---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507/blob/main/LICENSE
language:
- en
base_model: huihui-ai/Huihui-Qwen3-30B-A3B-Instruct-2507-abliterated
pipeline_tag: text-generation
library_name: transformers
tags:
- abliterated
- uncensored
- mlx
- mlx-my-repo
---

# cs2764/Huihui-Qwen3-30B-A3B-Instruct-2507-abliterated-mlx-6Bit-gs32

The Model [cs2764/Huihui-Qwen3-30B-A3B-Instruct-2507-abliterated-mlx-6Bit-gs32](https://huggingface.co/cs2764/Huihui-Qwen3-30B-A3B-Instruct-2507-abliterated-mlx-6Bit-gs32) was converted to MLX format from [huihui-ai/Huihui-Qwen3-30B-A3B-Instruct-2507-abliterated](https://huggingface.co/huihui-ai/Huihui-Qwen3-30B-A3B-Instruct-2507-abliterated) using mlx-lm version **0.26.2**.

## Quantization Details

This model was converted with the following quantization settings:

- **Quantization Strategy**: 6-bit quantization
- **Group Size**: 32
- **Average bits per weight**: 7.000

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("cs2764/Huihui-Qwen3-30B-A3B-Instruct-2507-abliterated-mlx-6Bit-gs32")

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)
```