Instructions to use GreenBitAI/QwQ-32B-layer-mix-bpw-4.0-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use GreenBitAI/QwQ-32B-layer-mix-bpw-4.0-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir QwQ-32B-layer-mix-bpw-4.0-mlx GreenBitAI/QwQ-32B-layer-mix-bpw-4.0-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
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license: apache-2.0
tags:
- mlx
base_model: GreenBitAI/QwQ-32B-layer-mix-bpw-4.0
---
# GreenBitAI/QwQ-32B-layer-mix-bpw-4.0-mlx
This quantized low-bit model [GreenBitAI/QwQ-32B-layer-mix-bpw-4.0-mlx](https://huggingface.co/GreenBitAI/QwQ-32B-layer-mix-bpw-4.0-mlx) was converted to MLX format from [`GreenBitAI/QwQ-32B-layer-mix-bpw-4.0`](https://huggingface.co/GreenBitAI/QwQ-32B-layer-mix-bpw-4.0) using gbx-lm version **0.3.9**.
Refer to the [original model card](https://huggingface.co/GreenBitAI/QwQ-32B-layer-mix-bpw-4.0) for more details on the model.
## Use with mlx
```bash
pip install gbx-lm
```
```python
from gbx_lm import load, generate
model, tokenizer = load("GreenBitAI/QwQ-32B-layer-mix-bpw-4.0-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
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