Introduction to CPM

Related tags

Deep Learning CPM
Overview

CPM

CPM is an open-source program on large-scale pre-trained models, which is conducted by Beijing Academy of Artificial Intelligence and Tsinghua University, with the goal of building large-scale Chinese-centered pre-trained models. The open-source models can be widely used in Chinese natural language understanding, generative tasks, and all of them are free and open for download for research use.

CPM-1

CPM: A Large-scale Generative Chinese Pre-trained Language Model. [paper]

Codes:

Models: [Download]

CPM-2

CPM-2: Large-scale Cost-effective Pre-trained Language Models. [paper]

Codes:

Models: [Download]

PLM Survey

Pre-Trained Models: Past, Present and Future. [paper]

Useful Links

  • Article Generation with CPM-1. [link]

Cite

@article{cpm-v1,
  title={CPM: A Large-scale Generative Chinese Pre-trained Language Model},
  author={Zhang, Zhengyan and Han, Xu, and Zhou, Hao, and Ke, Pei, and Gu, Yuxian and Ye, Deming and Qin, Yujia and Su, Yusheng and Ji, Haozhe and Guan, Jian and Qi, Fanchao and Wang, Xiaozhi and Zheng, Yanan and Zeng, Guoyang and Cao, Huanqi and Chen, Shengqi and Li, Daixuan and Sun, Zhenbo and Liu, Zhiyuan and Huang, Minlie and Han, Wentao and Tang, Jie and Li, Juanzi and Sun, Maosong},
  year={2020}
}

@article{cpm-v2,
  title={CPM-2: Large-scale Cost-efficient Pre-trained Language Models},
  author={Zhang, Zhengyan and Gu, Yuxian and Han, Xu and Chen, Shengqi and Xiao, Chaojun and Sun, Zhenbo and Yao, Yuan and Qi, Fanchao and Guan, Jian and Ke, Pei and Cai, Yanzheng and Zeng, Guoyang and Tan, Zhixing and Liu, Zhiyuan and Huang, Minlie and Han, Wentao and Liu, Yang and Zhu, Xiaoyan and Sun, Maosong},
  year={2021}
}

@article{han2021pretrained,
      title={Pre-Trained Models: Past, Present and Future}, 
      author={Xu Han and Zhengyan Zhang and Ning Ding and Yuxian Gu and Xiao Liu and Yuqi Huo and Jiezhong Qiu and Liang Zhang and Wentao Han and Minlie Huang and Qin Jin and Yanyan Lan and Yang Liu and Zhiyuan Liu and Zhiwu Lu and Xipeng Qiu and Ruihua Song and Jie Tang and Ji-Rong Wen and Jinhui Yuan and Wayne Xin Zhao and Jun Zhu},
      year={2021}
}

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Comments
  • Missing References in the Prompt Tuning section.

    Missing References in the Prompt Tuning section.

    There is a large body of prior and concurrent work in prompt tuning that has been left out of this paper. For example:

    • Learning How to Ask, one of the first to learn a continuous version of a prompt https://arxiv.org/abs/2104.06599
    • Prefix-Tuning https://arxiv.org/abs/2101.00190
    • Prompt Tuning, where you seem to have gotten the term model-tuning from https://arxiv.org/abs/2104.08691
    • GPT Understands Too, which jointly learns a prompt and updates the model like the second step of your two step pipeline https://arxiv.org/abs/2103.10385
    • WARP https://arxiv.org/abs/2101.00121
    opened by MLWatcher 1
  • CPM 2 198b model API

    CPM 2 198b model API

    Hi, I am a student studying computer science and I'm into NLP text generation. I want to know if someone could make an API to use CPM 2 on python or the web. The model is extremely large and hosting the model somewhere would really help to avoid downloading the entire model onto a pc like mine that doesn't support Cuda or GPU acceleration. I could use a different model but I would like to use CPM 2 Moe 198b since is the latest and biggest model so far

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Tsinghua AI
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