Instructions to use macabdul9/SmolVLM2-500M-Video-Instruct-video-feedback with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use macabdul9/SmolVLM2-500M-Video-Instruct-video-feedback with Transformers:

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

pipe = pipeline("image-text-to-text", model="macabdul9/SmolVLM2-500M-Video-Instruct-video-feedback")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("macabdul9/SmolVLM2-500M-Video-Instruct-video-feedback")
model = AutoModelForMultimodalLM.from_pretrained("macabdul9/SmolVLM2-500M-Video-Instruct-video-feedback", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use macabdul9/SmolVLM2-500M-Video-Instruct-video-feedback with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "macabdul9/SmolVLM2-500M-Video-Instruct-video-feedback"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "macabdul9/SmolVLM2-500M-Video-Instruct-video-feedback",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/macabdul9/SmolVLM2-500M-Video-Instruct-video-feedback

SGLang

How to use macabdul9/SmolVLM2-500M-Video-Instruct-video-feedback 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 "macabdul9/SmolVLM2-500M-Video-Instruct-video-feedback" \
    --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": "macabdul9/SmolVLM2-500M-Video-Instruct-video-feedback",
		"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 "macabdul9/SmolVLM2-500M-Video-Instruct-video-feedback" \
        --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": "macabdul9/SmolVLM2-500M-Video-Instruct-video-feedback",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use macabdul9/SmolVLM2-500M-Video-Instruct-video-feedback with Docker Model Runner:
```
docker model run hf.co/macabdul9/SmolVLM2-500M-Video-Instruct-video-feedback
```

SmolVLM2-500M-Video-Instruct-video-feedback / generation_config.json

macabdul9

End of training

79914cd verified over 1 year ago

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136 Bytes

{
  "_from_model_config": true,
  "bos_token_id": 0,
  "eos_token_id": 49279,
  "pad_token_id": 2,
  "transformers_version": "4.51.3"
}

	{
	"_from_model_config": true,
	"bos_token_id": 0,
	"eos_token_id": 49279,
	"pad_token_id": 2,
	"transformers_version": "4.51.3"
	}