---
library_name: transformers
tags:
- Uncensored
- Abliterated
- Cubed Reasoning
- QwQ-32B
- reasoning
- thinking
- r1
- cot
- deepseek
- Qwen2.5
- Hermes
- DeepHermes
- DeepSeek
- DeepSeek-R1-Distill
- 128k context
- not-for-all-audiences
- merge
- mlx
- mlx-my-repo
base_model: DavidAU/Qwen2.5-QwQ-37B-Eureka-Triple-Cubed-abliterated-uncensored
---

# cs2764/Qwen2.5-QwQ-37B-Eureka-Triple-Cubed-abliterated-uncensored-mlx-4Bit

The Model [cs2764/Qwen2.5-QwQ-37B-Eureka-Triple-Cubed-abliterated-uncensored-mlx-4Bit](https://huggingface.co/cs2764/Qwen2.5-QwQ-37B-Eureka-Triple-Cubed-abliterated-uncensored-mlx-4Bit) was converted to MLX format from [DavidAU/Qwen2.5-QwQ-37B-Eureka-Triple-Cubed-abliterated-uncensored](https://huggingface.co/DavidAU/Qwen2.5-QwQ-37B-Eureka-Triple-Cubed-abliterated-uncensored) using mlx-lm version **0.22.1**.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("cs2764/Qwen2.5-QwQ-37B-Eureka-Triple-Cubed-abliterated-uncensored-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)
```