💻 Github Repo • 🐦 Twitter • 📃 [GLM@ACL 22] [GitHub] • 📃 [GLM-130B@ICLR 23] [GitHub]
ChatGLM2-6B 是开源中英双语对话模型 ChatGLM-6B 的第二代版本,在保留了初代模型对话流畅、部署门槛较低等众多优秀特性的基础之上,ChatGLM2-6B 引入了如下新特性:
ChatGLM2-6B is the second-generation version of the open-source bilingual (Chinese-English) chat model ChatGLM-6B. It retains the smooth conversation flow and low deployment threshold of the first-generation model, while introducing the following new features:
pip install cpm_kernels
可以通过如下代码调用 ChatGLM-6B 模型来生成对话:
from modelscope import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("ZhipuAI/chatglm2-6b-int4", trust_remote_code=True)
model = AutoModel.from_pretrained("ZhipuAI/chatglm2-6b-int4", trust_remote_code=True).half().cuda()
model = model.eval()
response, history = model.chat(tokenizer, "你好", history=[])
response, history = model.chat(tokenizer, "晚上睡不着应该怎么办", history=history)
print(response)
关于更多的使用说明,包括如何运行命令行和网页版本的 DEMO,以及使用模型量化以节省显存,请参考我们的 Github Repo。
For more instructions, including how to run CLI and web demos, and model quantization, please refer to our Github Repo.
本仓库的代码依照 Apache-2.0 协议开源,ChatGLM2-6B 模型的权重的使用则需要遵循 Model License。
如果你觉得我们的工作有帮助的话,请考虑引用下列论文,ChatGLM2-6B 的论文会在近期公布,尽情期待~
@article{zeng2022glm,
title={Glm-130b: An open bilingual pre-trained model},
author={Zeng, Aohan and Liu, Xiao and Du, Zhengxiao and Wang, Zihan and Lai, Hanyu and Ding, Ming and Yang, Zhuoyi and Xu, Yifan and Zheng, Wendi and Xia, Xiao and others},
journal={arXiv preprint arXiv:2210.02414},
year={2022}
}
@inproceedings{du2022glm,
title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling},
author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages={320--335},
year={2022}
}