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💻 Github Repo • 🐦 Twitter • 📃 [GLM@ACL 22] [GitHub] • 📃 [GLM-130B@ICLR 23] [GitHub]

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介绍 (Introduction)

ChatGLM3-6B-128K在ChatGLM3-6B的基础上进一步强化了对于长文本的理解能力,能够更好的处理最多128K长度的上下文。具体地,我们对位置编码进行了更新,并设计了更有针对性的长文本训练方法,在对话阶段使用 128K 的上下文长度训练。在实际的使用中,如果您面临的上下文长度基本在 8K 以内,我们推荐使用ChatGLM3-6B;如果您需要处理超过 8K 的上下文长度,我们推荐使用ChatGLM3-6B-128K。

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:

  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。
  2. 更完整的功能支持: ChatGLM3-6B 采用了全新设计的 Prompt 格式,除正常的多轮对话外。同时原生支持工具调用(Function Call)、代码执行(Code Interpreter)和 Agent 任务等复杂场景。
  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-128K。以上所有权重对学术研究完全开放,在填写问卷进行登记后亦允许免费商业使用

Based on ChatGLM3-6B, ChatGLM3-6B-128K further strengthens the ability to understand long texts and can better handle contexts up to 128K in length. Specifically, we update the position encoding and design a more targeted long text training method, using a context length of 128K for training in the conversation stage. In actual use, if the context length you face is basically within 8K, we recommend using ChatGLM3-6B; if you need to handle For context lengths exceeding 8K, we recommend using ChatGLM3-6B-128K.

ChatGLM3-6B is the latest open-source model in the ChatGLM series. While retaining many excellent features such as smooth dialogue and low deployment threshold from the previous two generations, ChatGLM3-6B introduces the following features:

  1. More Powerful Base Model: The base model of ChatGLM3-6B, ChatGLM3-6B-Base, employs a more diverse training dataset, more sufficient training steps, and a more reasonable training strategy. Evaluations on datasets such as semantics, mathematics, reasoning, code, knowledge, etc., show that ChatGLM3-6B-Base has the strongest performance among pre-trained models under 10B.
  2. More Comprehensive Function Support: ChatGLM3-6B adopts a newly designed Prompt format, in addition to the normal multi-turn dialogue. It also natively supports function call, code interpreter, and complex scenarios such as agent tasks.
  3. More Comprehensive Open-source Series: In addition to the dialogue model ChatGLM3-6B, the base model ChatGLM-6B-Base and the long-text dialogue model ChatGLM3-6B-128K are also open-sourced. All the weights are fully open for academic research, and after completing the questionnaire registration, they are also allowed for free commercial use.

软件依赖 (Dependencies)

pip install protobuf transformers==4.30.2 cpm_kernels torch>=2.0 gradio mdtex2html sentencepiece accelerate

代码调用 (Code Usage)

可以通过如下代码调用 ChatGLM3-6B 模型来生成对话:

>>> from transformers import AutoTokenizer, AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm3-6b-128k", trust_remote_code=True)
>>> model = AutoModel.from_pretrained("THUDM/chatglm3-6b-128k", trust_remote_code=True).half().cuda()
>>> model = model.eval()
>>> response, history = model.chat(tokenizer, "你好", history=[])
>>> print(response)
你好👋!我是人工智能助手 ChatGLM-6B,很高兴见到你,欢迎问我任何问题。
>>> response, history = model.chat(tokenizer, "晚上睡不着应该怎么办", history=history)
>>> print(response)

1. 制定规律的睡眠时间表:保持规律的睡眠时间表可以帮助你建立健康的睡眠习惯,使你更容易入睡。尽量在每天的相同时间上床,并在同一时间起床。
2. 创造一个舒适的睡眠环境:确保睡眠环境舒适,安静,黑暗且温度适宜。可以使用舒适的床上用品,并保持房间通风。
3. 放松身心:在睡前做些放松的活动,例如泡个热水澡,听些轻柔的音乐,阅读一些有趣的书籍等,有助于缓解紧张和焦虑,使你更容易入睡。
4. 避免饮用含有咖啡因的饮料:咖啡因是一种刺激性物质,会影响你的睡眠质量。尽量避免在睡前饮用含有咖啡因的饮料,例如咖啡,茶和可乐。
5. 避免在床上做与睡眠无关的事情:在床上做些与睡眠无关的事情,例如看电影,玩游戏或工作等,可能会干扰你的睡眠。
6. 尝试呼吸技巧:深呼吸是一种放松技巧,可以帮助你缓解紧张和焦虑,使你更容易入睡。试着慢慢吸气,保持几秒钟,然后缓慢呼气。


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.

协议 (License)

本仓库的代码依照 Apache-2.0 协议开源,ChatGLM3-6B 模型的权重的使用则需要遵循 Model License

The code in this repository is open-sourced under the Apache-2.0 license, while the use of the ChatGLM3-6B model weights needs to comply with the Model License.

引用 (Citation)


If you find our work helpful, please consider citing the following papers.

  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},
  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)},
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