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---
language:
- zh
- en
tags:
- glm
- chatglm
- thudm
---
# ChatGLM3-6B
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💻 < a href = "https://github.com/THUDM/ChatGLM" target = "_blank" > Github Repo< / a > • 🐦 < a href = "https://twitter.com/thukeg" target = "_blank" > Twitter< / a > • 📃 < a href = "https://arxiv.org/abs/2103.10360" target = "_blank" > [GLM@ACL 22]< / a > < a href = "https://github.com/THUDM/GLM" target = "_blank" > [GitHub]< / a > • 📃 < a href = "https://arxiv.org/abs/2210.02414" target = "_blank" > [GLM-130B@ICLR 23]< / a > < a href = "https://github.com/THUDM/GLM-130B" target = "_blank" > [GitHub]< / a > < br >
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2024-11-01 14:46:06 +08:00
2024-11-01 14:49:37 +08:00
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👋 Join our < a href = "https://join.slack.com/t/chatglm/shared_invite/zt-25ti5uohv-A_hs~am_D3Q8XPZMpj7wwQ" target = "_blank" > Slack< / a > and < a href = "https://github.com/THUDM/ChatGLM/blob/main/resources/WECHAT.md" target = "_blank" > WeChat< / a >
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📍Experience the larger-scale ChatGLM model at < a href = "https://www.chatglm.cn" > chatglm.cn< / a >
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## 介绍
ChatGLM3-6B 是 ChatGLM 系列最新一代的开源模型， ，
1. **更强大的基础模型：** ChatGLM3-6B 的基础模型 ChatGLM3-6B-Base 采用了更多样的训练数据、更充分的训练步数和更合理的训练策略。在语义、数学、推理、代码、知识等不同角度的数据集上测评显示，
2. **更完整的功能支持：** ChatGLM3-6B 采用了全新设计的 [Prompt 格式 ](PROMPT.md )，除正常的多轮对话外。同时原生支持[工具调用](tool_using/README.md)（ ） （ ）
3. **更全面的开源序列：** 除了对话模型 ChatGLM3-6B 外，还开源了基础模型 ChatGLM-6B-Base、长文本对话模型 ChatGLM3-6B-32K。以上所有权重对学术研究**完全开放**，在填写[问卷](https://open.bigmodel.cn/mla/form)进行登记后**亦允许免费商业使用**。
## 软件依赖
```shell
pip install protobuf 'transformers>=4.30.2' cpm_kernels 'torch>=2.0' gradio mdtex2html sentencepiece accelerate
```
## 模型下载
modelscope API下载
```shell
pip install modelscope
```
```python
from modelscope import snapshot_download
model_dir = snapshot_download("ZhipuAI/chatglm3-6b", revision = "v1.0.0")
```
git下载
```shell
git lfs install
git clone https://www.modelscope.cn/ZhipuAI/chatglm3-6b.git
```
## 代码调用
可以通过如下代码调用 ChatGLM3-6B 模型来生成对话：
```python
from modelscope import AutoTokenizer, AutoModel, snapshot_download
model_dir = snapshot_download("ZhipuAI/chatglm3-6b", revision = "v1.0.0")
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
model = AutoModel.from_pretrained(model_dir, trust_remote_code=True).half().cuda()
model = model.eval()
response, history = model.chat(tokenizer, "你好", history=[])
print(response)
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， ， Github Repo ](https://github.com/THUDM/ChatGLM )。
For more instructions, including how to run CLI and web demos, and model quantization, please refer to our [Github Repo ](https://github.com/THUDM/ChatGLM ).
## 协议
本仓库的代码依照 [Apache-2.0 ](LICENSE ) 协议开源， Model License ](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。
```
@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}
}
```
