Qwen2.5 Bakeneko 32b Instruct V2
基于Qwen2.5 Bakeneko 32B的指令调优变体,通过Chat Vector和ORPO优化增强指令跟随能力,在日语MT-Bench上表现出色。
下载量 140
发布时间 : 3/16/2025
模型简介
该模型是专为日语优化的指令跟随大语言模型,通过两阶段训练(模型合并与ORPO微调)增强性能,适用于对话和指令理解任务。
模型特点
日语指令优化
通过Chat Vector合并和ORPO微调专门优化日语指令跟随能力
高性能对话
在日语MT-Bench多轮对话评测中达到8.53分,接近推理模型水平
模型融合技术
结合Qwen2.5 Bakeneko基础模型与QwQ Bakeneko的指令能力进行参数融合
模型能力
日语文本生成
多轮对话处理
复杂指令理解
知识问答
使用案例
智能助手
日语对话机器人
构建能理解复杂日语指令的AI助手
在MT-Bench评测中表现优异
内容创作
日语故事生成
生成符合日本文化背景的连贯故事
🚀 Qwen2.5 Bakeneko 32B Instruct V2 (rinna/qwen2.5-bakeneko-32b-instruct-v2)
本模型是基于Qwen2.5
架构的日语指令微调模型,通过先进的训练方法增强了指令跟随能力,在日语基准测试中表现出色,无需额外推理过程即可达到接近推理模型的性能。
🚀 快速开始
以下是使用该模型进行文本生成的示例代码:
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "rinna/qwen2.5-bakeneko-32b-instruct-v2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype=torch.bfloat16,
)
messages = [
{"role": "system", "content": "あなたは誠実で優秀な日本人のアシスタントです。"},
{"role": "user", "content": "ゲーム・小説・アニメに登場するアイテムボックスの特徴と、その原理を詳細に推測してください。"},
]
prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
input_ids = tokenizer.encode(
prompt,
add_special_tokens=False,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
input_ids,
max_new_tokens=1024,
do_sample=True,
temperature=0.7,
top_k=20,
top_p=0.8,
repetition_penalty=1.05,
)
response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
print(response)
✨ 主要特性
- 指令跟随能力强:通过Chat Vector和ORPO优化,增强了模型的指令跟随能力,在日语MT - Bench测试中表现出色。
- 无需额外推理过程:在日语MT - Bench测试中,无需额外推理过程即可达到接近推理模型的性能。
- 遵循Qwen2.5聊天格式:方便与现有基于Qwen2.5的系统集成。
📚 详细文档
模型概述
本模型是rinna/qwen2.5-bakeneko-32b的指令微调变体,通过Chat Vector和Odds Ratio Preference Optimization (ORPO)进行微调。它利用了rinna/qwq-bakeneko-32b的先进指令跟随能力,增强了rinna/qwen2.5-bakeneko-32b-instruct的性能。
模型类型
属性 | 详情 |
---|---|
模型类型 | 日语持续预训练模型、指令微调模型、DeepSeek R1蒸馏Qwen2.5合并推理模型、QwQ合并推理模型、QwQ Bakeneko合并指令微调模型 |
具体模型 | Qwen2.5 Bakeneko 32B [HF]、Qwen2.5 Bakeneko 32B Instruct [HF][AWQ][GGUF][GPTQ int8][GPTQ int4]、DeepSeek R1 Distill Qwen2.5 Bakeneko 32B [HF][AWQ][GGUF][GPTQ int8][GPTQ int4]、QwQ Bakeneko 32B [HF][AWQ][GGUF][GPTQ int8][GPTQ int4]、Qwen2.5 Bakeneko 32B Instruct V2 [HF][AWQ][GGUF][GPTQ int8][GPTQ int4] |
模型架构
这是一个64层、隐藏层大小为5120的基于Transformer的语言模型。如需全面了解架构,请参考Qwen2.5技术报告。
训练过程
本模型通过两阶段训练过程开发:
- 模型合并:通过添加Chat Vector增强指令跟随能力。
在这个过程中,执行参数向量的减法和加法时排除了嵌入层。rinna/qwen2.5-bakeneko-32b-instruct + 0.8 * (rinna/qwq-bakeneko-32b - rinna/qwen2.5-bakeneko-32b)
- 蒸馏和ORPO:使用ORPO进一步优化合并后的模型,在由DeepSeek - R1生成的1300个精心策划的样本上进行训练。
贡献者
发布日期
2025年2月19日
基准测试
模型 | 日语LM评估套件 | 日语MT - Bench(首轮) | 日语MT - Bench(多轮) |
---|---|---|---|
Qwen/Qwen2.5 - 32B | 79.46 | - | - |
rinna/qwen2.5 - bakeneko - 32b | 79.18 | - | - |
Qwen/Qwen2.5 - 32B - Instruct | 78.29 | 8.13 | 7.54 |
rinna/qwen2.5 - bakeneko - 32b - instruct | 79.62 | 8.17 | 7.66 |
rinna/qwen2.5 - bakeneko - 32b - instruct - v2 | 77.92 | 8.86 | 8.53 |
deepseek - ai/DeepSeek - R1 - Distill - Qwen - 32B | 73.51 | 7.39 | 6.88 |
rinna/deepseek - r1 - distill - qwen2.5 - bakeneko - 32b | 77.43 | 8.58 | 8.19 |
Qwen/QwQ - 32B | 76.12 | 8.58 | 8.25 |
rinna/qwq - bakeneko - 32b | 78.31 | 8.81 | 8.52 |
详细的基准测试结果请参考rinna的LM基准测试页面(表20250319)。
分词
本模型继承了原始Qwen/Qwen2.5 - 32B - Instruct的分词器。
引用格式
@misc{rinna-qwen2.5-bakeneko-32b-instruct-v2,
title = {rinna/qwen2.5-bakeneko-32b-instruct-v2},
author = {Chen, Xinqi and Wakatsuki, Toshiaki and Sawada, Kei},
url = {https://huggingface.co/rinna/qwen2.5-bakeneko-32b-instruct-v2}
}
@inproceedings{sawada2024release,
title = {Release of Pre-Trained Models for the {J}apanese Language},
author = {Sawada, Kei and Zhao, Tianyu and Shing, Makoto and Mitsui, Kentaro and Kaga, Akio and Hono, Yukiya and Wakatsuki, Toshiaki and Mitsuda, Koh},
booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
month = {5},
year = {2024},
pages = {13898--13905},
url = {https://aclanthology.org/2024.lrec-main.1213},
note = {\url{https://arxiv.org/abs/2404.01657}}
}
参考文献
@article{qwen2.5,
title = {Qwen2.5 Technical Report},
author = {An Yang and Baosong Yang and Beichen Zhang and Binyuan Hui and Bo Zheng and Bowen Yu and Chengyuan Li and Dayiheng Liu and Fei Huang and Haoran Wei and Huan Lin and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Yang and Jiaxi Yang and Jingren Zhou and Junyang Lin and Kai Dang and Keming Lu and Keqin Bao and Kexin Yang and Le Yu and Mei Li and Mingfeng Xue and Pei Zhang and Qin Zhu and Rui Men and Runji Lin and Tianhao Li and Tianyi Tang and Tingyu Xia and Xingzhang Ren and Xuancheng Ren and Yang Fan and Yang Su and Yichang Zhang and Yu Wan and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zihan Qiu},
journal = {arXiv preprint arXiv:2412.15115},
year = {2024}
}
@misc{qwq32b,
title = {QwQ-32B: Embracing the Power of Reinforcement Learning},
url = {https://qwenlm.github.io/blog/qwq-32b/},
author = {Qwen Team},
month = {March},
year = {2025}
}
@misc{deepseekai2025deepseekr1incentivizingreasoningcapability,
title = {DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning},
author = {DeepSeek-AI and Daya Guo and Dejian Yang and Haowei Zhang and Junxiao Song and Ruoyu Zhang and Runxin Xu and Qihao Zhu and Shirong Ma and Peiyi Wang and Xiao Bi and Xiaokang Zhang and Xingkai Yu and Yu Wu and Z. F. Wu and Zhibin Gou and Zhihong Shao and Zhuoshu Li and Ziyi Gao and Aixin Liu and Bing Xue and Bingxuan Wang and Bochao Wu and Bei Feng and Chengda Lu and Chenggang Zhao and Chengqi Deng and Chenyu Zhang and Chong Ruan and Damai Dai and Deli Chen and Dongjie Ji and Erhang Li and Fangyun Lin and Fucong Dai and Fuli Luo and Guangbo Hao and Guanting Chen and Guowei Li and H. Zhang and Han Bao and Hanwei Xu and Haocheng Wang and Honghui Ding and Huajian Xin and Huazuo Gao and Hui Qu and Hui Li and Jianzhong Guo and Jiashi Li and Jiawei Wang and Jingchang Chen and Jingyang Yuan and Junjie Qiu and Junlong Li and J. L. Cai and Jiaqi Ni and Jian Liang and Jin Chen and Kai Dong and Kai Hu and Kaige Gao and Kang Guan and Kexin Huang and Kuai Yu and Lean Wang and Lecong Zhang and Liang Zhao and Litong Wang and Liyue Zhang and Lei Xu and Leyi Xia and Mingchuan Zhang and Minghua Zhang and Minghui Tang and Meng Li and Miaojun Wang and Mingming Li and Ning Tian and Panpan Huang and Peng Zhang and Qiancheng Wang and Qinyu Chen and Qiushi Du and Ruiqi Ge and Ruisong Zhang and Ruizhe Pan and Runji Wang and R. J. Chen and R. L. Jin and Ruyi Chen and Shanghao Lu and Shangyan Zhou and Shanhuang Chen and Shengfeng Ye and Shiyu Wang and Shuiping Yu and Shunfeng Zhou and Shuting Pan and S. S. Li and Shuang Zhou and Shaoqing Wu and Shengfeng Ye and Tao Yun and Tian Pei and Tianyu Sun and T. Wang and Wangding Zeng and Wanjia Zhao and Wen Liu and Wenfeng Liang and Wenjun Gao and Wenqin Yu and Wentao Zhang and W. L. Xiao and Wei An and Xiaodong Liu and Xiaohan Wang and Xiaokang Chen and Xiaotao Nie and Xin Cheng and Xin Liu and Xin Xie and Xingchao Liu and Xinyu Yang and Xinyuan Li and Xuecheng Su and Xuheng Lin and X. Q. Li and Xiangyue Jin and Xiaojin Shen and Xiaosha Chen and Xiaowen Sun and Xiaoxiang Wang and Xinnan Song and Xinyi Zhou and Xianzu Wang and Xinxia Shan and Y. K. Li and Y. Q. Wang and Y. X. Wei and Yang Zhang and Yanhong Xu and Yao Li and Yao Zhao and Yaofeng Sun and Yaohui Wang and Yi Yu and Yichao Zhang and Yifan Shi and Yiliang Xiong and Ying He and Yishi Piao and Yisong Wang and Yixuan Tan and Yiyang Ma and Yiyuan Liu and Yongqiang Guo and Yuan Ou and Yuduan Wang and Yue Gong and Yuheng Zou and Yujia He and Yunfan Xiong and Yuxiang Luo and Yuxiang You and Yuxuan Liu and Yuyang Zhou and Y. X. Zhu and Yanhong Xu and Yanping Huang and Yaohui Li and Yi Zheng and Yuchen Zhu and Yunxian Ma and Ying Tang and Yukun Zha and Yuting Yan and Z. Z. Ren and Zehui Ren and Zhangli Sha and Zhe Fu and Zhean Xu and Zhenda Xie and Zhengyan Zhang and Zhewen Hao and Zhicheng Ma and Zhigang Yan and Zhiyu Wu and Zihui Gu and Zijia Zhu and Zijun Liu and Zilin Li and Ziwei Xie and Ziyang Song and Zizheng Pan and Zhen Huang and Zhipeng Xu and Zhongyu Zhang and Zhen Zhang},
year = {2025},
eprint = {2501.12948},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2501.12948},
}
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