Instructions to use AlejandroOlmedo/zeta-4bit-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AlejandroOlmedo/zeta-4bit-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download AlejandroOlmedo/zeta-4bit-mlx --local-dir zeta-4bit-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
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- mlx
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# Alejandroolmedo/zeta-4bit-mlx
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The Model [Alejandroolmedo/zeta-4bit-mlx](https://huggingface.co/Alejandroolmedo/zeta-4bit-mlx) was
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- mlx
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# **About:**
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**Tuned from Qwen2.5 coder for coding tasks**
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- Its a fine-tuned version of Qwen2.5-Coder-7B to support [**__edit prediction__**](https://zed.dev/edit-prediction) in Zed. Fine-tuned using [__zeta dataset__](https://huggingface.co/datasets/zed-industries/zeta).
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*Special thanks to the folks at Zed Industries for fine-tuning this version of* *Qwen2.5-Coder-7B*. More information about the model can be found here:
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[https://huggingface.co/zed-industries/zeta](https://huggingface.co/zed-industries/zeta) (Base Model)
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[https://huggingface.co/lmstudio-community/zeta-GGUF](https://huggingface.co/lmstudio-community/zeta-GGUF) (GGUF Version)
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I simply converted it to MLX format (using mlx-lm version **0.21.4**.) with a quantization of 4-bit for better performance on Apple Silicon Macs (M1,M2,M3,M4 Chips).
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# Alejandroolmedo/zeta-4bit-mlx
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The Model [Alejandroolmedo/zeta-4bit-mlx](https://huggingface.co/Alejandroolmedo/zeta-4bit-mlx) was
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