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---
license: apache-2.0
license_link: https://huggingface.co/huihui-ai/QwQ-32B-Preview-abliterated/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
base_model:
- huihui-ai/QwQ-32B-Preview-abliterated
tags:
- chat
- abliterated
- uncensored
library_name: transformers
---
# _**I kindly ask that you [follow me](https://huggingface.co/ibrahimkettaneh) for more quantizations, please, because it lets me know you are interested in more work like this**_ 😁
# Credits goes to all those who have contributed to the community, in this case, specifically: [huihui-ai](https://huggingface.co/huihui-ai)/[QwQ-32B-Preview-abliterated](https://huggingface.co/huihui-ai/QwQ-32B-Preview-abliterated)
# Benchmark
Source: [🐺🐦⬛ LLM Comparison/Test: 25 SOTA LLMs (including QwQ) through 59 MMLU-Pro CS benchmark runs](https://huggingface.co/blog/wolfram/llm-comparison-test-2024-12-04)
Credits go to for their helpful and informative benchmark: [Wolfram Ravenwolf](https://huggingface.co/wolfram)
# Recommendation for Best Performance
To increase performance, increase the max new output when running inference from the default to 16384 tokens.
## Detailed Table
| Duration | Total | % | TIGER-Lab | Correct Random Guesses | Prompt tokens | tk/s | Completion tokens | tk/s |
|----------|--------|---|-----------|----------------------|----------------|-------|-------------------|-------|
| QwQ-32B-Preview (8.0bpw EXL2, max_tokens=16384) | bartowski/QwQ-32B-Preview-exl2_8_0 | Qwen/Qwen2.5-Coder-0.5B-Instruct | 32B | EXL2 | TabbyAPI | RTX 6000 | 38436MiB | 1/2 | 2h 3m 30s | 325/410 | 79.27% | | 0/2, 0.00% | 656716 | 88.58 | 327825 | 44.22 |
| QwQ-32B-Preview (8.0bpw EXL2, max_tokens=16384) | bartowski/QwQ-32B-Preview-exl2_8_0 | Qwen/Qwen2.5-Coder-0.5B-Instruct | 32B | EXL2 | TabbyAPI | RTX 6000 | 38436MiB | 2/2 | 2h 3m 35s | 324/410 | 79.02% | | | 656716 | 88.52 | 343440 | 46.29 |
| QwQ-32B-Preview (4.25bpw EXL2, max_tokens=16384) | bartowski/QwQ-32B-Preview-exl2_4_25 | Qwen/Qwen2.5-Coder-0.5B-Instruct | 32B | EXL2 | TabbyAPI | RTX 6000 | 27636MiB | 1/2 | 1h 56m 8s | 319/410 | 77.80% | | 0/1, 0.00% | 656716 | 94.20 | 374973 | 53.79 |
| QwQ-32B-Preview (4.25bpw EXL2, max_tokens=16384) | bartowski/QwQ-32B-Preview-exl2_4_25 | Qwen/Qwen2.5-Coder-0.5B-Instruct | 32B | EXL2 | TabbyAPI | RTX 6000 | 27636MiB | 2/2 | 1h 55m 44s | 318/410 | 77.56% | | | 656716 | 94.45 | 377638 | 54.31 |
| QwQ-32B-Preview (8.0bpw EXL2) | bartowski/QwQ-32B-Preview-exl2_8_0 | Qwen/Qwen2.5-Coder-0.5B-Instruct | 32B | EXL2 | TabbyAPI | RTX 6000 | 38528MiB | 1/4 | 1h 29m 49s | 324/410 | 79.02% | | 0/1, 0.00% | 656716 | 121.70 | 229008 | 42.44 |
| QwQ-32B-Preview (8.0bpw EXL2) | bartowski/QwQ-32B-Preview-exl2_8_0 | Qwen/Qwen2.5-Coder-0.5B-Instruct | 32B | EXL2 | TabbyAPI | RTX 6000 | 38528MiB | 2/4 | 1h 32m 30s | 314/410 | 76.59% | | 0/2, 0.00% | 656716 | 118.24 | 239161 | 43.06 |
| QwQ-32B-Preview (8.0bpw EXL2) | bartowski/QwQ-32B-Preview-exl2_8_0 | - | 32B | EXL2 | TabbyAPI | RTX 6000 | 37000MiB | 3/4 | 2h 25m 24s | 308/410 | 75.12% | | 0/2, 0.00% | 656716 | 75.23 | 232208 | 26.60 |
| QwQ-32B-Preview (8.0bpw EXL2) | bartowski/QwQ-32B-Preview-exl2_8_0 | - | 32B | EXL2 | TabbyAPI | RTX 6000 | 37000MiB | 4/4 | 2h 27m 27s | 305/410 | 74.39% | | 0/3, 0.00% | 656716 | 74.19 | 235650 | 26.62 |
| QwQ-32B-Preview-abliterated (4.5bpw EXL2, max_tokens=16384) | ibrahimkettaneh_QwQ-32B-Preview-abliterated-4.5bpw-h8-exl2 | Qwen/Qwen2.5-Coder-0.5B-Instruct | 32B | EXL2 | TabbyAPI | RTX 6000 | 28556MiB | 1/2 | 2h 10m 53s | 310/410 | 75.61% | | | 656716 | 83.59 | 412512 | 52.51 |
| QwQ-32B-Preview-abliterated (4.5bpw EXL2, max_tokens=16384) | ibrahimkettaneh_QwQ-32B-Preview-abliterated-4.5bpw-h8-exl2 | Qwen/Qwen2.5-Coder-0.5B-Instruct | 32B | EXL2 | TabbyAPI | RTX 6000 | 28556MiB | 2/2 | 2h 25m 29s | 310/410 | 75.61% | | | 656716 | 75.20 | 478590 | 54.80 |
| QwQ-32B-Preview (4.25bpw EXL2) | bartowski/QwQ-32B-Preview-exl2_4_25 | - | 32B | EXL2 | TabbyAPI | RTX 6000 | 26198MiB | 1/4 | 1h 39m 49s | 308/410 | 75.12% | | 0/1, 0.00% | 656716 | 109.59 | 243552 | 40.64 |
| QwQ-32B-Preview (4.25bpw EXL2) | bartowski/QwQ-32B-Preview-exl2_4_25 | Qwen/Qwen2.5-Coder-0.5B-Instruct | 32B | EXL2 | TabbyAPI | RTX 6000 | 27750MiB | 2/4 | 1h 22m 12s | 304/410 | 74.15% | | | 656716 | 133.04 | 247314 | 50.10 |
| QwQ-32B-Preview (4.25bpw EXL2) | bartowski/QwQ-32B-Preview-exl2_4_25 | Qwen/Qwen2.5-Coder-0.5B-Instruct | 32B | EXL2 | TabbyAPI | RTX 6000 | 27750MiB | 3/4 | 1h 21m 39s | 296/410 | 72.20% | | | 656716 | 133.94 | 246020 | 50.18 |
| QwQ-32B-Preview (4.25bpw EXL2) | bartowski/QwQ-32B-Preview-exl2_4_25 | - | 32B | EXL2 | TabbyAPI | RTX 6000 | 26198MiB | 4/4 | 1h 42m 33s | 294/410 | 71.71% | | | 656716 | 106.63 | 250222 | 40.63 |
| QwQ-32B-Preview (3.0bpw EXL2, max_tokens=8192) | bartowski/QwQ-32B-Preview-exl2_3_0 | Qwen/Qwen2.5-Coder-0.5B-Instruct | 32B | EXL2 | TabbyAPI | RTX 6000 | 22990MiB | 1/2 | 1h 15m 18s | 289/410 | 70.49% | | | 656716 | 145.23 | 269937 | 59.69 |
| QwQ-32B-Preview (3.0bpw EXL2, max_tokens=8192) | bartowski/QwQ-32B-Preview-exl2_3_0 | Qwen/Qwen2.5-Coder-0.5B-Instruct | 32B | EXL2 | TabbyAPI | RTX 6000 | 22990MiB | 2/2 | 1h 19m 50s | 274/410 | 66.83% | | 0/2, 0.00% | 656716 | 137.01 | 291818 | 60.88 |
| QwQ-32B-Preview (3.0bpw EXL2) | bartowski/QwQ-32B-Preview-exl2_3_0 | - | 32B | EXL2 | TabbyAPI | RTX 6000 | 21574MiB | 1/2 | 1h 5m 30s | 268/410 | 65.37% | | 1/3, 33.33% | 656716 | 166.95 | 205218 | 52.17 |
| QwQ-32B-Preview (3.0bpw EXL2) | bartowski/QwQ-32B-Preview-exl2_3_0 | - | 32B | EXL2 | TabbyAPI | RTX 6000 | 21574MiB | 2/2 | 1h 8m 44s | 266/410 | 64.88% | | | 656716 | 159.10 | 215616 | 52.24 |
For more context, details, and comparisons, you can refer to [the original article by Ravenwolf](https://huggingface.co/blog/wolfram/llm-comparison-test-2024-12-04).
# Context
This is an uncensored version of [Qwen/QwQ-32B-Preview](https://huggingface.co/Qwen/QwQ-32B-Preview) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).
This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.
# QwQ-32B-Preview
## Introduction
**QwQ-32B-Preview** is an experimental research model developed by the Qwen Team, focused on advancing AI reasoning capabilities. As a preview release, it demonstrates promising analytical abilities while having several important limitations:
1. **Language Mixing and Code-Switching**: The model may mix languages or switch between them unexpectedly, affecting response clarity.
2. **Recursive Reasoning Loops**: The model may enter circular reasoning patterns, leading to lengthy responses without a conclusive answer.
3. **Safety and Ethical Considerations**: The model requires enhanced safety measures to ensure reliable and secure performance, and users should exercise caution when deploying it.
4. **Performance and Benchmark Limitations**: The model excels in math and coding but has room for improvement in other areas, such as common sense reasoning and nuanced language understanding.
**Specification**:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
- Number of Parameters: 32.5B
- Number of Paramaters (Non-Embedding): 31.0B
- Number of Layers: 64
- Number of Attention Heads (GQA): 40 for Q and 8 for KV
- Context Length: Full 32,768 tokens
For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwq-32b-preview/). You can also check Qwen2.5 [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Requirements
The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.37.0`, you will encounter the following error:
```
KeyError: 'qwen2'
```
## Quickstart
Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/QwQ-32B-Preview"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "How many r in strawberry."
messages = [
{"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
## Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwq-32b-preview,
title = {QwQ: Reflect Deeply on the Boundaries of the Unknown},
url = {https://qwenlm.github.io/blog/qwq-32b-preview/},
author = {Qwen Team},
month = {November},
year = {2024}
}
@article{qwen2,
title={Qwen2 Technical Report},
author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
journal={arXiv preprint arXiv:2407.10671},
year={2024}
}
``` |