Instructions to use sahilchachra/fastcontext-1.0-4b-sft-mxfp4-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-mxfp4-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-mxfp4-mlx sahilchachra/fastcontext-1.0-4b-sft-mxfp4-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
- Xet hash:
- 618fd707f39d65143bbfa1e41760137150c914c4e93ba54acd23558a5fed62f6
- Size of remote file:
- 2.14 GB
- SHA256:
- 89e94f6aaed9b652ef0d0f76057093612381cb9ca750c42c5a1926400b67fcd8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.