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 this https URL and
this https URL

介绍了 ChatGLM 的发展历程以及 GLM-4 系列语言模型的特点和优势，包括它们通过多阶段的预训练过程实现高质量的对齐，并且在多项评估中表现出与 GPT-4 相媲美甚至超越的性能，同时还介绍了一系列开源模型所取得的成功成果。

ChatGLM: 从 GLM-130B 到 GLM-4 的大型语言模型工具家族

ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All  Tools

Large language models (LLMs) have showcased profound capabilities in language
understanding and generation, facilitating a wide array of applications.
However, there is a notable paucity of detailed, open-sourced methodologies on
efficiently scaling LLMs beyond 50 billion parameters with minimum
trial-and-error cost and computational resources. In this report, we introduce
Tele-FLM (aka FLM-2), a 52B open-sourced multilingual large language model that
features a stable, efficient pre-training paradigm and enhanced factual
judgment capabilities. Tele-FLM demonstrates superior multilingual language
modeling abilities, measured by BPB on textual corpus. Besides, in both English
and Chinese foundation model evaluation, it is comparable to strong
open-sourced models that involve larger pre-training FLOPs, such as Llama2-70B
and DeepSeek-67B. In addition to the model weights, we share the core designs,
engineering practices, and training details, which we expect to benefit both
the academic and industrial communities.

大语言模型的扩展和效率提升方法的开放资源，以及展示优于其他模型的跨语言语言建模能力和核心设计。