Instructions to use mxmcc/KAT-Dev-72B-Exp-mlx-6Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mxmcc/KAT-Dev-72B-Exp-mlx-6Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir KAT-Dev-72B-Exp-mlx-6Bit mxmcc/KAT-Dev-72B-Exp-mlx-6Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
File size: 847 Bytes
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license: apache-2.0
tags:
- mlx
base_model: Kwaipilot/KAT-Dev-72B-Exp
---
# mxmcc/KAT-Dev-72B-Exp-mlx-6Bit
The Model [mxmcc/KAT-Dev-72B-Exp-mlx-6Bit](https://huggingface.co/mxmcc/KAT-Dev-72B-Exp-mlx-6Bit) was converted to MLX format from [Kwaipilot/KAT-Dev-72B-Exp](https://huggingface.co/Kwaipilot/KAT-Dev-72B-Exp) using mlx-lm version **0.26.4**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mxmcc/KAT-Dev-72B-Exp-mlx-6Bit")
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)
```
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