Instructions to use RESMP-DEV/LFM2.5-Encoder-350M-Code-MXFP4-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use RESMP-DEV/LFM2.5-Encoder-350M-Code-MXFP4-GPTQ with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir LFM2.5-Encoder-350M-Code-MXFP4-GPTQ RESMP-DEV/LFM2.5-Encoder-350M-Code-MXFP4-GPTQ
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Xet hash:
- 06e46418f748786bc329393ba5fe059870658a9711e3dd512669f5f97e29d744
- Size of remote file:
- 287 MB
- SHA256:
- 7f9e7a155619d202495591ed4794fe707967433a6d5c997f3e83a048204dca8c
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