nomagick / chatglm2-6b

ChatGLM2-6B: An Open Bilingual Chat LLM | 开源双语对话语言模型

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Replicate cog image author note:

This is an unofficial fork of the ChatGLM2-6B repo.
LICENSE: The model weights is only licensed for research purposes by default.
You may apply for commercial license HERE (Chinese)

The code had been patched to accept a single prompt input.
In order to stay “on-distribution” and get the expected results. You need to construct the proper prompt.

For single round conversation, the prompt should look like:

[Round 1]

问：User input

答：

For multi round conversations, increase the Round number :

[Round 1]

问：User input

答：Assistant response

[Round 2]

问：User input

答：

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

👋 Join our Slack and WeChat

Introduction

ChatGLM2-6B is the second-generation version of the open-source bilingual (Chinese-English) chat model ChatGLM-6B. It retains the smooth conversation flow and low deployment threshold of the first-generation model, while introducing the following new features:

  1. Stronger Performance: Based on the development experience of the first-generation ChatGLM model, we have fully upgraded the base model of ChatGLM2-6B. ChatGLM2-6B uses the hybrid objective function of GLM, and has undergone pre-training with 1.4T bilingual tokens and human preference alignment training. The evaluation results show that, compared to the first-generation model, ChatGLM2-6B has achieved substantial improvements in performance on datasets like MMLU (+23%), CEval (+33%), GSM8K (+571%), BBH (+60%), showing strong competitiveness among models of the same size.
  2. Longer Context: Based on FlashAttention technique, we have extended the context length of the base model from 2K in ChatGLM-6B to 32K, and trained with a context length of 8K during the dialogue alignment, allowing for more rounds of dialogue. However, the current version of ChatGLM2-6B has limited understanding of single-round ultra-long documents, which we will focus on optimizing in future iterations.
  3. More Efficient Inference: Based on Multi-Query Attention technique, ChatGLM2-6B has more efficient inference speed and lower GPU memory usage: under the official implementation, the inference speed has increased by 42% compared to the first generation; under INT4 quantization, the dialogue length supported by 6G GPU memory has increased from 1K to 8K.
  4. More Open License: ChatGLM2-6B weights are completely open for academic research, and free commercial use is also allowed after completing the questionnaire.

The open-source ChatGLM2-6B is intended to promote the development of LLMs together with the open-source community. We earnestly request developers and everyone to abide by the open-source license. Do not use the open-source model, code, or any derivatives from the open-source project for any purposes that may harm nations or societies, or for any services that have not undergone safety assessments and legal approval. At present, our project team has not developed any applications based on ChatGLM2-6B, including web, Android, Apple iOS, and Windows App applications.

Although the model strives to ensure the compliance and accuracy of data at each stage of training, due to the smaller scale of the ChatGLM2-6B model, and its susceptibility to probabilistic randomness, the accuracy of output content cannot be guaranteed, and the model can easily be misled. Our project does not assume any risks or responsibilities arising from data security, public opinion risks, or any instances of the model being misled, abused, disseminated, or improperly used due to the open-source model and code.

Getting Started

Environment Setup

Install dependencies with pip: pip install -r requirements.txt. It’s recommended to use version 4.27.1 for the transformers library and use version 2.0 or higher for torch to achieve the best inference performance.

We provide a web page demo and a command line demo. You need to download this repository to use them:

git clone https://github.com/THUDM/ChatGLM2-6B
cd ChatGLM2-6B

Usage

Generate dialogue with the following code:

>>> from transformers import AutoTokenizer, AutoModel
>>> tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
>>> model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True, device='cuda').eval()
>>> response, history = model.chat(tokenizer, "你好", history=[])
>>> print(response)
你好👋!我是人工智能助手 ChatGLM2-6B,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)
晚上睡不着可能会让你感到焦虑或不舒服,但以下是一些可以帮助你入睡的方法:

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。

如果这些方法无法帮助你入睡,你可以考虑咨询医生或睡眠专家,寻求进一步的建议。

The implementation of the model is still in development. If you want to fix the used model implementation to ensure compatibility, you can add the revision="v1.0" parameter in the from_pretrained call. v1.0 is the latest version number. For a complete list of versions, see Change Log.

License

The code of this repository is licensed under Apache-2.0. The use of the ChatGLM2-6B model weights is subject to the Model License. ChatGLM2-6B weights are completely open for academic research, and commercial use is also allowed after obtaining official written permission. If you find our open source model useful for your business, we welcome your donations towards the development of the next generation model, ChatGLM3. For related matters, please contact yiwen.xu@zhipuai.cn.

Citation

If you find our work useful, please consider citing the following papers. The technical report for ChatGLM2-6B will be out soon.

@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}
}