Instructions to use AsadIsmail/SmolVLM2-256M-Video-Instruct-ternary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AsadIsmail/SmolVLM2-256M-Video-Instruct-ternary with Transformers:

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

pipe = pipeline("image-text-to-text", model="AsadIsmail/SmolVLM2-256M-Video-Instruct-ternary")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AsadIsmail/SmolVLM2-256M-Video-Instruct-ternary", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use AsadIsmail/SmolVLM2-256M-Video-Instruct-ternary with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AsadIsmail/SmolVLM2-256M-Video-Instruct-ternary"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AsadIsmail/SmolVLM2-256M-Video-Instruct-ternary",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AsadIsmail/SmolVLM2-256M-Video-Instruct-ternary

SGLang

How to use AsadIsmail/SmolVLM2-256M-Video-Instruct-ternary 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 "AsadIsmail/SmolVLM2-256M-Video-Instruct-ternary" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AsadIsmail/SmolVLM2-256M-Video-Instruct-ternary",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "AsadIsmail/SmolVLM2-256M-Video-Instruct-ternary" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AsadIsmail/SmolVLM2-256M-Video-Instruct-ternary",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AsadIsmail/SmolVLM2-256M-Video-Instruct-ternary with Docker Model Runner:
```
docker model run hf.co/AsadIsmail/SmolVLM2-256M-Video-Instruct-ternary
```

SmolVLM2-256M-Video-Instruct — Ternary Quantized (tritplane3)

Ternary-quantized version of HuggingFaceTB/SmolVLM2-256M-Video-Instruct — the smallest video-understanding VLM, now even smaller. Ideal for edge deployment.

Specifications

Property	Value
Base Model	HuggingFaceTB/SmolVLM2-256M-Video-Instruct
Parameters	256M
Quantization	tritplane3 (211 linear layers)
Full-model effective bits	12.91
Compression ratio	1.24×
Avg reconstruction error	0.1488