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

Libraries

How to use chutesai/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="chutesai/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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("chutesai/Llama-4-Scout-17B-16E-Instruct")
model = AutoModelForImageTextToText.from_pretrained("chutesai/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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use chutesai/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 "chutesai/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": "chutesai/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/chutesai/Llama-4-Scout-17B-16E-Instruct

SGLang

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

jondurbin commited on Apr 10, 2025

Commit

1f87ff1

verified ·

1 Parent(s): 2d8f8a1

Upload folder using huggingface_hub

Browse files

Files changed (4) hide show

README.md +3 -3
config.json +2 -2
special_tokens_map.json +1 -1
tokenizer_config.json +1 -9

README.md CHANGED Viewed

@@ -20,7 +20,7 @@ tags:
 - meta
 - pytorch
 - llama
-- llama-4
 extra_gated_prompt: >-
     **LLAMA 4 COMMUNITY LICENSE AGREEMENT**
@@ -176,7 +176,7 @@ Please, make sure you have transformers `v4.51.0` installed, or upgrade using `p
 from transformers import AutoProcessor, Llama4ForConditionalGeneration
 import torch
-model_id = "meta-llama/Llama-4-Maverick-17B-128E-Instruct"
 processor = AutoProcessor.from_pretrained(model_id)
 model = Llama4ForConditionalGeneration.from_pretrained(
@@ -269,7 +269,7 @@ In this section, we report the results for Llama 4 relative to our previous mode
 | Image Understanding | ChartQA | 0 | relaxed\_accuracy |  |  | 88.8 | 90.0 |
 |  | DocVQA (test) | 0 | anls |  |  | 94.4 | 94.4 |
 | Coding | LiveCodeBench (10/01/2024-02/01/2025) | 0 | pass@1 | 33.3 | 27.7 | 32.8 | 43.4 |
-| Reasoning & Knowledge | MMLU Pro | 0 | macro\_avg/em | 68.9 | 73.4 | 74.3 | 80.5 |
 |  | GPQA Diamond | 0 | accuracy | 50.5 | 49.0 | 57.2 | 69.8 |
 | Multilingual | MGSM | 0 | average/em | 91.1 | 91.6 | 90.6 | 92.3 |
 | Long context | MTOB (half book) eng-\>kgv/kgv-\>eng | \- | chrF | Context window is 128K |  | 42.2/36.6 | 54.0/46.4 |

 - meta
 - pytorch
 - llama
+- llama4
 extra_gated_prompt: >-
     **LLAMA 4 COMMUNITY LICENSE AGREEMENT**
 from transformers import AutoProcessor, Llama4ForConditionalGeneration
 import torch
+model_id = "meta-llama/Llama-4-Scout-17B-16E-Instruct"
 processor = AutoProcessor.from_pretrained(model_id)
 model = Llama4ForConditionalGeneration.from_pretrained(
 | Image Understanding | ChartQA | 0 | relaxed\_accuracy |  |  | 88.8 | 90.0 |
 |  | DocVQA (test) | 0 | anls |  |  | 94.4 | 94.4 |
 | Coding | LiveCodeBench (10/01/2024-02/01/2025) | 0 | pass@1 | 33.3 | 27.7 | 32.8 | 43.4 |
+| Reasoning & Knowledge | MMLU Pro | 0 | macro\_avg/acc | 68.9 | 73.4 | 74.3 | 80.5 |
 |  | GPQA Diamond | 0 | accuracy | 50.5 | 49.0 | 57.2 | 69.8 |
 | Multilingual | MGSM | 0 | average/em | 91.1 | 91.6 | 90.6 | 92.3 |
 | Long context | MTOB (half book) eng-\>kgv/kgv-\>eng | \- | chrF | Context window is 128K |  | 42.2/36.6 | 54.0/46.4 |

config.json CHANGED Viewed

@@ -37,8 +37,8 @@
     "pad_token_id": 200018,
     "rms_norm_eps": 1e-05,
     "rope_scaling": {
-      "factor": 8.0,
-      "high_freq_factor": 4.0,
       "low_freq_factor": 1.0,
       "original_max_position_embeddings": 8192,
       "rope_type": "llama3"

     "pad_token_id": 200018,
     "rms_norm_eps": 1e-05,
     "rope_scaling": {
+      "factor": 16.0,
+      "high_freq_factor": 1.0,
       "low_freq_factor": 1.0,
       "original_max_position_embeddings": 8192,
       "rope_type": "llama3"

special_tokens_map.json CHANGED Viewed

@@ -1,5 +1,5 @@
 {
   "bos_token": "<|begin_of_text|>",
   "eos_token": "<|eot|>",
-  "pad_token": "<|finetune_right_pad_id|>"
 }

 {
   "bos_token": "<|begin_of_text|>",
   "eos_token": "<|eot|>",
+  "pad_token": "<|finetune_right_pad|>"
 }

tokenizer_config.json CHANGED Viewed

@@ -9071,14 +9071,6 @@
       "rstrip": false,
       "single_word": false,
       "special": true
-    },
-    "201134": {
-      "content": "<|finetune_right_pad_id|>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
     }
   },
   "bos_token": "<|begin_of_text|>",
@@ -9091,7 +9083,7 @@
     "attention_mask"
   ],
   "model_max_length": 10485760,
-  "pad_token": "<|finetune_right_pad_id|>",
   "processor_class": "Llama4Processor",
   "tokenizer_class": "PreTrainedTokenizer"
 }

       "rstrip": false,
       "single_word": false,
       "special": true
     }
   },
   "bos_token": "<|begin_of_text|>",
     "attention_mask"
   ],
   "model_max_length": 10485760,
+  "pad_token": "<|finetune_right_pad|>",
   "processor_class": "Llama4Processor",
   "tokenizer_class": "PreTrainedTokenizer"
 }