Instructions to use theroadnotbacon/BLS-Mini-Code-1.0-Q4-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use theroadnotbacon/BLS-Mini-Code-1.0-Q4-MLX with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir BLS-Mini-Code-1.0-Q4-MLX theroadnotbacon/BLS-Mini-Code-1.0-Q4-MLX
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
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README.md
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- mlx
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library_name: mlx
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base_model:
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- CohereLabs/BLS-Mini-Code-1.0
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base_model_relation: quantized
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library_name: mlx
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Generated via:
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```
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git clone https://github.com/eauchs/mlx-lm --single-branch --depth=1 --branch add-cohere2-moe
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uv venv venv
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source venv/bin/activate.fish
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uv pip install mlx[cpu] ./mlx-lm
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python -m mlx_lm.convert \
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--hf-path "./BLS-Mini-Code-1.0" \
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-q \
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--q-bits 4 --q-group-size 64 \
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--mlx-path ./cohere-mlx-q4
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```
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Requires this fork of mlx_lm to run (main doesn't include support for cohere2_moe yet):
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