diff --git a/lcb_runner/lm_styles.py b/lcb_runner/lm_styles.py
index d10b2117..405ccf32 100644
--- a/lcb_runner/lm_styles.py
+++ b/lcb_runner/lm_styles.py
@@ -31,6 +31,7 @@ class LMStyle(Enum):
     QwQ = "QwQ"
     LLaMa3 = "LLaMa3"
     DeepSeekR1 = "DeepSeekR1"
+    GraniteInstruct = "GraniteInstruct"
 
     TogetherAI = "TogetherAI"
 
@@ -861,6 +862,21 @@ def to_dict(self) -> dict:
         datetime(2024, 4, 1),
         "https://huggingface.co/agentica-org/DeepCoder-14B-Preview",
     ),
+    ## Granite 3.3 Instruct (2B and 8B)
+    LanguageModel(
+        "ibm-granite/granite-3.3-2b-instruct",
+        "Granite-3.3-2B-Ins",
+        LMStyle.GraniteInstruct,
+        datetime(2025, 4, 16),
+        "https://huggingface.co/ibm-granite/granite-3.3-2b-instruct",
+    ),
+    LanguageModel(
+        "ibm-granite/granite-3.3-8b-instruct",
+        "Granite-3.3-8B-Ins",
+        LMStyle.GraniteInstruct,
+        datetime(2025, 4, 16),
+        "https://huggingface.co/ibm-granite/granite-3.3-8b-instruct",
+    ),
 ]
 
 LanguageModelStore: dict[str, LanguageModel] = {
diff --git a/lcb_runner/prompts/code_generation.py b/lcb_runner/prompts/code_generation.py
index 22ba3f82..37e96a5a 100644
--- a/lcb_runner/prompts/code_generation.py
+++ b/lcb_runner/prompts/code_generation.py
@@ -335,6 +335,31 @@ def format_prompt_generation(
         prompt += f"{get_deepseek_r1_question_template_answer(question)}"
         return prompt
 
+    if LanguageModelStyle == LMStyle.GraniteInstruct:
+        chat_messages = [
+            {
+                "role": "system",
+                "content": PromptConstants.SYSTEM_MESSAGE_GENERIC,
+            },
+            {
+                "role": "user",
+                "content": get_generic_question_template_answer(question),
+            },
+        ]
+        from transformers import AutoTokenizer
+
+        # Granite 3.x instruct models share one chat template; only the template is used here.
+        tokenizer = AutoTokenizer.from_pretrained(
+            "ibm-granite/granite-3.3-2b-instruct", padding_side="left"
+        )
+        return tokenizer.apply_chat_template(
+            chat_messages,
+            tokenize=False,
+            add_generation_prompt=True,
+            truncation=False,
+            padding=False,
+        )
+
     if LanguageModelStyle == LMStyle.GenericBase:
         prompt = get_base_model_question_template_answer(question)
         return prompt