---
license: apache-2.0
datasets:
- Tongyi-Zhiwen/DocQA-RL-1.6K
base_model: Tongyi-Zhiwen/QwenLong-L1-32B
tags:
- long-context
- large-reasoning-model
- mlx
- mlx-my-repo
---

# WaveCut/QwenLong-L1-32B-mlx-4Bit

The Model [WaveCut/QwenLong-L1-32B-mlx-4Bit](https://huggingface.co/WaveCut/QwenLong-L1-32B-mlx-4Bit) was converted to MLX format from [Tongyi-Zhiwen/QwenLong-L1-32B](https://huggingface.co/Tongyi-Zhiwen/QwenLong-L1-32B) using mlx-lm version **0.22.3**.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("WaveCut/QwenLong-L1-32B-mlx-4Bit")

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)
```