def llama_kv_cache_seq_div(
-    ctx: llama_context_p,
+def llama_memory_seq_div(
+    mem: llama_memory_t,
     seq_id: Union[llama_seq_id, int],
     p0: Union[llama_pos, int],
     p1: Union[llama_pos, int],
@@ -1874,39 +2425,52 @@ def llama_kv_cache_seq_div(
     /,
 ):
     """Integer division of the positions by factor of `d > 1`
-    If the KV cache is RoPEd, the KV data is updated accordingly
     p0 < 0 : [0,  p1]
     p1 < 0 : [p0, inf)"""
     ...
 
 
-# // Defragment the KV cache
-# // This will be applied:
-# //   - lazily on next llama_decode()
-# //   - explicitly with llama_kv_cache_update()
-# LLAMA_API void llama_kv_cache_defrag(struct llama_context * ctx);
-@ctypes_function("llama_kv_cache_defrag", [llama_context_p_ctypes], None)
-def llama_kv_cache_defrag(ctx: llama_context_p, /):
-    """Defragment the KV cache
-    This will be applied:
-    - lazily on next llama_decode()
-    - explicitly with llama_kv_cache_update()"""
+# // Returns the smallest position present in the memory for the specified sequence
+# // This is typically non-zero only for SWA caches
+# // Note that all positions in the range [pos_min, pos_max] are guaranteed to be present in the memory
+# // Return -1 if the sequence is empty
+# LLAMA_API llama_pos llama_memory_seq_pos_min(
+#         llama_memory_t mem,
+#           llama_seq_id seq_id);
+@ctypes_function(
+    "llama_memory_seq_pos_min", [llama_memory_t_ctypes, llama_seq_id], llama_pos
+)
+def llama_memory_seq_pos_min(
+    mem: llama_memory_t, seq_id: Union[llama_seq_id, int], /
+) -> int:
+    """Returns the smallest position present in the memory for the specified sequence
+    This is typically non-zero only for SWA caches
+    Return -1 if the sequence is empty"""
     ...
 
 
-# // Apply the KV cache updates (such as K-shifts, defragmentation, etc.)
-# LLAMA_API void llama_kv_cache_update(struct llama_context * ctx);
-@ctypes_function("llama_kv_cache_update", [llama_context_p_ctypes], None)
-def llama_kv_cache_update(ctx: llama_context_p, /):
-    """Apply the KV cache updates (such as K-shifts, defragmentation, etc.)"""
+# // Returns the largest position present in the memory for the specified sequence
+# // Note that all positions in the range [pos_min, pos_max] are guaranteed to be present in the memory
+# // Return -1 if the sequence is empty
+# LLAMA_API llama_pos llama_memory_seq_pos_max(
+#         llama_memory_t mem,
+#           llama_seq_id seq_id);
+@ctypes_function(
+    "llama_memory_seq_pos_max", [llama_memory_t_ctypes, llama_seq_id], llama_pos
+)
+def llama_memory_seq_pos_max(
+    mem: llama_memory_t, seq_id: Union[llama_seq_id, int], /
+) -> int:
+    """Returns the largest position present in the memory for the specified sequence
+    Return -1 if the sequence is empty"""
     ...
 
 
-# // Check if the context supports KV cache shifting
-# LLAMA_API bool llama_kv_cache_can_shift(struct llama_context * ctx);
-@ctypes_function("llama_kv_cache_can_shift", [llama_context_p_ctypes], ctypes.c_bool)
-def llama_kv_cache_can_shift(ctx: llama_context_p, /) -> bool:
-    """Check if the context supports KV cache shifting"""
+# // Check if the memory supports shifting
+# LLAMA_API bool llama_memory_can_shift(llama_memory_t mem);
+@ctypes_function("llama_memory_can_shift", [llama_memory_t_ctypes], ctypes.c_bool)
+def llama_memory_can_shift(mem: llama_memory_t, /) -> bool:
+    """Check if the memory supports shifting"""
     ...
 
 
@@ -1916,12 +2480,12 @@ def llama_kv_cache_can_shift(ctx: llama_context_p, /) -> bool:
 
 
 # // Returns the *actual* size in bytes of the state
-# // (logits, embedding and kv_cache)
+# // (logits, embedding and memory)
 # // Only use when saving the state, not when restoring it, otherwise the size may be too small.
 # LLAMA_API size_t llama_state_get_size(struct llama_context * ctx);
 @ctypes_function("llama_state_get_size", [llama_context_p_ctypes], ctypes.c_size_t)
 def llama_state_get_size(ctx: llama_context_p, /) -> int:
-    """Returns the *actual* size in bytes of the state (rng, logits, embedding and kv_cache) - will often be smaller after compacting tokens"""
+    """Returns the *actual* size in bytes of the state (logits, embedding and memory)"""
     ...
 
 
@@ -1929,11 +2493,18 @@ def llama_state_get_size(ctx: llama_context_p, /) -> int:
 #     "use llama_state_get_size instead");
 @ctypes_function("llama_get_state_size", [llama_context_p_ctypes], ctypes.c_size_t)
 def llama_get_state_size(ctx: llama_context_p, /) -> int:
-    """Returns the maximum size in bytes of the state (rng, logits, embedding
-    and kv_cache) - will often be smaller after compacting tokens"""
+    """Returns the size in bytes of the state (DEPRECATED)"""
     ...
 
 
+_llama_get_state_size = llama_get_state_size
+
+
+def llama_get_state_size(ctx: llama_context_p, /) -> int:
+    _warn_deprecated("llama_get_state_size", "use llama_state_get_size instead")
+    return _llama_get_state_size(ctx)
+
+
 # // Copies the state to the specified destination address.
 # // Destination needs to have allocated enough memory.
 # // Returns the number of bytes copied
@@ -1977,12 +2548,20 @@ def llama_state_get_data(
 def llama_copy_state_data(
     ctx: llama_context_p, dst: CtypesArray[ctypes.c_uint8], /
 ) -> int:
-    """Copies the state to the specified destination address.
-    Destination needs to have allocated enough memory.
-    Returns the number of bytes copied"""
+    """Copies the state to the specified destination address (DEPRECATED)"""
     ...
 
 
+_llama_copy_state_data = llama_copy_state_data
+
+
+def llama_copy_state_data(
+    ctx: llama_context_p, dst: CtypesArray[ctypes.c_uint8], /
+) -> int:
+    _warn_deprecated("llama_copy_state_data", "use llama_state_get_data instead")
+    return _llama_copy_state_data(ctx, dst)
+
+
 # // Set the state reading from the specified address
 # // Returns the number of bytes read
 # LLAMA_API size_t llama_state_set_data(
@@ -2017,10 +2596,20 @@ def llama_state_set_data(
 def llama_set_state_data(
     ctx: llama_context_p, src: CtypesArray[ctypes.c_uint8], /
 ) -> int:
-    """Set the state reading from the specified address"""
+    """Set the state reading from the specified address (DEPRECATED)"""
     ...
 
 
+_llama_set_state_data = llama_set_state_data
+
+
+def llama_set_state_data(
+    ctx: llama_context_p, src: CtypesArray[ctypes.c_uint8], /
+) -> int:
+    _warn_deprecated("llama_set_state_data", "use llama_state_set_data instead")
+    return _llama_set_state_data(ctx, src)
+
+
 # Save/load session file
 # LLAMA_API bool llama_state_load_file(
 #         struct llama_context * ctx,
@@ -2046,8 +2635,7 @@ def llama_state_load_file(
     n_token_capacity: Union[ctypes.c_size_t, int],
     n_token_count_out: CtypesPointerOrRef[ctypes.c_size_t],
     /,
-) -> bool:
-    ...
+) -> bool: ...
 
 
 # LLAMA_API DEPRECATED(bool llama_load_session_file(
@@ -2066,7 +2654,7 @@ def llama_state_load_file(
         ctypes.c_size_t,
         ctypes.POINTER(ctypes.c_size_t),
     ],
-    ctypes.c_size_t,
+    ctypes.c_bool,
 )
 def llama_load_session_file(
     ctx: llama_context_p,
@@ -2075,8 +2663,24 @@ def llama_load_session_file(
     n_token_capacity: Union[ctypes.c_size_t, int],
     n_token_count_out: CtypesPointerOrRef[ctypes.c_size_t],
     /,
-) -> int:
-    ...
+) -> bool: ...
+
+
+_llama_load_session_file = llama_load_session_file
+
+
+def llama_load_session_file(
+    ctx: llama_context_p,
+    path_session: bytes,
+    tokens_out: CtypesArray[llama_token],
+    n_token_capacity: Union[ctypes.c_size_t, int],
+    n_token_count_out: CtypesPointerOrRef[ctypes.c_size_t],
+    /,
+) -> bool:
+    _warn_deprecated("llama_load_session_file", "use llama_state_load_file instead")
+    return _llama_load_session_file(
+        ctx, path_session, tokens_out, n_token_capacity, n_token_count_out
+    )
 
 
 # LLAMA_API bool llama_state_save_file(
@@ -2100,8 +2704,7 @@ def llama_state_save_file(
     tokens: CtypesArray[llama_token],
     n_token_count: Union[ctypes.c_size_t, int],
     /,
-) -> bool:
-    ...
+) -> bool: ...
 
 
 # LLAMA_API DEPRECATED(bool llama_save_session_file(
@@ -2118,7 +2721,7 @@ def llama_state_save_file(
         llama_token_p,
         ctypes.c_size_t,
     ],
-    ctypes.c_size_t,
+    ctypes.c_bool,
 )
 def llama_save_session_file(
     ctx: llama_context_p,
@@ -2126,11 +2729,24 @@ def llama_save_session_file(
     tokens: CtypesArray[llama_token],
     n_token_count: Union[ctypes.c_size_t, int],
     /,
-) -> int:
-    ...
+) -> bool: ...
+
+
+_llama_save_session_file = llama_save_session_file
+
+
+def llama_save_session_file(
+    ctx: llama_context_p,
+    path_session: bytes,
+    tokens: CtypesArray[llama_token],
+    n_token_count: Union[ctypes.c_size_t, int],
+    /,
+) -> bool:
+    _warn_deprecated("llama_save_session_file", "use llama_state_save_file instead")
+    return _llama_save_session_file(ctx, path_session, tokens, n_token_count)
 
 
-# // Get the exact size needed to copy the KV cache of a single sequence
+# // Get the exact size needed to copy the state of a single sequence
 # LLAMA_API size_t llama_state_seq_get_size(
 #         struct llama_context * ctx,
 #                 llama_seq_id   seq_id);
@@ -2140,11 +2756,11 @@ def llama_save_session_file(
     ctypes.c_size_t,
 )
 def llama_state_seq_get_size(ctx: llama_context_p, seq_id: llama_seq_id, /) -> int:
-    """Get the exact size needed to copy the KV cache of a single sequence"""
+    """Get the exact size needed to copy the state of a single sequence"""
     ...
 
 
-# // Copy the KV cache of a single sequence into the specified buffer
+# // Copy the state of a single sequence into the specified buffer
 # LLAMA_API size_t llama_state_seq_get_data(
 #         struct llama_context * ctx,
 #                      uint8_t * dst,
@@ -2167,7 +2783,7 @@ def llama_state_seq_get_data(
     seq_id: llama_seq_id,
     /,
 ) -> int:
-    """Copy the KV cache of a single sequence into the specified buffer"""
+    """Copy the state of a single sequence into the specified buffer"""
     ...
 
 
@@ -2197,7 +2813,7 @@ def llama_state_seq_set_data(
     dest_seq_id: llama_seq_id,
     /,
 ) -> int:
-    """Copy the sequence data (originally copied with `llama_state_seq_get_data`) into the specified sequence"""
+    """Copy the sequence data into the specified sequence"""
     ...
 
 
@@ -2225,8 +2841,7 @@ def llama_state_seq_save_file(
     tokens: CtypesArray[llama_token],
     n_token_count: Union[ctypes.c_size_t, int],
     /,
-) -> int:
-    ...
+) -> int: ...
 
 
 # LLAMA_API size_t llama_state_seq_load_file(
@@ -2256,19 +2871,107 @@ def llama_state_seq_load_file(
     n_token_capacity: Union[ctypes.c_size_t, int],
     n_token_count_out: CtypesPointerOrRef[ctypes.c_size_t],
     /,
-) -> int:
-    ...
+) -> int: ...
 
 
-# //
-# // Decoding
-# //
+# define LLAMA_STATE_SEQ_FLAGS_NONE 0
+LLAMA_STATE_SEQ_FLAGS_NONE = 0
 
+# for backwards-compat
+# define LLAMA_STATE_SEQ_FLAGS_SWA_ONLY 1
+LLAMA_STATE_SEQ_FLAGS_SWA_ONLY = 1
 
-# // Return batch for single sequence of tokens
-# // The sequence ID will be fixed to 0
-# // The position of the tokens will be tracked automatically by llama_decode
-# //
+# work only with partial states, such as SWA KV cache or recurrent cache
+# (e.g. Mamba)
+# define LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY 1
+LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY = 1
+
+# keeps the tensor data on device buffers
+# (i.e. not accessible in host memory, but faster save/load)
+# define LLAMA_STATE_SEQ_FLAGS_ON_DEVICE 2
+LLAMA_STATE_SEQ_FLAGS_ON_DEVICE = 2
+
+
+# LLAMA_API size_t llama_state_seq_get_size_ext(
+#         struct llama_context * ctx,
+#                 llama_seq_id   seq_id,
+#        llama_state_seq_flags   flags);
+@ctypes_function(
+    "llama_state_seq_get_size_ext",
+    [llama_context_p_ctypes, llama_seq_id, llama_state_seq_flags],
+    ctypes.c_size_t,
+)
+def llama_state_seq_get_size_ext(
+    ctx: llama_context_p,
+    seq_id: llama_seq_id,
+    flags: llama_state_seq_flags,
+    /,
+) -> int: ...
+
+
+# LLAMA_API size_t llama_state_seq_get_data_ext(
+#         struct llama_context * ctx,
+#                      uint8_t * dst,
+#                       size_t   size,
+#                 llama_seq_id   seq_id,
+#        llama_state_seq_flags   flags);
+@ctypes_function(
+    "llama_state_seq_get_data_ext",
+    [
+        llama_context_p_ctypes,
+        ctypes.POINTER(ctypes.c_uint8),
+        ctypes.c_size_t,
+        llama_seq_id,
+        llama_state_seq_flags,
+    ],
+    ctypes.c_size_t,
+)
+def llama_state_seq_get_data_ext(
+    ctx: llama_context_p,
+    dst: CtypesArray[ctypes.c_uint8],
+    size: Union[ctypes.c_size_t, int],
+    seq_id: llama_seq_id,
+    flags: llama_state_seq_flags,
+    /,
+) -> int: ...
+
+
+# LLAMA_API size_t llama_state_seq_set_data_ext(
+#         struct llama_context * ctx,
+#                const uint8_t * src,
+#                       size_t   size,
+#                 llama_seq_id   dest_seq_id,
+#        llama_state_seq_flags   flags);
+@ctypes_function(
+    "llama_state_seq_set_data_ext",
+    [
+        llama_context_p_ctypes,
+        ctypes.POINTER(ctypes.c_uint8),
+        ctypes.c_size_t,
+        llama_seq_id,
+        llama_state_seq_flags,
+    ],
+    ctypes.c_size_t,
+)
+def llama_state_seq_set_data_ext(
+    ctx: llama_context_p,
+    src: CtypesArray[ctypes.c_uint8],
+    size: Union[ctypes.c_size_t, int],
+    dest_seq_id: llama_seq_id,
+    flags: llama_state_seq_flags,
+    /,
+) -> int: ...
+
+
+# //
+# // Decoding
+# //
+
+
+# // Return batch for single sequence of tokens
+# // The sequence ID will be fixed to 0
+# // The position of the tokens will be tracked automatically by llama_decode
+# //
 # // NOTE: this is a helper function to facilitate transition to the new batch API - avoid using it
 # //
 # LLAMA_API struct llama_batch llama_batch_get_one(
@@ -2287,7 +2990,7 @@ def llama_batch_get_one(
     n_tokens: Union[ctypes.c_int, int],
     /,
 ) -> llama_batch:
-    """Return batch for single sequence of tokens starting at pos_0
+    """Return batch for single sequence of tokens
 
     NOTE: this is a helper function to facilitate transition to the new batch API - avoid using it
     """
@@ -2332,35 +3035,46 @@ def llama_batch_free(batch: llama_batch, /):
     ...
 
 
-# // Processes a batch of tokens with the ecoder part of the encoder-decoder model.
-# // Stores the encoder output internally for later use by the decoder cross-attention layers.
+# // Process a batch of tokens.
+# // In contrast to llama_decode() - this call does not use KV cache.
+# // For encode-decoder contexts, processes the batch using the encoder.
+# // Can store the encoder output internally for later use by the decoder's cross-attention layers.
 # //   0 - success
-# // < 0 - error
+# // < 0 - error. the memory state is restored to the state before this call
 # LLAMA_API int32_t llama_encode(
 #         struct llama_context * ctx,
 #           struct llama_batch   batch);
 @ctypes_function("llama_encode", [llama_context_p_ctypes, llama_batch], ctypes.c_int32)
 def llama_encode(ctx: llama_context_p, batch: llama_batch, /) -> int:
-    """Processes a batch of tokens with the ecoder part of the encoder-decoder model.
-    Stores the encoder output internally for later use by the decoder cross-attention layers.
+    """Process a batch of tokens using the encoder.
     0 - success
     < 0 - error"""
     ...
 
 
+# // Process a batch of tokens.
+# // Requires the context to have a memory.
+# // For encode-decoder contexts, processes the batch using the decoder.
 # // Positive return values does not mean a fatal error, but rather a warning.
-# //   0 - success
-# //   1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
-# // < 0 - error
+# // Upon fatal-error or abort, the ubatches that managed to be been processed will remain in the memory state of the context
+# //   To handle this correctly, query the memory state using llama_memory_seq_pos_min() and llama_memory_seq_pos_max()
+# // Upon other return values, the memory state is restored to the state before this call
+# //    0 - success
+# //    1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
+# //    2 - aborted     (processed ubatches will remain in the context's memory)
+# //   -1 - invalid input batch
+# // < -1 - fatal error (processed ubatches will remain in the context's memory)
 # LLAMA_API int32_t llama_decode(
 #         struct llama_context * ctx,
 #           struct llama_batch   batch);
 @ctypes_function("llama_decode", [llama_context_p_ctypes, llama_batch], ctypes.c_int32)
 def llama_decode(ctx: llama_context_p, batch: llama_batch, /) -> int:
-    """Positive return values does not mean a fatal error, but rather a warning.
+    """Process a batch of tokens.
     0 - success
     1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
-    < 0 - error"""
+    2 - aborted (processed ubatches will remain in the context's memory)
+    -1 - invalid input batch
+    < -1 - fatal error (processed ubatches will remain in the context's memory)"""
     ...
 
 
@@ -2406,13 +3120,12 @@ def llama_n_threads_batch(ctx: llama_context_p, /) -> int:
     ...
 
 
-# // Set whether the model is in embeddings mode or not
-# // If true, embeddings will be returned but logits will not
+# // Set whether the context outputs embeddings or not
+# // TODO: rename to avoid confusion with llama_get_embeddings()
 # LLAMA_API void llama_set_embeddings(struct llama_context * ctx, bool embeddings);
 @ctypes_function("llama_set_embeddings", [llama_context_p_ctypes, ctypes.c_bool], None)
 def llama_set_embeddings(ctx: llama_context_p, embeddings: bool, /):
-    """Set whether the model is in embeddings model or not
-    If true, embeddings will be returned but logits will not"""
+    """Set whether the context outputs embeddings or not"""
     ...
 
 
@@ -2426,6 +3139,16 @@ def llama_set_causal_attn(ctx: llama_context_p, causal_attn: bool, /):
     ...
 
 
+# // Set whether the model is in warmup mode or not
+# // If true, all model tensors are activated during llama_decode() to load and cache their weights.
+# LLAMA_API void llama_set_warmup(struct llama_context * ctx, bool warmup);
+@ctypes_function("llama_set_warmup", [llama_context_p_ctypes, ctypes.c_bool], None)
+def llama_set_warmup(ctx: llama_context_p, warmup: bool, /):
+    """Set whether the model is in warmup mode or not
+    If true, all model tensors are activated during llama_decode() to load and cache their weights."""
+    ...
+
+
 # // Set abort callback
 # LLAMA_API void llama_set_abort_callback(struct llama_context * ctx, ggml_abort_callback abort_callback, void * abort_callback_data);
 @ctypes_function(
@@ -2460,15 +3183,16 @@ def llama_synchronize(ctx: llama_context_p, /):
 # // in the order they have appeared in the batch.
 # // Rows: number of tokens for which llama_batch.logits[i] != 0
 # // Cols: n_vocab
+# // TODO: deprecate in favor of llama_get_logits_ith() (ref: https://github.com/ggml-org/llama.cpp/pull/14853#issuecomment-3113143522)
 # LLAMA_API float * llama_get_logits(struct llama_context * ctx);
 @ctypes_function(
     "llama_get_logits", [llama_context_p_ctypes], ctypes.POINTER(ctypes.c_float)
 )
 def llama_get_logits(ctx: llama_context_p, /) -> CtypesArray[ctypes.c_float]:
-    """Token logits obtained from the last call to llama_eval()
-    The logits for the last token are stored in the last row
-    Logits for which llama_batch.logits[i] == 0 are undefined
-    Rows: n_tokens provided with llama_batch
+    """Token logits obtained from the last call to llama_decode()
+    The logits for which llama_batch.logits[i] != 0 are stored contiguously
+    in the order they have appeared in the batch.
+    Rows: number of tokens for which llama_batch.logits[i] != 0
     Cols: n_vocab
 
     Returns:
@@ -2500,6 +3224,7 @@ def llama_get_logits_ith(
 # // in the order they have appeared in the batch.
 # // shape: [n_outputs*n_embd]
 # // Otherwise, returns NULL.
+# // TODO: deprecate in favor of llama_get_embeddings_ith() (ref: https://github.com/ggml-org/llama.cpp/pull/14853#issuecomment-3113143522)
 # LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
 @ctypes_function(
     "llama_get_embeddings", [llama_context_p_ctypes], ctypes.POINTER(ctypes.c_float)
@@ -2531,7 +3256,7 @@ def llama_get_embeddings_ith(
 
 # // Get the embeddings for a sequence id
 # // Returns NULL if pooling_type is LLAMA_POOLING_TYPE_NONE
-# // when pooling_type == LLAMA_POOLING_TYPE_RANK, returns float[1] with the rank of the sequence
+# // when pooling_type == LLAMA_POOLING_TYPE_RANK, returns float[n_cls_out] with the rank(s) of the sequence
 # // otherwise: float[n_embd] (1-dimensional)
 # LLAMA_API float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id);
 @ctypes_function(
@@ -2548,169 +3273,665 @@ def llama_get_embeddings_seq(
     ...
 
 
-# //
-# // Vocab
-# //
+# // Get the backend sampled token for the ith token.
+# // Returns LLAMA_TOKEN_NULL if no token was sampled.
+# LLAMA_API llama_token llama_get_sampled_token_ith(struct llama_context * ctx, int32_t i);
+@ctypes_function(
+    "llama_get_sampled_token_ith", [llama_context_p_ctypes, ctypes.c_int32], llama_token
+)
+def llama_get_sampled_token_ith(
+    ctx: llama_context_p, i: Union[ctypes.c_int32, int], /
+) -> int:
+    """Get the backend sampled token for the ith token."""
+    ...
 
 
-# LLAMA_API const char * llama_token_get_text(const struct llama_model * model, llama_token token);
+# // Get the backend sampled probabilities for the ith token
+# LLAMA_API float *  llama_get_sampled_probs_ith      (struct llama_context * ctx, int32_t i);
 @ctypes_function(
-    "llama_token_get_text", [llama_model_p_ctypes, llama_token], ctypes.c_char_p
+    "llama_get_sampled_probs_ith",
+    [llama_context_p_ctypes, ctypes.c_int32],
+    ctypes.POINTER(ctypes.c_float),
 )
-def llama_token_get_text(
-    model: llama_model_p, token: Union[llama_token, int], /
-) -> bytes:
+def llama_get_sampled_probs_ith(
+    ctx: llama_context_p, i: Union[ctypes.c_int32, int], /
+) -> Optional[CtypesPointer[ctypes.c_float]]:
+    """Get the backend sampled probabilities for the ith token."""
     ...
 
 
-# LLAMA_API float llama_token_get_score(const struct llama_model * model, llama_token token);
+# LLAMA_API uint32_t llama_get_sampled_probs_count_ith(struct llama_context * ctx, int32_t i);
 @ctypes_function(
-    "llama_token_get_score", [llama_model_p_ctypes, llama_token], ctypes.c_float
+    "llama_get_sampled_probs_count_ith",
+    [llama_context_p_ctypes, ctypes.c_int32],
+    ctypes.c_uint32,
 )
-def llama_token_get_score(
-    model: llama_model_p, token: Union[llama_token, int], /
-) -> float:
+def llama_get_sampled_probs_count_ith(
+    ctx: llama_context_p, i: Union[ctypes.c_int32, int], /
+) -> int:
+    """Get the backend sampled probability count for the ith token."""
     ...
 
 
-# LLAMA_API enum llama_token_attr llama_token_get_attr(const struct llama_model * model, llama_token token);
+# // Get the backend sampled logits for the ith token
+# LLAMA_API float *  llama_get_sampled_logits_ith      (struct llama_context * ctx, int32_t i);
 @ctypes_function(
-    "llama_token_get_attr", [llama_model_p_ctypes, llama_token], ctypes.c_int
+    "llama_get_sampled_logits_ith",
+    [llama_context_p_ctypes, ctypes.c_int32],
+    ctypes.POINTER(ctypes.c_float),
 )
-def llama_token_get_attr(
-    model: llama_model_p, token: Union[llama_token, int], /
+def llama_get_sampled_logits_ith(
+    ctx: llama_context_p, i: Union[ctypes.c_int32, int], /
+) -> Optional[CtypesPointer[ctypes.c_float]]:
+    """Get the backend sampled logits for the ith token."""
+    ...
+
+
+# LLAMA_API uint32_t llama_get_sampled_logits_count_ith(struct llama_context * ctx, int32_t i);
+@ctypes_function(
+    "llama_get_sampled_logits_count_ith",
+    [llama_context_p_ctypes, ctypes.c_int32],
+    ctypes.c_uint32,
+)
+def llama_get_sampled_logits_count_ith(
+    ctx: llama_context_p, i: Union[ctypes.c_int32, int], /
+) -> int:
+    """Get the backend sampled logit count for the ith token."""
+    ...
+
+
+# // Get the backend sampled candidates for the ith token
+# LLAMA_API llama_token * llama_get_sampled_candidates_ith      (struct llama_context * ctx, int32_t i);
+@ctypes_function(
+    "llama_get_sampled_candidates_ith",
+    [llama_context_p_ctypes, ctypes.c_int32],
+    ctypes.POINTER(llama_token),
+)
+def llama_get_sampled_candidates_ith(
+    ctx: llama_context_p, i: Union[ctypes.c_int32, int], /
+) -> Optional[CtypesPointer[llama_token]]:
+    """Get the backend sampled candidates for the ith token."""
+    ...
+
+
+# LLAMA_API uint32_t      llama_get_sampled_candidates_count_ith(struct llama_context * ctx, int32_t i);
+@ctypes_function(
+    "llama_get_sampled_candidates_count_ith",
+    [llama_context_p_ctypes, ctypes.c_int32],
+    ctypes.c_uint32,
+)
+def llama_get_sampled_candidates_count_ith(
+    ctx: llama_context_p, i: Union[ctypes.c_int32, int], /
 ) -> int:
+    """Get the backend sampled candidate count for the ith token."""
     ...
 
 
+# //
+# // Vocab
+# //
+
+
+# LLAMA_API const char * llama_vocab_get_text(const struct llama_vocab * vocab, llama_token token);
+@ctypes_function(
+    "llama_vocab_get_text", [llama_vocab_p_ctypes, llama_token], ctypes.c_char_p
+)
+def llama_vocab_get_text(
+    vocab: llama_vocab_p, token: Union[llama_token, int], /
+) -> bytes: ...
+
+
+# LLAMA_API float llama_vocab_get_score(const struct llama_vocab * vocab, llama_token token);
+@ctypes_function(
+    "llama_vocab_get_score", [llama_vocab_p_ctypes, llama_token], ctypes.c_float
+)
+def llama_vocab_get_score(
+    vocab: llama_vocab_p, token: Union[llama_token, int], /
+) -> float: ...
+
+
+# LLAMA_API enum llama_token_attr llama_vocab_get_attr(const struct llama_vocab * vocab, llama_token token);
+@ctypes_function(
+    "llama_vocab_get_attr", [llama_vocab_p_ctypes, llama_token], ctypes.c_int
+)
+def llama_vocab_get_attr(
+    vocab: llama_vocab_p, token: Union[llama_token, int], /
+) -> int: ...
+
+
 # // Check if the token is supposed to end generation (end-of-generation, eg. EOS, EOT, etc.)
-# LLAMA_API bool llama_token_is_eog(const struct llama_model * model, llama_token token);
+# LLAMA_API bool llama_vocab_is_eog(const struct llama_vocab * vocab, llama_token token);
 @ctypes_function(
-    "llama_token_is_eog", [llama_model_p_ctypes, llama_token], ctypes.c_bool
+    "llama_vocab_is_eog", [llama_vocab_p_ctypes, llama_token], ctypes.c_bool
 )
-def llama_token_is_eog(model: llama_model_p, token: Union[llama_token, int], /) -> bool:
+def llama_vocab_is_eog(vocab: llama_vocab_p, token: Union[llama_token, int], /) -> bool:
     """Check if the token is supposed to end generation (end-of-generation, eg. EOS, EOT, etc.)"""
     ...
 
 
 # // Identify if Token Id is a control token or a render-able token
-# LLAMA_API bool llama_token_is_control(const struct llama_model * model, llama_token token);
+# LLAMA_API bool llama_vocab_is_control(const struct llama_vocab * vocab, llama_token token);
 @ctypes_function(
-    "llama_token_is_control", [llama_model_p_ctypes, llama_token], ctypes.c_bool
+    "llama_vocab_is_control", [llama_vocab_p_ctypes, llama_token], ctypes.c_bool
 )
-def llama_token_is_control(
-    model: llama_model_p, token: Union[llama_token, int], /
+def llama_vocab_is_control(
+    vocab: llama_vocab_p, token: Union[llama_token, int], /
 ) -> bool:
     """Identify if Token Id is a control token or a render-able token"""
     ...
 
 
 # // Special tokens
-
-
-# LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence
-@ctypes_function("llama_token_bos", [llama_model_p_ctypes], llama_token)
-def llama_token_bos(model: llama_model_p, /) -> int:
+# LLAMA_API llama_token llama_vocab_bos(const struct llama_vocab * vocab); // beginning-of-sentence
+@ctypes_function("llama_vocab_bos", [llama_vocab_p_ctypes], llama_token)
+def llama_vocab_bos(vocab: llama_vocab_p, /) -> llama_token:
     """beginning-of-sentence"""
     ...
 
 
-# LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence
-@ctypes_function("llama_token_eos", [llama_model_p_ctypes], llama_token)
-def llama_token_eos(model: llama_model_p, /) -> int:
+# LLAMA_API llama_token llama_vocab_eos(const struct llama_vocab * vocab); // end-of-sentence
+@ctypes_function("llama_vocab_eos", [llama_vocab_p_ctypes], llama_token)
+def llama_vocab_eos(vocab: llama_vocab_p, /) -> llama_token:
     """end-of-sentence"""
     ...
 
 
-# LLAMA_API llama_token llama_token_eot(const struct llama_model * model); // end-of-turn
-@ctypes_function("llama_token_eot", [llama_model_p_ctypes], llama_token)
-def llama_token_eot(model: llama_model_p, /) -> int:
+# LLAMA_API llama_token llama_vocab_eot(const struct llama_vocab * vocab); // end-of-turn
+@ctypes_function("llama_vocab_eot", [llama_vocab_p_ctypes], llama_token)
+def llama_vocab_eot(vocab: llama_vocab_p, /) -> llama_token:
     """end-of-turn"""
     ...
 
 
-# LLAMA_API llama_token llama_token_cls(const struct llama_model * model); // classification
-@ctypes_function("llama_token_cls", [llama_model_p_ctypes], llama_token)
-def llama_token_cls(model: llama_model_p, /) -> int:
-    """classification"""
-    ...
-
-
-# LLAMA_API llama_token llama_token_sep(const struct llama_model * model); // sentence separator
-@ctypes_function("llama_token_sep", [llama_model_p_ctypes], llama_token)
-def llama_token_sep(model: llama_model_p, /) -> int:
+# LLAMA_API llama_token llama_vocab_sep(const struct llama_vocab * vocab); // sentence separator
+@ctypes_function("llama_vocab_sep", [llama_vocab_p_ctypes], llama_token)
+def llama_vocab_sep(vocab: llama_vocab_p, /) -> llama_token:
     """sentence separator"""
     ...
 
 
-# LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line
-@ctypes_function("llama_token_nl", [llama_model_p_ctypes], llama_token)
-def llama_token_nl(model: llama_model_p, /) -> int:
+# LLAMA_API llama_token llama_vocab_nl (const struct llama_vocab * vocab); // next-line
+@ctypes_function("llama_vocab_nl", [llama_vocab_p_ctypes], llama_token)
+def llama_vocab_nl(vocab: llama_vocab_p, /) -> llama_token:
     """next-line"""
     ...
 
 
-# LLAMA_API bool llama_add_bos_token(const struct llama_model * model);
-@ctypes_function("llama_add_bos_token", [llama_model_p_ctypes], ctypes.c_bool)
-def llama_add_bos_token(model: llama_model_p, /) -> bool:
+# LLAMA_API llama_token llama_vocab_pad(const struct llama_vocab * vocab); // padding
+@ctypes_function("llama_vocab_pad", [llama_vocab_p_ctypes], llama_token)
+def llama_vocab_pad(vocab: llama_vocab_p, /) -> llama_token:
+    """padding"""
     ...
 
 
-# LLAMA_API bool llama_add_eos_token(const struct llama_model * model);
-@ctypes_function("llama_add_eos_token", [llama_model_p_ctypes], ctypes.c_bool)
-def llama_add_eos_token(model: llama_model_p, /) -> bool:
+# LLAMA_API llama_token llama_vocab_mask(const struct llama_vocab * vocab); // mask
+@ctypes_function("llama_vocab_mask", [llama_vocab_p_ctypes], llama_token)
+def llama_vocab_mask(vocab: llama_vocab_p, /) -> llama_token:
+    """mask"""
     ...
 
 
-# // Codellama infill tokens
-# DEPRECATED(LLAMA_API llama_token llama_token_prefix(const struct llama_model * model), "use llama_token_fim_pre instead");
-@ctypes_function("llama_token_prefix", [llama_model_p_ctypes], llama_token)
-def llama_token_prefix(model: llama_model_p) -> int:
-    """codellama infill tokens"""
-    ...
+# LLAMA_API bool llama_vocab_get_add_bos(const struct llama_vocab * vocab);
+@ctypes_function(
+    "llama_vocab_get_add_bos",
+    [llama_vocab_p_ctypes],
+    ctypes.c_bool,
+)
+def llama_vocab_get_add_bos(vocab: llama_vocab_p, /) -> bool: ...
 
 
-# DEPRECATED(LLAMA_API llama_token llama_token_middle(const struct llama_model * model), "use llama_token_fim_mid instead");
-@ctypes_function("llama_token_middle", [llama_model_p_ctypes], llama_token)
-def llama_token_middle(model: llama_model_p, /) -> int:
-    ...
+# LLAMA_API bool llama_vocab_get_add_eos(const struct llama_vocab * vocab);
+@ctypes_function(
+    "llama_vocab_get_add_eos",
+    [llama_vocab_p_ctypes],
+    ctypes.c_bool,
+)
+def llama_vocab_get_add_eos(vocab: llama_vocab_p, /) -> bool: ...
 
 
-# DEPRECATED(LLAMA_API llama_token llama_token_suffix(const struct llama_model * model), "use llama_token_fim_suf instead");
-@ctypes_function("llama_token_suffix", [llama_model_p_ctypes], llama_token)
-def llama_token_suffix(model: llama_model_p, /) -> int:
-    ...
+# LLAMA_API bool llama_vocab_get_add_sep(const struct llama_vocab * vocab);
+@ctypes_function(
+    "llama_vocab_get_add_sep",
+    [llama_vocab_p_ctypes],
+    ctypes.c_bool,
+)
+def llama_vocab_get_add_sep(vocab: llama_vocab_p, /) -> bool: ...
+
+
+# LLAMA_API llama_token llama_vocab_fim_pre(const struct llama_vocab * vocab);
+@ctypes_function(
+    "llama_vocab_fim_pre",
+    [llama_vocab_p_ctypes],
+    llama_token,
+)
+def llama_vocab_fim_pre(vocab: llama_vocab_p, /) -> llama_token: ...
+
+
+# LLAMA_API llama_token llama_vocab_fim_suf(const struct llama_vocab * vocab);
+@ctypes_function(
+    "llama_vocab_fim_suf",
+    [llama_vocab_p_ctypes],
+    llama_token,
+)
+def llama_vocab_fim_suf(vocab: llama_vocab_p, /) -> llama_token: ...
+
+
+# LLAMA_API llama_token llama_vocab_fim_mid(const struct llama_vocab * vocab);
+@ctypes_function(
+    "llama_vocab_fim_mid",
+    [llama_vocab_p_ctypes],
+    llama_token,
+)
+def llama_vocab_fim_mid(vocab: llama_vocab_p, /) -> llama_token: ...
+
+
+# LLAMA_API llama_token llama_vocab_fim_pad(const struct llama_vocab * vocab);
+@ctypes_function(
+    "llama_vocab_fim_pad",
+    [llama_vocab_p_ctypes],
+    llama_token,
+)
+def llama_vocab_fim_pad(vocab: llama_vocab_p, /) -> llama_token: ...
+
+
+# LLAMA_API llama_token llama_vocab_fim_rep(const struct llama_vocab * vocab);
+@ctypes_function(
+    "llama_vocab_fim_rep",
+    [llama_vocab_p_ctypes],
+    llama_token,
+)
+def llama_vocab_fim_rep(vocab: llama_vocab_p, /) -> llama_token: ...
+
+
+# LLAMA_API llama_token llama_vocab_fim_sep(const struct llama_vocab * vocab);
+@ctypes_function(
+    "llama_vocab_fim_sep",
+    [llama_vocab_p_ctypes],
+    llama_token,
+)
+def llama_vocab_fim_sep(vocab: llama_vocab_p, /) -> llama_token: ...
+
+
+# DEPRECATED functions
+# DEPRECATED(LLAMA_API const char * llama_token_get_text(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_get_text instead");
+@ctypes_function(
+    "llama_token_get_text",
+    [llama_vocab_p_ctypes, llama_token],
+    ctypes.c_char_p,
+)
+def llama_token_get_text(
+    vocab: llama_vocab_p, token: Union[llama_token, int], /
+) -> bytes: ...
+
+
+_llama_token_get_text = llama_token_get_text
+
+
+def llama_token_get_text(
+    vocab: llama_vocab_p, token: Union[llama_token, int], /
+) -> bytes:
+    _warn_deprecated("llama_token_get_text", "use llama_vocab_get_text instead")
+    return _llama_token_get_text(vocab, token)
+
+
+# DEPRECATED(LLAMA_API float llama_token_get_score(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_get_score instead");
+@ctypes_function(
+    "llama_token_get_score",
+    [llama_vocab_p_ctypes, llama_token],
+    ctypes.c_float,
+)
+def llama_token_get_score(
+    vocab: llama_vocab_p, token: Union[llama_token, int], /
+) -> float: ...
+
+
+_llama_token_get_score = llama_token_get_score
+
+
+def llama_token_get_score(
+    vocab: llama_vocab_p, token: Union[llama_token, int], /
+) -> float:
+    _warn_deprecated("llama_token_get_score", "use llama_vocab_get_score instead")
+    return _llama_token_get_score(vocab, token)
+
+
+# DEPRECATED(LLAMA_API enum llama_token_attr llama_token_get_attr(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_get_attr instead");
+@ctypes_function(
+    "llama_token_get_attr",
+    [llama_vocab_p_ctypes, llama_token],
+    ctypes.c_int,
+)
+def llama_token_get_attr(
+    vocab: llama_vocab_p, token: Union[llama_token, int], /
+) -> int: ...
+
+
+_llama_token_get_attr = llama_token_get_attr
+
+
+def llama_token_get_attr(
+    vocab: llama_vocab_p, token: Union[llama_token, int], /
+) -> int:
+    _warn_deprecated("llama_token_get_attr", "use llama_vocab_get_attr instead")
+    return _llama_token_get_attr(vocab, token)
+
+
+# DEPRECATED(LLAMA_API bool llama_token_is_eog(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_is_eog instead");
+@ctypes_function(
+    "llama_token_is_eog",
+    [llama_vocab_p_ctypes, llama_token],
+    ctypes.c_bool,
+)
+def llama_token_is_eog(
+    vocab: llama_vocab_p, token: Union[llama_token, int], /
+) -> bool: ...
+
+
+_llama_token_is_eog = llama_token_is_eog
+
+
+def llama_token_is_eog(vocab: llama_vocab_p, token: Union[llama_token, int], /) -> bool:
+    _warn_deprecated("llama_token_is_eog", "use llama_vocab_is_eog instead")
+    return _llama_token_is_eog(vocab, token)
+
+
+# DEPRECATED(LLAMA_API bool llama_token_is_control(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_is_control instead");
+@ctypes_function(
+    "llama_token_is_control",
+    [llama_vocab_p_ctypes, llama_token],
+    ctypes.c_bool,
+)
+def llama_token_is_control(
+    vocab: llama_vocab_p, token: Union[llama_token, int], /
+) -> bool: ...
+
+
+_llama_token_is_control = llama_token_is_control
+
+
+def llama_token_is_control(
+    vocab: llama_vocab_p, token: Union[llama_token, int], /
+) -> bool:
+    _warn_deprecated(
+        "llama_token_is_control",
+        "use llama_vocab_is_control instead",
+    )
+    return _llama_token_is_control(vocab, token)
+
+
+# DEPRECATED(LLAMA_API llama_token llama_token_bos(const struct llama_vocab * vocab), "use llama_vocab_bos instead");
+@ctypes_function(
+    "llama_token_bos",
+    [llama_vocab_p_ctypes],
+    llama_token,
+)
+def llama_token_bos(vocab: llama_vocab_p, /) -> int: ...
+
+
+_llama_token_bos = llama_token_bos
+
+
+def llama_token_bos(vocab: llama_vocab_p, /) -> int:
+    _warn_deprecated("llama_token_bos", "use llama_vocab_bos instead")
+    return _llama_token_bos(vocab)
+
+
+# DEPRECATED(LLAMA_API llama_token llama_token_eos(const struct llama_vocab * vocab), "use llama_vocab_eos instead");
+@ctypes_function(
+    "llama_token_eos",
+    [llama_vocab_p_ctypes],
+    llama_token,
+)
+def llama_token_eos(vocab: llama_vocab_p, /) -> int: ...
+
+
+_llama_token_eos = llama_token_eos
+
+
+def llama_token_eos(vocab: llama_vocab_p, /) -> int:
+    _warn_deprecated("llama_token_eos", "use llama_vocab_eos instead")
+    return _llama_token_eos(vocab)
+
+
+# DEPRECATED(LLAMA_API llama_token llama_token_eot(const struct llama_vocab * vocab), "use llama_vocab_eot instead");
+@ctypes_function(
+    "llama_token_eot",
+    [llama_vocab_p_ctypes],
+    llama_token,
+)
+def llama_token_eot(vocab: llama_vocab_p, /) -> int: ...
+
+
+_llama_token_eot = llama_token_eot
+
+
+def llama_token_eot(vocab: llama_vocab_p, /) -> int:
+    _warn_deprecated("llama_token_eot", "use llama_vocab_eot instead")
+    return _llama_token_eot(vocab)
+
+
+# DEPRECATED(LLAMA_API llama_token llama_token_cls(const struct llama_vocab * vocab), "use llama_vocab_cls instead");
+@ctypes_function(
+    "llama_token_cls",
+    [llama_vocab_p_ctypes],
+    llama_token,
+)
+def llama_token_cls(vocab: llama_vocab_p, /) -> int: ...
+
+
+_llama_token_cls = llama_token_cls
+
+
+def llama_token_cls(vocab: llama_vocab_p, /) -> int:
+    _warn_deprecated("llama_token_cls", "use llama_vocab_cls instead")
+    return _llama_token_cls(vocab)
+
+
+# DEPRECATED(LLAMA_API llama_token llama_token_sep(const struct llama_vocab * vocab), "use llama_vocab_sep instead");
+@ctypes_function(
+    "llama_token_sep",
+    [llama_vocab_p_ctypes],
+    llama_token,
+)
+def llama_token_sep(vocab: llama_vocab_p, /) -> int: ...
+
+
+_llama_token_sep = llama_token_sep
+
+
+def llama_token_sep(vocab: llama_vocab_p, /) -> int:
+    _warn_deprecated("llama_token_sep", "use llama_vocab_sep instead")
+    return _llama_token_sep(vocab)
+
+
+# DEPRECATED(LLAMA_API llama_token llama_token_nl (const struct llama_vocab * vocab), "use llama_vocab_nl instead");
+@ctypes_function(
+    "llama_token_nl",
+    [llama_vocab_p_ctypes],
+    llama_token,
+)
+def llama_token_nl(vocab: llama_vocab_p, /) -> int: ...
+
+
+_llama_token_nl = llama_token_nl
+
+
+def llama_token_nl(vocab: llama_vocab_p, /) -> int:
+    _warn_deprecated("llama_token_nl", "use llama_vocab_nl instead")
+    return _llama_token_nl(vocab)
+
+
+# DEPRECATED(LLAMA_API llama_token llama_token_pad(const struct llama_vocab * vocab), "use llama_vocab_pad instead");
+@ctypes_function(
+    "llama_token_pad",
+    [llama_vocab_p_ctypes],
+    llama_token,
+)
+def llama_token_pad(vocab: llama_vocab_p, /) -> int: ...
+
+
+_llama_token_pad = llama_token_pad
+
+
+def llama_token_pad(vocab: llama_vocab_p, /) -> int:
+    _warn_deprecated("llama_token_pad", "use llama_vocab_pad instead")
+    return _llama_token_pad(vocab)
+
+
+# DEPRECATED(LLAMA_API bool llama_add_bos_token(const struct llama_vocab * vocab), "use llama_vocab_get_add_bos instead");
+@ctypes_function(
+    "llama_add_bos_token",
+    [llama_vocab_p_ctypes],
+    ctypes.c_bool,
+)
+def llama_add_bos_token(vocab: llama_vocab_p, /) -> bool: ...
+
+
+_llama_add_bos_token = llama_add_bos_token
+
+
+def llama_add_bos_token(vocab: llama_vocab_p, /) -> bool:
+    _warn_deprecated("llama_add_bos_token", "use llama_vocab_get_add_bos instead")
+    return _llama_add_bos_token(vocab)
+
+
+# DEPRECATED(LLAMA_API bool llama_add_eos_token(const struct llama_vocab * vocab), "use llama_vocab_get_add_eos instead");
+@ctypes_function(
+    "llama_add_eos_token",
+    [llama_vocab_p_ctypes],
+    ctypes.c_bool,
+)
+def llama_add_eos_token(vocab: llama_vocab_p, /) -> bool: ...
+
+
+_llama_add_eos_token = llama_add_eos_token
+
+
+def llama_add_eos_token(vocab: llama_vocab_p, /) -> bool:
+    _warn_deprecated("llama_add_eos_token", "use llama_vocab_get_add_eos instead")
+    return _llama_add_eos_token(vocab)
+
+
+# DEPRECATED(LLAMA_API llama_token llama_token_fim_pre(const struct llama_vocab * vocab), "use llama_vocab_fim_pre instead");
+@ctypes_function(
+    "llama_token_fim_pre",
+    [llama_vocab_p_ctypes],
+    llama_token,
+)
+def llama_token_fim_pre(vocab: llama_vocab_p, /) -> llama_token: ...
+
+
+_llama_token_fim_pre = llama_token_fim_pre
+
+
+def llama_token_fim_pre(vocab: llama_vocab_p, /) -> llama_token:
+    _warn_deprecated("llama_token_fim_pre", "use llama_vocab_fim_pre instead")
+    return _llama_token_fim_pre(vocab)
+
+
+# DEPRECATED(LLAMA_API llama_token llama_token_fim_suf(const struct llama_vocab * vocab), "use llama_vocab_fim_suf instead");
+@ctypes_function(
+    "llama_token_fim_suf",
+    [llama_vocab_p_ctypes],
+    llama_token,
+)
+def llama_token_fim_suf(vocab: llama_vocab_p, /) -> llama_token: ...
+
+
+_llama_token_fim_suf = llama_token_fim_suf
+
+
+def llama_token_fim_suf(vocab: llama_vocab_p, /) -> llama_token:
+    _warn_deprecated("llama_token_fim_suf", "use llama_vocab_fim_suf instead")
+    return _llama_token_fim_suf(vocab)
+
+
+# DEPRECATED(LLAMA_API llama_token llama_token_fim_mid(const struct llama_vocab * vocab), "use llama_vocab_fim_mid instead");
+@ctypes_function(
+    "llama_token_fim_mid",
+    [llama_vocab_p_ctypes],
+    llama_token,
+)
+def llama_token_fim_mid(vocab: llama_vocab_p, /) -> llama_token: ...
+
+
+_llama_token_fim_mid = llama_token_fim_mid
+
+
+def llama_token_fim_mid(vocab: llama_vocab_p, /) -> llama_token:
+    _warn_deprecated("llama_token_fim_mid", "use llama_vocab_fim_mid instead")
+    return _llama_token_fim_mid(vocab)
+
+
+# DEPRECATED(LLAMA_API llama_token llama_token_fim_pad(const struct llama_vocab * vocab), "use llama_vocab_fim_pad instead");
+@ctypes_function(
+    "llama_token_fim_pad",
+    [llama_vocab_p_ctypes],
+    llama_token,
+)
+def llama_token_fim_pad(vocab: llama_vocab_p, /) -> llama_token: ...
+
+
+_llama_token_fim_pad = llama_token_fim_pad
+
+
+def llama_token_fim_pad(vocab: llama_vocab_p, /) -> llama_token:
+    _warn_deprecated("llama_token_fim_pad", "use llama_vocab_fim_pad instead")
+    return _llama_token_fim_pad(vocab)
+
+
+# DEPRECATED(LLAMA_API llama_token llama_token_fim_rep(const struct llama_vocab * vocab), "use llama_vocab_fim_rep instead");
+@ctypes_function(
+    "llama_token_fim_rep",
+    [llama_vocab_p_ctypes],
+    llama_token,
+)
+def llama_token_fim_rep(vocab: llama_vocab_p, /) -> llama_token: ...
+
+
+_llama_token_fim_rep = llama_token_fim_rep
+
+
+def llama_token_fim_rep(vocab: llama_vocab_p, /) -> llama_token:
+    _warn_deprecated("llama_token_fim_rep", "use llama_vocab_fim_rep instead")
+    return _llama_token_fim_rep(vocab)
+
+
+# DEPRECATED(LLAMA_API llama_token llama_token_fim_sep(const struct llama_vocab * vocab), "use llama_vocab_fim_sep instead");
+@ctypes_function(
+    "llama_token_fim_sep",
+    [llama_vocab_p_ctypes],
+    llama_token,
+)
+def llama_token_fim_sep(vocab: llama_vocab_p, /) -> llama_token: ...
+
+
+_llama_token_fim_sep = llama_token_fim_sep
+
+
+def llama_token_fim_sep(vocab: llama_vocab_p, /) -> llama_token:
+    _warn_deprecated("llama_token_fim_sep", "use llama_vocab_fim_sep instead")
+    return _llama_token_fim_sep(vocab)
 
 
-# LLAMA_API llama_token llama_token_fim_pre(const struct llama_model * model);
-@ctypes_function("llama_token_fim_pre", [llama_model_p_ctypes], llama_token)
-def llama_token_fim_pre(model: llama_model_p, /) -> int:
-    ...
+# // CLS is equivalent to BOS
+# DEPRECATED(LLAMA_API llama_token llama_vocab_cls(const struct llama_vocab * vocab), // classification
+#         "use llama_vocab_bos instead");
+@ctypes_function(
+    "llama_vocab_cls",
+    [llama_vocab_p_ctypes],
+    llama_token,
+)
+def llama_vocab_cls(vocab: llama_vocab_p, /) -> llama_token: ...
 
-# LLAMA_API llama_token llama_token_fim_suf(const struct llama_model * model);
-@ctypes_function("llama_token_fim_suf", [llama_model_p_ctypes], llama_token)
-def llama_token_fim_suf(model: llama_model_p, /) -> int:
-    ...
 
-# LLAMA_API llama_token llama_token_fim_mid(const struct llama_model * model);
-@ctypes_function("llama_token_fim_mid", [llama_model_p_ctypes], llama_token)
-def llama_token_fim_mid(model: llama_model_p, /) -> int:
-    ...
+_llama_vocab_cls = llama_vocab_cls
 
-# LLAMA_API llama_token llama_token_fim_pad(const struct llama_model * model);
-@ctypes_function("llama_token_fim_pad", [llama_model_p_ctypes], llama_token)
-def llama_token_fim_pad(model: llama_model_p, /) -> int:
-    ...
 
-# LLAMA_API llama_token llama_token_fim_rep(const struct llama_model * model);
-@ctypes_function("llama_token_fim_rep", [llama_model_p_ctypes], llama_token)
-def llama_token_fim_rep(model: llama_model_p, /) -> int:
-    ...
+def llama_vocab_cls(vocab: llama_vocab_p, /) -> llama_token:
+    _warn_deprecated("llama_vocab_cls", "use llama_vocab_bos instead")
+    return _llama_vocab_cls(vocab)
 
-# LLAMA_API llama_token llama_token_fim_sep(const struct llama_model * model);
-@ctypes_function("llama_token_fim_sep", [llama_model_p_ctypes], llama_token)
-def llama_token_fim_sep(model: llama_model_p, /) -> int:
-    ...
 
 # //
 # // Tokenization
@@ -2723,11 +3944,12 @@ def llama_token_fim_sep(model: llama_model_p, /) -> int:
 # /// @param tokens The tokens pointer must be large enough to hold the resulting tokens.
 # /// @return Returns the number of tokens on success, no more than n_tokens_max
 # /// @return Returns a negative number on failure - the number of tokens that would have been returned
+# /// @return Returns INT32_MIN on overflow (e.g., tokenization result size exceeds int32_t limit)
 # /// @param add_special Allow to add BOS and EOS tokens if model is configured to do so.
 # /// @param parse_special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated
 # ///                      as plaintext. Does not insert a leading space.
 # LLAMA_API int32_t llama_tokenize(
-#     const struct llama_model * model,
+#     const struct llama_vocab * vocab,
 #                   const char * text,
 #                      int32_t   text_len,
 #                  llama_token * tokens,
@@ -2737,7 +3959,7 @@ def llama_token_fim_sep(model: llama_model_p, /) -> int:
 @ctypes_function(
     "llama_tokenize",
     [
-        llama_model_p_ctypes,
+        llama_vocab_p_ctypes,
         ctypes.c_char_p,
         ctypes.c_int32,
         llama_token_p,
@@ -2748,7 +3970,7 @@ def llama_token_fim_sep(model: llama_model_p, /) -> int:
     ctypes.c_int32,
 )
 def llama_tokenize(
-    model: llama_model_p,
+    vocab: llama_vocab_p,
     text: bytes,
     text_len: Union[ctypes.c_int, int],
     tokens: CtypesArray[llama_token],
@@ -2760,12 +3982,12 @@ def llama_tokenize(
     """Convert the provided text into tokens.
 
     Args:
-        model: The model to use for tokenization.
+        vocab: The vocabulary to use for tokenization.
         text: The text to tokenize.
         text_len: The length of the text.
         tokens: The tokens pointer must be large enough to hold the resulting tokens.
         n_max_tokens: The maximum number of tokens to return.
-        add_special: Allow adding special tokenns if the model is configured to do so.
+        add_special: Allow adding special tokens if the model is configured to do so.
         parse_special: Allow parsing special tokens.
 
     Returns:
@@ -2781,7 +4003,7 @@ def llama_tokenize(
 # // User can skip up to 'lstrip' leading spaces before copying (useful when encoding/decoding multiple tokens with 'add_space_prefix')
 # // @param special If true, special tokens are rendered in the output.
 # LLAMA_API int32_t llama_token_to_piece(
-#           const struct llama_model * model,
+#           const struct llama_vocab * vocab,
 #                        llama_token   token,
 #                               char * buf,
 #                            int32_t   length,
@@ -2790,7 +4012,7 @@ def llama_tokenize(
 @ctypes_function(
     "llama_token_to_piece",
     [
-        llama_model_p_ctypes,
+        llama_vocab_p_ctypes,
         llama_token,
         ctypes.c_char_p,
         ctypes.c_int32,
@@ -2800,7 +4022,7 @@ def llama_tokenize(
     ctypes.c_int32,
 )
 def llama_token_to_piece(
-    model: llama_model_p,
+    vocab: llama_vocab_p,
     token: Union[llama_token, int],
     buf: Union[ctypes.c_char_p, bytes, CtypesArray[ctypes.c_char]],
     length: Union[ctypes.c_int, int],
@@ -2814,7 +4036,7 @@ def llama_token_to_piece(
     User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
 
     Args:
-        model: The model to use for tokenization.
+        vocab: The vocabulary to use for tokenization.
         token: The token to convert.
         buf: The buffer to write the token to.
         length: The length of the buffer.
@@ -2823,23 +4045,6 @@ def llama_token_to_piece(
     ...
 
 
-# # // check if token0 is contained as a prefix in token1
-# # LLAMA_API bool llama_token_is_prefix(
-# #           const struct llama_model * model,
-# #                        llama_token   token0,
-# #                        llama_token   token1);
-# @ctypes_function(
-#     "llama_token_is_prefix",
-#     [llama_model_p_ctypes, llama_token, llama_token],
-#     ctypes.c_bool,
-# )
-# def llama_token_is_prefix(
-#     model: llama_model_p, token0: Union[llama_token, int], token1: Union[llama_token, int], /
-# ) -> bool:
-#     """Check if token0 is contained as a prefix in token1"""
-#     ...
-
-
 # /// @details Convert the provided tokens into text (inverse of llama_tokenize()).
 # /// @param text The char pointer must be large enough to hold the resulting text.
 # /// @return Returns the number of chars/bytes on success, no more than text_len_max.
@@ -2847,7 +4052,7 @@ def llama_token_to_piece(
 # /// @param remove_special Allow to remove BOS and EOS tokens if model is configured to do so.
 # /// @param unparse_special If true, special tokens are rendered in the output.
 # LLAMA_API int32_t llama_detokenize(
-#     const struct llama_model * model,
+#     const struct llama_vocab * vocab,
 #            const llama_token * tokens,
 #                      int32_t   n_tokens,
 #                         char * text,
@@ -2857,7 +4062,7 @@ def llama_token_to_piece(
 @ctypes_function(
     "llama_detokenize",
     [
-        llama_model_p_ctypes,
+        llama_vocab_p_ctypes,
         ctypes.POINTER(llama_token),
         ctypes.c_int32,
         ctypes.c_char_p,
@@ -2868,7 +4073,7 @@ def llama_token_to_piece(
     ctypes.c_int32,
 )
 def llama_detokenize(
-    model: llama_model_p,
+    vocab: llama_vocab_p,
     tokens: CtypesArray[llama_token],
     n_tokens: Union[ctypes.c_int, int],
     text: bytes,
@@ -2880,7 +4085,7 @@ def llama_detokenize(
     """Convert the provided tokens into text (inverse of llama_tokenize()).
 
     Args:
-        model: The model to use for tokenization.
+        vocab: The vocabulary to use for tokenization.
         tokens: The tokens to convert.
         n_tokens: The number of tokens.
         text: The buffer to write the text to.
@@ -2897,8 +4102,8 @@ def llama_detokenize(
 
 # /// Apply chat template. Inspired by hf apply_chat_template() on python.
 # /// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model"
-# /// NOTE: This function does not use a jinja parser. It only support a pre-defined list of template. See more: https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
-# /// @param tmpl A Jinja template to use for this chat. If this is nullptr, the model’s default chat template will be used instead.
+# /// NOTE: This function does not use a jinja parser. It only support a pre-defined list of template. See more: https://github.com/ggml-org/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
+# /// @param tmpl A Jinja template to use for this chat. If this is nullptr, the model's default chat template will be used instead.
 # /// @param chat Pointer to a list of multiple llama_chat_message
 # /// @param n_msg Number of llama_chat_message in this chat
 # /// @param add_ass Whether to end the prompt with the token(s) that indicate the start of an assistant message.
@@ -2906,7 +4111,6 @@ def llama_detokenize(
 # /// @param length The size of the allocated buffer
 # /// @return The total number of bytes of the formatted prompt. If is it larger than the size of buffer, you may need to re-alloc it and then re-apply the template.
 # LLAMA_API int32_t llama_chat_apply_template(
-#           const struct llama_model * model,
 #                         const char * tmpl,
 #    const struct llama_chat_message * chat,
 #                             size_t   n_msg,
@@ -2916,67 +4120,91 @@ def llama_detokenize(
 @ctypes_function(
     "llama_chat_apply_template",
     [
-        ctypes.c_void_p,
-        ctypes.c_char_p,
-        ctypes.POINTER(llama_chat_message),
-        ctypes.c_size_t,
+        ctypes.c_char_p,  # tmpl
+        ctypes.POINTER(llama_chat_message),  # chat
+        ctypes.c_size_t,  # n_msg
+        ctypes.c_bool,  # add_ass (added)
+        ctypes.c_char_p,  # buf
+        ctypes.c_int32,  # length
     ],
     ctypes.c_int32,
 )
 def llama_chat_apply_template(
-    model: llama_model_p,
     tmpl: bytes,
     chat: CtypesArray[llama_chat_message],
     n_msg: int,
+    add_ass: bool,  # Added parameter
+    buf: bytes,
+    length: int,
+    /,
+) -> int:
+    """Apply chat template.
+
+    Args:
+        tmpl: Template to use. If None, uses model's default
+        chat: Array of chat messages
+        n_msg: Number of messages
+        add_ass: Whether to end prompt with assistant token
+        buf: Output buffer
+        length: Buffer length
+
+    Returns:
+        Number of bytes written, or needed if buffer too small
+    """
+    ...
+
+
+# // Get list of built-in chat templates
+# LLAMA_API int32_t llama_chat_builtin_templates(const char ** output, size_t len);
+@ctypes_function(
+    "llama_chat_builtin_templates",
+    [
+        ctypes.POINTER(ctypes.c_char_p),
+        ctypes.c_size_t,
+    ],
+    ctypes.c_int32,
+)
+def llama_chat_builtin_templates(
+    output: CtypesArray[bytes],
+    len: Union[ctypes.c_size_t, int],
     /,
 ) -> int:
+    """Get list of built-in chat templates.
+
+    Args:
+        output: Output buffer to store template names.
+        len: Length of the output buffer.
+
+    Returns:
+        Number of templates available.
+        Returns a negative number on error.
+    """
     ...
 
 
 # //
 # // Sampling API
 # //
-# // Sample usage:
-# //
-# //    // prepare the sampling chain at the start
-# //    auto sparams = llama_sampler_chain_default_params();
-# //
-# //    llama_sampler * smpl = llama_sampler_chain_init(sparams);
-# //
-# //    llama_sampler_chain_add(smpl, llama_sampler_init_top_k(50));
-# //    llama_sampler_chain_add(smpl, llama_sampler_init_top_p(0.9, 1));
-# //    llama_sampler_chain_add(smpl, llama_sampler_init_temp (0.8));
-# //
-# //    // typically, the chain should end with a sampler such as "greedy", "dist" or "mirostat"
-# //    // this sampler will be responsible to select the actual token
-# //    llama_sampler_chain_add(smpl, llama_sampler_init_dist(seed));
-# //
-# //    ...
-# //
-# //    // decoding loop:
-# //    while (...) {
-# //        ...
-# //
-# //        llama_decode(ctx, batch);
-# //
-# //        // sample from the logits of the last token in the batch
-# //        const llama_token id = llama_sampler_sample(smpl, ctx, -1);
-# //
-# //        // accepting the token updates the internal state of certain samplers (e.g. grammar, repetition, etc.)
-# //        llama_sampler_accept(smpl, id);
-# //        ...
-# //    }
-# //
-# //    llama_sampler_free(smpl);
-# //
-# // TODO: In the future, llama_sampler will be utilized to offload the sampling to the backends (e.g. GPU).
-# // TODO: in the future, the entire sampling API that uses llama_model should start using llama_vocab
-# //
 
 # typedef void * llama_sampler_context_t;
 llama_sampler_context_t = ctypes.c_void_p
 
 
+# struct llama_sampler_data {
+#     struct ggml_tensor * logits;
+#     struct ggml_tensor * probs;
+#     struct ggml_tensor * sampled;
+#     struct ggml_tensor * candidates;
+# };
+class llama_sampler_data(ctypes.Structure):
+    _fields_ = [
+        ("logits", ctypes.c_void_p),
+        ("probs", ctypes.c_void_p),
+        ("sampled", ctypes.c_void_p),
+        ("candidates", ctypes.c_void_p),
+    ]
+
+
 # // user code can implement the interface below in order to create custom llama_sampler
 # struct llama_sampler_i {
 #     const char *           (*name)  (const struct llama_sampler * smpl);                                 // can be NULL
@@ -2985,17 +4213,17 @@ def llama_chat_apply_template(
 #     void                   (*reset) (      struct llama_sampler * smpl);                                 // can be NULL
 #     struct llama_sampler * (*clone) (const struct llama_sampler * smpl);                                 // can be NULL if ctx is NULL
 #     void                   (*free)  (      struct llama_sampler * smpl);                                 // can be NULL if ctx is NULL
-#
+
+
 #     // TODO: API for internal libllama usage for appending the sampling to an existing ggml_cgraph
 #     //void (*apply_ggml) (struct llama_sampler * smpl, ...);
 # };
-class llama_sampler_i(ctypes.Structure):
-    ...
+class llama_sampler_i(ctypes.Structure): ...
 
 
 # struct llama_sampler {
-#     struct llama_sampler_i  * iface;
-#     llama_sampler_context_t   ctx;
+#     const struct llama_sampler_i * iface;
+#     llama_sampler_context_t        ctx;
 # };
 class llama_sampler(ctypes.Structure):
     _fields_ = [
@@ -3017,6 +4245,24 @@ class llama_sampler(ctypes.Structure):
 llama_sampler_i_reset = ctypes.CFUNCTYPE(None, llama_sampler_p_ctypes)
 llama_sampler_i_clone = ctypes.CFUNCTYPE(llama_sampler_p_ctypes, llama_sampler_p_ctypes)
 llama_sampler_i_free = ctypes.CFUNCTYPE(None, llama_sampler_p_ctypes)
+llama_sampler_i_backend_init = ctypes.CFUNCTYPE(
+    ctypes.c_bool, llama_sampler_p_ctypes, ctypes.c_void_p
+)
+llama_sampler_i_backend_accept = ctypes.CFUNCTYPE(
+    None,
+    llama_sampler_p_ctypes,
+    ctypes.c_void_p,
+    ctypes.c_void_p,
+    ctypes.c_void_p,
+)
+llama_sampler_i_backend_apply = ctypes.CFUNCTYPE(
+    None,
+    llama_sampler_p_ctypes,
+    ctypes.c_void_p,
+    ctypes.c_void_p,
+    ctypes.POINTER(llama_sampler_data),
+)
+llama_sampler_i_backend_set_input = ctypes.CFUNCTYPE(None, llama_sampler_p_ctypes)
 
 llama_sampler_i._fields_ = [
     ("name", llama_sampler_i_name),
@@ -3025,18 +4271,46 @@ class llama_sampler(ctypes.Structure):
     ("reset", llama_sampler_i_reset),
     ("clone", llama_sampler_i_clone),
     ("free", llama_sampler_i_free),
+    ("backend_init", llama_sampler_i_backend_init),
+    ("backend_accept", llama_sampler_i_backend_accept),
+    ("backend_apply", llama_sampler_i_backend_apply),
+    ("backend_set_input", llama_sampler_i_backend_set_input),
 ]
 
 
+# // attach a sampler to the context
+# LLAMA_API bool llama_set_sampler(struct llama_context * ctx, llama_seq_id seq_id, struct llama_sampler * smpl);
+@ctypes_function(
+    "llama_set_sampler",
+    [llama_context_p_ctypes, llama_seq_id, llama_sampler_p_ctypes],
+    ctypes.c_bool,
+)
+def llama_set_sampler(
+    ctx: llama_context_p, seq_id: Union[llama_seq_id, int], smpl: llama_sampler_p, /
+) -> bool:
+    """Attach a sampler to the context."""
+    ...
+
+
 # // mirror of llama_sampler_i:
+# LLAMA_API struct llama_sampler * llama_sampler_init  (const struct llama_sampler_i * iface, llama_sampler_context_t ctx);
+@ctypes_function(
+    "llama_sampler_init",
+    [ctypes.POINTER(llama_sampler_i), llama_sampler_context_t],
+    llama_sampler_p_ctypes,
+)
+def llama_sampler_init(
+    iface: ctypes.POINTER(llama_sampler_i), ctx: llama_sampler_context_t, /
+) -> llama_sampler_p: ...
+
+
 # LLAMA_API const char *           llama_sampler_name  (const struct llama_sampler * smpl);
 @ctypes_function(
     "llama_sampler_name",
     [llama_sampler_p_ctypes],
     ctypes.c_char_p,
 )
-def llama_sampler_name(smpl: llama_sampler_p, /) -> bytes:
-    ...
+def llama_sampler_name(smpl: llama_sampler_p, /) -> bytes: ...
 
 
 # LLAMA_API void                   llama_sampler_accept(      struct llama_sampler * smpl, llama_token token);
@@ -3045,8 +4319,7 @@ def llama_sampler_name(smpl: llama_sampler_p, /) -> bytes:
     [llama_sampler_p_ctypes, llama_token],
     None,
 )
-def llama_sampler_accept(smpl: llama_sampler_p, token: Union[llama_token, int], /):
-    ...
+def llama_sampler_accept(smpl: llama_sampler_p, token: Union[llama_token, int], /): ...
 
 
 # LLAMA_API void                   llama_sampler_apply (      struct llama_sampler * smpl, llama_token_data_array * cur_p);
@@ -3057,8 +4330,7 @@ def llama_sampler_accept(smpl: llama_sampler_p, token: Union[llama_token, int],
 )
 def llama_sampler_apply(
     smpl: llama_sampler_p, cur_p: CtypesArray[llama_token_data_array], /
-):
-    ...
+): ...
 
 
 # LLAMA_API void                   llama_sampler_reset (      struct llama_sampler * smpl);
@@ -3067,8 +4339,7 @@ def llama_sampler_apply(
     [llama_sampler_p_ctypes],
     None,
 )
-def llama_sampler_reset(smpl: llama_sampler_p, /):
-    ...
+def llama_sampler_reset(smpl: llama_sampler_p, /): ...
 
 
 # LLAMA_API struct llama_sampler * llama_sampler_clone (const struct llama_sampler * smpl);
@@ -3077,8 +4348,7 @@ def llama_sampler_reset(smpl: llama_sampler_p, /):
     [llama_sampler_p_ctypes],
     llama_sampler_p_ctypes,
 )
-def llama_sampler_clone(smpl: llama_sampler_p, /) -> llama_sampler_p:
-    ...
+def llama_sampler_clone(smpl: llama_sampler_p, /) -> llama_sampler_p: ...
 
 
 # // important: do not free if the sampler has been added to a llama_sampler_chain (via llama_sampler_chain_add)
@@ -3088,21 +4358,22 @@ def llama_sampler_clone(smpl: llama_sampler_p, /) -> llama_sampler_p:
     [llama_sampler_p_ctypes],
     None,
 )
-def llama_sampler_free(smpl: llama_sampler_p, /):
-    ...
+def llama_sampler_free(smpl: llama_sampler_p, /): ...
 
 
 # // llama_sampler_chain
 # // a type of llama_sampler that can chain multiple samplers one after another
-#
+
+
 # LLAMA_API struct llama_sampler * llama_sampler_chain_init(struct llama_sampler_chain_params params);
 @ctypes_function(
     "llama_sampler_chain_init",
     [llama_sampler_chain_params],
     llama_sampler_p_ctypes,
 )
-def llama_sampler_chain_init(params: llama_sampler_chain_params, /) -> llama_sampler_p:
-    ...
+def llama_sampler_chain_init(
+    params: llama_sampler_chain_params, /
+) -> llama_sampler_p: ...
 
 
 # // important: takes ownership of the sampler object and will free it when llama_sampler_free is called
@@ -3112,8 +4383,7 @@ def llama_sampler_chain_init(params: llama_sampler_chain_params, /) -> llama_sam
     [llama_sampler_p_ctypes, llama_sampler_p_ctypes],
     None,
 )
-def llama_sampler_chain_add(chain: llama_sampler_p, smpl: llama_sampler_p, /):
-    ...
+def llama_sampler_chain_add(chain: llama_sampler_p, smpl: llama_sampler_p, /): ...
 
 
 # LLAMA_API struct llama_sampler * llama_sampler_chain_get(const struct llama_sampler * chain, int32_t i);
@@ -3124,8 +4394,7 @@ def llama_sampler_chain_add(chain: llama_sampler_p, smpl: llama_sampler_p, /):
 )
 def llama_sampler_chain_get(
     chain: llama_sampler_p, i: Union[ctypes.c_int32, int], /
-) -> llama_sampler_p:
-    ...
+) -> llama_sampler_p: ...
 
 
 # LLAMA_API int                    llama_sampler_chain_n  (const struct llama_sampler * chain);
@@ -3134,8 +4403,7 @@ def llama_sampler_chain_get(
     [llama_sampler_p_ctypes],
     ctypes.c_int,
 )
-def llama_sampler_chain_n(chain: llama_sampler_p, /) -> int:
-    ...
+def llama_sampler_chain_n(chain: llama_sampler_p, /) -> int: ...
 
 
 # // after removing a sampler, the chain will no longer own it, and it will not be freed when the chain is freed
@@ -3147,38 +4415,27 @@ def llama_sampler_chain_n(chain: llama_sampler_p, /) -> int:
 )
 def llama_sampler_chain_remove(
     chain: llama_sampler_p, i: Union[ctypes.c_int32, int], /
-) -> llama_sampler_p:
-    ...
+) -> llama_sampler_p: ...
 
 
 # // available samplers:
-#
+
+
 # LLAMA_API struct llama_sampler * llama_sampler_init_greedy(void);
 @ctypes_function("llama_sampler_init_greedy", [], llama_sampler_p_ctypes)
-def llama_sampler_init_greedy() -> llama_sampler_p:
-    ...
+def llama_sampler_init_greedy() -> llama_sampler_p: ...
 
 
 # LLAMA_API struct llama_sampler * llama_sampler_init_dist  (uint32_t seed);
 @ctypes_function("llama_sampler_init_dist", [ctypes.c_uint32], llama_sampler_p_ctypes)
-def llama_sampler_init_dist(seed: int) -> llama_sampler_p:
-    ...
-
-
-# /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
-# /// NOTE: Avoid using on the full vocabulary as the sorting can become slow. For example, apply top-k or top-p sampling first.
-# DEPRECATED(LLAMA_API struct llama_sampler * llama_sampler_init_softmax    (void),
-#     "will be removed in the future (see https://github.com/ggerganov/llama.cpp/pull/9896#discussion_r1800920915)");
-@ctypes_function("llama_sampler_init_softmax", [], llama_sampler_p_ctypes)
-def llama_sampler_init_softmax() -> llama_sampler_p:
-    ...
+def llama_sampler_init_dist(seed: int) -> llama_sampler_p: ...
 
 
 # /// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
+# /// Setting k <= 0 makes this a noop
 # LLAMA_API struct llama_sampler * llama_sampler_init_top_k      (int32_t k);
 @ctypes_function("llama_sampler_init_top_k", [ctypes.c_int32], llama_sampler_p_ctypes)
-def llama_sampler_init_top_k(k: int) -> llama_sampler_p:
-    ...
+def llama_sampler_init_top_k(k: int) -> llama_sampler_p: ...
 
 
 # /// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
@@ -3188,19 +4445,17 @@ def llama_sampler_init_top_k(k: int) -> llama_sampler_p:
     [ctypes.c_float, ctypes.c_size_t],
     llama_sampler_p_ctypes,
 )
-def llama_sampler_init_top_p(p: float, min_keep: int) -> llama_sampler_p:
-    ...
+def llama_sampler_init_top_p(p: float, min_keep: int) -> llama_sampler_p: ...
 
 
-# /// @details Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841
+# /// @details Minimum P sampling as described in https://github.com/ggml-org/llama.cpp/pull/3841
 # LLAMA_API struct llama_sampler * llama_sampler_init_min_p      (float   p, size_t min_keep);
 @ctypes_function(
     "llama_sampler_init_min_p",
     [ctypes.c_float, ctypes.c_size_t],
     llama_sampler_p_ctypes,
 )
-def llama_sampler_init_min_p(p: float, min_keep: int) -> llama_sampler_p:
-    ...
+def llama_sampler_init_min_p(p: float, min_keep: int) -> llama_sampler_p: ...
 
 
 # /// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
@@ -3210,14 +4465,13 @@ def llama_sampler_init_min_p(p: float, min_keep: int) -> llama_sampler_p:
     [ctypes.c_float, ctypes.c_size_t],
     llama_sampler_p_ctypes,
 )
-def llama_sampler_init_typical(p: float, min_keep: int) -> llama_sampler_p:
-    ...
+def llama_sampler_init_typical(p: float, min_keep: int) -> llama_sampler_p: ...
 
 
+# /// #details Updates the logits l_i` = l_i/t. When t <= 0.0f, the maximum logit is kept at it's original value, the rest are set to -inf
 # LLAMA_API struct llama_sampler * llama_sampler_init_temp       (float   t);
 @ctypes_function("llama_sampler_init_temp", [ctypes.c_float], llama_sampler_p_ctypes)
-def llama_sampler_init_temp(t: float) -> llama_sampler_p:
-    ...
+def llama_sampler_init_temp(t: float) -> llama_sampler_p: ...
 
 
 # /// @details Dynamic temperature implementation (a.k.a. entropy) described in the paper https://arxiv.org/abs/2309.02772.
@@ -3229,8 +4483,7 @@ def llama_sampler_init_temp(t: float) -> llama_sampler_p:
 )
 def llama_sampler_init_temp_ext(
     t: float, delta: float, exponent: float
-) -> llama_sampler_p:
-    ...
+) -> llama_sampler_p: ...
 
 
 # /// @details XTC sampler as described in https://github.com/oobabooga/text-generation-webui/pull/6335
@@ -3242,16 +4495,20 @@ def llama_sampler_init_temp_ext(
 )
 def llama_sampler_init_xtc(
     p: float, t: float, min_keep: int, seed: int, /
-) -> llama_sampler_p:
-    ...
+) -> llama_sampler_p: ...
+
+
+# /// @details Top n sigma sampling as described in academic paper "Top-nσ: Not All Logits Are You Need" https://arxiv.org/pdf/2411.07641
+# LLAMA_API struct llama_sampler * llama_sampler_init_top_n_sigma(float   n);
+@ctypes_function(
+    "llama_sampler_init_top_n_sigma",
+    [ctypes.c_float],
+    llama_sampler_p_ctypes,
+)
+def llama_sampler_init_top_n_sigma(n: float, /) -> llama_sampler_p: ...
 
 
 # /// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
-# /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
-# /// @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
-# /// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
-# /// @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm.
-# /// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
 # LLAMA_API struct llama_sampler * llama_sampler_init_mirostat(
 #                          int32_t   n_vocab,
 #                         uint32_t   seed,
@@ -3265,15 +4522,10 @@ def llama_sampler_init_xtc(
 )
 def llama_sampler_init_mirostat(
     n_vocab: int, seed: int, tau: float, eta: float, m: int, /
-) -> llama_sampler_p:
-    ...
+) -> llama_sampler_p: ...
 
 
 # /// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
-# /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
-# /// @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
-# /// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
-# /// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
 # LLAMA_API struct llama_sampler * llama_sampler_init_mirostat_v2(
 #                         uint32_t   seed,
 #                            float   tau,
@@ -3285,68 +4537,144 @@ def llama_sampler_init_mirostat(
 )
 def llama_sampler_init_mirostat_v2(
     seed: int, tau: float, eta: float, /
-) -> llama_sampler_p:
-    ...
+) -> llama_sampler_p: ...
 
 
+# /// @details Intializes a GBNF grammar, see grammars/README.md for details.
 # LLAMA_API struct llama_sampler * llama_sampler_init_grammar(
-#         const struct llama_model * model,
+#         const struct llama_vocab * vocab,
 #                       const char * grammar_str,
 #                       const char * grammar_root);
 @ctypes_function(
     "llama_sampler_init_grammar",
-    [llama_model_p_ctypes, ctypes.c_char_p, ctypes.c_char_p],
+    [llama_vocab_p_ctypes, ctypes.c_char_p, ctypes.c_char_p],
     llama_sampler_p_ctypes,
 )
 def llama_sampler_init_grammar(
-    model: llama_model_p, grammar_str: bytes, grammar_root: bytes, /
+    vocab: llama_vocab_p, grammar_str: bytes, grammar_root: bytes, /
+) -> llama_sampler_p: ...
+
+
+# DEPRECATED(LLAMA_API struct llama_sampler * llama_sampler_init_grammar_lazy(
+#         const struct llama_vocab * vocab,
+#                       const char * grammar_str,
+#                       const char * grammar_root,
+#                      const char ** trigger_words,
+#                             size_t num_trigger_words,
+#                const llama_token * trigger_tokens,
+#                             size_t num_trigger_tokens),
+#     "use llama_sampler_init_grammar_lazy_patterns instead");
+@ctypes_function(
+    "llama_sampler_init_grammar_lazy",
+    [
+        llama_vocab_p_ctypes,
+        ctypes.c_char_p,
+        ctypes.c_char_p,
+        ctypes.POINTER(ctypes.c_char_p),
+        ctypes.c_size_t,
+        ctypes.POINTER(llama_token),
+        ctypes.c_size_t,
+    ],
+    llama_sampler_p_ctypes,
+)
+def llama_sampler_init_grammar_lazy(
+    vocab: llama_vocab_p,
+    grammar_str: bytes,
+    grammar_root: bytes,
+    trigger_words: CtypesArray[bytes],
+    num_trigger_words: int,
+    trigger_tokens: CtypesArray[llama_token],
+    num_trigger_tokens: int,
+    /,
+) -> llama_sampler_p: ...
+
+
+_llama_sampler_init_grammar_lazy = llama_sampler_init_grammar_lazy
+
+
+def llama_sampler_init_grammar_lazy(
+    vocab: llama_vocab_p,
+    grammar_str: bytes,
+    grammar_root: bytes,
+    trigger_words: CtypesArray[bytes],
+    num_trigger_words: int,
+    trigger_tokens: CtypesArray[llama_token],
+    num_trigger_tokens: int,
+    /,
 ) -> llama_sampler_p:
-    ...
+    _warn_deprecated(
+        "llama_sampler_init_grammar_lazy",
+        "use llama_sampler_init_grammar_lazy_patterns instead",
+    )
+    return _llama_sampler_init_grammar_lazy(
+        vocab,
+        grammar_str,
+        grammar_root,
+        trigger_words,
+        num_trigger_words,
+        trigger_tokens,
+        num_trigger_tokens,
+    )
 
 
-# LLAMA_API struct llama_sampler * llama_sampler_init_penalties(
-#                          int32_t   n_vocab,         // llama_n_vocab()
-#                      llama_token   special_eos_id,  // llama_token_eos()
-#                      llama_token   linefeed_id,     // llama_token_nl()
-#                          int32_t   penalty_last_n,  // last n tokens to penalize (0 = disable penalty, -1 = context size)
-#                            float   penalty_repeat,  // 1.0 = disabled
-#                            float   penalty_freq,    // 0.0 = disabled
-#                            float   penalty_present, // 0.0 = disabled
-#                             bool   penalize_nl,     // consider newlines as a repeatable token
-#                             bool   ignore_eos);     // ignore the end-of-sequence token
+# /// @details Lazy grammar sampler, introduced in https://github.com/ggml-org/llama.cpp/pull/9639
+# LLAMA_API struct llama_sampler * llama_sampler_init_grammar_lazy_patterns(
+#     const struct llama_vocab * vocab,
+#                   const char * grammar_str,
+#                   const char * grammar_root,
+#                  const char ** trigger_patterns,
+#                         size_t num_trigger_patterns,
+#            const llama_token * trigger_tokens,
+#                         size_t num_trigger_tokens);
 @ctypes_function(
-    "llama_sampler_init_penalties",
+    "llama_sampler_init_grammar_lazy_patterns",
     [
-        ctypes.c_int32,
-        llama_token,
-        llama_token,
-        ctypes.c_int32,
-        ctypes.c_float,
-        ctypes.c_float,
-        ctypes.c_float,
-        ctypes.c_bool,
-        ctypes.c_bool,
+        llama_vocab_p_ctypes,
+        ctypes.c_char_p,
+        ctypes.c_char_p,
+        ctypes.POINTER(ctypes.c_char_p),
+        ctypes.c_size_t,
+        ctypes.POINTER(llama_token),
+        ctypes.c_size_t,
     ],
     llama_sampler_p_ctypes,
 )
+def llama_sampler_init_grammar_lazy_patterns(
+    vocab: llama_vocab_p,
+    grammar_str: bytes,
+    grammar_root: bytes,
+    trigger_patterns: CtypesArray[bytes],
+    num_trigger_patterns: int,
+    trigger_tokens: CtypesArray[llama_token],
+    num_trigger_tokens: int,
+    /,
+) -> llama_sampler_p: ...
+
+
+# /// NOTE: Avoid using on the full vocabulary as searching for repeated tokens can become slow. For example, apply top-k or top-p sampling first.
+# LLAMA_API struct llama_sampler * llama_sampler_init_penalties(
+#                          int32_t   penalty_last_n,   // last n tokens to penalize (0 = disable penalty, -1 = context size)
+#                            float   penalty_repeat,   // 1.0 = disabled
+#                            float   penalty_freq,     // 0.0 = disabled
+#                            float   penalty_present); // 0.0 = disabled
+@ctypes_function(
+    "llama_sampler_init_penalties",
+    [ctypes.c_int32, ctypes.c_float, ctypes.c_float, ctypes.c_float],
+    llama_sampler_p_ctypes,
+)
 def llama_sampler_init_penalties(
-    n_vocab: int,
-    special_eos_id: int,
-    linefeed_id: int,
     penalty_last_n: int,
     penalty_repeat: float,
     penalty_freq: float,
     penalty_present: float,
-    penalize_nl: bool,
-    ignore_eos: bool,
     /,
-) -> llama_sampler_p:
-    ...
+) -> llama_sampler_p: ...
 
 
 # ///  @details DRY sampler, designed by p-e-w, as described in: https://github.com/oobabooga/text-generation-webui/pull/5677, porting Koboldcpp implementation authored by pi6am: https://github.com/LostRuins/koboldcpp/pull/982
 # LLAMA_API struct llama_sampler *    llama_sampler_init_dry(
-#         const struct llama_model *  model,
+#         const struct llama_vocab *  vocab,
+#                          int32_t    n_ctx_train,
 #                            float    dry_multiplier,
 #                            float    dry_base,
 #                          int32_t    dry_allowed_length,
@@ -3356,7 +4684,8 @@ def llama_sampler_init_penalties(
 @ctypes_function(
     "llama_sampler_init_dry",
     [
-        llama_model_p_ctypes,
+        llama_vocab_p_ctypes,
+        ctypes.c_int32,
         ctypes.c_float,
         ctypes.c_float,
         ctypes.c_int32,
@@ -3367,15 +4696,31 @@ def llama_sampler_init_penalties(
     llama_sampler_p_ctypes,
 )
 def llama_sampler_init_dry(
-    model: llama_model_p,
+    vocab: llama_vocab_p,
+    n_ctx_train: int,
     dry_multiplier: float,
     dry_base: float,
     dry_allowed_length: int,
     dry_penalty_last_n: int,
-    seq_breakers: CtypesArray[bytes],
+    seq_breakers,
     num_breakers: int,
     /,
+) -> llama_sampler_p: ...
+
+
+# LLAMA_API struct llama_sampler * llama_sampler_init_adaptive_p(
+#                            float   target,
+#                            float   decay,
+#                         uint32_t   seed);
+@ctypes_function(
+    "llama_sampler_init_adaptive_p",
+    [ctypes.c_float, ctypes.c_float, ctypes.c_uint32],
+    llama_sampler_p_ctypes,
+)
+def llama_sampler_init_adaptive_p(
+    target: float, decay: float, seed: int, /
 ) -> llama_sampler_p:
+    """Initialize an adaptive-p sampler."""
     ...
 
 
@@ -3390,41 +4735,17 @@ def llama_sampler_init_dry(
 )
 def llama_sampler_init_logit_bias(
     n_vocab: int, n_logit_bias: int, logit_bias: CtypesArray[llama_logit_bias], /
-) -> llama_sampler_p:
-    ...
+) -> llama_sampler_p: ...
 
 
 # // this sampler is meant to be used for fill-in-the-middle infilling
-# // it's supposed to be used after top_k + top_p sampling
-# //
-# // 1. if the sum of the EOG probs times the number of candidates is higher than the sum of the other probs -> pick EOG
-# // 2. combine probs of tokens that have the same prefix
-# //
-# // example:
-# //
-# // - before:
-# //   "hel":   0.5
-# //   "hell":  0.2
-# //   "hello": 0.1
-# //   "dummy": 0.1
-# //
-# // - after:
-# //   "hel":   0.8
-# //   "dummy": 0.1
-# //
-# // 3. discard non-EOG tokens with low prob
-# // 4. if no tokens are left -> pick EOT
-# //
-# LLAMA_API struct llama_sampler * llama_sampler_init_infill(const struct llama_model * model);
+# LLAMA_API struct llama_sampler * llama_sampler_init_infill(const struct llama_vocab * vocab);
 @ctypes_function(
     "llama_sampler_init_infill",
-    [llama_model_p_ctypes],
+    [llama_vocab_p_ctypes],
     llama_sampler_p_ctypes,
 )
-def llama_sampler_init_infill(model: llama_model_p, /) -> llama_sampler_p:
-    """This sampler is meant to be used for fill-in-the-middle infilling.
-    """
-    ...
+def llama_sampler_init_infill(vocab: llama_vocab_p, /) -> llama_sampler_p: ...
 
 
 # // Returns the seed used by the sampler if applicable, LLAMA_DEFAULT_SEED otherwise
@@ -3434,20 +4755,10 @@ def llama_sampler_init_infill(model: llama_model_p, /) -> llama_sampler_p:
     [llama_sampler_p_ctypes],
     ctypes.c_uint32,
 )
-def llama_sampler_get_seed(smpl: llama_sampler_p, /) -> int:
-    ...
+def llama_sampler_get_seed(smpl: llama_sampler_p, /) -> int: ...
 
 
 # /// @details Sample and accept a token from the idx-th output of the last evaluation
-# //
-# // Shorthand for:
-# //    const auto * logits = llama_get_logits_ith(ctx, idx);
-# //    llama_token_data_array cur_p = { ... init from logits ... };
-# //    llama_sampler_apply(smpl, &cur_p);
-# //    auto token = cur_p.data[cur_p.selected].id;
-# //    llama_sampler_accept(smpl, token);
-# //    return token;
-# // Returns the sampled token
 # LLAMA_API llama_token llama_sampler_sample(struct llama_sampler * smpl, struct llama_context * ctx, int32_t idx);
 @ctypes_function(
     "llama_sampler_sample",
@@ -3456,8 +4767,7 @@ def llama_sampler_get_seed(smpl: llama_sampler_p, /) -> int:
 )
 def llama_sampler_sample(
     smpl: llama_sampler_p, ctx: llama_context_p, idx: int, /
-) -> int:
-    ...
+) -> int: ...
 
 
 # //
@@ -3466,8 +4776,6 @@ def llama_sampler_sample(
 
 
 # /// @details Build a split GGUF final path for this chunk.
-# ///          llama_split_path(split_path, sizeof(split_path), "/models/ggml-model-q4_0", 2, 4) => split_path = "/models/ggml-model-q4_0-00002-of-00004.gguf"
-# //  Returns the split_path length.
 # LLAMA_API int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count);
 @ctypes_function(
     "llama_split_path",
@@ -3487,8 +4795,6 @@ def llama_split_path(
 
 
 # /// @details Extract the path prefix from the split_path if and only if the split_no and split_count match.
-# ///          llama_split_prefix(split_prefix, 64, "/models/ggml-model-q4_0-00002-of-00004.gguf", 2, 4) => split_prefix = "/models/ggml-model-q4_0"
-# //  Returns the split_prefix length.
 # LLAMA_API int llama_split_prefix(char * split_prefix, size_t maxlen, const char * split_path, int split_no, int split_count);
 @ctypes_function(
     "llama_split_prefix",
@@ -3510,16 +4816,31 @@ def llama_split_prefix(
 # // Print system information
 # LLAMA_API const char * llama_print_system_info(void);
 @ctypes_function("llama_print_system_info", [], ctypes.c_char_p)
-def llama_print_system_info() -> bytes:
-    ...
+def llama_print_system_info() -> bytes: ...
 
 
 # // Set callback for all future logging events.
 # // If this is not called, or NULL is supplied, everything is output on stderr.
+# // The logger state is global so these functions are NOT thread safe.
+# LLAMA_API void llama_log_get(ggml_log_callback * log_callback, void ** user_data);
+@ctypes_function(
+    "llama_log_get",
+    [ctypes.POINTER(llama_log_callback), ctypes.POINTER(ctypes.c_void_p)],
+    None,
+)
+def llama_log_get(
+    log_callback: CtypesPointerOrRef[llama_log_callback],
+    user_data: CtypesPointerOrRef[ctypes.c_void_p],
+    /,
+):
+    """Get the current logging callback and user data."""
+    ...
+
+
 # LLAMA_API void llama_log_set(ggml_log_callback log_callback, void * user_data);
 @ctypes_function(
     "llama_log_set",
-    [ctypes.c_void_p, ctypes.c_void_p],
+    [llama_log_callback, ctypes.c_void_p],
     None,
 )
 def llama_log_set(
@@ -3536,18 +4857,17 @@ def llama_log_set(
 # //
 # // Performance utils
 # //
-# // NOTE: Used by llama.cpp examples, avoid using in third-party apps. Instead, do your own performance measurements.
-# //
-
 
 # struct llama_perf_context_data {
 #     double t_start_ms;
 #     double t_load_ms;
 #     double t_p_eval_ms;
 #     double t_eval_ms;
-#
+
+
 #     int32_t n_p_eval;
 #     int32_t n_eval;
+#     int32_t n_reused; // number of times a ggml compute graph had been reused
 # };
 class llama_perf_context_data(ctypes.Structure):
     _fields_ = [
@@ -3557,12 +4877,14 @@ class llama_perf_context_data(ctypes.Structure):
         ("t_eval_ms", ctypes.c_double),
         ("n_p_eval", ctypes.c_int32),
         ("n_eval", ctypes.c_int32),
+        ("n_reused", ctypes.c_int32),
     ]
 
 
 # struct llama_perf_sampler_data {
 #     double t_sample_ms;
-#
+
+
 #     int32_t n_sample;
 # };
 class llama_perf_sampler_data(ctypes.Structure):
@@ -3578,8 +4900,7 @@ class llama_perf_sampler_data(ctypes.Structure):
     [llama_context_p_ctypes],
     llama_perf_context_data,
 )
-def llama_perf_context(ctx: llama_context_p, /) -> llama_perf_context_data:
-    ...
+def llama_perf_context(ctx: llama_context_p, /) -> llama_perf_context_data: ...
 
 
 # LLAMA_API void                           llama_perf_context_print(const struct llama_context * ctx);
@@ -3588,8 +4909,7 @@ def llama_perf_context(ctx: llama_context_p, /) -> llama_perf_context_data:
     [llama_context_p_ctypes],
     None,
 )
-def llama_perf_context_print(ctx: llama_context_p, /):
-    ...
+def llama_perf_context_print(ctx: llama_context_p, /): ...
 
 
 # LLAMA_API void                           llama_perf_context_reset(      struct llama_context * ctx);
@@ -3598,8 +4918,7 @@ def llama_perf_context_print(ctx: llama_context_p, /):
     [llama_context_p_ctypes],
     None,
 )
-def llama_perf_context_reset(ctx: llama_context_p, /):
-    ...
+def llama_perf_context_reset(ctx: llama_context_p, /): ...
 
 
 # // NOTE: the following work only with samplers constructed via llama_sampler_chain_init
@@ -3609,8 +4928,7 @@ def llama_perf_context_reset(ctx: llama_context_p, /):
     [llama_sampler_p_ctypes],
     llama_perf_sampler_data,
 )
-def llama_perf_sampler(chain: llama_sampler_p, /) -> llama_perf_sampler_data:
-    ...
+def llama_perf_sampler(chain: llama_sampler_p, /) -> llama_perf_sampler_data: ...
 
 
 # LLAMA_API void                           llama_perf_sampler_print(const struct llama_sampler * chain);
@@ -3619,8 +4937,7 @@ def llama_perf_sampler(chain: llama_sampler_p, /) -> llama_perf_sampler_data:
     [llama_sampler_p_ctypes],
     None,
 )
-def llama_perf_sampler_print(chain: llama_sampler_p, /):
-    ...
+def llama_perf_sampler_print(chain: llama_sampler_p, /): ...
 
 
 # LLAMA_API void                           llama_perf_sampler_reset(      struct llama_sampler * chain);
@@ -3629,7 +4946,94 @@ def llama_perf_sampler_print(chain: llama_sampler_p, /):
     [llama_sampler_p_ctypes],
     None,
 )
-def llama_perf_sampler_reset(chain: llama_sampler_p, /):
-    ...
+def llama_perf_sampler_reset(chain: llama_sampler_p, /): ...
+
+
+# //
+# // training
+# //
+
+# // function that returns whether or not a given tensor contains trainable parameters
+# typedef bool (*llama_opt_param_filter)(const struct ggml_tensor * tensor, void * userdata);
+llama_opt_param_filter = ctypes.CFUNCTYPE(
+    ctypes.c_bool, ctypes.c_void_p, ctypes.c_void_p
+)
+
+
+# // always returns true
+# LLAMA_API bool llama_opt_param_filter_all(const struct ggml_tensor * tensor, void * userdata);
+@ctypes_function(
+    "llama_opt_param_filter_all",
+    [ctypes.c_void_p, ctypes.c_void_p],
+    ctypes.c_bool,
+)
+def llama_opt_param_filter_all(
+    tensor: ctypes.c_void_p, userdata: ctypes.c_void_p, /
+) -> bool: ...
+
+
+# struct llama_opt_params {
+#     uint32_t n_ctx_train; // assumed context size post training, use context size specified in llama_context if 0
+
+#     llama_opt_param_filter param_filter; // callback for determining which tensors contain trainable parameters
+#     void * param_filter_ud;              // userdata for determining which tensors contain trainable parameters
 
 
+#     ggml_opt_get_optimizer_params get_opt_pars; // callback for calculating optimizer parameters
+#     void * get_opt_pars_ud;                     // userdata for calculating optimizer parameters
+# };
+class llama_opt_params(ctypes.Structure):
+    _fields_ = [
+        ("n_ctx_train", ctypes.c_uint32),
+        ("param_filter", llama_opt_param_filter),
+        ("param_filter_ud", ctypes.c_void_p),
+        (
+            "get_opt_pars",
+            ctypes.c_void_p,
+        ),  # ggml_opt_get_optimizer_params - not implemented here
+        ("get_opt_pars_ud", ctypes.c_void_p),
+    ]
+
+
+# LLAMA_API void llama_opt_init(struct llama_context * lctx, struct llama_model * model, struct llama_opt_params lopt_params);
+@ctypes_function(
+    "llama_opt_init",
+    [llama_context_p_ctypes, llama_model_p_ctypes, llama_opt_params],
+    None,
+)
+def llama_opt_init(
+    lctx: llama_context_p, model: llama_model_p, lopt_params: llama_opt_params, /
+): ...
+
+
+# LLAMA_API void llama_opt_epoch(
+#         struct llama_context    * lctx,
+#         ggml_opt_dataset_t        dataset,
+#         ggml_opt_result_t         result_train,
+#         ggml_opt_result_t         result_eval,
+#         int64_t                   idata_split,
+#         ggml_opt_epoch_callback   callback_train,
+#         ggml_opt_epoch_callback   callback_eval);
+@ctypes_function(
+    "llama_opt_epoch",
+    [
+        llama_context_p_ctypes,
+        ctypes.c_void_p,  # ggml_opt_dataset_t
+        ctypes.c_void_p,  # ggml_opt_result_t
+        ctypes.c_void_p,  # ggml_opt_result_t
+        ctypes.c_int64,
+        ctypes.c_void_p,  # ggml_opt_epoch_callback
+        ctypes.c_void_p,  # ggml_opt_epoch_callback
+    ],
+    None,
+)
+def llama_opt_epoch(
+    lctx: llama_context_p,
+    dataset: ctypes.c_void_p,
+    result_train: ctypes.c_void_p,
+    result_eval: ctypes.c_void_p,
+    idata_split: int,
+    callback_train: ctypes.c_void_p,
+    callback_eval: ctypes.c_void_p,
+    /,
+): ...
diff --git a/llama_cpp/llama_cpp_ext.py b/llama_cpp/llama_cpp_ext.py
new file mode 100644
index 0000000000..a4b424eb63
--- /dev/null
+++ b/llama_cpp/llama_cpp_ext.py
@@ -0,0 +1,136 @@
+"""Experimental bindings for non-public llama.cpp APIs from `llama-ext.h`.
+
+This module is not part of the stable llama-cpp-python public API.
+Downstream code should not import or depend on it directly.
+"""
+
+from __future__ import annotations
+
+import ctypes
+import functools
+
+from typing import Any, Iterable, Union
+
+from . import llama_cpp
+
+_lib = llama_cpp._lib
+
+
+def _ctypes_function_from_names(
+    names: Iterable[str],
+    argtypes: list[Any],
+    restype: Any,
+):
+    """Decorator for extension functions whose exported symbol name can vary by ABI."""
+
+    def decorator(f):
+        missing: list[str] = []
+        for name in names:
+            try:
+                func = getattr(_lib, name)
+            except AttributeError:
+                missing.append(name)
+                continue
+            func.argtypes = argtypes
+            func.restype = restype
+            functools.wraps(f)(func)
+            return func
+        raise AttributeError(
+            f"None of the shared library symbols were found: {', '.join(missing)}"
+        )
+
+    return decorator
+
+
+# LLAMA_API void llama_set_embeddings_nextn(struct llama_context * ctx, bool value, bool masked);
+@_ctypes_function_from_names(
+    (
+        "llama_set_embeddings_nextn",
+        "_Z26llama_set_embeddings_nextnP13llama_contextbb",
+        "?llama_set_embeddings_nextn@@YAXPEAUllama_context@@_N1@Z",
+    ),
+    [llama_cpp.llama_context_p_ctypes, ctypes.c_bool, ctypes.c_bool],
+    None,
+)
+def llama_set_embeddings_nextn(
+    ctx: llama_cpp.llama_context_p,
+    value: bool,
+    masked: bool,
+    /,
+):
+    """Set whether the context outputs nextn embeddings or not."""
+    ...
+
+
+# LLAMA_API void llama_set_nextn_layer_offset(struct llama_context * ctx, int32_t offset);
+@_ctypes_function_from_names(
+    (
+        "llama_set_nextn_layer_offset",
+        "_Z28llama_set_nextn_layer_offsetP13llama_contexti",
+        "?llama_set_nextn_layer_offset@@YAXPEAUllama_context@@H@Z",
+    ),
+    [llama_cpp.llama_context_p_ctypes, ctypes.c_int32],
+    None,
+)
+def llama_set_nextn_layer_offset(
+    ctx: llama_cpp.llama_context_p,
+    offset: Union[ctypes.c_int32, int],
+    /,
+):
+    """Select which appended NextN block the decoder MTP graph runs."""
+    ...
+
+
+# LLAMA_API float * llama_get_embeddings_nextn(struct llama_context * ctx);
+@_ctypes_function_from_names(
+    (
+        "llama_get_embeddings_nextn",
+        "_Z26llama_get_embeddings_nextnP13llama_context",
+        "?llama_get_embeddings_nextn@@YAPEAMPEAUllama_context@@@Z",
+    ),
+    [llama_cpp.llama_context_p_ctypes],
+    ctypes.POINTER(ctypes.c_float),
+)
+def llama_get_embeddings_nextn(
+    ctx: llama_cpp.llama_context_p,
+    /,
+):
+    """Get the nextn embeddings from the last evaluation."""
+    ...
+
+
+# LLAMA_API float * llama_get_embeddings_nextn_ith(struct llama_context * ctx, int32_t i);
+@_ctypes_function_from_names(
+    (
+        "llama_get_embeddings_nextn_ith",
+        "_Z30llama_get_embeddings_nextn_ithP13llama_contexti",
+        "?llama_get_embeddings_nextn_ith@@YAPEAMPEAUllama_context@@H@Z",
+    ),
+    [llama_cpp.llama_context_p_ctypes, ctypes.c_int32],
+    ctypes.POINTER(ctypes.c_float),
+)
+def llama_get_embeddings_nextn_ith(
+    ctx: llama_cpp.llama_context_p,
+    i: Union[ctypes.c_int32, int],
+    /,
+):
+    """Get the nextn embeddings for the ith output row from the last evaluation."""
+    ...
+
+
+# LLAMA_API llama_context * llama_get_ctx_other(struct llama_context * ctx);
+@_ctypes_function_from_names(
+    (
+        "llama_get_ctx_other",
+        "_Z19llama_get_ctx_otherP13llama_context",
+        "?llama_get_ctx_other@@YAPEAUllama_context@@PEAU1@@Z",
+    ),
+    [llama_cpp.llama_context_p_ctypes],
+    llama_cpp.llama_context_p_ctypes,
+)
+def llama_get_ctx_other(
+    ctx: llama_cpp.llama_context_p,
+    /,
+):
+    """Get the context linked through llama_context_params.ctx_other."""
+    ...
diff --git a/llama_cpp/llama_grammar.py b/llama_cpp/llama_grammar.py
index b95c77ab5e..ba34dda831 100644
--- a/llama_cpp/llama_grammar.py
+++ b/llama_cpp/llama_grammar.py
@@ -297,7 +297,7 @@ def opt_repetitions(up_to_n, prefix_with_sep=False):
     if max_items is not None:
         result += opt_repetitions(max_items - min_items, prefix_with_sep=min_items > 0)
     else:
-        item_operator = f'({separator_rule + " " if separator_rule else ""}{item_rule})'
+        item_operator = f"({separator_rule + ' ' if separator_rule else ''}{item_rule})"
 
         if min_items == 0 and separator_rule:
             result = f"({item_rule} {item_operator}*)?"
@@ -450,9 +450,9 @@ def visit(n: dict):
                 ref = n.get("$ref")
                 if ref is not None and ref not in self._refs:
                     if ref.startswith("https://"):
-                        assert (
-                            self._allow_fetch
-                        ), "Fetching remote schemas is not allowed (use --allow-fetch for force)"
+                        assert self._allow_fetch, (
+                            "Fetching remote schemas is not allowed (use --allow-fetch for force)"
+                        )
                         import requests
 
                         frag_split = ref.split("#")
@@ -475,9 +475,9 @@ def visit(n: dict):
                         raise ValueError(f"Unsupported ref {ref}")
 
                     for sel in ref.split("#")[-1].split("/")[1:]:
-                        assert (
-                            target is not None and sel in target
-                        ), f"Error resolving ref {ref}: {sel} not in {target}"
+                        assert target is not None and sel in target, (
+                            f"Error resolving ref {ref}: {sel} not in {target}"
+                        )
                         target = target[sel]
 
                     self._refs[ref] = target
@@ -492,7 +492,7 @@ def visit(n: dict):
     def _generate_union_rule(self, name, alt_schemas):
         return " | ".join(
             (
-                self.visit(alt_schema, f'{name}{"-" if name else "alternative-"}{i}')
+                self.visit(alt_schema, f"{name}{'-' if name else 'alternative-'}{i}")
                 for i, alt_schema in enumerate(alt_schemas)
             )
         )
@@ -510,9 +510,9 @@ def _visit_pattern(self, pattern, name):
         we define sub-rules to keep the output lean.
         """
 
-        assert pattern.startswith("^") and pattern.endswith(
-            "$"
-        ), 'Pattern must start with "^" and end with "$"'
+        assert pattern.startswith("^") and pattern.endswith("$"), (
+            'Pattern must start with "^" and end with "$"'
+        )
         pattern = pattern[1:-1]
         sub_rule_ids = {}
 
@@ -566,15 +566,15 @@ def join_seq():
                 elif c == "(":
                     i += 1
                     if i < length:
-                        assert (
-                            pattern[i] != "?"
-                        ), f'Unsupported pattern syntax "{pattern[i]}" at index {i} of /{pattern}/'
+                        assert pattern[i] != "?", (
+                            f'Unsupported pattern syntax "{pattern[i]}" at index {i} of /{pattern}/'
+                        )
                     seq.append((f"({to_rule(transform())})", False))
                 elif c == ")":
                     i += 1
-                    assert (
-                        start > 0 and pattern[start - 1] == "("
-                    ), f"Unbalanced parentheses; start = {start}, i = {i}, pattern = {pattern}"
+                    assert start > 0 and pattern[start - 1] == "(", (
+                        f"Unbalanced parentheses; start = {start}, i = {i}, pattern = {pattern}"
+                    )
                     return join_seq()
                 elif c == "[":
                     square_brackets = c
@@ -586,9 +586,9 @@ def join_seq():
                         else:
                             square_brackets += pattern[i]
                             i += 1
-                    assert (
-                        i < length
-                    ), f"Unbalanced square brackets; start = {start}, i = {i}, pattern = {pattern}"
+                    assert i < length, (
+                        f"Unbalanced square brackets; start = {start}, i = {i}, pattern = {pattern}"
+                    )
                     square_brackets += "]"
                     i += 1
                     seq.append((square_brackets, False))
@@ -604,9 +604,9 @@ def join_seq():
                     while i < length and pattern[i] != "}":
                         curly_brackets += pattern[i]
                         i += 1
-                    assert (
-                        i < length
-                    ), f"Unbalanced curly brackets; start = {start}, i = {i}, pattern = {pattern}"
+                    assert i < length, (
+                        f"Unbalanced curly brackets; start = {start}, i = {i}, pattern = {pattern}"
+                    )
                     curly_brackets += "}"
                     i += 1
                     nums = [s.strip() for s in curly_brackets[1:-1].split(",")]
@@ -777,13 +777,13 @@ def add_component(comp_schema, is_required):
                     rule_name,
                     '"[" space '
                     + ' "," space '.join(
-                        self.visit(item, f'{name}{"-" if name else ""}tuple-{i}')
+                        self.visit(item, f"{name}{'-' if name else ''}tuple-{i}")
                         for i, item in enumerate(items)
                     )
                     + ' "]" space',
                 )
             else:
-                item_rule_name = self.visit(items, f'{name}{"-" if name else ""}item')
+                item_rule_name = self.visit(items, f"{name}{'-' if name else ''}item")
                 min_items = schema.get("minItems", 0)
                 max_items = schema.get("maxItems")
                 return self._add_rule(
@@ -873,17 +873,17 @@ def _build_object_rule(
         prop_kv_rule_names = {}
         for prop_name, prop_schema in properties:
             prop_rule_name = self.visit(
-                prop_schema, f'{name}{"-" if name else ""}{prop_name}'
+                prop_schema, f"{name}{'-' if name else ''}{prop_name}"
             )
             prop_kv_rule_names[prop_name] = self._add_rule(
-                f'{name}{"-" if name else ""}{prop_name}-kv',
+                f"{name}{'-' if name else ''}{prop_name}-kv",
                 rf'{self._format_literal(json.dumps(prop_name))} space ":" space {prop_rule_name}',
             )
         required_props = [k for k in sorted_props if k in required]
         optional_props = [k for k in sorted_props if k not in required]
 
         if additional_properties == True or isinstance(additional_properties, dict):
-            sub_name = f'{name}{"-" if name else ""}additional'
+            sub_name = f"{name}{'-' if name else ''}additional"
             value_rule = self.visit(
                 {} if additional_properties == True else additional_properties,
                 f"{sub_name}-value",
@@ -908,7 +908,7 @@ def get_recursive_refs(ks, first_is_optional):
                 kv_rule_name = prop_kv_rule_names[k]
                 if k == "*":
                     res = self._add_rule(
-                        f'{name}{"-" if name else ""}additional-kvs',
+                        f"{name}{'-' if name else ''}additional-kvs",
                         f'{kv_rule_name} ( "," space ' + kv_rule_name + " )*",
                     )
                 elif first_is_optional:
@@ -917,7 +917,7 @@ def get_recursive_refs(ks, first_is_optional):
                     res = kv_rule_name
                 if len(rest) > 0:
                     res += " " + self._add_rule(
-                        f'{name}{"-" if name else ""}{k}-rest',
+                        f"{name}{'-' if name else ''}{k}-rest",
                         get_recursive_refs(rest, first_is_optional=True),
                     )
                 return res
diff --git a/llama_cpp/llama_types.py b/llama_cpp/llama_types.py
index bbb58afc35..f647822ff5 100644
--- a/llama_cpp/llama_types.py
+++ b/llama_cpp/llama_types.py
@@ -82,10 +82,28 @@ class ChatCompletionFunction(TypedDict):
     parameters: Dict[str, JsonType]  # TODO: make this more specific
 
 
+class ChatCompletionTopLogprobToken(TypedDict):
+    token: str
+    logprob: float
+    bytes: Optional[List[int]]
+
+
+class ChatCompletionLogprobToken(ChatCompletionTopLogprobToken):
+    token: str
+    logprob: float
+    bytes: Optional[List[int]]
+    top_logprobs: List[ChatCompletionTopLogprobToken]
+
+
+class ChatCompletionLogprobs(TypedDict):
+    content: Optional[List[ChatCompletionLogprobToken]]
+    refusal: Optional[List[ChatCompletionLogprobToken]]
+
+
 class ChatCompletionResponseChoice(TypedDict):
     index: int
     message: "ChatCompletionResponseMessage"
-    logprobs: Optional[CompletionLogprobs]
+    logprobs: Optional[ChatCompletionLogprobs]
     finish_reason: Optional[str]
 
 
@@ -134,7 +152,7 @@ class ChatCompletionStreamResponseChoice(TypedDict):
         ChatCompletionStreamResponseDelta, ChatCompletionStreamResponseDeltaEmpty
     ]
     finish_reason: Optional[Literal["stop", "length", "tool_calls", "function_call"]]
-    logprobs: NotRequired[Optional[CompletionLogprobs]]
+    logprobs: NotRequired[Optional[ChatCompletionLogprobs]]
 
 
 class CreateChatCompletionStreamResponse(TypedDict):
@@ -214,7 +232,7 @@ class ChatCompletionRequestAssistantMessageFunctionCall(TypedDict):
 
 class ChatCompletionRequestAssistantMessage(TypedDict):
     role: Literal["assistant"]
-    content: Optional[str]
+    content: NotRequired[str]
     tool_calls: NotRequired[ChatCompletionMessageToolCalls]
     function_call: NotRequired[
         ChatCompletionRequestAssistantMessageFunctionCall
diff --git a/llama_cpp/llava_cpp.py b/llama_cpp/llava_cpp.py
index d9dfaf5fd0..ea9b216f7d 100644
--- a/llama_cpp/llava_cpp.py
+++ b/llama_cpp/llava_cpp.py
@@ -36,7 +36,11 @@
 # Specify the base name of the shared library to load
 _libllava_base_name = "llava"
 _libllava_override_path = os.environ.get("LLAVA_CPP_LIB")
-_libllava_base_path = pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib" if _libllava_override_path is None else pathlib.Path()
+_libllava_base_path = (
+    pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib"
+    if _libllava_override_path is None
+    else pathlib.Path(_libllava_override_path)
+)
 
 # Load the library
 _libllava = load_shared_library(_libllava_base_name, _libllava_base_path)
@@ -73,8 +77,7 @@ class llava_image_embed(Structure):
 )
 def llava_validate_embed_size(
     ctx_llama: llama_cpp.llama_context_p, ctx_clip: clip_ctx_p, /
-) -> bool:
-    ...
+) -> bool: ...
 
 
 # /** build an image embed from image file bytes */
@@ -90,8 +93,7 @@ def llava_image_embed_make_with_bytes(
     image_bytes: CtypesArray[c_uint8],
     image_bytes_length: Union[c_int, int],
     /,
-) -> "_Pointer[llava_image_embed]":
-    ...
+) -> "_Pointer[llava_image_embed]": ...
 
 
 # /** build an image embed from a path to an image filename */
@@ -103,15 +105,13 @@ def llava_image_embed_make_with_bytes(
 )
 def llava_image_embed_make_with_filename(
     ctx_clip: clip_ctx_p, n_threads: Union[c_int, int], image_path: bytes, /
-) -> "_Pointer[llava_image_embed]":
-    ...
+) -> "_Pointer[llava_image_embed]": ...
 
 
 # LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed);
 # /** free an embedding made with llava_image_embed_make_* */
 @ctypes_function("llava_image_embed_free", [POINTER(llava_image_embed)], None)
-def llava_image_embed_free(embed: "_Pointer[llava_image_embed]", /):
-    ...
+def llava_image_embed_free(embed: "_Pointer[llava_image_embed]", /): ...
 
 
 # /** write the image represented by embed into the llama context with batch size n_batch, starting at context pos n_past. on completion, n_past points to the next position in the context after the image embed. */
@@ -132,8 +132,7 @@ def llava_eval_image_embed(
     n_batch: Union[c_int, int],
     n_past: "_Pointer[c_int]",
     /,
-) -> bool:
-    ...
+) -> bool: ...
 
 
 ################################################
@@ -146,13 +145,10 @@ def llava_eval_image_embed(
 @ctypes_function("clip_model_load", [c_char_p, c_int], clip_ctx_p_ctypes)
 def clip_model_load(
     fname: bytes, verbosity: Union[c_int, int], /
-) -> Optional[clip_ctx_p]:
-    ...
+) -> Optional[clip_ctx_p]: ...
 
 
 # /** free mmproj model */
 # CLIP_API void clip_free(struct clip_ctx * ctx);
 @ctypes_function("clip_free", [clip_ctx_p_ctypes], None)
-def clip_free(ctx: clip_ctx_p, /):
-    ...
-
+def clip_free(ctx: clip_ctx_p, /): ...
diff --git a/llama_cpp/mtmd_cpp.py b/llama_cpp/mtmd_cpp.py
new file mode 100644
index 0000000000..35357a3279
--- /dev/null
+++ b/llama_cpp/mtmd_cpp.py
@@ -0,0 +1,1019 @@
+from __future__ import annotations
+
+import os
+import warnings
+from ctypes import (
+    CFUNCTYPE,
+    c_bool,
+    c_char_p,
+    c_int,
+    c_int64,
+    c_uint8,
+    c_uint32,
+    c_size_t,
+    c_float,
+    c_void_p,
+    POINTER,
+    _Pointer,  # type: ignore
+    Structure,
+    byref,
+)
+import pathlib
+from typing import (
+    Callable,
+    Union,
+    NewType,
+    Optional,
+    TYPE_CHECKING,
+)
+
+import llama_cpp.llama_cpp as llama_cpp
+
+from llama_cpp._ctypes_extensions import (
+    load_shared_library,
+    ctypes_function_for_shared_library,
+)
+
+if TYPE_CHECKING:
+    from llama_cpp._ctypes_extensions import (
+        CtypesArray,
+    )
+
+
+# Specify the base name of the shared library to load
+_libmtmd_base_name = "mtmd"
+_libmtmd_override_path = os.environ.get("MTMD_CPP_LIB")
+_libmtmd_base_path = (
+    pathlib.Path(os.path.abspath(os.path.dirname(__file__))) / "lib"
+    if _libmtmd_override_path is None
+    else pathlib.Path(_libmtmd_override_path)
+)
+
+# Load the library
+_libmtmd = load_shared_library(_libmtmd_base_name, _libmtmd_base_path)
+
+ctypes_function = ctypes_function_for_shared_library(_libmtmd)
+
+################################################
+# mtmd.h types
+################################################
+
+# Opaque types
+mtmd_context_p = NewType("mtmd_context_p", int)
+mtmd_context_p_ctypes = c_void_p
+
+mtmd_bitmap_p = NewType("mtmd_bitmap_p", int)
+mtmd_bitmap_p_ctypes = c_void_p
+
+mtmd_helper_video_p = NewType("mtmd_helper_video_p", int)
+mtmd_helper_video_p_ctypes = c_void_p
+
+mtmd_image_tokens_p = NewType("mtmd_image_tokens_p", int)
+mtmd_image_tokens_p_ctypes = c_void_p
+
+mtmd_input_chunk_p = NewType("mtmd_input_chunk_p", int)
+mtmd_input_chunk_p_ctypes = c_void_p
+
+mtmd_input_chunks_p = NewType("mtmd_input_chunks_p", int)
+mtmd_input_chunks_p_ctypes = c_void_p
+
+mtmd_batch_p = NewType("mtmd_batch_p", int)
+mtmd_batch_p_ctypes = c_void_p
+
+# Enums
+MTMD_INPUT_CHUNK_TYPE_TEXT = 0
+MTMD_INPUT_CHUNK_TYPE_IMAGE = 1
+MTMD_INPUT_CHUNK_TYPE_AUDIO = 2
+
+mtmd_progress_callback = CFUNCTYPE(c_bool, c_float, c_void_p)
+
+
+# Structures
+class mtmd_context_params(Structure):
+    """Context parameters for MTMD initialization.
+
+    `image_marker` is deprecated upstream and kept for compatibility; use
+    `media_marker` for multimodal prompt placeholders.
+    """
+
+    if TYPE_CHECKING:
+        use_gpu: bool
+        print_timings: bool
+        n_threads: int
+        image_marker: Optional[bytes]
+        media_marker: Optional[bytes]
+        flash_attn_type: int
+        warmup: bool
+        image_min_tokens: int
+        image_max_tokens: int
+        cb_eval: llama_cpp.ggml_backend_sched_eval_callback
+        cb_eval_user_data: c_void_p
+        batch_max_tokens: int
+        progress_callback: Callable[[float, c_void_p], bool]
+        progress_callback_user_data: c_void_p
+
+    _fields_ = [
+        ("use_gpu", c_bool),
+        ("print_timings", c_bool),
+        ("n_threads", c_int),
+        ("image_marker", c_char_p),
+        ("media_marker", c_char_p),
+        ("flash_attn_type", c_int),
+        ("warmup", c_bool),
+        ("image_min_tokens", c_int),
+        ("image_max_tokens", c_int),
+        ("cb_eval", llama_cpp.ggml_backend_sched_eval_callback),
+        ("cb_eval_user_data", c_void_p),
+        ("batch_max_tokens", c_int),
+        ("progress_callback", mtmd_progress_callback),
+        ("progress_callback_user_data", c_void_p),
+    ]
+
+
+class mtmd_input_text(Structure):
+    """Text input passed to `mtmd_tokenize`."""
+
+    _fields_ = [
+        ("text", c_char_p),
+        ("add_special", c_bool),
+        ("parse_special", c_bool),
+    ]
+
+
+class mtmd_decoder_pos(Structure):
+    """Decoder attention position for M-RoPE models."""
+
+    _fields_ = [
+        ("t", c_uint32),
+        ("x", c_uint32),
+        ("y", c_uint32),
+        ("z", c_uint32),
+    ]
+
+
+# struct mtmd_caps {
+#     bool inp_vision;
+#     bool inp_audio;
+# };
+class mtmd_caps(Structure):
+    """Capabilities exposed by an mmproj file."""
+
+    if TYPE_CHECKING:
+        inp_vision: bool
+        inp_audio: bool
+
+    _fields_ = [
+        ("inp_vision", c_bool),
+        ("inp_audio", c_bool),
+    ]
+
+
+mtmd_bitmap_lazy_callback = CFUNCTYPE(
+    c_int,
+    c_size_t,
+    c_void_p,
+    POINTER(mtmd_bitmap_p_ctypes),
+    POINTER(c_char_p),
+)
+
+mtmd_helper_post_decode_callback = CFUNCTYPE(
+    c_int,
+    llama_cpp.llama_batch,
+    c_void_p,
+)
+
+
+class mtmd_helper_bitmap_wrapper(Structure):
+    """Bitmap wrapper returned by MTMD helper media loaders."""
+
+    if TYPE_CHECKING:
+        bitmap: Optional[mtmd_bitmap_p]
+        video_ctx: Optional[mtmd_helper_video_p]
+
+    _fields_ = [
+        ("bitmap", mtmd_bitmap_p_ctypes),
+        ("video_ctx", mtmd_helper_video_p_ctypes),
+    ]
+
+
+class mtmd_helper_video_info(Structure):
+    """Metadata for a decoded video stream."""
+
+    if TYPE_CHECKING:
+        width: int
+        height: int
+        fps: float
+        n_frames: int
+
+    _fields_ = [
+        ("width", c_uint32),
+        ("height", c_uint32),
+        ("fps", c_float),
+        ("n_frames", c_int),
+    ]
+
+
+class mtmd_helper_video_init_params(Structure):
+    """Parameters for initializing an MTMD helper video stream."""
+
+    if TYPE_CHECKING:
+        fps_target: float
+        ffmpeg_bin_dir: Optional[bytes]
+        timestamp_interval_ms: int
+
+    _fields_ = [
+        ("fps_target", c_float),
+        ("ffmpeg_bin_dir", c_char_p),
+        ("timestamp_interval_ms", c_int64),
+    ]
+
+
+################################################
+# mtmd.h functions
+################################################
+
+
+# MTMD_API const char * mtmd_default_marker(void);
+@ctypes_function("mtmd_default_marker", [], c_char_p)
+def mtmd_default_marker() -> bytes:
+    """Return the default media marker."""
+    ...
+
+
+# MTMD_API struct mtmd_context_params mtmd_context_params_default(void);
+@ctypes_function("mtmd_context_params_default", [], mtmd_context_params)
+def mtmd_context_params_default() -> mtmd_context_params:
+    """Return the default MTMD context parameters."""
+    ...
+
+
+# MTMD_API mtmd_context * mtmd_init_from_file(const char * mmproj_fname,
+#                                             const struct llama_model * text_model,
+#                                             const struct mtmd_context_params ctx_params);
+@ctypes_function(
+    "mtmd_init_from_file",
+    [c_char_p, llama_cpp.llama_model_p_ctypes, mtmd_context_params],
+    mtmd_context_p_ctypes,
+)
+def mtmd_init_from_file(
+    mmproj_fname: bytes,
+    text_model: llama_cpp.llama_model_p,
+    ctx_params: mtmd_context_params,
+    /,
+) -> Optional[mtmd_context_p]:
+    """Initialize the MTMD context from a projector file. Returns None on failure."""
+    ...
+
+
+# MTMD_API void mtmd_free(mtmd_context * ctx);
+@ctypes_function("mtmd_free", [mtmd_context_p_ctypes], None)
+def mtmd_free(ctx: mtmd_context_p, /): ...
+
+
+# MTMD_API bool mtmd_decode_use_non_causal(const mtmd_context * ctx, const mtmd_input_chunk * chunk);
+@ctypes_function(
+    "mtmd_decode_use_non_causal",
+    [mtmd_context_p_ctypes, mtmd_input_chunk_p_ctypes],
+    c_bool,
+)
+def mtmd_decode_use_non_causal(
+    ctx: mtmd_context_p, chunk: Optional[mtmd_input_chunk_p], /
+) -> bool:
+    """Check whether MTMD decoding uses non-causal attention."""
+    ...
+
+
+# MTMD_API bool mtmd_decode_use_mrope(const mtmd_context * ctx);
+@ctypes_function("mtmd_decode_use_mrope", [mtmd_context_p_ctypes], c_bool)
+def mtmd_decode_use_mrope(ctx: mtmd_context_p, /) -> bool:
+    """Check whether MTMD decoding uses mRoPE."""
+    ...
+
+
+# MTMD_API bool mtmd_support_vision(const mtmd_context * ctx);
+@ctypes_function("mtmd_support_vision", [mtmd_context_p_ctypes], c_bool)
+def mtmd_support_vision(ctx: mtmd_context_p, /) -> bool:
+    """Check whether the current model supports vision input."""
+    ...
+
+
+# MTMD_API bool mtmd_support_audio(const mtmd_context * ctx);
+@ctypes_function("mtmd_support_audio", [mtmd_context_p_ctypes], c_bool)
+def mtmd_support_audio(ctx: mtmd_context_p, /) -> bool:
+    """Check whether MTMD supports audio."""
+    ...
+
+
+# MTMD_API int mtmd_get_audio_sample_rate(const mtmd_context * ctx);
+@ctypes_function("mtmd_get_audio_sample_rate", [mtmd_context_p_ctypes], c_int)
+def mtmd_get_audio_sample_rate(ctx: mtmd_context_p, /) -> int:
+    """Get the audio sample rate in Hz. Returns -1 if audio is not supported."""
+    ...
+
+
+# MTMD_API const char * mtmd_get_marker(const mtmd_context * ctx);
+@ctypes_function("mtmd_get_marker", [mtmd_context_p_ctypes], c_char_p)
+def mtmd_get_marker(ctx: mtmd_context_p, /) -> Optional[bytes]:
+    """Get the current media marker string."""
+    ...
+
+
+# Deprecated compatibility wrapper for the renamed mtmd_get_audio_sample_rate().
+def mtmd_get_audio_bitrate(ctx: mtmd_context_p, /) -> int:
+    warnings.warn(
+        "mtmd_get_audio_bitrate is deprecated; use mtmd_get_audio_sample_rate instead",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+    return mtmd_get_audio_sample_rate(ctx)
+
+
+# MTMD_API mtmd_bitmap * mtmd_bitmap_init(uint32_t nx, uint32_t ny, const unsigned char * data);
+@ctypes_function(
+    "mtmd_bitmap_init", [c_uint32, c_uint32, POINTER(c_uint8)], mtmd_bitmap_p_ctypes
+)
+def mtmd_bitmap_init(
+    nx: Union[c_uint32, int],
+    ny: Union[c_uint32, int],
+    data: CtypesArray[c_uint8],
+    /,
+) -> Optional[mtmd_bitmap_p]: ...
+
+
+# MTMD_API mtmd_bitmap * mtmd_bitmap_init_from_audio(size_t n_samples, const float * data);
+@ctypes_function(
+    "mtmd_bitmap_init_from_audio",
+    [c_size_t, POINTER(c_float)],
+    mtmd_bitmap_p_ctypes,
+)
+def mtmd_bitmap_init_from_audio(
+    n_samples: Union[c_size_t, int],
+    data: CtypesArray[c_float],
+    /,
+) -> Optional[mtmd_bitmap_p]:
+    """Initialize an MTMD bitmap from audio samples."""
+    ...
+
+
+# MTMD_API void mtmd_bitmap_free(mtmd_bitmap * bitmap);
+@ctypes_function("mtmd_bitmap_free", [mtmd_bitmap_p_ctypes], None)
+def mtmd_bitmap_free(bitmap: mtmd_bitmap_p, /): ...
+
+
+# MTMD_API uint32_t mtmd_bitmap_get_nx(const mtmd_bitmap * bitmap);
+@ctypes_function("mtmd_bitmap_get_nx", [mtmd_bitmap_p_ctypes], c_uint32)
+def mtmd_bitmap_get_nx(bitmap: mtmd_bitmap_p, /) -> int:
+    """Get the bitmap width in pixels."""
+    ...
+
+
+# MTMD_API uint32_t mtmd_bitmap_get_ny(const mtmd_bitmap * bitmap);
+@ctypes_function("mtmd_bitmap_get_ny", [mtmd_bitmap_p_ctypes], c_uint32)
+def mtmd_bitmap_get_ny(bitmap: mtmd_bitmap_p, /) -> int:
+    """Get the bitmap height in pixels."""
+    ...
+
+
+# MTMD_API const unsigned char * mtmd_bitmap_get_data(const mtmd_bitmap * bitmap);
+@ctypes_function("mtmd_bitmap_get_data", [mtmd_bitmap_p_ctypes], POINTER(c_uint8))
+def mtmd_bitmap_get_data(bitmap: mtmd_bitmap_p, /) -> Optional[CtypesArray[c_uint8]]:
+    """Get the raw bitmap data buffer."""
+    ...
+
+
+# MTMD_API size_t mtmd_bitmap_get_n_bytes(const mtmd_bitmap * bitmap);
+@ctypes_function("mtmd_bitmap_get_n_bytes", [mtmd_bitmap_p_ctypes], c_size_t)
+def mtmd_bitmap_get_n_bytes(bitmap: mtmd_bitmap_p, /) -> int:
+    """Get the bitmap data size in bytes."""
+    ...
+
+
+# MTMD_API bool mtmd_bitmap_is_audio(const mtmd_bitmap * bitmap);
+@ctypes_function("mtmd_bitmap_is_audio", [mtmd_bitmap_p_ctypes], c_bool)
+def mtmd_bitmap_is_audio(bitmap: mtmd_bitmap_p, /) -> bool:
+    """Check whether the bitmap contains audio data."""
+    ...
+
+
+# MTMD_API const char * mtmd_bitmap_get_id(const mtmd_bitmap * bitmap);
+@ctypes_function("mtmd_bitmap_get_id", [mtmd_bitmap_p_ctypes], c_char_p)
+def mtmd_bitmap_get_id(bitmap: mtmd_bitmap_p, /) -> Optional[bytes]:
+    """Get the optional bitmap identifier."""
+    ...
+
+
+# MTMD_API void mtmd_bitmap_set_id(mtmd_bitmap * bitmap, const char * id);
+@ctypes_function("mtmd_bitmap_set_id", [mtmd_bitmap_p_ctypes, c_char_p], None)
+def mtmd_bitmap_set_id(bitmap: mtmd_bitmap_p, id: Optional[bytes], /):
+    """Set the optional bitmap identifier."""
+    ...
+
+
+# MTMD_API mtmd_bitmap * mtmd_bitmap_init_lazy(mtmd_context * ctx,
+#                                              const char * id,
+#                                              void * user_data,
+#                                              mtmd_bitmap_lazy_callback callback);
+@ctypes_function(
+    "mtmd_bitmap_init_lazy",
+    [mtmd_context_p_ctypes, c_char_p, c_void_p, mtmd_bitmap_lazy_callback],
+    mtmd_bitmap_p_ctypes,
+)
+def mtmd_bitmap_init_lazy(
+    ctx: mtmd_context_p,
+    id: Optional[bytes],
+    user_data: c_void_p,
+    callback: mtmd_bitmap_lazy_callback,
+    /,
+) -> Optional[mtmd_bitmap_p]:
+    """Initialize a lazy MTMD bitmap."""
+    ...
+
+
+# MTMD_API mtmd_input_chunks * mtmd_input_chunks_init(void);
+@ctypes_function("mtmd_input_chunks_init", [], mtmd_input_chunks_p_ctypes)
+def mtmd_input_chunks_init() -> Optional[mtmd_input_chunks_p]: ...
+
+
+# MTMD_API void mtmd_input_chunks_free(mtmd_input_chunks * chunks);
+@ctypes_function("mtmd_input_chunks_free", [mtmd_input_chunks_p_ctypes], None)
+def mtmd_input_chunks_free(chunks: mtmd_input_chunks_p, /): ...
+
+
+# MTMD_API size_t mtmd_input_chunks_size(const mtmd_input_chunks * chunks);
+@ctypes_function("mtmd_input_chunks_size", [mtmd_input_chunks_p_ctypes], c_size_t)
+def mtmd_input_chunks_size(chunks: mtmd_input_chunks_p, /) -> int: ...
+
+
+# MTMD_API const mtmd_input_chunk * mtmd_input_chunks_get(const mtmd_input_chunks * chunks, size_t idx);
+@ctypes_function(
+    "mtmd_input_chunks_get",
+    [mtmd_input_chunks_p_ctypes, c_size_t],
+    mtmd_input_chunk_p_ctypes,
+)
+def mtmd_input_chunks_get(
+    chunks: mtmd_input_chunks_p, idx: Union[c_size_t, int], /
+) -> Optional[mtmd_input_chunk_p]: ...
+
+
+# MTMD_API int32_t mtmd_tokenize(mtmd_context * ctx,
+#                                mtmd_input_chunks * output,
+#                                const mtmd_input_text * text,
+#                                const mtmd_bitmap ** bitmaps,
+#                                size_t n_bitmaps);
+@ctypes_function(
+    "mtmd_tokenize",
+    [
+        mtmd_context_p_ctypes,
+        mtmd_input_chunks_p_ctypes,
+        POINTER(mtmd_input_text),
+        POINTER(mtmd_bitmap_p_ctypes),
+        c_size_t,
+    ],
+    c_int,
+)
+def mtmd_tokenize(
+    ctx: mtmd_context_p,
+    output: mtmd_input_chunks_p,
+    text: "_Pointer[mtmd_input_text]",
+    bitmaps: CtypesArray[mtmd_bitmap_p_ctypes],
+    n_bitmaps: Union[c_size_t, int],
+    /,
+) -> int: ...
+
+
+# MTMD_API size_t mtmd_input_chunk_get_n_tokens(const mtmd_input_chunk * chunk);
+@ctypes_function("mtmd_input_chunk_get_n_tokens", [mtmd_input_chunk_p_ctypes], c_size_t)
+def mtmd_input_chunk_get_n_tokens(chunk: mtmd_input_chunk_p, /) -> int: ...
+
+
+# MTMD_API enum mtmd_input_chunk_type mtmd_input_chunk_get_type(const mtmd_input_chunk * chunk);
+@ctypes_function("mtmd_input_chunk_get_type", [mtmd_input_chunk_p_ctypes], c_int)
+def mtmd_input_chunk_get_type(chunk: mtmd_input_chunk_p, /) -> int: ...
+
+
+# MTMD_API const llama_token * mtmd_input_chunk_get_tokens_text(const mtmd_input_chunk * chunk, size_t * n_tokens_output);
+@ctypes_function(
+    "mtmd_input_chunk_get_tokens_text",
+    [mtmd_input_chunk_p_ctypes, POINTER(c_size_t)],
+    POINTER(llama_cpp.llama_token),
+)
+def mtmd_input_chunk_get_tokens_text(
+    chunk: mtmd_input_chunk_p, n_tokens_output: "_Pointer[c_size_t]", /
+) -> Optional["_Pointer[llama_cpp.llama_token]"]: ...
+
+
+# MTMD_API const mtmd_image_tokens * mtmd_input_chunk_get_tokens_image(const mtmd_input_chunk * chunk);
+@ctypes_function(
+    "mtmd_input_chunk_get_tokens_image",
+    [mtmd_input_chunk_p_ctypes],
+    mtmd_image_tokens_p_ctypes,
+)
+def mtmd_input_chunk_get_tokens_image(
+    chunk: mtmd_input_chunk_p, /
+) -> Optional[mtmd_image_tokens_p]: ...
+
+
+# MTMD_API const char * mtmd_input_chunk_get_id(const mtmd_input_chunk * chunk);
+@ctypes_function("mtmd_input_chunk_get_id", [mtmd_input_chunk_p_ctypes], c_char_p)
+def mtmd_input_chunk_get_id(chunk: mtmd_input_chunk_p, /) -> Optional[bytes]:
+    """Get the optional chunk identifier."""
+    ...
+
+
+# MTMD_API llama_pos mtmd_input_chunk_get_n_pos(const mtmd_input_chunk * chunk);
+@ctypes_function(
+    "mtmd_input_chunk_get_n_pos",
+    [mtmd_input_chunk_p_ctypes],
+    llama_cpp.llama_pos,
+)
+def mtmd_input_chunk_get_n_pos(chunk: mtmd_input_chunk_p, /) -> int:
+    """Get the number of positions consumed by the chunk."""
+    ...
+
+
+# MTMD_API mtmd_input_chunk * mtmd_input_chunk_copy(const mtmd_input_chunk * chunk);
+@ctypes_function(
+    "mtmd_input_chunk_copy", [mtmd_input_chunk_p_ctypes], mtmd_input_chunk_p_ctypes
+)
+def mtmd_input_chunk_copy(chunk: mtmd_input_chunk_p, /) -> Optional[mtmd_input_chunk_p]:
+    """Copy an input chunk and transfer ownership to the caller."""
+    ...
+
+
+# MTMD_API void mtmd_input_chunk_free(mtmd_input_chunk * chunk);
+@ctypes_function("mtmd_input_chunk_free", [mtmd_input_chunk_p_ctypes], None)
+def mtmd_input_chunk_free(chunk: mtmd_input_chunk_p, /):
+    """Free an owned input chunk."""
+    ...
+
+
+# MTMD_API size_t mtmd_image_tokens_get_n_tokens(const mtmd_image_tokens * image_tokens);
+@ctypes_function(
+    "mtmd_image_tokens_get_n_tokens", [mtmd_image_tokens_p_ctypes], c_size_t
+)
+def mtmd_image_tokens_get_n_tokens(image_tokens: mtmd_image_tokens_p, /) -> int:
+    """Get the number of image tokens."""
+    ...
+
+
+# DEPRECATED(MTMD_API size_t mtmd_image_tokens_get_nx(const mtmd_image_tokens * image_tokens),
+#            "use mtmd_image_tokens_get_decoder_pos() instead");
+@ctypes_function("mtmd_image_tokens_get_nx", [mtmd_image_tokens_p_ctypes], c_size_t)
+def mtmd_image_tokens_get_nx(image_tokens: mtmd_image_tokens_p, /) -> int:
+    """Get the image token grid width."""
+    ...
+
+
+# DEPRECATED(MTMD_API size_t mtmd_image_tokens_get_ny(const mtmd_image_tokens * image_tokens),
+#            "use mtmd_image_tokens_get_decoder_pos() instead");
+@ctypes_function("mtmd_image_tokens_get_ny", [mtmd_image_tokens_p_ctypes], c_size_t)
+def mtmd_image_tokens_get_ny(image_tokens: mtmd_image_tokens_p, /) -> int:
+    """Get the image token grid height."""
+    ...
+
+
+# MTMD_API const char * mtmd_image_tokens_get_id(const mtmd_image_tokens * image_tokens);
+@ctypes_function("mtmd_image_tokens_get_id", [mtmd_image_tokens_p_ctypes], c_char_p)
+def mtmd_image_tokens_get_id(image_tokens: mtmd_image_tokens_p, /) -> Optional[bytes]:
+    """Get the optional image token identifier."""
+    ...
+
+
+# MTMD_API llama_pos mtmd_image_tokens_get_n_pos(const mtmd_image_tokens * image_tokens);
+@ctypes_function(
+    "mtmd_image_tokens_get_n_pos",
+    [mtmd_image_tokens_p_ctypes],
+    llama_cpp.llama_pos,
+)
+def mtmd_image_tokens_get_n_pos(image_tokens: mtmd_image_tokens_p, /) -> int:
+    """Get the number of positions consumed by the image tokens."""
+    ...
+
+
+# MTMD_API struct mtmd_decoder_pos mtmd_image_tokens_get_decoder_pos(
+#     const mtmd_image_tokens * image_tokens, llama_pos pos_0, size_t i);
+@ctypes_function(
+    "mtmd_image_tokens_get_decoder_pos",
+    [mtmd_image_tokens_p_ctypes, llama_cpp.llama_pos, c_size_t],
+    mtmd_decoder_pos,
+)
+def mtmd_image_tokens_get_decoder_pos(
+    image_tokens: mtmd_image_tokens_p,
+    pos_0: llama_cpp.llama_pos,
+    i: Union[c_size_t, int],
+    /,
+) -> mtmd_decoder_pos:
+    """Get decoder attention position for an image embedding token."""
+    ...
+
+
+# MTMD_API int32_t mtmd_encode(mtmd_context * ctx, const mtmd_image_tokens * image_tokens);
+@ctypes_function(
+    "mtmd_encode",
+    [mtmd_context_p_ctypes, mtmd_image_tokens_p_ctypes],
+    c_int,
+)
+def mtmd_encode(ctx: mtmd_context_p, image_tokens: mtmd_image_tokens_p, /) -> int:
+    """Run a deprecated MTMD encode pass for image tokens."""
+    ...
+
+
+# MTMD_API int32_t mtmd_encode_chunk(mtmd_context * ctx, const mtmd_input_chunk * chunk);
+@ctypes_function(
+    "mtmd_encode_chunk",
+    [mtmd_context_p_ctypes, mtmd_input_chunk_p_ctypes],
+    c_int,
+)
+def mtmd_encode_chunk(ctx: mtmd_context_p, chunk: mtmd_input_chunk_p, /) -> int:
+    """Run an MTMD encode pass for a single chunk."""
+    ...
+
+
+# MTMD_API float * mtmd_get_output_embd(mtmd_context * ctx);
+@ctypes_function("mtmd_get_output_embd", [mtmd_context_p_ctypes], POINTER(c_float))
+def mtmd_get_output_embd(ctx: mtmd_context_p, /) -> Optional[CtypesArray[c_float]]:
+    """Get output embeddings from the last encode pass."""
+    ...
+
+
+# MTMD_API mtmd_batch * mtmd_batch_init(mtmd_context * ctx);
+@ctypes_function("mtmd_batch_init", [mtmd_context_p_ctypes], mtmd_batch_p_ctypes)
+def mtmd_batch_init(ctx: mtmd_context_p, /) -> Optional[mtmd_batch_p]:
+    """Initialize an MTMD media chunk batch for a context."""
+    ...
+
+
+# MTMD_API void mtmd_batch_free(mtmd_batch * batch);
+@ctypes_function("mtmd_batch_free", [mtmd_batch_p_ctypes], None)
+def mtmd_batch_free(batch: mtmd_batch_p, /): ...
+
+
+# MTMD_API int32_t mtmd_batch_add_chunk(mtmd_batch * batch, const mtmd_input_chunk * chunk);
+@ctypes_function(
+    "mtmd_batch_add_chunk",
+    [mtmd_batch_p_ctypes, mtmd_input_chunk_p_ctypes],
+    c_int,
+)
+def mtmd_batch_add_chunk(
+    batch: mtmd_batch_p,
+    chunk: mtmd_input_chunk_p,
+    /,
+) -> int:
+    """Add a media chunk to an MTMD batch."""
+    ...
+
+
+# MTMD_API int32_t mtmd_batch_encode(mtmd_batch * batch);
+@ctypes_function("mtmd_batch_encode", [mtmd_batch_p_ctypes], c_int)
+def mtmd_batch_encode(batch: mtmd_batch_p, /) -> int:
+    """Run an MTMD encode pass for all chunks in a batch."""
+    ...
+
+
+# MTMD_API float * mtmd_batch_get_output_embd(mtmd_batch * batch, const mtmd_input_chunk * chunk);
+@ctypes_function(
+    "mtmd_batch_get_output_embd",
+    [mtmd_batch_p_ctypes, mtmd_input_chunk_p_ctypes],
+    POINTER(c_float),
+)
+def mtmd_batch_get_output_embd(
+    batch: mtmd_batch_p,
+    chunk: mtmd_input_chunk_p,
+    /,
+) -> Optional[CtypesArray[c_float]]:
+    """Get output embeddings for a chunk from the last batch encode pass."""
+    ...
+
+
+# MTMD_API struct mtmd_caps mtmd_get_cap_from_file(const char * mmproj_fname);
+@ctypes_function("mtmd_get_cap_from_file", [c_char_p], mtmd_caps)
+def mtmd_get_cap_from_file(mmproj_fname: bytes, /) -> mtmd_caps:
+    """Get mmproj capabilities without initializing a full MTMD context."""
+    ...
+
+
+# MTMD_API mtmd_input_chunks * mtmd_test_create_input_chunks(void);
+@ctypes_function("mtmd_test_create_input_chunks", [], mtmd_input_chunks_p_ctypes)
+def mtmd_test_create_input_chunks() -> Optional[mtmd_input_chunks_p]:
+    """Create MTMD test chunks for the C API tests."""
+    ...
+
+
+################################################
+# mtmd-helper.h functions
+################################################
+
+
+# MTMD_API bool mtmd_helper_support_video(mtmd_context * ctx);
+@ctypes_function(
+    "mtmd_helper_support_video",
+    [mtmd_context_p_ctypes],
+    c_bool,
+)
+def mtmd_helper_support_video(ctx: mtmd_context_p, /) -> bool:
+    """Check whether MTMD helper video support is available."""
+    ...
+
+
+# MTMD_API struct mtmd_helper_bitmap_wrapper mtmd_helper_bitmap_init_from_file(mtmd_context * ctx, const char * fname, bool placeholder);
+@ctypes_function(
+    "mtmd_helper_bitmap_init_from_file",
+    [mtmd_context_p_ctypes, c_char_p, c_bool],
+    mtmd_helper_bitmap_wrapper,
+)
+def mtmd_helper_bitmap_init_from_file_wrapper(
+    ctx: mtmd_context_p, fname: bytes, placeholder: Union[c_bool, bool], /
+) -> mtmd_helper_bitmap_wrapper:
+    """Initialize an MTMD bitmap wrapper from a file."""
+    ...
+
+
+def mtmd_helper_bitmap_init_from_file(
+    ctx: mtmd_context_p, fname: bytes, placeholder: Union[c_bool, bool], /
+) -> Optional[mtmd_bitmap_p]:
+    """Initialize an MTMD bitmap from a file."""
+    return mtmd_helper_bitmap_init_from_file_wrapper(ctx, fname, placeholder).bitmap
+
+
+# MTMD_API struct mtmd_helper_bitmap_wrapper mtmd_helper_bitmap_init_from_buf(mtmd_context * ctx, const unsigned char * buf, size_t len, bool placeholder);
+@ctypes_function(
+    "mtmd_helper_bitmap_init_from_buf",
+    [mtmd_context_p_ctypes, POINTER(c_uint8), c_size_t, c_bool],
+    mtmd_helper_bitmap_wrapper,
+)
+def mtmd_helper_bitmap_init_from_buf_wrapper(
+    ctx: mtmd_context_p,
+    buf: CtypesArray[c_uint8],
+    length: Union[c_size_t, int],
+    placeholder: Union[c_bool, bool],
+    /,
+) -> mtmd_helper_bitmap_wrapper: ...
+
+
+def mtmd_helper_bitmap_init_from_buf(
+    ctx: mtmd_context_p,
+    buf: CtypesArray[c_uint8],
+    length: Union[c_size_t, int],
+    placeholder: Union[c_bool, bool],
+    /,
+) -> Optional[mtmd_bitmap_p]:
+    """Initialize an MTMD bitmap from a buffer."""
+    return mtmd_helper_bitmap_init_from_buf_wrapper(
+        ctx, buf, length, placeholder
+    ).bitmap
+
+
+# MTMD_API size_t mtmd_helper_get_n_tokens(const mtmd_input_chunks * chunks);
+@ctypes_function("mtmd_helper_get_n_tokens", [mtmd_input_chunks_p_ctypes], c_size_t)
+def mtmd_helper_get_n_tokens(chunks: mtmd_input_chunks_p, /) -> int: ...
+
+
+# MTMD_API llama_pos mtmd_helper_get_n_pos(const mtmd_input_chunks * chunks);
+@ctypes_function(
+    "mtmd_helper_get_n_pos",
+    [mtmd_input_chunks_p_ctypes],
+    llama_cpp.llama_pos,
+)
+def mtmd_helper_get_n_pos(chunks: mtmd_input_chunks_p, /) -> int:
+    """Count the total positions consumed by the chunks."""
+    ...
+
+
+# MTMD_API void mtmd_helper_image_get_decoder_pos(
+#     const mtmd_image_tokens * image, llama_pos pos_0, struct mtmd_decoder_pos * out_pos);
+@ctypes_function(
+    "mtmd_helper_image_get_decoder_pos",
+    [mtmd_image_tokens_p_ctypes, llama_cpp.llama_pos, POINTER(mtmd_decoder_pos)],
+    None,
+)
+def mtmd_helper_image_get_decoder_pos(
+    image: mtmd_image_tokens_p,
+    pos_0: llama_cpp.llama_pos,
+    out_pos: "_Pointer[mtmd_decoder_pos]",
+    /,
+):
+    """Fill decoder attention positions for all image embedding tokens."""
+    ...
+
+
+# MTMD_API int32_t mtmd_helper_eval_chunks(mtmd_context * ctx,
+#                                          struct llama_context * lctx,
+#                                          const mtmd_input_chunks * chunks,
+#                                          llama_pos n_past,
+#                                          llama_seq_id seq_id,
+#                                          int32_t n_batch,
+#                                          bool logits_last,
+#                                          llama_pos * new_n_past);
+@ctypes_function(
+    "mtmd_helper_eval_chunks",
+    [
+        mtmd_context_p_ctypes,
+        llama_cpp.llama_context_p_ctypes,
+        mtmd_input_chunks_p_ctypes,
+        llama_cpp.llama_pos,
+        llama_cpp.llama_seq_id,
+        c_int,
+        c_bool,
+        POINTER(llama_cpp.llama_pos),
+    ],
+    c_int,
+)
+def mtmd_helper_eval_chunks(
+    ctx: mtmd_context_p,
+    lctx: llama_cpp.llama_context_p,
+    chunks: mtmd_input_chunks_p,
+    n_past: llama_cpp.llama_pos,
+    seq_id: llama_cpp.llama_seq_id,
+    n_batch: Union[c_int, int],
+    logits_last: Union[c_bool, bool],
+    new_n_past: "_Pointer[llama_cpp.llama_pos]",
+    /,
+) -> int: ...
+
+
+# MTMD_API int32_t mtmd_helper_eval_chunk_single(mtmd_context * ctx,
+#                                                struct llama_context * lctx,
+#                                                const mtmd_input_chunk * chunk,
+#                                                llama_pos n_past,
+#                                                llama_seq_id seq_id,
+#                                                int32_t n_batch,
+#                                                bool logits_last,
+#                                                llama_pos * new_n_past);
+@ctypes_function(
+    "mtmd_helper_eval_chunk_single",
+    [
+        mtmd_context_p_ctypes,
+        llama_cpp.llama_context_p_ctypes,
+        mtmd_input_chunk_p_ctypes,
+        llama_cpp.llama_pos,
+        llama_cpp.llama_seq_id,
+        c_int,
+        c_bool,
+        POINTER(llama_cpp.llama_pos),
+    ],
+    c_int,
+)
+def mtmd_helper_eval_chunk_single(
+    ctx: mtmd_context_p,
+    lctx: llama_cpp.llama_context_p,
+    chunk: mtmd_input_chunk_p,
+    n_past: llama_cpp.llama_pos,
+    seq_id: llama_cpp.llama_seq_id,
+    n_batch: Union[c_int, int],
+    logits_last: Union[c_bool, bool],
+    new_n_past: "_Pointer[llama_cpp.llama_pos]",
+    /,
+) -> int: ...
+
+
+# MTMD_API int32_t mtmd_helper_decode_image_chunk(mtmd_context * ctx,
+#                                                 struct llama_context * lctx,
+#                                                 const mtmd_input_chunk * chunk,
+#                                                 float * encoded_embd,
+#                                                 llama_pos n_past,
+#                                                 llama_seq_id seq_id,
+#                                                 int32_t n_batch,
+#                                                 llama_pos * new_n_past,
+#                                                 mtmd_helper_post_decode_callback callback,
+#                                                 void * user_data);
+@ctypes_function(
+    "mtmd_helper_decode_image_chunk",
+    [
+        mtmd_context_p_ctypes,
+        llama_cpp.llama_context_p_ctypes,
+        mtmd_input_chunk_p_ctypes,
+        POINTER(c_float),
+        llama_cpp.llama_pos,
+        llama_cpp.llama_seq_id,
+        c_int,
+        POINTER(llama_cpp.llama_pos),
+        mtmd_helper_post_decode_callback,
+        c_void_p,
+    ],
+    c_int,
+)
+def mtmd_helper_decode_image_chunk(
+    ctx: mtmd_context_p,
+    lctx: llama_cpp.llama_context_p,
+    chunk: mtmd_input_chunk_p,
+    encoded_embd: CtypesArray[c_float],
+    n_past: llama_cpp.llama_pos,
+    seq_id: llama_cpp.llama_seq_id,
+    n_batch: Union[c_int, int],
+    new_n_past: "_Pointer[llama_cpp.llama_pos]",
+    callback: Optional[mtmd_helper_post_decode_callback],
+    user_data: c_void_p,
+    /,
+) -> int:
+    """Decode a pre-encoded image chunk."""
+    ...
+
+
+# MTMD_API struct mtmd_helper_video_init_params mtmd_helper_video_init_params_default(void);
+@ctypes_function(
+    "mtmd_helper_video_init_params_default", [], mtmd_helper_video_init_params
+)
+def mtmd_helper_video_init_params_default() -> mtmd_helper_video_init_params:
+    """Return the default MTMD helper video initialization parameters."""
+    ...
+
+
+# MTMD_API mtmd_helper_video * mtmd_helper_video_init(
+#                     struct mtmd_context * mctx,
+#                     const char * path,
+#                     struct mtmd_helper_video_init_params params);
+@ctypes_function(
+    "mtmd_helper_video_init",
+    [mtmd_context_p_ctypes, c_char_p, mtmd_helper_video_init_params],
+    mtmd_helper_video_p_ctypes,
+)
+def mtmd_helper_video_init(
+    ctx: mtmd_context_p,
+    path: bytes,
+    params: mtmd_helper_video_init_params,
+    /,
+) -> Optional[mtmd_helper_video_p]:
+    """Initialize an MTMD helper video stream from a file path."""
+    ...
+
+
+# MTMD_API mtmd_helper_video * mtmd_helper_video_init_from_buf(
+#                     struct mtmd_context * mctx,
+#                     const unsigned char * buf, size_t len,
+#                     struct mtmd_helper_video_init_params params);
+@ctypes_function(
+    "mtmd_helper_video_init_from_buf",
+    [mtmd_context_p_ctypes, POINTER(c_uint8), c_size_t, mtmd_helper_video_init_params],
+    mtmd_helper_video_p_ctypes,
+)
+def mtmd_helper_video_init_from_buf(
+    ctx: mtmd_context_p,
+    buf: CtypesArray[c_uint8],
+    length: Union[c_size_t, int],
+    params: mtmd_helper_video_init_params,
+    /,
+) -> Optional[mtmd_helper_video_p]:
+    """Initialize an MTMD helper video stream from a buffer."""
+    ...
+
+
+# MTMD_API void mtmd_helper_video_free(mtmd_helper_video * ctx);
+@ctypes_function("mtmd_helper_video_free", [mtmd_helper_video_p_ctypes], None)
+def mtmd_helper_video_free(ctx: mtmd_helper_video_p, /):
+    """Free an MTMD helper video stream."""
+    ...
+
+
+# MTMD_API struct mtmd_helper_video_info mtmd_helper_video_get_info(const mtmd_helper_video * ctx);
+@ctypes_function(
+    "mtmd_helper_video_get_info",
+    [mtmd_helper_video_p_ctypes],
+    mtmd_helper_video_info,
+)
+def mtmd_helper_video_get_info(ctx: mtmd_helper_video_p, /) -> mtmd_helper_video_info:
+    """Get metadata for an MTMD helper video stream."""
+    ...
+
+
+# MTMD_API int32_t mtmd_helper_video_read_next(mtmd_helper_video * ctx,
+#             mtmd_bitmap ** out_bitmap,
+#             char ** out_text);
+@ctypes_function(
+    "mtmd_helper_video_read_next",
+    [
+        mtmd_helper_video_p_ctypes,
+        POINTER(mtmd_bitmap_p_ctypes),
+        POINTER(c_char_p),
+    ],
+    c_int,
+)
+def mtmd_helper_video_read_next(
+    ctx: mtmd_helper_video_p,
+    out_bitmap: "_Pointer[mtmd_bitmap_p_ctypes]",
+    out_text: "_Pointer[c_char_p]",
+    /,
+) -> int:
+    """Read the next bitmap or text chunk from an MTMD helper video stream."""
+    ...
+
+
+# MTMD_API void mtmd_log_set(ggml_log_callback log_callback, void * user_data);
+@ctypes_function(
+    "mtmd_log_set",
+    [llama_cpp.llama_log_callback, c_void_p],
+    None,
+)
+def mtmd_log_set(log_callback, user_data: c_void_p, /):
+    """Set the MTMD logging callback."""
+    ...
+
+
+# MTMD_API void mtmd_helper_log_set(ggml_log_callback log_callback, void * user_data);
+@ctypes_function(
+    "mtmd_helper_log_set",
+    [llama_cpp.llama_log_callback, c_void_p],
+    None,
+)
+def mtmd_helper_log_set(log_callback, user_data: c_void_p, /):
+    """Set the MTMD helper logging callback."""
+    ...
diff --git a/llama_cpp/server/app.py b/llama_cpp/server/app.py
index cd3255176f..f776fe159c 100644
--- a/llama_cpp/server/app.py
+++ b/llama_cpp/server/app.py
@@ -5,9 +5,9 @@
 import typing
 import contextlib
 
-from threading import Lock
+from anyio import Lock
 from functools import partial
-from typing import Iterator, List, Optional, Union, Dict
+from typing import List, Optional, Union, Dict
 
 import llama_cpp
 
@@ -70,14 +70,14 @@ def set_llama_proxy(model_settings: List[ModelSettings]):
     _llama_proxy = LlamaProxy(models=model_settings)
 
 
-def get_llama_proxy():
+async def get_llama_proxy():
     # NOTE: This double lock allows the currently streaming llama model to
     # check if any other requests are pending in the same thread and cancel
     # the stream if so.
-    llama_outer_lock.acquire()
+    await llama_outer_lock.acquire()
     release_outer_lock = True
     try:
-        llama_inner_lock.acquire()
+        await llama_inner_lock.acquire()
         try:
             llama_outer_lock.release()
             release_outer_lock = False
@@ -125,9 +125,9 @@ def create_app(
         server_settings = ServerSettings.model_validate(settings)
         model_settings = [ModelSettings.model_validate(settings)]
 
-    assert (
-        server_settings is not None and model_settings is not None
-    ), "server_settings and model_settings must be provided together"
+    assert server_settings is not None and model_settings is not None, (
+        "server_settings and model_settings must be provided together"
+    )
 
     set_server_settings(server_settings)
     middleware = [Middleware(RawContextMiddleware, plugins=(RequestIdPlugin(),))]
@@ -155,34 +155,71 @@ def create_app(
     return app
 
 
+def prepare_request_resources(
+    body: CreateCompletionRequest | CreateChatCompletionRequest,
+    llama_proxy: LlamaProxy,
+    body_model: str | None,
+    kwargs,
+) -> llama_cpp.Llama:
+    if llama_proxy is None:
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail="Service is not available",
+        )
+    llama = llama_proxy(body_model)
+    if body.logit_bias is not None:
+        kwargs["logit_bias"] = (
+            _logit_bias_tokens_to_input_ids(llama, body.logit_bias)
+            if body.logit_bias_type == "tokens"
+            else body.logit_bias
+        )
+
+    if body.grammar is not None:
+        kwargs["grammar"] = llama_cpp.LlamaGrammar.from_string(body.grammar)
+
+    if body.min_tokens > 0:
+        _min_tokens_logits_processor = llama_cpp.LogitsProcessorList(
+            [llama_cpp.MinTokensLogitsProcessor(body.min_tokens, llama.token_eos())]
+        )
+        if "logits_processor" not in kwargs:
+            kwargs["logits_processor"] = _min_tokens_logits_processor
+        else:
+            kwargs["logits_processor"].extend(_min_tokens_logits_processor)
+    return llama
+
+
 async def get_event_publisher(
     request: Request,
     inner_send_chan: MemoryObjectSendStream[typing.Any],
-    iterator: Iterator[typing.Any],
-    on_complete: typing.Optional[typing.Callable[[], None]] = None,
+    body: CreateCompletionRequest | CreateChatCompletionRequest,
+    body_model: str | None,
+    llama_call,
+    kwargs,
 ):
     server_settings = next(get_server_settings())
     interrupt_requests = (
         server_settings.interrupt_requests if server_settings else False
     )
-    async with inner_send_chan:
-        try:
-            async for chunk in iterate_in_threadpool(iterator):
-                await inner_send_chan.send(dict(data=json.dumps(chunk)))
-                if await request.is_disconnected():
-                    raise anyio.get_cancelled_exc_class()()
-                if interrupt_requests and llama_outer_lock.locked():
-                    await inner_send_chan.send(dict(data="[DONE]"))
-                    raise anyio.get_cancelled_exc_class()()
-            await inner_send_chan.send(dict(data="[DONE]"))
-        except anyio.get_cancelled_exc_class() as e:
-            print("disconnected")
-            with anyio.move_on_after(1, shield=True):
-                print(f"Disconnected from client (via refresh/close) {request.client}")
-                raise e
-        finally:
-            if on_complete:
-                on_complete()
+    async with contextlib.asynccontextmanager(get_llama_proxy)() as llama_proxy:
+        llama = prepare_request_resources(body, llama_proxy, body_model, kwargs)
+        async with inner_send_chan:
+            try:
+                iterator = await run_in_threadpool(llama_call, llama, **kwargs)
+                async for chunk in iterate_in_threadpool(iterator):
+                    await inner_send_chan.send(dict(data=json.dumps(chunk)))
+                    if await request.is_disconnected():
+                        raise anyio.get_cancelled_exc_class()()
+                    if interrupt_requests and llama_outer_lock.locked():
+                        await inner_send_chan.send(dict(data="[DONE]"))
+                        raise anyio.get_cancelled_exc_class()()
+                await inner_send_chan.send(dict(data="[DONE]"))
+            except anyio.get_cancelled_exc_class() as e:
+                print("disconnected")
+                with anyio.move_on_after(1, shield=True):
+                    print(
+                        f"Disconnected from client (via refresh/close) {request.client}"
+                    )
+                    raise e
 
 
 def _logit_bias_tokens_to_input_ids(
@@ -267,20 +304,11 @@ async def create_completion(
     request: Request,
     body: CreateCompletionRequest,
 ) -> llama_cpp.Completion:
-    exit_stack = contextlib.ExitStack()
-    llama_proxy = await run_in_threadpool(
-        lambda: exit_stack.enter_context(contextlib.contextmanager(get_llama_proxy)())
-    )
-    if llama_proxy is None:
-        raise HTTPException(
-            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
-            detail="Service is not available",
-        )
     if isinstance(body.prompt, list):
         assert len(body.prompt) <= 1
         body.prompt = body.prompt[0] if len(body.prompt) > 0 else ""
 
-    llama = llama_proxy(
+    body_model = (
         body.model
         if request.url.path != "/v1/engines/copilot-codex/completions"
         else "copilot-codex"
@@ -295,41 +323,8 @@ async def create_completion(
     }
     kwargs = body.model_dump(exclude=exclude)
 
-    if body.logit_bias is not None:
-        kwargs["logit_bias"] = (
-            _logit_bias_tokens_to_input_ids(llama, body.logit_bias)
-            if body.logit_bias_type == "tokens"
-            else body.logit_bias
-        )
-
-    if body.grammar is not None:
-        kwargs["grammar"] = llama_cpp.LlamaGrammar.from_string(body.grammar)
-
-    if body.min_tokens > 0:
-        _min_tokens_logits_processor = llama_cpp.LogitsProcessorList(
-            [llama_cpp.MinTokensLogitsProcessor(body.min_tokens, llama.token_eos())]
-        )
-        if "logits_processor" not in kwargs:
-            kwargs["logits_processor"] = _min_tokens_logits_processor
-        else:
-            kwargs["logits_processor"].extend(_min_tokens_logits_processor)
-
-    iterator_or_completion: Union[
-        llama_cpp.CreateCompletionResponse,
-        Iterator[llama_cpp.CreateCompletionStreamResponse],
-    ] = await run_in_threadpool(llama, **kwargs)
-
-    if isinstance(iterator_or_completion, Iterator):
-        # EAFP: It's easier to ask for forgiveness than permission
-        first_response = await run_in_threadpool(next, iterator_or_completion)
-
-        # If no exception was raised from first_response, we can assume that
-        # the iterator is valid and we can use it to stream the response.
-        def iterator() -> Iterator[llama_cpp.CreateCompletionStreamResponse]:
-            yield first_response
-            yield from iterator_or_completion
-            exit_stack.close()
-
+    # handle streaming request
+    if kwargs.get("stream", False):
         send_chan, recv_chan = anyio.create_memory_object_stream(10)
         return EventSourceResponse(
             recv_chan,
@@ -337,14 +332,29 @@ def iterator() -> Iterator[llama_cpp.CreateCompletionStreamResponse]:
                 get_event_publisher,
                 request=request,
                 inner_send_chan=send_chan,
-                iterator=iterator(),
-                on_complete=exit_stack.close,
+                body=body,
+                body_model=body_model,
+                llama_call=llama_cpp.Llama.__call__,
+                kwargs=kwargs,
             ),
             sep="\n",
             ping_message_factory=_ping_message_factory,
         )
-    else:
-        return iterator_or_completion
+
+    # handle regular request
+    async with contextlib.asynccontextmanager(get_llama_proxy)() as llama_proxy:
+        llama = prepare_request_resources(body, llama_proxy, body_model, kwargs)
+
+        if await request.is_disconnected():
+            print(
+                f"Disconnected from client (via refresh/close) before llm invoked {request.client}"
+            )
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail="Client closed request",
+            )
+
+        return await run_in_threadpool(llama, **kwargs)
 
 
 @router.post(
@@ -472,15 +482,8 @@ async def create_chat_completion(
     # where the dependency is cleaned up before a StreamingResponse
     # is complete.
     # https://github.com/tiangolo/fastapi/issues/11143
-    exit_stack = contextlib.ExitStack()
-    llama_proxy = await run_in_threadpool(
-        lambda: exit_stack.enter_context(contextlib.contextmanager(get_llama_proxy)())
-    )
-    if llama_proxy is None:
-        raise HTTPException(
-            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
-            detail="Service is not available",
-        )
+
+    body_model = body.model
     exclude = {
         "n",
         "logit_bias_type",
@@ -488,41 +491,9 @@ async def create_chat_completion(
         "min_tokens",
     }
     kwargs = body.model_dump(exclude=exclude)
-    llama = llama_proxy(body.model)
-    if body.logit_bias is not None:
-        kwargs["logit_bias"] = (
-            _logit_bias_tokens_to_input_ids(llama, body.logit_bias)
-            if body.logit_bias_type == "tokens"
-            else body.logit_bias
-        )
-
-    if body.grammar is not None:
-        kwargs["grammar"] = llama_cpp.LlamaGrammar.from_string(body.grammar)
-
-    if body.min_tokens > 0:
-        _min_tokens_logits_processor = llama_cpp.LogitsProcessorList(
-            [llama_cpp.MinTokensLogitsProcessor(body.min_tokens, llama.token_eos())]
-        )
-        if "logits_processor" not in kwargs:
-            kwargs["logits_processor"] = _min_tokens_logits_processor
-        else:
-            kwargs["logits_processor"].extend(_min_tokens_logits_processor)
-
-    iterator_or_completion: Union[
-        llama_cpp.ChatCompletion, Iterator[llama_cpp.ChatCompletionChunk]
-    ] = await run_in_threadpool(llama.create_chat_completion, **kwargs)
-
-    if isinstance(iterator_or_completion, Iterator):
-        # EAFP: It's easier to ask for forgiveness than permission
-        first_response = await run_in_threadpool(next, iterator_or_completion)
-
-        # If no exception was raised from first_response, we can assume that
-        # the iterator is valid and we can use it to stream the response.
-        def iterator() -> Iterator[llama_cpp.ChatCompletionChunk]:
-            yield first_response
-            yield from iterator_or_completion
-            exit_stack.close()
 
+    # handle streaming request
+    if kwargs.get("stream", False):
         send_chan, recv_chan = anyio.create_memory_object_stream(10)
         return EventSourceResponse(
             recv_chan,
@@ -530,15 +501,29 @@ def iterator() -> Iterator[llama_cpp.ChatCompletionChunk]:
                 get_event_publisher,
                 request=request,
                 inner_send_chan=send_chan,
-                iterator=iterator(),
-                on_complete=exit_stack.close,
+                body=body,
+                body_model=body_model,
+                llama_call=llama_cpp.Llama.create_chat_completion,
+                kwargs=kwargs,
             ),
             sep="\n",
             ping_message_factory=_ping_message_factory,
         )
-    else:
-        exit_stack.close()
-        return iterator_or_completion
+
+    # handle regular request
+    async with contextlib.asynccontextmanager(get_llama_proxy)() as llama_proxy:
+        llama = prepare_request_resources(body, llama_proxy, body_model, kwargs)
+
+        if await request.is_disconnected():
+            print(
+                f"Disconnected from client (via refresh/close) before llm invoked {request.client}"
+            )
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail="Client closed request",
+            )
+
+        return await run_in_threadpool(llama.create_chat_completion, **kwargs)
 
 
 @router.get(
diff --git a/llama_cpp/server/cli.py b/llama_cpp/server/cli.py
index 3dd0076767..171b8db30c 100644
--- a/llama_cpp/server/cli.py
+++ b/llama_cpp/server/cli.py
@@ -1,8 +1,9 @@
 from __future__ import annotations
 
 import argparse
+import json
 
-from typing import List, Literal, Union, Any, Type, TypeVar
+from typing import List, Literal, Union, Any, Type, TypeVar, Dict
 
 from pydantic import BaseModel
 
@@ -14,7 +15,9 @@ def _get_base_type(annotation: Type[Any]) -> Type[Any]:
     elif getattr(annotation, "__origin__", None) is Union:
         assert hasattr(annotation, "__args__") and len(annotation.__args__) >= 1  # type: ignore
         non_optional_args: List[Type[Any]] = [
-            arg for arg in annotation.__args__ if arg is not type(None)  # type: ignore
+            arg
+            for arg in annotation.__args__
+            if arg is not type(None)  # type: ignore
         ]
         if non_optional_args:
             return _get_base_type(non_optional_args[0])
@@ -38,6 +41,17 @@ def _contains_list_type(annotation: Type[Any] | None) -> bool:
         return False
 
 
+def _contains_dict_type(annotation: Type[Any] | None) -> bool:
+    origin = getattr(annotation, "__origin__", None)
+
+    if origin is dict or origin is Dict:
+        return True
+    elif origin in (Literal, Union):
+        return any(_contains_dict_type(arg) for arg in annotation.__args__)  # type: ignore
+    else:
+        return False
+
+
 def _parse_bool_arg(arg: str | bytes | bool) -> bool:
     if isinstance(arg, bytes):
         arg = arg.decode("utf-8")
@@ -55,6 +69,16 @@ def _parse_bool_arg(arg: str | bytes | bool) -> bool:
         raise ValueError(f"Invalid boolean argument: {arg}")
 
 
+def _parse_json_object_arg(arg: str | bytes) -> dict[str, Any]:
+    if isinstance(arg, bytes):
+        arg = arg.decode("utf-8")
+
+    value = json.loads(arg)
+    if not isinstance(value, dict):
+        raise ValueError(f"Invalid JSON object argument: {arg}")
+    return value
+
+
 def add_args_from_model(parser: argparse.ArgumentParser, model: Type[BaseModel]):
     """Add arguments from a pydantic model to an argparse parser."""
 
@@ -66,7 +90,15 @@ def add_args_from_model(parser: argparse.ArgumentParser, model: Type[BaseModel])
             _get_base_type(field.annotation) if field.annotation is not None else str
         )
         list_type = _contains_list_type(field.annotation)
-        if base_type is not bool:
+        dict_type = _contains_dict_type(field.annotation)
+        if dict_type:
+            parser.add_argument(
+                f"--{name}",
+                dest=name,
+                type=_parse_json_object_arg,
+                help=description,
+            )
+        elif base_type is not bool:
             parser.add_argument(
                 f"--{name}",
                 dest=name,
diff --git a/llama_cpp/server/errors.py b/llama_cpp/server/errors.py
index fbf9fd80d5..d0eda5664b 100644
--- a/llama_cpp/server/errors.py
+++ b/llama_cpp/server/errors.py
@@ -134,8 +134,6 @@ def error_message_wrapper(
         ] = None,
     ) -> Tuple[int, ErrorResponse]:
         """Wraps error message in OpenAI style error response"""
-        print(f"Exception: {str(error)}", file=sys.stderr)
-        traceback.print_exc(file=sys.stderr)
         if body is not None and isinstance(
             body,
             (
@@ -149,6 +147,10 @@ def error_message_wrapper(
                 if match is not None:
                     return callback(body, match)
 
+        # Only print the trace on unexpected exceptions
+        print(f"Exception: {str(error)}", file=sys.stderr)
+        traceback.print_exc(file=sys.stderr)
+
         # Wrap other errors as internal server error
         return 500, ErrorResponse(
             message=str(error),
diff --git a/llama_cpp/server/model.py b/llama_cpp/server/model.py
index c6716f9196..8aa929202c 100644
--- a/llama_cpp/server/model.py
+++ b/llama_cpp/server/model.py
@@ -115,6 +115,23 @@ def load_llama_from_model_settings(settings: ModelSettings) -> llama_cpp.Llama:
                 chat_handler = llama_cpp.llama_chat_format.Llava16ChatHandler(
                     clip_model_path=settings.clip_model_path, verbose=settings.verbose
                 )
+        elif settings.chat_format in ("mtmd", "gemma4"):
+            assert settings.clip_model_path is not None, "clip model not found"
+            chat_handler_cls = (
+                llama_cpp.llama_chat_format.MTMDChatHandler
+                if settings.chat_format == "mtmd"
+                else llama_cpp.llama_chat_format.Gemma4ChatHandler
+            )
+            if settings.hf_model_repo_id is not None:
+                chat_handler = chat_handler_cls.from_pretrained(
+                    repo_id=settings.hf_model_repo_id,
+                    filename=settings.clip_model_path,
+                    verbose=settings.verbose,
+                )
+            else:
+                chat_handler = chat_handler_cls(
+                    clip_model_path=settings.clip_model_path, verbose=settings.verbose
+                )
         elif settings.chat_format == "moondream":
             assert settings.clip_model_path is not None, "clip model not found"
             if settings.hf_model_repo_id is not None:
@@ -171,19 +188,33 @@ def load_llama_from_model_settings(settings: ModelSettings) -> llama_cpp.Llama:
                 chat_handler = llama_cpp.llama_chat_format.MiniCPMv26ChatHandler(
                     clip_model_path=settings.clip_model_path, verbose=settings.verbose
                 )
+        elif settings.chat_format == "qwen2.5-vl":
+            assert settings.clip_model_path is not None, "clip model not found"
+            if settings.hf_model_repo_id is not None:
+                chat_handler = (
+                    llama_cpp.llama_chat_format.Qwen25VLChatHandler.from_pretrained(
+                        repo_id=settings.hf_model_repo_id,
+                        filename=settings.clip_model_path,
+                        verbose=settings.verbose,
+                    )
+                )
+            else:
+                chat_handler = llama_cpp.llama_chat_format.Qwen25VLChatHandler(
+                    clip_model_path=settings.clip_model_path, verbose=settings.verbose
+                )
         elif settings.chat_format == "hf-autotokenizer":
-            assert (
-                settings.hf_pretrained_model_name_or_path is not None
-            ), "hf_pretrained_model_name_or_path must be set for hf-autotokenizer"
+            assert settings.hf_pretrained_model_name_or_path is not None, (
+                "hf_pretrained_model_name_or_path must be set for hf-autotokenizer"
+            )
             chat_handler = (
                 llama_cpp.llama_chat_format.hf_autotokenizer_to_chat_completion_handler(
                     settings.hf_pretrained_model_name_or_path
                 )
             )
         elif settings.chat_format == "hf-tokenizer-config":
-            assert (
-                settings.hf_tokenizer_config_path is not None
-            ), "hf_tokenizer_config_path must be set for hf-tokenizer-config"
+            assert settings.hf_tokenizer_config_path is not None, (
+                "hf_tokenizer_config_path must be set for hf-tokenizer-config"
+            )
             chat_handler = llama_cpp.llama_chat_format.hf_tokenizer_config_to_chat_completion_handler(
                 json.load(open(settings.hf_tokenizer_config_path))
             )
@@ -285,6 +316,21 @@ def load_llama_from_model_settings(settings: ModelSettings) -> llama_cpp.Llama:
             # Misc
             verbose=settings.verbose,
         )
+        if settings.chat_template_kwargs:
+            base_chat_handler = (
+                _model.chat_handler
+                or _model._chat_handlers.get(_model.chat_format)
+                or llama_cpp.llama_chat_format.get_chat_completion_handler(
+                    _model.chat_format
+                )
+            )
+
+            def chat_handler_with_kwargs(*args, **kwargs):
+                return base_chat_handler(
+                    *args, **{**settings.chat_template_kwargs, **kwargs}
+                )
+
+            _model.chat_handler = chat_handler_with_kwargs
         if settings.cache:
             if settings.cache_type == "disk":
                 if settings.verbose:
diff --git a/llama_cpp/server/settings.py b/llama_cpp/server/settings.py
index 13c9512419..78dd7cdeb8 100644
--- a/llama_cpp/server/settings.py
+++ b/llama_cpp/server/settings.py
@@ -2,7 +2,7 @@
 
 import multiprocessing
 
-from typing import Optional, List, Literal, Union, Dict, cast
+from typing import Any, Optional, List, Literal, Union, Dict, cast
 from typing_extensions import Self
 
 from pydantic import Field, model_validator
@@ -60,7 +60,7 @@ class ModelSettings(BaseSettings):
     )
     rpc_servers: Optional[str] = Field(
         default=None,
-        description="comma seperated list of rpc servers for offloading",
+        description="comma separated list of rpc servers for offloading",
     )
     # Context Params
     seed: int = Field(
@@ -131,6 +131,10 @@ class ModelSettings(BaseSettings):
         default=None,
         description="Chat format to use.",
     )
+    chat_template_kwargs: Optional[Dict[str, Any]] = Field(
+        default=None,
+        description="Extra keyword arguments forwarded to chat templates at model load time. Matches llama.cpp server `chat_template_kwargs`.",
+    )
     clip_model_path: Optional[str] = Field(
         default=None,
         description="Path to a CLIP model to use for multi-modal chat completion.",
diff --git a/llama_cpp/server/types.py b/llama_cpp/server/types.py
index fdd1644568..eda2b92801 100644
--- a/llama_cpp/server/types.py
+++ b/llama_cpp/server/types.py
@@ -107,6 +107,7 @@
 
 
 class CreateCompletionRequest(BaseModel):
+    model: Optional[str] = model_field
     prompt: Union[str, List[str]] = Field(
         default="", description="The prompt to generate completions for."
     )
@@ -138,7 +139,6 @@ class CreateCompletionRequest(BaseModel):
     seed: Optional[int] = Field(None)
 
     # ignored or currently unsupported
-    model: Optional[str] = model_field
     n: Optional[int] = 1
     best_of: Optional[int] = 1
     user: Optional[str] = Field(default=None)
@@ -190,6 +190,7 @@ class ChatCompletionRequestMessage(BaseModel):
 
 
 class CreateChatCompletionRequest(BaseModel):
+    model: Optional[str] = model_field
     messages: List[llama_cpp.ChatCompletionRequestMessage] = Field(
         default=[], description="A list of messages to generate completions for."
     )
@@ -237,7 +238,6 @@ class CreateChatCompletionRequest(BaseModel):
     )
 
     # ignored or currently unsupported
-    model: Optional[str] = model_field
     n: Optional[int] = 1
     user: Optional[str] = Field(None)
 
diff --git a/mkdocs.yml b/mkdocs.yml
index 79a9e67a1a..37e1002e8d 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -36,7 +36,7 @@ plugins:
               - typing
               - typing_extensions
               - ctypes
-          import:
+          inventories:
             - https://docs.python.org/3/objects.inv
             - https://numpy.org/doc/stable/objects.inv
 
diff --git a/pyproject.toml b/pyproject.toml
index 9983ef777d..6bfacf279c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -25,6 +25,8 @@ classifiers = [
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
+    "Programming Language :: Python :: 3.14",
 ]
 
 
@@ -48,7 +50,7 @@ test = [
     "huggingface-hub>=0.23.0"
 ]
 dev = [
-    "black>=23.3.0",
+    "ruff>=0.15.7",
     "twine>=4.0.2",
     "mkdocs>=1.4.3",
     "mkdocstrings[python]>=0.22.0",
@@ -62,6 +64,7 @@ all = [
 
 [tool.scikit-build]
 wheel.packages = ["llama_cpp"]
+wheel.py-api = "py3"
 cmake.verbose = true
 cmake.minimum-version = "3.21"
 minimum-version = "0.5.1"
@@ -77,5 +80,16 @@ Issues = "https://github.com/abetlen/llama-cpp-python/issues"
 Documentation = "https://llama-cpp-python.readthedocs.io/en/latest/"
 Changelog = "https://llama-cpp-python.readthedocs.io/en/latest/changelog/"
 
+[tool.ruff]
+target-version = "py38"
+line-length = 88
+required-version = ">=0.15.7"
+src = ["llama_cpp", "tests"]
+extend-exclude = ["vendor", "examples/notebooks"]
+
+[tool.ruff.lint]
+select = ["E4", "E7", "E9"]
+ignore = ["E712"]
+
 [tool.pytest.ini_options]
 testpaths = "tests"
diff --git a/scripts/releases-to-pep-503.sh b/scripts/releases-to-pep-503.sh
index 71910efcbf..8359624492 100755
--- a/scripts/releases-to-pep-503.sh
+++ b/scripts/releases-to-pep-503.sh
@@ -54,8 +54,12 @@ cat << EOF > "$output_dir/llama-cpp-python/index.html"
     <h1>Links for llama-cpp-python</h1>
 EOF
 
-# Filter releases by pattern
-releases=$(grep -E "$pattern" "$current_dir/all_releases.txt")
+# Filter releases by pattern. Some backend indexes are valid even when there
+# are no matching releases yet.
+releases=$(grep -E "$pattern" "$current_dir/all_releases.txt" || true)
+if [ -z "$releases" ]; then
+    log_info "No releases found matching pattern: $pattern"
+fi
 
 # Prepare curl headers
 headers=('--header' 'Accept: application/vnd.github.v3+json')
@@ -81,16 +85,16 @@ for release in $releases; do
         continue
     fi
 
-    # Get release version from release ie v0.1.0-cu121 -> v0.1.0
-    release_version=$(echo "$release" | grep -oE "^[v]?[0-9]+\.[0-9]+\.[0-9]+")
-    echo "    <h2>$release_version</h2>" >> "$output_dir/llama-cpp-python/index.html"
-    
     wheel_urls=$(echo "$response" | jq -r '.assets[] | select(.name | endswith(".whl")) | .browser_download_url')
     if [ -z "$wheel_urls" ]; then
         log_error "No wheel files found for release $release"
         continue
     fi
 
+    # Get release version from release ie v0.1.0-cu121 -> v0.1.0
+    release_version=$(echo "$release" | grep -oE "^[v]?[0-9]+\.[0-9]+\.[0-9]+")
+    echo "    <h2>$release_version</h2>" >> "$output_dir/llama-cpp-python/index.html"
+
     echo "$wheel_urls" | while read -r asset; do
         echo "    <a href=\"$asset\">$asset</a>" >> "$output_dir/llama-cpp-python/index.html"
         echo "    <br>" >> "$output_dir/llama-cpp-python/index.html"
diff --git a/tests/test_llama.py b/tests/test_llama.py
index fc182ae20f..70fce12d8e 100644
--- a/tests/test_llama.py
+++ b/tests/test_llama.py
@@ -1,4 +1,5 @@
 import ctypes
+import itertools
 import multiprocessing
 
 import numpy as np
@@ -58,14 +59,47 @@ def test_llama_cpp_tokenization():
 
 @pytest.fixture
 def llama_cpp_model_path():
-    repo_id = "Qwen/Qwen2-0.5B-Instruct-GGUF"
-    filename = "qwen2-0_5b-instruct-q8_0.gguf"
+    repo_id = "lmstudio-community/Qwen3.5-0.8B-GGUF"
+    filename = "Qwen3.5-0.8B-Q8_0.gguf"
+    model_path = hf_hub_download(repo_id, filename)
+    return model_path
+
+
+@pytest.fixture
+def llama_cpp_transformer_model_path():
+    repo_id = "ggml-org/models"
+    filename = "tinyllamas/stories15M-q4_0.gguf"
+    model_path = hf_hub_download(repo_id, filename)
+    return model_path
+
+
+@pytest.fixture
+def llama_cpp_embedding_model_path():
+    repo_id = "CompendiumLabs/bge-small-en-v1.5-gguf"
+    filename = "bge-small-en-v1.5-q4_k_m.gguf"
+    model_path = hf_hub_download(repo_id, filename)
+    return model_path
+
+
+@pytest.fixture
+def llama_cpp_recurrent_model_path():
+    repo_id = "QuantFactory/mamba-130m-hf-GGUF"
+    filename = "mamba-130m-hf.Q2_K.gguf"
+    model_path = hf_hub_download(repo_id, filename)
+    return model_path
+
+
+@pytest.fixture
+def llama_cpp_hybrid_model_path():
+    repo_id = "tiiuae/Falcon-H1-Tiny-90M-Instruct-GGUF"
+    filename = "Falcon-H1-Tiny-90M-Instruct-Q2_K.gguf"
     model_path = hf_hub_download(repo_id, filename)
     return model_path
 
 
 def test_real_model(llama_cpp_model_path):
     import os
+
     assert os.path.exists(llama_cpp_model_path)
 
     params = llama_cpp.llama_model_default_params()
@@ -82,14 +116,19 @@ def test_real_model(llama_cpp_model_path):
     cparams.n_threads = multiprocessing.cpu_count()
     cparams.n_threads_batch = multiprocessing.cpu_count()
     cparams.logits_all = False
-    cparams.flash_attn = True
+    cparams.flash_attn_type = llama_cpp.LLAMA_FLASH_ATTN_TYPE_ENABLED
 
     context = internals.LlamaContext(model=model, params=cparams)
     tokens = model.tokenize(b"Hello, world!", add_bos=True, special=True)
 
-    assert tokens == [9707, 11, 1879, 0]
+    assert tokens == [9419, 11, 1814, 0]
 
-    tokens = model.tokenize(b"The quick brown fox jumps", add_bos=True, special=True)
+    tokens = model.tokenize(
+        b"The quick brown fox jumps over the lazy dog. The quick brown fox jumps ",
+        add_bos=True,
+        special=True,
+    )
+    prompt_token_count = len(tokens)
 
     batch = internals.LlamaBatch(n_tokens=len(tokens), embd=0, n_seq_max=1)
 
@@ -110,9 +149,12 @@ def test_real_model(llama_cpp_model_path):
         tokens = [token_id]
         result += tokens
 
-    output = result[5:]
+    output = result[prompt_token_count:]
     output_text = model.detokenize(output, special=True)
-    assert output_text == b" over the lazy dog"
+    # Low-level sampling output varies across CPU and Metal backends.
+    assert len(output) == 4
+    assert output_text
+
 
 def test_real_llama(llama_cpp_model_path):
     model = llama_cpp.Llama(
@@ -127,15 +169,14 @@ def test_real_llama(llama_cpp_model_path):
     )
 
     output = model.create_completion(
-        "The quick brown fox jumps",
-        max_tokens=4,
+        "The quick brown fox jumps over the lazy dog. The quick brown fox",
+        max_tokens=6,
         top_k=50,
         top_p=0.9,
-        temperature=0.8,
-        seed=1337
+        temperature=0.0,
+        seed=1337,
     )
-    assert output["choices"][0]["text"] == " over the lazy dog"
-
+    assert output["choices"][0]["text"] == " jumps over the lazy dog."
 
     output = model.create_completion(
         "The capital of france is paris, 'true' or 'false'?:\n",
@@ -146,20 +187,19 @@ def test_real_llama(llama_cpp_model_path):
         seed=1337,
         grammar=llama_cpp.LlamaGrammar.from_string("""
 root ::= "true" | "false"
-""")
+"""),
     )
     assert output["choices"][0]["text"] == "true"
 
     suffix = b"rot"
     tokens = model.tokenize(suffix, add_bos=True, special=True)
+
     def logit_processor_func(input_ids, logits):
         for token in tokens:
             logits[token] *= 1000
         return logits
 
-    logit_processors = llama_cpp.LogitsProcessorList(
-        [logit_processor_func]
-    )
+    logit_processors = llama_cpp.LogitsProcessorList([logit_processor_func])
 
     output = model.create_completion(
         "The capital of france is par",
@@ -168,7 +208,7 @@ def logit_processor_func(input_ids, logits):
         top_p=0.9,
         temperature=0.8,
         seed=1337,
-        logits_processor=logit_processors
+        logits_processor=logit_processors,
     )
     assert output["choices"][0]["text"].lower().startswith("rot")
 
@@ -181,10 +221,10 @@ def logit_processor_func(input_ids, logits):
         max_tokens=4,
         top_k=50,
         top_p=0.9,
-        temperature=0.8,
+        temperature=1.0,
         grammar=llama_cpp.LlamaGrammar.from_string("""
 root ::= "1" | "2" | "3" | "4" | "5" | "6" | "7" | "8" | "9" | "10"
-""")
+"""),
     )
     number_1 = output["choices"][0]["text"]
 
@@ -193,10 +233,10 @@ def logit_processor_func(input_ids, logits):
         max_tokens=4,
         top_k=50,
         top_p=0.9,
-        temperature=0.8,
+        temperature=1.0,
         grammar=llama_cpp.LlamaGrammar.from_string("""
 root ::= "1" | "2" | "3" | "4" | "5" | "6" | "7" | "8" | "9" | "10"
-""")
+"""),
     )
     number_2 = output["choices"][0]["text"]
 
@@ -207,12 +247,409 @@ def logit_processor_func(input_ids, logits):
         max_tokens=4,
         top_k=50,
         top_p=0.9,
-        temperature=0.8,
+        temperature=1.0,
         grammar=llama_cpp.LlamaGrammar.from_string("""
 root ::= "1" | "2" | "3" | "4" | "5" | "6" | "7" | "8" | "9" | "10"
-""")
+"""),
     )
     number_3 = output["choices"][0]["text"]
 
     assert number_1 != number_2
     assert number_1 == number_3
+
+
+def test_real_llama_repeated_prompt_cache(llama_cpp_model_path):
+    model = llama_cpp.Llama(
+        llama_cpp_model_path,
+        n_ctx=32,
+        n_batch=32,
+        n_ubatch=32,
+        n_threads=multiprocessing.cpu_count(),
+        n_threads_batch=multiprocessing.cpu_count(),
+        logits_all=False,
+        flash_attn=True,
+        verbose=False,
+    )
+    prompt = "The quick brown fox jumps over the lazy dog. The quick brown fox"
+
+    output_1 = model.create_completion(
+        prompt,
+        max_tokens=6,
+        temperature=0.0,
+        seed=1337,
+    )
+    output_2 = model.create_completion(
+        prompt,
+        max_tokens=6,
+        temperature=0.0,
+        seed=1337,
+    )
+
+    assert output_1["choices"][0]["text"] == " jumps over the lazy dog."
+    assert output_2["choices"][0]["text"] == output_1["choices"][0]["text"]
+
+
+def _assert_prompt_cache_reset_handles_history_edit(
+    model_path,
+    *,
+    is_recurrent: bool,
+    is_hybrid: bool,
+):
+    model = llama_cpp.Llama(
+        model_path,
+        n_ctx=32,
+        n_batch=32,
+        n_ubatch=32,
+        n_threads=multiprocessing.cpu_count(),
+        n_threads_batch=multiprocessing.cpu_count(),
+        logits_all=False,
+        verbose=False,
+    )
+
+    assert model._is_recurrent is is_recurrent
+    assert model._is_hybrid is is_hybrid
+
+    first_prompt = "The quick brown fox"
+    second_prompt = "The slow brown fox"
+    first_tokens = model.tokenize(first_prompt.encode(), add_bos=True, special=True)
+    second_tokens = model.tokenize(second_prompt.encode(), add_bos=True, special=True)
+
+    assert first_tokens != second_tokens
+    assert first_tokens[0] == second_tokens[0]
+
+    first_output = model.create_completion(
+        first_prompt,
+        max_tokens=1,
+        temperature=0.0,
+    )
+    assert isinstance(first_output["choices"][0]["text"], str)
+
+    second_output = model.create_completion(
+        second_prompt,
+        max_tokens=1,
+        temperature=0.0,
+    )
+    assert isinstance(second_output["choices"][0]["text"], str)
+
+
+def test_recurrent_model_prompt_cache_reset(llama_cpp_recurrent_model_path):
+    _assert_prompt_cache_reset_handles_history_edit(
+        llama_cpp_recurrent_model_path,
+        is_recurrent=True,
+        is_hybrid=False,
+    )
+
+
+def test_hybrid_model_prompt_cache_reset(llama_cpp_hybrid_model_path):
+    _assert_prompt_cache_reset_handles_history_edit(
+        llama_cpp_hybrid_model_path,
+        is_recurrent=False,
+        is_hybrid=True,
+    )
+
+
+def _create_test_model(model_path):
+    return llama_cpp.Llama(
+        model_path,
+        n_ctx=64,
+        n_batch=64,
+        n_ubatch=64,
+        n_threads=multiprocessing.cpu_count(),
+        n_threads_batch=multiprocessing.cpu_count(),
+        logits_all=False,
+        verbose=False,
+    )
+
+
+def _generate_test_tokens(model, tokens, max_tokens=3):
+    return list(
+        itertools.islice(
+            model.generate(
+                tokens,
+                temp=0.0,
+            ),
+            max_tokens,
+        )
+    )
+
+
+MODEL_CACHE_CASES = (
+    ("llama_cpp_transformer_model_path", False, False),
+    ("llama_cpp_recurrent_model_path", True, False),
+    ("llama_cpp_hybrid_model_path", False, True),
+)
+
+RESTORED_CACHE_CASES = MODEL_CACHE_CASES
+
+
+def _eval_alternate_same_length_prompt(model, tokens, expected_next_token):
+    replacement_tokens = (
+        model.token_eos(),
+        model.token_nl(),
+        0,
+        1,
+        2,
+        model.n_vocab() - 1,
+    )
+
+    for replacement_token in replacement_tokens:
+        alternate_tokens = list(tokens)
+        alternate_tokens[-1] = replacement_token
+        if alternate_tokens == tokens:
+            continue
+
+        model.reset()
+        model.eval(alternate_tokens)
+        if model.sample(temp=0.0, idx=len(tokens) - 1) != expected_next_token:
+            return
+
+    raise AssertionError("failed to find an alternate same-length prompt")
+
+
+def _assert_exact_cached_prompt_reuse_matches_fresh(
+    model_path,
+    *,
+    is_recurrent: bool,
+    is_hybrid: bool,
+):
+    prompt = "The quick brown fox"
+    fresh = _create_test_model(model_path)
+    tokens = fresh.tokenize(prompt.encode(), add_bos=True, special=True)
+
+    assert fresh._is_recurrent is is_recurrent
+    assert fresh._is_hybrid is is_hybrid
+
+    expected_tokens = _generate_test_tokens(fresh, tokens)
+
+    cached = _create_test_model(model_path)
+    assert cached._is_recurrent is is_recurrent
+    assert cached._is_hybrid is is_hybrid
+
+    cached.eval(tokens)
+    assert cached.n_tokens == len(tokens)
+    assert cached.input_ids[: cached.n_tokens].tolist() == tokens
+    assert cached.sample(temp=0.0, idx=len(tokens) - 1) == expected_tokens[0]
+
+    reset_calls = 0
+    original_reset = cached.reset
+
+    def reset_tracker():
+        nonlocal reset_calls
+        reset_calls += 1
+        original_reset()
+
+    cached.reset = reset_tracker
+
+    cached_tokens = _generate_test_tokens(cached, tokens)
+    assert reset_calls == 0
+    assert cached_tokens == expected_tokens
+    assert cached.n_tokens == len(tokens) + len(cached_tokens) - 1
+
+
+def _assert_loaded_exact_cached_prompt_reuse_matches_fresh(
+    model_path,
+    *,
+    is_recurrent: bool,
+    is_hybrid: bool,
+):
+    prompt = "The quick brown fox"
+    fresh = _create_test_model(model_path)
+    tokens = fresh.tokenize(prompt.encode(), add_bos=True, special=True)
+    expected_tokens = _generate_test_tokens(fresh, tokens)
+
+    source = _create_test_model(model_path)
+    assert source._is_recurrent is is_recurrent
+    assert source._is_hybrid is is_hybrid
+
+    source.eval(tokens)
+    state = source.save_state()
+
+    loaded = _create_test_model(model_path)
+    assert loaded._is_recurrent is is_recurrent
+    assert loaded._is_hybrid is is_hybrid
+
+    _eval_alternate_same_length_prompt(
+        loaded,
+        tokens,
+        expected_tokens[0],
+    )
+    loaded.load_state(state)
+
+    assert loaded.n_tokens == len(tokens)
+    assert loaded.input_ids[: loaded.n_tokens].tolist() == tokens
+
+    loaded_tokens = _generate_test_tokens(loaded, tokens)
+    assert loaded_tokens == expected_tokens
+    assert loaded.n_tokens == len(tokens) + len(loaded_tokens) - 1
+
+
+def _assert_ram_cache_exact_prompt_hit_matches_fresh(
+    model_path,
+    *,
+    is_recurrent: bool,
+    is_hybrid: bool,
+):
+    prompt = "The quick brown fox"
+    fresh = _create_test_model(model_path)
+    tokens = fresh.tokenize(prompt.encode(), add_bos=True, special=True)
+    expected = fresh.create_completion(
+        tokens,
+        max_tokens=1,
+        temperature=0.0,
+        seed=1337,
+    )
+
+    cache = llama_cpp.LlamaRAMCache()
+    writer = _create_test_model(model_path)
+    writer.set_cache(cache)
+    writer.create_completion(
+        tokens,
+        max_tokens=1,
+        temperature=0.0,
+        seed=1337,
+    )
+
+    cached = _create_test_model(model_path)
+    assert cached._is_recurrent is is_recurrent
+    assert cached._is_hybrid is is_hybrid
+    cached.set_cache(cache)
+
+    load_state_calls = 0
+    original_load_state = cached.load_state
+
+    def load_state_tracker(state):
+        nonlocal load_state_calls
+        load_state_calls += 1
+        original_load_state(state)
+
+    cached.load_state = load_state_tracker
+
+    actual = cached.create_completion(
+        tokens,
+        max_tokens=1,
+        temperature=0.0,
+        seed=1337,
+    )
+
+    assert load_state_calls == 1
+    assert actual["choices"][0]["text"] == expected["choices"][0]["text"]
+    assert (
+        actual["usage"]["completion_tokens"] == expected["usage"]["completion_tokens"]
+    )
+
+
+def _assert_shorter_prompt_prefix_reuse_matches_fresh(
+    model_path,
+    *,
+    is_recurrent: bool,
+    is_hybrid: bool,
+):
+    prompt = "The quick brown fox"
+    history = " jumps over the lazy dog"
+    fresh = _create_test_model(model_path)
+    tokens = fresh.tokenize(prompt.encode(), add_bos=True, special=True)
+    history_tokens = fresh.tokenize(history.encode(), add_bos=False, special=True)
+    expected_tokens = _generate_test_tokens(fresh, tokens)
+
+    cached = _create_test_model(model_path)
+    assert cached._is_recurrent is is_recurrent
+    assert cached._is_hybrid is is_hybrid
+
+    cached.eval(tokens + history_tokens)
+    assert cached.n_tokens > len(tokens)
+    assert cached.input_ids[: len(tokens)].tolist() == tokens
+
+    cached_tokens = _generate_test_tokens(cached, tokens)
+    assert cached_tokens == expected_tokens
+
+
+@pytest.mark.parametrize(
+    ("model_path_fixture", "is_recurrent", "is_hybrid"), MODEL_CACHE_CASES
+)
+def test_exact_cached_prompt_reuse_matches_fresh(
+    request,
+    model_path_fixture,
+    is_recurrent,
+    is_hybrid,
+):
+    _assert_exact_cached_prompt_reuse_matches_fresh(
+        request.getfixturevalue(model_path_fixture),
+        is_recurrent=is_recurrent,
+        is_hybrid=is_hybrid,
+    )
+
+
+@pytest.mark.parametrize(
+    ("model_path_fixture", "is_recurrent", "is_hybrid"), RESTORED_CACHE_CASES
+)
+def test_loaded_exact_cached_prompt_reuse_matches_fresh(
+    request,
+    model_path_fixture,
+    is_recurrent,
+    is_hybrid,
+):
+    _assert_loaded_exact_cached_prompt_reuse_matches_fresh(
+        request.getfixturevalue(model_path_fixture),
+        is_recurrent=is_recurrent,
+        is_hybrid=is_hybrid,
+    )
+
+
+@pytest.mark.parametrize(
+    ("model_path_fixture", "is_recurrent", "is_hybrid"), RESTORED_CACHE_CASES
+)
+def test_ram_cache_exact_prompt_hit_matches_fresh(
+    request,
+    model_path_fixture,
+    is_recurrent,
+    is_hybrid,
+):
+    _assert_ram_cache_exact_prompt_hit_matches_fresh(
+        request.getfixturevalue(model_path_fixture),
+        is_recurrent=is_recurrent,
+        is_hybrid=is_hybrid,
+    )
+
+
+@pytest.mark.parametrize(
+    ("model_path_fixture", "is_recurrent", "is_hybrid"), MODEL_CACHE_CASES
+)
+def test_shorter_prompt_prefix_reuse_matches_fresh(
+    request,
+    model_path_fixture,
+    is_recurrent,
+    is_hybrid,
+):
+    _assert_shorter_prompt_prefix_reuse_matches_fresh(
+        request.getfixturevalue(model_path_fixture),
+        is_recurrent=is_recurrent,
+        is_hybrid=is_hybrid,
+    )
+
+
+def test_real_llama_embeddings(llama_cpp_embedding_model_path):
+    model = llama_cpp.Llama(
+        llama_cpp_embedding_model_path,
+        n_ctx=32,
+        n_batch=32,
+        n_ubatch=32,
+        n_threads=multiprocessing.cpu_count(),
+        n_threads_batch=multiprocessing.cpu_count(),
+        logits_all=False,
+        flash_attn=True,
+        embedding=True,
+    )
+    embedding = model.embed("Hello World")
+    assert len(embedding) > 0
+
+    prompts = ["Hello World", "A different prompt"]
+    individual_embeddings = [model.embed(prompt) for prompt in prompts]
+    batched_embeddings = model.embed(prompts)
+
+    assert len(batched_embeddings) == len(prompts)
+    for individual, batched in zip(individual_embeddings, batched_embeddings):
+        np.testing.assert_allclose(batched, individual, rtol=1e-4, atol=1e-4)
+
+    repeated_embeddings = model.embed(list(reversed(prompts)))
+    assert len(repeated_embeddings) == len(prompts)
+    assert all(len(repeated) == len(embedding) for repeated in repeated_embeddings)
diff --git a/tests/test_llama_chat_format.py b/tests/test_llama_chat_format.py
index f031bf72b7..18c7279cf0 100644
--- a/tests/test_llama_chat_format.py
+++ b/tests/test_llama_chat_format.py
@@ -10,13 +10,20 @@
 
 from llama_cpp.llama_chat_format import hf_tokenizer_config_to_chat_formatter
 
+
 def test_mistral_instruct():
     chat_template = "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}"
     chat_formatter = jinja2.Template(chat_template)
     messages = [
-        llama_types.ChatCompletionRequestUserMessage(role="user", content="Instruction"),
-        llama_types.ChatCompletionRequestAssistantMessage(role="assistant", content="Model answer"),
-        llama_types.ChatCompletionRequestUserMessage(role="user", content="Follow-up instruction"),
+        llama_types.ChatCompletionRequestUserMessage(
+            role="user", content="Instruction"
+        ),
+        llama_types.ChatCompletionRequestAssistantMessage(
+            role="assistant", content="Model answer"
+        ),
+        llama_types.ChatCompletionRequestUserMessage(
+            role="user", content="Follow-up instruction"
+        ),
     ]
     response = llama_chat_format.format_mistral_instruct(
         messages=messages,
@@ -77,13 +84,11 @@ def test_mistral_instruct():
 
 def test_hf_tokenizer_config_str_to_chat_formatter():
     tokenizer_config = json.loads(mistral_7b_tokenizer_config)
-    chat_formatter = hf_tokenizer_config_to_chat_formatter(
-        tokenizer_config
-    )
+    chat_formatter = hf_tokenizer_config_to_chat_formatter(tokenizer_config)
     chat_formatter_respoonse = chat_formatter(
         messages=[
             ChatCompletionRequestUserMessage(role="user", content="Hello, world!"),
         ]
     )
 
-    assert chat_formatter_respoonse.prompt == ("<s>[INST] Hello, world! [/INST]</s>" "")
+    assert chat_formatter_respoonse.prompt == ("<s>[INST] Hello, world! [/INST]</s>")
diff --git a/tests/test_llama_speculative.py b/tests/test_llama_speculative.py
index b5d450567b..d28c9ca9c6 100644
--- a/tests/test_llama_speculative.py
+++ b/tests/test_llama_speculative.py
@@ -2,15 +2,20 @@
 
 from llama_cpp.llama_speculative import LlamaPromptLookupDecoding
 
+
 def test_find_candidate_pred_tokens():
     find_candidate_pred_tokens = LlamaPromptLookupDecoding.find_candidate_pred_tokens
 
     # Test Case 1: Matching ngram is found
     input_ids1 = np.array([1, 2, 3, 1, 2, 3, 1, 2, 3])
-    result1 = find_candidate_pred_tokens(input_ids1, max_ngram_size=3, num_pred_tokens=2)
+    result1 = find_candidate_pred_tokens(
+        input_ids1, max_ngram_size=3, num_pred_tokens=2
+    )
     assert np.array_equal(result1, np.array([1, 2]))
 
     # Test Case 2: Matching ngram is not found
     input_ids2 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
-    result2 = find_candidate_pred_tokens(input_ids2, max_ngram_size=3, num_pred_tokens=2)
+    result2 = find_candidate_pred_tokens(
+        input_ids2, max_ngram_size=3, num_pred_tokens=2
+    )
     assert np.array_equal(result2, np.array([]))
diff --git a/vendor/llama.cpp b/vendor/llama.cpp
index dc22344088..92e854ab83 160000
--- a/vendor/llama.cpp
+++ b/vendor/llama.cpp
@@ -1 +1 @@
-Subproject commit dc22344088a7ee81a1e4f096459b03a72f24ccdc
+Subproject commit 92e854ab836254bb7f2eb49babd5613474bdb700