缩略图: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png 许可证: apache-2.0 语言:

• 日语 标签:
• qwen2
• 对话式
• gguf 基础模型: rinna/qwq-bakeneko-32b 基础模型关系: 量化 管道标签: 文本生成

QwQ Bakeneko 32B GGUF (rinna/qwq-bakeneko-32b-gguf)

概述

本模型是基于rinna/qwq-bakeneko-32b使用llama.cpp量化的模型，兼容多数基于llama.cpp的应用。

模型架构与数据详情请参阅rinna/qwq-bakeneko-32b。

• 贡献者

  • Toshiaki Wakatsuki
  • Xinqi Chen
  • Kei Sawada

• 发布日期

  2025年3月13日

基准测试

详细基准测试结果请参考rinna的LM基准页面(20250313表格)。

引用方式

@misc{rinna-qwq-bakeneko-32b-gguf,
    title = {rinna/qwq-bakeneko-32b-gguf},
    author = {Wakatsuki, Toshiaki and Chen, Xinqi and Sawada, Kei},
    url = {https://huggingface.co/rinna/qwq-bakeneko-32b-gguf}
}

@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技术报告}, 
      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预印本 arXiv:2412.15115},
      year = {2024}
}

@misc{qwq32b,
    title = {QwQ-32B：拥抱强化学习的力量},
    url = {https://qwenlm.github.io/blog/qwq-32b/},
    author = {Qwen团队},
    month = {3月},
    year = {2025}
}

@misc{deepseekai2025deepseekr1incentivizingreasoningcapability,
    title = {DeepSeek-R1：通过强化学习激励LLM的推理能力}, 
    author = {DeepSeek-AI团队及合作者},
    year = {2025},
    eprint = {2501.12948},
    archivePrefix = {arXiv},
    primaryClass = {cs.CL},
    url = {https://arxiv.org/abs/2501.12948}, 
}

@article{huang2023chat,
    title = {Chat Vector：一种为LLM适配新语言指令跟随与模型对齐的简易方法},
    author = {Huang, Shih-Cheng and Li, Pin-Zu and Hsu, Yu-Chi and Chen, Kuang-Ming and Lin, Yu Tung and Hsiao, Shih-Kai and Tzong-Han Tsai, Richard and Lee, Hung-yi},
    year = {2023},
    url = {https://arxiv.org/abs/2310.04799}
}

@inproceedings{hong2024orpo,
    title = {ORPO：无需参考模型的统一偏好优化},
    author = {Hong, Jiwoo and Lee, Noah and Thorne, James},
    booktitle = {2024年自然语言处理实证方法会议论文集},
    pages = {11170--11189},
    year = {2024}
}

@misc{llamacpp,
    title = {llama.cpp},
    author = {Gerganov, Georgi},
    howpublished = {\url{https://github.com/ggerganov/llama.cpp}},
    year = {2023}
}

许可证

Apache许可证2.0版