[Model] Support Llama4 in vLLM (#16104)

examples/offline_inference/vision_language.py

@@ -582,6 +582,42 @@ def run_mllama(questions: list[str], modality: str) -> ModelRequestData:
     )
 
 
+def run_llama4(questions: list[str], modality: str):
+    assert modality == "image"
+
+    model_name = "meta-llama/Llama-4-Scout-17B-16E-Instruct"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=8192,
+        max_num_seqs=4,
+        tensor_parallel_size=8,
+        disable_mm_preprocessor_cache=args.disable_mm_preprocessor_cache,
+        gpu_memory_utilization=0.4,
+    )
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    messages = [[{
+        "role":
+        "user",
+        "content": [{
+            "type": "image"
+        }, {
+            "type": "text",
+            "text": f"{question}"
+        }]
+    }] for question in questions]
+    prompts = tokenizer.apply_chat_template(messages,
+                                            add_generation_prompt=True,
+                                            tokenize=False)
+    stop_token_ids = None
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+        stop_token_ids=stop_token_ids,
+    )
+
+
 # Molmo
 def run_molmo(questions: list[str], modality: str) -> ModelRequestData:
     assert modality == "image"
@@ -907,6 +943,7 @@ model_example_map = {
     "minicpmv": run_minicpmv,
     "mistral3": run_mistral3,
     "mllama": run_mllama,
+    "llama4": run_llama4,
     "molmo": run_molmo,
     "NVLM_D": run_nvlm_d,
     "paligemma": run_paligemma,

examples/offline_inference/vision_language_multi_image.py

@@ -253,6 +253,43 @@ def load_internvl(question: str, image_urls: list[str]) -> ModelRequestData:
     )
 
 
+def load_llama4(question: str, image_urls: list[str]) -> ModelRequestData:
+    model_name = "meta-llama/Llama-4-Scout-17B-16E-Instruct"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=8192,
+        max_num_seqs=4,
+        tensor_parallel_size=8,
+        limit_mm_per_prompt={"image": len(image_urls)},
+    )
+
+    placeholders = [{"type": "image", "image": url} for url in image_urls]
+    messages = [{
+        "role":
+        "user",
+        "content": [
+            *placeholders,
+            {
+                "type": "text",
+                "text": question
+            },
+        ],
+    }]
+
+    processor = AutoProcessor.from_pretrained(model_name)
+
+    prompt = processor.apply_chat_template(messages,
+                                           tokenize=False,
+                                           add_generation_prompt=True)
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompt=prompt,
+        image_data=[fetch_image(url) for url in image_urls],
+    )
+
+
 def load_mistral3(question: str, image_urls: list[str]) -> ModelRequestData:
     model_name = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
 
@@ -567,6 +604,7 @@ model_example_map = {
     "h2ovl_chat": load_h2ovl,
     "idefics3": load_idefics3,
     "internvl_chat": load_internvl,
+    "llama4": load_llama4,
     "mistral3": load_mistral3,
     "mllama": load_mllama,
     "NVLM_D": load_nvlm_d,