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--- |
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datasets: |
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- zed-industries/zeta |
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license: apache-2.0 |
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tags: |
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- mlx |
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base_model: zed-industries/zeta |
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--- |
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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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- Converted it to MLX format (using mlx-lm version **0.20.5**.) with a quantization of 8-bit for better performance on Apple Silicon Macs (M1,M2,M3,M4 Chips). |
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- If looking for a larger or smaller (quantized) mlx model, see the models below. |
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## Other Types: |
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| Link | Type | Size| Notes | |
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|-------|-----------|-----------|-----------| |
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| [MLX] (https://huggingface.co/AlejandroOlmedo/zeta-mlx) | Full | 15.2 GB | **Best Quality** | |
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| [MLX] (https://huggingface.co/AlejandroOlmedo/zeta-8bit-mlx) | 8-bit | 8.10 GB | **Better 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-8bit-mlx |
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The Model [AlejandroOlmedo/zeta-8bit-mlx](https://huggingface.co/AlejandroOlmedo/zeta-8bit-mlx) was converted to MLX format from [zed-industries/zeta](https://huggingface.co/zed-industries/zeta) using mlx-lm version **0.20.5**. |
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## Use with mlx |
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```bash |
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pip install mlx-lm |
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``` |
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```python |
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from mlx_lm import load, generate |
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model, tokenizer = load("AlejandroOlmedo/zeta-8bit-mlx") |
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prompt="hello" |
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if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: |
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messages = [{"role": "user", "content": prompt}] |
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prompt = tokenizer.apply_chat_template( |
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messages, tokenize=False, add_generation_prompt=True |
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) |
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response = generate(model, tokenizer, prompt=prompt, verbose=True) |
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``` |
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