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:
# 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") - 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] huggingface-cli download --local-dir SmolVLM2-256M-Video-Instruct-mlx mlx-community/SmolVLM2-256M-Video-Instruct-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Update 500M to 256M for the title
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by rudrankriyam - opened
README.md
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# HuggingFaceTB/SmolVLM2-
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This model was converted to MLX format from [`HuggingFaceTB/SmolVLM2-256M-Video-Instruct`]() using mlx-vlm version **0.1.13**.
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Refer to the [original model card](https://huggingface.co/HuggingFaceTB/SmolVLM2-256M-Video-Instruct) for more details on the model.
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## Use with mlx
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- mlx
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# HuggingFaceTB/SmolVLM2-256M-Video-Instruct-mlx
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This model was converted to MLX format from [`HuggingFaceTB/SmolVLM2-256M-Video-Instruct`]() using mlx-vlm version **0.1.13**.
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Refer to the [original model card](https://huggingface.co/HuggingFaceTB/SmolVLM2-256M-Video-Instruct) for more details on the model.
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## Use with mlx
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