ChatGLM-6B模型介绍

ChatGLM-6B 是一个开源的、支持中英双语问答的对话语言模型，基于 General Language Model (GLM) 架构，具有 62 亿参数。结合模型量化技术，用户可以在消费级的显卡上进行本地部署（INT4 量化级别下最低只需 6GB 显存）。ChatGLM-6B 使用了和 ChatGLM 相同的技术，针对中文问答和对话进行了优化。经过约 1T 标识符的中英双语训练，辅以监督微调、反馈自助、人类反馈强化学习等技术的加持，62 亿参数的 ChatGLM-6B 已经能生成相当符合人类偏好的回答。

ChatGLM-6B is an open bilingual language model based on General Language Model (GLM) framework, with 6.2 billion parameters. With the quantization technique, users can deploy locally on consumer-grade graphics cards (only 6GB of GPU memory is required at the INT4 quantization level). ChatGLM-6B uses technology similar to ChatGPT, optimized for Chinese QA and dialogue. The model is trained for about 1T tokens of Chinese and English corpus, supplemented by supervised fine-tuning, feedback bootstrap, and reinforcement learning wit human feedback. With only about 6.2 billion parameters, the model is able to generate answers that are in line with human preference.

环境搭建

本模型环境需安装最新版的modelscope

pip install modelscope==1.4.3 -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

软件依赖

pip install protobuf==3.20.0 transformers==4.27.1 icetk cpm_kernels

代码范例

from modelscope.utils.constant import Tasks
from modelscope import Model
from modelscope.pipelines import pipeline
model = Model.from_pretrained('ZhipuAI/ChatGLM-6B', device_map='auto', revision='v1.0.19').half().cuda()
pipe = pipeline(task=Tasks.chat, model=model)
inputs = {'text':'你好', 'history': []}
result = pipe(inputs)
inputs = {'text':'介绍下江南大学', 'history': result['history']}
result = pipe(inputs)
print(result)

如果modelscope版本小于1.7.0，请使用如下代码范例：

from modelscope.utils.constant import Tasks
from modelscope.pipelines import pipeline
pipe = pipeline(task=Tasks.chat, model='ZhipuAI/ChatGLM-6B', model_revision='v1.0.16', device_map='auto')
inputs = {'text':'你好', 'history': []}
result = pipe(inputs)
inputs = {'text':'介绍下清华大学', 'history': result['history']}
result = pipe(inputs)
print(result)

协议

本仓库的代码依照 Apache-2.0 协议开源，ChatGLM-6B 模型的权重的使用则需要遵循 Model License。

引用

如果你觉得我们的工作有帮助的话，请考虑引用下列论文：

@inproceedings{
  zeng2023glm-130b,
  title={{GLM}-130B: An Open Bilingual Pre-trained Model},
  author={Aohan Zeng and Xiao Liu and Zhengxiao Du and Zihan Wang and Hanyu Lai and Ming Ding and Zhuoyi Yang and Yifan Xu and Wendi Zheng and Xiao Xia and Weng Lam Tam and Zixuan Ma and Yufei Xue and Jidong Zhai and Wenguang Chen and Zhiyuan Liu and Peng Zhang and Yuxiao Dong and Jie Tang},
  booktitle={The Eleventh International Conference on Learning Representations (ICLR)},
  year={2023},
  url={https://openreview.net/forum?id=-Aw0rrrPUF}
}

@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}
}

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modelhub clone

 git clone https://www.modelscope.cn/ZhipuAI/ChatGLM-6B.git