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
```

Bug: Llama4 Multimodal (Llama4ForConditionalGeneration) Fails with Optimized Attention (sdpa, eager) and KV Cache for Effective Sequence Lengths > attention_chunk_size (8192)

#84

by Peter-233234 - opened May 24, 2025

Discussion

Peter-233234

May 24, 2025

•

edited May 24, 2025

Model: meta-llama/Llama-4-Scout-17B-16E-Instruct
Transformers Version: 4.53.2
PyTorch Version: 2.7
Accelerate: 1.7.0

Description:
When performing multimodal inference with Llama4ForConditionalGeneration using use_cache=True and either attn_implementation="sdpa" or attn_implementation="eager", runtime errors occur if the total effective sequence length (text tokens + image feature representations + past_key_values_length) exceeds the model's config.text_config.attention_chunk_size (which is 8192, also matching config.text_config.rope_scaling.original_max_position_embeddings).

The issue is reproducible when using AutoProcessor.apply_chat_template with multiple image inputs as demonstrated in documentation examples, provided the resulting effective sequence length crosses this 8192 threshold.

Key Configuration Parameters (from model's config.json):
"text_config": {
"attention_chunk_size": 8192,
"max_position_embeddings": 10485760,
"rope_scaling": {
"original_max_position_embeddings": 8192,
"factor": 16.0,
"rope_type": "llama3"
}
}

Errors Encountered (with use_cache=True and effective length > 8192):

With attn_implementation="sdpa":
Input causing error (example from apply_chat_template with 16 images):
input_ids shape: [1, text_len_with_placeholders]
attention_mask shape: [1, text_len_with_placeholders]
pixel_values shape: [num_images, 3, H, W] (e.g., [16, 3, 336, 336])
(Note: processor.apply_chat_template loads images and passes pixel_values correctly to model.generate)

Error:
RuntimeError: The size of tensor a (e.g., 39497) must match the size of tensor b (e.g., 40008) at non-singleton dimension 5
Traceback points to: transformers/models/llama4/modeling_llama4.py, line ~739, in _update_causal_mask: chunked_attention_mask = chunked_attention_mask * local_attention_mask[:, None, None, :]

attn_implementation="sdpa" with use_cache=True works correctly and performantly if the total effective input sequence length (text + image features from pixel_values) is kept at or below 8192 tokens. This was observed when max_length was not passed to model.generate(), and the input itself was short enough.

Hypothesis:
The sliding_window logic to compute the causal_attention_mask is defective.

Suggested Investigation for Maintainers:

Examine Llama4Model._update_causal_mask for multimodal inputs with use_cache=True.
Focus on the logic paths triggered when past_key_values_length + current_input_length > config.text_config.attention_chunk_size (8192).
For the sdpa path: Analyze how chunked_attention_mask (representing current effective sequence length) and local_attention_mask (derived from initial input padding and attention_chunk_size) are generated and why their key dimensions (e.g., 39497 vs. 40008) become incompatible for the multiplication at line ~739. The derivation of local_attention_mask's effective length seems particularly relevant.

Logs:

Exception has occurred: RuntimeError (note: full exception trace is shown but execution is paused at: _run_module_as_main)

Demo run failed: The size of tensor a (39497) must match the size of tensor b (40008) at non-singleton dimension 5
File "/home/electric/Llama4Engine/src/model/server/inference/engine.py", line 268, in outputs = lm.model.generate( File "/home/electric/.pyenv/versions/Llama4Engine/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context return func(*args, **kwargs) File "/home/electric/.pyenv/versions/Llama4Engine/lib/python3.10/site-packages/transformers/generation/utils.py", line 2597, in generate result = self._sample( File "/home/electric/.pyenv/versions/Llama4Engine/lib/python3.10/site-packages/transformers/generation/utils.py", line 3557, in _sample outputs = self(**model_inputs, return_dict=True) File "/home/electric/.pyenv/versions/Llama4Engine/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/home/electric/.pyenv/versions/Llama4Engine/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl return forward_call(*args, **kwargs) File "/home/electric/.pyenv/versions/Llama4Engine/lib/python3.10/site-packages/accelerate/hooks.py", line 175, in new_forward output = module._old_forward(*args, **kwargs) File "/home/electric/.pyenv/versions/Llama4Engine/lib/python3.10/site-packages/transformers/models/llama4/modeling_llama4.py", line 1652, in forward outputs = self.language_model( File "/home/electric/.pyenv/versions/Llama4Engine/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/home/electric/.pyenv/versions/Llama4Engine/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl return forward_call(*args, **kwargs) File "/home/electric/.pyenv/versions/Llama4Engine/lib/python3.10/site-packages/transformers/utils/generic.py", line 969, in wrapper output = func(self, *args, **kwargs) File "/home/electric/.pyenv/versions/Llama4Engine/lib/python3.10/site-packages/transformers/models/llama4/modeling_llama4.py", line 936, in forward outputs = self.model( File "/home/electric/.pyenv/versions/Llama4Engine/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/home/electric/.pyenv/versions/Llama4Engine/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl return forward_call(*args, **kwargs) File "/home/electric/.pyenv/versions/Llama4Engine/lib/python3.10/site-packages/transformers/utils/generic.py", line 969, in wrapper output = func(self, *args, **kwargs) File "/home/electric/.pyenv/versions/Llama4Engine/lib/python3.10/site-packages/transformers/models/llama4/modeling_llama4.py", line 578, in forward causal_mask, chunk_causal_mask = self._update_causal_mask( File "/home/electric/.pyenv/versions/Llama4Engine/lib/python3.10/site-packages/transformers/models/llama4/modeling_llama4.py", line 739, in _update_causal_mask chunked_attention_mask = chunked_attention_mask * local_attention_mask[:, None, None, :]RuntimeError: The size of tensor a (39497) must match the size of tensor b (40008) at non-singleton dimension 5The above exception was the direct cause of the following exception: File "/home/electric/Llama4Engine/src/model/server/inference/engine.py", line 279, in raise RuntimeError(f"Demo run failed: {exc}") from exc File "/home/electric/.pyenv/versions/3.10.17/lib/python3.10/runpy.py", line 86, in _run_code exec(code, run_globals) File "/home/electric/.pyenv/versions/3.10.17/lib/python3.10/runpy.py", line 196, in _run_module_as_main (Current frame) return _run_code(code, main_globals, None,
RuntimeError: Demo run failed: The size of tensor a (39497) must match the size of tensor b (40008) at non-singleton dimension 5

Peter-233234 changed discussion title from Bug: Llama4 Multimodal (Llama4ForConditionalGeneration) Fails with Optimized Attention (sdpa, flex_attention) and KV Cache for Effective Sequence Lengths > attention_chunk_size (8192) to Bug: Llama4 Multimodal (Llama4ForConditionalGeneration) Fails with Optimized Attention (sdpa, eager) and KV Cache for Effective Sequence Lengths > attention_chunk_size (8192) May 24, 2025

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