Instructions to use meta-llama/Llama-4-Scout-17B-16E-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meta-llama/Llama-4-Scout-17B-16E-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="meta-llama/Llama-4-Scout-17B-16E-Instruct")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("meta-llama/Llama-4-Scout-17B-16E-Instruct")
model = AutoModelForMultimodalLM.from_pretrained("meta-llama/Llama-4-Scout-17B-16E-Instruct")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use meta-llama/Llama-4-Scout-17B-16E-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meta-llama/Llama-4-Scout-17B-16E-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meta-llama/Llama-4-Scout-17B-16E-Instruct",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/meta-llama/Llama-4-Scout-17B-16E-Instruct

SGLang

How to use meta-llama/Llama-4-Scout-17B-16E-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "meta-llama/Llama-4-Scout-17B-16E-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meta-llama/Llama-4-Scout-17B-16E-Instruct",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "meta-llama/Llama-4-Scout-17B-16E-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meta-llama/Llama-4-Scout-17B-16E-Instruct",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use meta-llama/Llama-4-Scout-17B-16E-Instruct with Docker Model Runner:
```
docker model run hf.co/meta-llama/Llama-4-Scout-17B-16E-Instruct
```

VLLM not loading meta-llama/Llama-4-Scout-17B-16E-Instruct

#53

by alokkrsahu - opened Apr 9, 2025

Discussion

alokkrsahu

Apr 9, 2025

meta-llama/Llama-4-Scout-17B-16E-Instruct
Above model is not loading correctly, however, meta-llama/Llama-4-Scout-17B-16E, works fine.

See the error logs below:

alokkrsahu

Apr 9, 2025

•

edited Apr 9, 2025

The core exact errors from the log are:

Assertion '-sizes[i] <= index && index < sizes[i] && "index out of bounds"' failed.
RuntimeError: CUDA error: device-side assert triggered
torch.distributed.DistBackendError: NCCL error in: /pytorch/torch/csrc/distributed/c10d/NCCLUtils.cpp:133, unhandled cuda error
torch._dynamo.exc.InternalTorchDynamoError: RuntimeError: CUDA error: device-side assert triggered

These errors indicate an index out of bounds problem during tensor operations, which cascaded into CUDA device errors and ultimately crashed the distributed processing system.

alokkrsahu changed discussion status to closed Apr 9, 2025

alokkrsahu changed discussion status to open Apr 9, 2025

taytun

Apr 9, 2025

I was able to load and do infer with following command in vllm.

VLLM_DISABLE_COMPILE_CACHE=1 python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-4-Scout-17B-16E-Instruct \
    --host 0.0.0.0 --port 8085 --max-model-len 8192 --dtype bfloat16 --tensor-parallel-size 8 --gpu-memory-utilization 0.9 --served-model-name llama4_scout_inst --override-generation-config='{"attn_temperature_tuning": true}'

My configs and hardware is in https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct/discussions/57

Huggingface load is failing so far for me.

kiranzxy007

Apr 22, 2025

@taytun
what memory was utilized by following command?

VLLM_DISABLE_COMPILE_CACHE=1 python -m vllm.entrypoints.openai.api_server
--model meta-llama/Llama-4-Scout-17B-16E-Instruct
--host 0.0.0.0 --port 8085 --max-model-len 8192 --dtype bfloat16 --tensor-parallel-size 8 --gpu-memory-utilization 0.9 --served-model-name llama4_scout_inst --override-generation-config='{"attn_temperature_tuning": true}'

I have 8 * 40 gb gpu of A100 will that work ?

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