Instructions to use arrow-hf/SmolVLM2-500M-Video-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arrow-hf/SmolVLM2-500M-Video-Instruct with Transformers:

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

pipe = pipeline("image-text-to-text", model="arrow-hf/SmolVLM2-500M-Video-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("arrow-hf/SmolVLM2-500M-Video-Instruct")
model = AutoModelForImageTextToText.from_pretrained("arrow-hf/SmolVLM2-500M-Video-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 arrow-hf/SmolVLM2-500M-Video-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "arrow-hf/SmolVLM2-500M-Video-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": "arrow-hf/SmolVLM2-500M-Video-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/arrow-hf/SmolVLM2-500M-Video-Instruct

SGLang

How to use arrow-hf/SmolVLM2-500M-Video-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 "arrow-hf/SmolVLM2-500M-Video-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": "arrow-hf/SmolVLM2-500M-Video-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 "arrow-hf/SmolVLM2-500M-Video-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": "arrow-hf/SmolVLM2-500M-Video-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 arrow-hf/SmolVLM2-500M-Video-Instruct with Docker Model Runner:
```
docker model run hf.co/arrow-hf/SmolVLM2-500M-Video-Instruct
```

SmolVLM2-500M-Video-Instruct (full mirror)

Full mirror of HuggingFaceTB/SmolVLM2-500M-Video-Instruct.

Includes:

model.safetensors (~1.9 GB PyTorch weights)
14 ONNX variants under onnx/ (fp16, int8, q4, uint8, etc. for decoder / embed_tokens / vision_encoder)
Tokenizer files (tokenizer.json, vocab.json, merges.txt, added_tokens.json, special_tokens_map.json)
Processor configs (processor_config.json, preprocessor_config.json, chat_template.json)
generation_config.json, config.json

Mirrored via huggingface_hub.snapshot_download.

Usage

from transformers import AutoModel, AutoProcessor, AutoTokenizer
model = AutoModel.from_pretrained("arrow-hf/SmolVLM2-500M-Video-Instruct")
processor = AutoProcessor.from_pretrained("arrow-hf/SmolVLM2-500M-Video-Instruct")
tokenizer = AutoTokenizer.from_pretrained("arrow-hf/SmolVLM2-500M-Video-Instruct")

The tokenizer is used by arrow-hf/smolvla-robotwin-stack-bowls-two-50pct (max_length=48). The SmolVLA policy is fine-tuned on top of this base VLM.

Downloads last month: 17

Safetensors

Model size

0.5B params

Tensor type

F32

arrow-hf
/

SmolVLM2-500M-Video-Instruct

SmolVLM2-500M-Video-Instruct (full mirror)

Usage

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