Instructions to use HuggingFaceTB/SmolVLM-256M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceTB/SmolVLM-256M-Instruct with Transformers:

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

pipe = pipeline("image-text-to-text", model="HuggingFaceTB/SmolVLM-256M-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("HuggingFaceTB/SmolVLM-256M-Instruct")
model = AutoModelForImageTextToText.from_pretrained("HuggingFaceTB/SmolVLM-256M-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 HuggingFaceTB/SmolVLM-256M-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceTB/SmolVLM-256M-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": "HuggingFaceTB/SmolVLM-256M-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/HuggingFaceTB/SmolVLM-256M-Instruct

SGLang

How to use HuggingFaceTB/SmolVLM-256M-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 "HuggingFaceTB/SmolVLM-256M-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": "HuggingFaceTB/SmolVLM-256M-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 "HuggingFaceTB/SmolVLM-256M-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": "HuggingFaceTB/SmolVLM-256M-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 HuggingFaceTB/SmolVLM-256M-Instruct with Docker Model Runner:
```
docker model run hf.co/HuggingFaceTB/SmolVLM-256M-Instruct
```

andito HF Staff commited on Jan 23, 2025

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@@ -166,15 +166,10 @@ The training data comes from [The Cauldron](https://huggingface.co/datasets/Hugg
 ## Evaluation
-| Model             | MMMU (val) | MathVista (testmini) | MMStar (val) | DocVQA (test) | TextVQA (val) | Min GPU RAM required (GB) |
-|-------------------|------------|----------------------|--------------|---------------|---------------|---------------------------|
-| SmolVLM 2.2B      | 38.8       | 44.6                | 42.1         | 81.6          | 72.7          | 5.02                      |
-| Qwen-VL 2B        | 41.1       | 47.8                | 47.5         | 90.1          | 79.7          | 13.70                     |
-| InternVL2 2B      | 34.3       | 46.3                | 49.8         | 86.9          | 73.4          | 10.52                     |
-| PaliGemma 3B 448px| 34.9       | 28.7                | 48.3         | 32.2          | 56.0          | 6.72                      |
-| moondream2        | 32.4       | 24.3                | 40.3         | 70.5          | 65.2          | 3.87                      |
-| MiniCPM-V-2       | 38.2       | 39.8                | 39.1         | 71.9          | 74.1          | 7.88                      |
-| MM1.5 1B          | 35.8       | 37.2                | 0.0          | 81.0          | 72.5          | NaN                       |

 ## Evaluation
+| Size  | Mathvista | MMMU | OCRBench | MMStar | AI2D  | ChartQA_Test | Science_QA | TextVQA Val | DocVQA Val |
+|-------|-----------|------|----------|--------|-------|--------------|------------|-------------|------------|
+| 256M  | 35.9      | 28.3 | 52.6     | 34.6   | 47    | 55.8         | 73.6       | 49.9        | 58.3       |
+| 500M  | 40.1      | 33.7 | 61       | 38.3   | 59.5  | 63.2         | 79.7       | 60.5        | 70.5       |
+| 2.2B  | 43.9      | 38.3 | 65.5     | 41.8   | 64    | 71.6         | 84.5       | 72.1        | 79.7       |