---
datasets:
- zed-industries/zeta
license: apache-2.0
tags:
- mlx
base_model: zed-industries/zeta
---

# **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 **0.20.5**.) with a quantization of 8-bit for better performance on Apple Silicon Macs (M1,M2,M3,M4 Chips).
- If looking for a larger or 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-8bit-mlx

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**.

## Use with mlx

```bash
pip install mlx-lm
```

```python
from mlx_lm import load, generate

model, tokenizer = load("AlejandroOlmedo/zeta-8bit-mlx")

prompt="hello"

if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)
```