title: ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All
    Tools
summary: 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.
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paper_title: ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All
    Tools
paper_summary: 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.
paper_ai_keywords: large language models ChatGLM GLM-4 series GLM-4 GLM-4-Air GLM-4-9B pre-trained tokens corpus Chinese English multi-stage post-training process supervised fine-tuning learning from human feedback MMLU superiorK MATH BBH GPQA HumanEval instruction following IFEval long context handling AlignBench GLM-4 All Tools tool alignment web browser Python interpreter text-to-image model user-defined functions complex task completion open-sourced models ChatGLM-3 (three generations) GLM-4-9B (128K, 1M) GLM-4V-9B