Instructions to use mlx-community/SmolVLM2-256M-Video-Instruct-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlx-community/SmolVLM2-256M-Video-Instruct-mlx with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("mlx-community/SmolVLM2-256M-Video-Instruct-mlx") model = AutoModelForMultimodalLM.from_pretrained("mlx-community/SmolVLM2-256M-Video-Instruct-mlx", device_map="auto") - MLX
How to use mlx-community/SmolVLM2-256M-Video-Instruct-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download mlx-community/SmolVLM2-256M-Video-Instruct-mlx --local-dir SmolVLM2-256M-Video-Instruct-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Commit History
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Update 500M to 256M for the title (#4)
Verified signature- Update README.md (8d8e83bd7255c14fe2ef9c2dcfa623d4bf40c13a) Co-authored-by: Rudrank Riyam <rudrankriyam@users.noreply.huggingface.co>7990165
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Update README.md (#3)
Verified signature- Update README.md (7dd37b2d4daed6a70dd4eb6709a2f77fc5edccd5) Co-authored-by: Justin Means <justinmeans@users.noreply.huggingface.co>46d206e
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Upload folder using huggingface_hub
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initial commit
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