Instructions to use AlejandroOlmedo/zeta-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AlejandroOlmedo/zeta-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download AlejandroOlmedo/zeta-mlx --local-dir zeta-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
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Download README.md from AlejandroOlmedo/zeta-mlx: direct link, hf CLI and curl.
- Browser
- Download file 2.04 kB
-
https://huggingface.co/AlejandroOlmedo/zeta-mlx/resolve/main/README.md
- Command line
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hf download hf://AlejandroOlmedo/zeta-mlx/README.md
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curl -L -o README.md https://huggingface.co/AlejandroOlmedo/zeta-mlx/resolve/main/README.md
2.04 kB
| datasets: | |
| - zed-industries/zeta | |
| license: apache-2.0 | |
| base_model: zed-industries/zeta | |
| tags: | |
| - mlx | |
| # **About:** | |
| **Tuned from Qwen2.5 coder for coding tasks** | |
| - 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). | |
| *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: | |
| [https://huggingface.co/zed-industries/zeta](https://huggingface.co/zed-industries/zeta) (Base Model) | |
| [https://huggingface.co/lmstudio-community/zeta-GGUF](https://huggingface.co/lmstudio-community/zeta-GGUF) (GGUF Version) | |
| - Converted it to MLX format (using mlx-lm version **0.21.4**.) for better performance on Apple Silicon Macs (M1,M2,M3,M4 Chips). | |
| - If you are looking for a smaller (quantized) mlx model, see the models below. | |
| ## Other Types: | |
| | Link | Type | Size| Notes | | |
| |-------|-----------|-----------|-----------| | |
| | [MLX] (https://huggingface.co/AlejandroOlmedo/zeta-mlx) | Full | 15.2 GB | **Best Quality** | | |
| | [MLX] (https://huggingface.co/AlejandroOlmedo/zeta-8bit-mlx) | 8-bit | 8.10 GB | **Better Quality** | | |
| | [MLX] (https://huggingface.co/AlejandroOlmedo/zeta-4bit-mlx) | 4-bit | 4.30 GB | Good Quality| | |
| # AlejandroOlmedo/zeta-mlx | |
| The Model [AlejandroOlmedo/zeta-mlx](https://huggingface.co/AlejandroOlmedo/zeta-mlx) was | |
| converted to MLX format from [zed-industries/zeta](https://huggingface.co/zed-industries/zeta) | |
| using mlx-lm version **0.21.4**. | |
| ## Use with mlx | |
| ```bash | |
| pip install mlx-lm | |
| ``` | |
| ```python | |
| from mlx_lm import load, generate | |
| model, tokenizer = load("AlejandroOlmedo/zeta-mlx") | |
| prompt = "hello" | |
| if tokenizer.chat_template is not None: | |
| messages = [{"role": "user", "content": prompt}] | |
| prompt = tokenizer.apply_chat_template( | |
| messages, add_generation_prompt=True | |
| ) | |
| response = generate(model, tokenizer, prompt=prompt, verbose=True) | |
| ``` | |