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。
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。
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，在填写问卷进行登记后亦允许免费商业使用。

本仓库为 ChatGLM3-6B 的基础模型 ChatGLM3-6B-Base。

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:

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.
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.
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-32K 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.

This repo is ChatGLM3-6B-Base, the base model of ChatGLM3-6B.

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)

作为没有经过人类意图对齐的模型，ChatGLM3-6B-Base 不能用于多轮对话。但是可以进行文本续写。

As a model that has not been aligned with human intent, ChatGLM3-6B-Base cannot be used for multi-turn conversations. However, text completion is possible.

python

from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm3-6b-base", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/chatglm3-6b-base", trust_remote_code=True).half().cuda()

inputs = tokenizer(["今天天气真不错"], return_tensors="pt").to('cuda')
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0].tolist()))

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)

如果你觉得我们的工作有帮助的话，请考虑引用下列论文。

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

@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}
}

grim3000/chatGLM3-6B-Base

ChatGLM3-6B-Base

介绍 (Introduction)

软件依赖 (Dependencies)

代码调用 (Code Usage)

协议 (License)

引用 (Citation)

grim3000/
chatGLM3-6B-Base