zai-org/GLM-4

zai-org/GLM-4

Description: GLM-4 series: Open Multilingual Multimodal Chat LMs | 开源多语言多模态对话模型

Language: Python

License: Apache-2.0

Stars: 7065

Forks: 616

Open issues: 38

Created: 2024-05-15T05:17:08Z

Pushed: 2025-07-04T04:16:36Z

Default branch: main

Fork: no

Archived: no

README:

GLM-4-0414 Model Series

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📍The open-source models released this time can be experienced for free at Z.ai; for GLM commercial model services, please visit bigmodel.cn.

Read this in [中文](README_zh.md)

Project Updates

• 🔥 News: ``2025/07/02``: We are releasing the GLM-4.1V-9B-Thinking series VLM, check this github repo to get more information.
• News: ``2025/04/14``: We are releasing the GLM-4-32B-0414 series models, scaled up to 32B parameters, including models with capabilities for dialogue, reasoning, and rumination.
• News: `2024/06/18`: We have released our Technical Report, feel free to check it out.
• News: `2024/06/05: We released the GLM-4-9B` series of open-source models. Details can be found [here](README_20240605.md).

Model Introduction

The GLM family welcomes new members, the GLM-4-32B-0414 series models, featuring 32 billion parameters. Its performance is comparable to OpenAI’s GPT series and DeepSeek’s V3/R1 series. It also supports very user-friendly local deployment features. GLM-4-32B-Base-0414 was pre-trained on 15T of high-quality data, including substantial reasoning-type synthetic data. This lays the foundation for subsequent reinforcement learning extensions. In the post-training stage, we employed human preference alignment for dialogue scenarios. Additionally, using techniques like rejection sampling and reinforcement learning, we enhanced the model’s performance in instruction following, engineering code, and function calling, thus strengthening the atomic capabilities required for agent tasks. GLM-4-32B-0414 achieves good results in engineering code, Artifact generation, function calling, search-based Q&A, and report generation. In particular, on several benchmarks, such as code generation or specific Q&A tasks, GLM-4-32B-Base-0414 achieves comparable performance with those larger models like GPT-4o and DeepSeek-V3-0324 (671B).

GLM-Z1-32B-0414 is a reasoning model with deep thinking capabilities. This was developed based on GLM-4-32B-0414 through cold start, extended reinforcement learning, and further training on tasks including mathematics, code, and logic. Compared to the base model, GLM-Z1-32B-0414 significantly improves mathematical abilities and the capability to solve complex tasks. During training, we also introduced general reinforcement learning based on pairwise ranking feedback, which enhances the model's general capabilities.

GLM-Z1-Rumination-32B-0414 is a deep reasoning model with rumination capabilities (against OpenAI's Deep Research). Unlike typical deep thinking models, the rumination model is capable of deeper and longer thinking to solve more open-ended and complex problems (e.g., writing a comparative analysis of AI development in two cities and their future development plans). Z1-Rumination is trained through scaling end-to-end reinforcement learning with responses graded by the ground truth answers or rubrics and can make use of search tools during its deep thinking process to handle complex tasks. The model shows significant improvements in research-style writing and complex tasks.

Finally, GLM-Z1-9B-0414 is a surprise. We employed all the aforementioned techniques to train a small model (9B). GLM-Z1-9B-0414 exhibits excellent capabilities in mathematical reasoning and general tasks. Its overall performance is top-ranked among all open-source models of the same size. Especially in resource-constrained scenarios, this model achieves an excellent balance between efficiency and effectiveness, providing a powerful option for users seeking lightweight deployment.

Showcase

Animation Generation

GLM-Z1-32B-0414

GLM-4-32B-0414

write a Python program that shows a ball bouncing inside a spinning hexagon. The ball should be affected by gravity and friction, and it must bounce off the rotating walls realistically

Use HTML to simulate the scenario of a small ball released from the center of a rotating hexagon. Consider the collision between the ball and the hexagon's edges, the gravity acting on the ball, and assume all collisions are perfectly elastic. (Prompt translated from Chinese)

Web Design

GLM-4-32B-0414

GLM-4-32B-0414

Design a drawing board that supports custom function plotting, allowing adding and deleting custom functions, and assigning colors to functions. (Prompt translated from Chinese)

Design a UI for a mobile machine learning platform, which should include interfaces for training tasks, storage management, and personal statistics. The personal statistics interface should use charts to display the user's resource usage over a period. Use Tailwind CSS to style the page, and display these 3 mobile interfaces tiled on a single HTML page. (Prompt translated from Chinese)

SVG Generation

GLM-4-32B-0414

GLM-4-32B-0414

Create a misty Jiangnan scene using SVG. (Prompt translated from Chinese)

Use SVG to illustrate the training process of an LLM. (Prompt translated from Chinese)

Analysis and Research Report Writing

Analysis of AI Development in Chinese Cities: A Comparative Study of Beijing and Hangzhou, Alongside an Investigation of International Cases of AI in Urban Governance. (Prompt translated from Chinese)

Model List

GLM-4-0414 Series Models

GLM-Z1-9B-0414 Open-Source Model Try it Online

| Model | Type | Seq Length* | Download | |:--------------------------:|:---------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | GLM-4-9B-0414 | Chat | 32K -> 128K | [🤗…