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
Update README.md
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README.md
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## Other Types:
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| Link | Type | Size| Notes |
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| [MLX] (https://huggingface.co/
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| [MLX] (https://huggingface.co/
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#
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The Model [
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converted to MLX format from [zed-industries/zeta](https://huggingface.co/zed-industries/zeta)
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using mlx-lm version **0.21.4**.
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```python
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from mlx_lm import load, generate
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model, tokenizer = load("
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prompt = "hello"
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## Other Types:
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| Link | Type | Size| Notes |
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| [MLX] (https://huggingface.co/AlejandroOlmedo/zeta-8bit-mlx) | 8-bit | 8.10 GB | **Best Quality** |
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| [MLX] (https://huggingface.co/AlejandroOlmedo/zeta-4bit-mlx) | 4-bit | 4.30 GB | Good Quality|
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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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converted to MLX format from [zed-industries/zeta](https://huggingface.co/zed-industries/zeta)
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using mlx-lm version **0.21.4**.
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```python
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from mlx_lm import load, generate
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model, tokenizer = load("AlejandroOlmedo/zeta-4bit-mlx")
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prompt = "hello"
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