Reinforcement Learning
MLX
Safetensors
English
Chinese
qwen3_5
CUDA
MUSA
GPU-Kernel
Reinforcement-Learning
mlx-my-repo
8-bit precision
Instructions to use cnfusion/MusaCoder-27B-mlx-8Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use cnfusion/MusaCoder-27B-mlx-8Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir MusaCoder-27B-mlx-8Bit cnfusion/MusaCoder-27B-mlx-8Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
File size: 977 Bytes
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license: apache-2.0
language:
- en
- zh
base_model: MooreThreads/MusaCoder-27B
pipeline_tag: reinforcement-learning
tags:
- CUDA
- MUSA
- GPU-Kernel
- Reinforcement-Learning
- mlx
- mlx-my-repo
---
# cnfusion/MusaCoder-27B-mlx-8Bit
The Model [cnfusion/MusaCoder-27B-mlx-8Bit](https://huggingface.co/cnfusion/MusaCoder-27B-mlx-8Bit) was converted to MLX format from [MooreThreads/MusaCoder-27B](https://huggingface.co/MooreThreads/MusaCoder-27B) using mlx-lm version **0.31.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("cnfusion/MusaCoder-27B-mlx-8Bit")
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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