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README.md
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README.md
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# GLM-4
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<p align="center">
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🤗 <a href="https://huggingface.co/collections/THUDM/glm-4-665fcf188c414b03c2f7e3b7" target="_blank">HF Repo</a> • 🤖 <a href="https://modelscope.cn/models/ZhipuAI/glm-4-9b-chat" target="_blank">ModelScope</a> • 🟣 <a href="https://wisemodel.cn/models/ZhipuAI/glm-4-9b-chat" target="_blank">WiseModel</a> • 🐦 <a href="https://twitter.com/thukeg" target="_blank">Twitter</a> • 👋 加入我们的 <a href="https://discord.gg/fK2dz4bg" target="_blank">Discord</a> 和 <a href="resources/WECHAT.md" target="_blank">微信</a>
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📄<a href="https://arxiv.org/pdf/2406.12793" target="_blank"> Report </a> • 🤗 <a href="https://huggingface.co/collections/THUDM/glm-4-665fcf188c414b03c2f7e3b7" target="_blank">HF Repo</a> • 🤖 <a href="https://modelscope.cn/models/ZhipuAI/glm-4-9b-chat" target="_blank">ModelScope</a> • 🟣 <a href="https://wisemodel.cn/models/ZhipuAI/glm-4-9b-chat" target="_blank">WiseModel</a> • 🐦 <a href="https://twitter.com/thukeg" target="_blank">Twitter</a> • 👋 加入我们的 <a href="https://discord.gg/fK2dz4bg" target="_blank">Discord</a> 和 <a href="resources/WECHAT.md" target="_blank">微信</a>
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</p>
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<p align="center">
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📍在 <a href="https://open.bigmodel.cn/?utm_campaign=open&_channel_track_key=OWTVNma9">智谱AI开放平台</a> 体验和使用更大规模的 GLM 商业模型。
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Read this in [English](README_en.md)
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## 项目更新
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- 🔥🔥 **News**: ``2024/6/19``: 我们更新了模型仓库的运行文件和配置文件，修复了部分已知的模型推理的问题，欢迎大家克隆最新的模型仓库。
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- 🔥 **News**: ``2024/6/18``: 我们发布 [技术报告](https://arxiv.org/pdf/2406.12793), 欢迎查看。
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- 🔥 **News**: ``2024/6/05``: 我们发布 GLM-4-9B 系列开源模型
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## 模型介绍
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GLM-4-9B 是智谱 AI 推出的最新一代预训练模型 GLM-4 系列中的开源版本。 在语义、数学、推理、代码和知识等多方面的数据集测评中，
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@ -257,22 +263,13 @@ with torch.no_grad():
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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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@inproceedings{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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booktitle={The Eleventh International Conference on Learning Representations,
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{ICLR} 2023, Kigali, Rwanda, May 1-5, 2023},
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year= {2023},
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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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@misc{glm2024chatglm,
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title={ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools},
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author={Team GLM and : and Aohan Zeng and Bin Xu and Bowen Wang and Chenhui Zhang and Da Yin and Diego Rojas and Guanyu Feng and Hanlin Zhao and Hanyu Lai and Hao Yu and Hongning Wang and Jiadai Sun and Jiajie Zhang and Jiale Cheng and Jiayi Gui and Jie Tang and Jing Zhang and Juanzi Li and Lei Zhao and Lindong Wu and Lucen Zhong and Mingdao Liu and Minlie Huang and Peng Zhang and Qinkai Zheng and Rui Lu and Shuaiqi Duan and Shudan Zhang and Shulin Cao and Shuxun Yang and Weng Lam Tam and Wenyi Zhao and Xiao Liu and Xiao Xia and Xiaohan Zhang and Xiaotao Gu and Xin Lv and Xinghan Liu and Xinyi Liu and Xinyue Yang and Xixuan Song and Xunkai Zhang and Yifan An and Yifan Xu and Yilin Niu and Yuantao Yang and Yueyan Li and Yushi Bai and Yuxiao Dong and Zehan Qi and Zhaoyu Wang and Zhen Yang and Zhengxiao Du and Zhenyu Hou and Zihan Wang},
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year={2024},
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eprint={2406.12793},
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archivePrefix={arXiv},
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primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
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}
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```
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README_en.md
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README_en.md
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@ -1,13 +1,18 @@
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# GLM-4
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<p align="center">
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🤗 <a href="https://huggingface.co/collections/THUDM/glm-4-665fcf188c414b03c2f7e3b7" target="_blank">HF Repo</a> • 🤖 <a href="https://modelscope.cn/models/ZhipuAI/glm-4-9b-chat" target="_blank">ModelScope</a> • 🟣 <a href="https://wisemodel.cn/models/ZhipuAI/glm-4-9b-chat" target="_blank">WiseModel</a> • 🐦 <a href="https://twitter.com/thukeg" target="_blank">Twitter</a> • 👋 Join <a href="https://discord.gg/fK2dz4bg" target="_blank">Discord</a> and <a href="resources/WECHAT.md" target="_blank">WeChat</a>
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📄<a href="https://arxiv.org/pdf/2406.12793" target="_blank"> Report </a> • 🤗 <a href="https://huggingface.co/collections/THUDM/glm-4-665fcf188c414b03c2f7e3b7" target="_blank">HF Repo</a> • 🤖 <a href="https://modelscope.cn/models/ZhipuAI/glm-4-9b-chat" target="_blank">ModelScope</a> • 🟣 <a href="https://wisemodel.cn/models/ZhipuAI/glm-4-9b-chat" target="_blank">WiseModel</a> • 🐦 <a href="https://twitter.com/thukeg" target="_blank">Twitter</a> • 👋 Join <a href="https://discord.gg/fK2dz4bg" target="_blank">Discord</a> and <a href="resources/WECHAT.md" target="_blank">WeChat</a>
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</p>
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<p align="center">
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📍Experience and use a larger-scale GLM business model on the <a href="https://open.bigmodel.cn/?utm_campaign=open&_channel_track_key=OWTVNma9">Zhipu AI Open Platform</a>
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</p>
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## Update
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- 🔥🔥 **News**: ``2024/6/19``: We updated the running files and configuration files of the model repository and fixed some model inference issues. Welcome to clone the latest model repository.
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- 🔥 **News**: ``2024/6/18``: We released a [technical report](https://arxiv.org/pdf/2406.12793), welcome to check it out.
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- 🔥 **News**: ``2024/6/05``: We released the GLM-4-9B series of open source models
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## Model Introduction
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GLM-4-9B is the open-source version of the latest generation of pre-trained models in the GLM-4 series launched by Zhipu
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@ -272,22 +277,13 @@ Please strictly follow the open source license.
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If you find our work helpful, please consider citing the following paper.
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```
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@inproceedings{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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booktitle={The Eleventh International Conference on Learning Representations,
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{ICLR} 2023, Kigali, Rwanda, May 1-5, 2023},
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year= {2023},
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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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@misc{glm2024chatglm,
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title={ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools},
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author={Team GLM and : and Aohan Zeng and Bin Xu and Bowen Wang and Chenhui Zhang and Da Yin and Diego Rojas and Guanyu Feng and Hanlin Zhao and Hanyu Lai and Hao Yu and Hongning Wang and Jiadai Sun and Jiajie Zhang and Jiale Cheng and Jiayi Gui and Jie Tang and Jing Zhang and Juanzi Li and Lei Zhao and Lindong Wu and Lucen Zhong and Mingdao Liu and Minlie Huang and Peng Zhang and Qinkai Zheng and Rui Lu and Shuaiqi Duan and Shudan Zhang and Shulin Cao and Shuxun Yang and Weng Lam Tam and Wenyi Zhao and Xiao Liu and Xiao Xia and Xiaohan Zhang and Xiaotao Gu and Xin Lv and Xinghan Liu and Xinyi Liu and Xinyue Yang and Xixuan Song and Xunkai Zhang and Yifan An and Yifan Xu and Yilin Niu and Yuantao Yang and Yueyan Li and Yushi Bai and Yuxiao Dong and Zehan Qi and Zhaoyu Wang and Zhen Yang and Zhengxiao Du and Zhenyu Hou and Zihan Wang},
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year={2024},
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eprint={2406.12793},
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archivePrefix={arXiv},
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primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
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}
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```
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@ -151,7 +151,7 @@ def process_response(output: str, tools: dict | List[dict] = None, use_tool: boo
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lines = output.strip().split("\n")
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arguments_json = None
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special_tools = ["cogview", "simple_browser"]
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tools = {tool['function']['name'] for tool in tools}
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tools = {tool['function']['name'] for tool in tools} if tools else {}
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# 这是一个简单的工具比较函数，不能保证拦截所有非工具输出的结果，比如参数未对齐等特殊情况。
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##TODO 如果你希望做更多判断，可以在这里进行逻辑完善。
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function_name = None
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response_id = generate_id('chatcmpl-', 29)
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system_fingerprint = generate_id('fp_', 9)
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tools = {tool['function']['name'] for tool in gen_params['tools']} if gen_params['tools'] else None
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tools = {tool['function']['name'] for tool in gen_params['tools']} if gen_params['tools'] else {}
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async for new_response in generate_stream_glm4(gen_params):
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decoded_unicode = new_response["text"]
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delta_text = decoded_unicode[len(output):]
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