Instructions to use sahilchachra/fastcontext-1.0-4b-sft-mxfp8-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use sahilchachra/fastcontext-1.0-4b-sft-mxfp8-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir fastcontext-1.0-4b-sft-mxfp8-mlx sahilchachra/fastcontext-1.0-4b-sft-mxfp8-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
- Xet hash:
- 6c206269edc2454f1f9bfa677e890cc861f466e93991b47a905529cc31ab87f8
- Size of remote file:
- 4.15 GB
- SHA256:
- 7c21d6e1cf2050d18725b5d5a5988f963086e381cd075fa86fe139e69deb30c7
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