---
license: apache-2.0
license_link: https://huggingface.co/huihui-ai/QwQ-32B-Coder-Fusion-8020/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
base_model: 
 - huihui-ai/QwQ-32B-Preview-abliterated
 - huihui-ai/Qwen2.5-Coder-32B-Instruct-abliterated
tags:
- chat
- abliterated
- uncensored
library_name: transformers
---

# huihui-ai/QwQ-32B-Coder-Fusion-8020


## Overview
`QwQ-32B-Coder-Fusion-8020` is a mixed model that combines the strengths of two powerful Qwen-based models: [huihui-ai/QwQ-32B-Preview-abliterated](https://huggingface.co/huihui-ai/QwQ-32B-Preview-abliterated) and 
[huihui-ai/Qwen2.5-Coder-32B-Instruct-abliterated](https://huggingface.co/huihui-ai/Qwen2.5-Coder-32B-Instruct-abliterated).   
The weights are blended in a 8:2 ratio, with 80% of the weights from QwQ-32B-Preview-abliterated and 20% from the abliterated Qwen2.5-Coder-32B-Instruct-abliterated model.
**Although it's a simple mix, the model is usable, and no gibberish has appeared**.
This is an experiment. I test the [9:1](https://huggingface.co/huihui-ai/QwQ-32B-Coder-Fusion-9010), 
[8:2](https://huggingface.co/huihui-ai/QwQ-32B-Coder-Fusion-8020), 
and [7:3](https://huggingface.co/huihui-ai/QwQ-32B-Coder-Fusion-7030) ratios separately to see how much impact they have on the model.   


Please refer to the mixed [source code](https://huggingface.co/huihui-ai/QwQ-32B-Coder-Fusion-9010/blob/main/merge_model_weights.py). 


## Model Details
- **Base Models:**
  - [huihui-ai/QwQ-32B-Preview-abliterated](https://huggingface.co/huihui-ai/QwQ-32B-Preview-abliterated) (80%)
  - [huihui-ai/Qwen2.5-Coder-32B-Instruct-abliterated](https://huggingface.co/huihui-ai/Qwen2.5-Coder-32B-Instruct-abliterated) (20%)
- **Model Size:** 32B parameters
- **Architecture:** Qwen 2.5
- **Mixing Ratio:** 8:2 (QwQ-32B-Preview-abliterated:Qwen2.5-Coder-32B-Instruct-abliterated)


## ollama

You can use [huihui_ai/qwq-fusion:32b-8020](https://ollama.com/huihui_ai/qwq-fusion:32b-8020) directly, 
```
ollama run huihui_ai/qwq-fusion:32b-8020
```

Other proportions can be obtained by visiting [huihui_ai/qwq-fusion](https://ollama.com/huihui_ai/qwq-fusion/tags).