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---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- chat
- abliterated
- uncensored
base_model:
- huihui-ai/QwQ-32B-Preview-abliterated
- huihui-ai/Qwen2.5-Coder-32B-Instruct-abliterated
license_link: https://huggingface.co/huihui-ai/QwQ-32B-Coder-Fusion-9010/blob/main/LICENSE
pipeline_tag: text-generation
model-index:
- name: QwQ-32B-Coder-Fusion-9010
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 57.78
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=huihui-ai/QwQ-32B-Coder-Fusion-9010
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 53.02
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=huihui-ai/QwQ-32B-Coder-Fusion-9010
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 40.26
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=huihui-ai/QwQ-32B-Coder-Fusion-9010
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 14.88
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=huihui-ai/QwQ-32B-Coder-Fusion-9010
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 19.52
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=huihui-ai/QwQ-32B-Coder-Fusion-9010
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 51.11
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=huihui-ai/QwQ-32B-Coder-Fusion-9010
name: Open LLM Leaderboard
---
# huihui-ai/QwQ-32B-Coder-Fusion-9010
## Overview
`QwQ-32B-Coder-Fusion-9010` 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 9:1 ratio, with 90% of the weights from the abliterated QwQ-32B-Preview-abliterated and 10% 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.
Now the effective ratios are 9:1, 8:2, and 7:3. Any other ratios (6:4,5:5) would result in mixed or unclear expressions.
## Model Details
- **Base Models:**
- [huihui-ai/QwQ-32B-Preview-abliterated](https://huggingface.co/huihui-ai/QwQ-32B-Preview-abliterated) (90%)
- [huihui-ai/Qwen2.5-Coder-32B-Instruct-abliterated](https://huggingface.co/huihui-ai/Qwen2.5-Coder-32B-Instruct-abliterated) (10%)
- **Model Size:** 32B parameters
- **Architecture:** Qwen 2.5
- **Mixing Ratio:** 9:1 (QwQ-32B-Preview-abliterated:Qwen2.5-Coder-32B-Instruct-abliterated)
## ollama
You can use [huihui_ai/qwq-fusion](https://ollama.com/huihui_ai/qwq-fusion) directly,
```
ollama run huihui_ai/qwq-fusion
```
Other proportions can be obtained by visiting [huihui_ai/qwq-fusion](https://ollama.com/huihui_ai/qwq-fusion/tags).
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/huihui-ai__QwQ-32B-Coder-Fusion-9010-details)
| Metric |Value|
|-------------------|----:|
|Avg. |39.43|
|IFEval (0-Shot) |57.78|
|BBH (3-Shot) |53.02|
|MATH Lvl 5 (4-Shot)|40.26|
|GPQA (0-shot) |14.88|
|MuSR (0-shot) |19.52|
|MMLU-PRO (5-shot) |51.11|
|