98 lines
4.4 KiB
Markdown
98 lines
4.4 KiB
Markdown
---
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language:
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- zh
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- en
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tags:
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- glm
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- chatglm
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- thudm
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---
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# ChatGLM3-6B
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<p align="center">
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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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</p>
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<p align="center">
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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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</p>
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<p align="center">
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📍Experience the larger-scale ChatGLM model at <a href="https://www.chatglm.cn">chatglm.cn</a>
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</p>
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## 介绍
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ChatGLM3-6B 是 ChatGLM 系列最新一代的开源模型，在保留了前两代模型对话流畅、部署门槛低等众多优秀特性的基础上，ChatGLM3-6B 引入了如下特性：
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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，ChatGLM3-6B-Base 具有在 10B 以下的预训练模型中最强的性能。
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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)（Function Call）、代码执行（Code Interpreter）和 Agent 任务等复杂场景。
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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**。
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## 软件依赖
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```shell
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pip install protobuf 'transformers>=4.30.2' cpm_kernels 'torch>=2.0' gradio mdtex2html sentencepiece accelerate
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```
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## 模型下载
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modelscope API下载
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```shell
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pip install modelscope
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```
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```python
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from modelscope import snapshot_download
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model_dir = snapshot_download("ZhipuAI/chatglm3-6b", revision = "v1.0.0")
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```
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git下载
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```shell
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git lfs install
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git clone https://www.modelscope.cn/ZhipuAI/chatglm3-6b.git
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```
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## 代码调用
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可以通过如下代码调用 ChatGLM3-6B 模型来生成对话：
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```python
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from modelscope import AutoTokenizer, AutoModel, snapshot_download
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model_dir = snapshot_download("ZhipuAI/chatglm3-6b", revision = "v1.0.0")
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tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
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model = AutoModel.from_pretrained(model_dir, trust_remote_code=True).half().cuda()
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model = model.eval()
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response, history = model.chat(tokenizer, "你好", history=[])
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print(response)
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response, history = model.chat(tokenizer, "晚上睡不着应该怎么办", history=history)
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print(response)
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```
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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](https://github.com/THUDM/ChatGLM)。
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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).
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## 协议
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本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源，ChatGLM3-6B 模型的权重的使用则需要遵循 [Model License](MODEL_LICENSE)。
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## 引用
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如果你觉得我们的工作有帮助的话，请考虑引用下列论文。
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```
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@article{zeng2022glm,
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title={Glm-130b: An open bilingual pre-trained model},
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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},
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journal={arXiv preprint arXiv:2210.02414},
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year={2022}
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}
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```
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```
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@inproceedings{du2022glm,
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title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling},
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author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie},
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booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
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pages={320--335},
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year={2022}
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}
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``` |