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
metadata
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 was converted to MLX format from Kwaipilot/KAT-Dev-72B-Exp using mlx-lm version 0.26.4.
Use with mlx
pip install mlx-lm
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)