ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All
  Tools
Authors: name: Team GLM, name: Aohan Zeng, name: Bin Xu, name: Bowen Wang, name: Chenhui Zhang, name: Da Yin, name: Diego Rojas, name: Guanyu Feng, name: Hanlin Zhao, name: Hanyu Lai, name: Hao Yu, name: Hongning Wang, name: Jiadai Sun, name: Jiajie Zhang, name: Jiale Cheng, name: Jiayi Gui, name: Jie Tang, name: Jing Zhang, name: Juanzi Li, name: Lei Zhao, name: Lindong Wu, name: Lucen Zhong, name: Mingdao Liu, name: Minlie Huang, name: Peng Zhang, name: Qinkai Zheng, name: Rui Lu, name: Shuaiqi Duan, name: Shudan Zhang, name: Shulin Cao, name: Shuxun Yang, name: Weng Lam Tam, name: Wenyi Zhao, name: Xiao Liu, name: Xiao Xia, name: Xiaohan Zhang, name: Xiaotao Gu, name: Xin Lv, name: Xinghan Liu, name: Xinyi Liu, name: Xinyue Yang, name: Xixuan Song, name: Xunkai Zhang, name: Yifan An, name: Yifan Xu, name: Yilin Niu, name: Yuantao Yang, name: Yueyan Li, name: Yushi Bai, name: Yuxiao Dong, name: Zehan Qi, name: Zhaoyu Wang, name: Zhen Yang, name: Zhengxiao Du, name: Zhenyu Hou, name: Zihan Wang

We introduce ChatGLM, an evolving family of large language models that we
have been developing over time. This report primarily focuses on the GLM-4
language series, which includes GLM-4, GLM-4-Air, and GLM-4-9B. They represent
our most capable models that are trained with all the insights and lessons
gained from the preceding three generations of ChatGLM. To date, the GLM-4
models are pre-trained on ten trillions of tokens mostly in Chinese and
English, along with a small set of corpus from 24 languages, and aligned
primarily for Chinese and English usage. The high-quality alignment is achieved
via a multi-stage post-training process, which involves supervised fine-tuning
and learning from human feedback. Evaluations show that GLM-4 1) closely rivals
or outperforms GPT-4 in terms of general metrics such as MMLU, GSM8K, MATH,
BBH, GPQA, and HumanEval, 2) gets close to GPT-4-Turbo in instruction following
as measured by IFEval, 3) matches GPT-4 Turbo (128K) and Claude 3 for long
context tasks, and 4) outperforms GPT-4 in Chinese alignments as measured by
AlignBench. The GLM-4 All Tools model is further aligned to understand user
intent and autonomously decide when and which tool(s) touse -- including web
browser, Python interpreter, text-to-image model, and user-defined functions --
to effectively complete complex tasks. In practical applications, it matches
and even surpasses GPT-4 All Tools in tasks like accessing online information
via web browsing and solving math problems using Python interpreter. Over the
course, we have open-sourced a series of models, including ChatGLM-6B (three
generations), GLM-4-9B (128K, 1M), GLM-4V-9B, WebGLM, and CodeGeeX, attracting
over 10 million downloads on Hugging face in the year 2023 alone. The open
models can be accessed through https://github.com/THUDM and
https://huggingface.co/THUDM.