🌟 [2022-10-06] Thrilled to present GLM-130B: An Open Bilingual Pre-trained Model. It is an open-sourced LLM outperforming GPT-3 175B over various benchmarks. Get model weights and do inference and P-Tuning with only 4 * RTX 3090 or 8 * RTX 2080 Ti FOR FREE!
🌟 [2022-07-14] Parameter-Efficient Prompt Tuning Makes Generalized and Calibrated Neural Text Retrievers is out! Check our code.
🌟 [2021-10-15] P-tuning v2 is out! Check our Github repo.
A novel method to tune language models. Codes and datasets for paper ``GPT understands, too''.
Xiao Liu*, Yanan Zheng*, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, Jie Tang
You may be also interested in our another work GLM: All NLP Tasks Are Generation Tasks: A General Pretraining Framework
We have released the code and datasets for LAMA and few-shot SuperGLUE (32-dev) experiments. Please check README.md and requirement.txt in the corresponding subdirectories for details.
The LAMA and FewGLUE_32dev datasets are available. The LAMA dataset should be placed in ./data directory, and the SuperGLUE dataset should be placed in the ./ (project root) directory.
If you find our work useful, please cite the following paper:
@article{liu2021gpt,
title={GPT Understands, Too},
author={Liu, Xiao and Zheng, Yanan and Du, Zhengxiao and Ding, Ming and Qian, Yujie and Yang, Zhilin and Tang, Jie},
journal={arXiv:2103.10385},
year={2021}
}