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GLM-4-32B - Zhipu Open Source's next-generation base model

GLM-4-32B is a new generation of pedestal model open-sourced by Zhipu Technology, with parameter version GLM-4-32B-0414. GLM-4-32B has been pre-trained on 15TB of high-quality data, enhancing its code generation, inference, and engineering task capabilities, and supports HTML, CS...

What is GLM-4-32B?

GLM-4-32B is a new generation of open-source pedestal model from Z.ai, with parameter version GLM-4-32B-0414. GLM-4-32B is pre-trained on 15TB of high-quality data, enhancing its code generation, inference, and engineering task capabilities. It supports real-time code display and execution in languages such as HTML, CSS, JS, and SVG. Its performance rivals mainstream models with larger parameter sets, such as GPT-4o and DeepSeek-V3-0324 (671B). It also adheres to the MIT License, is completely open-source, and does not restrict commercial use, allowing users to experience the model's powerful features for free on the Z.ai platform.

Main functions of GLM-4-32B

  • powerful language generation capabilitiesIt supports generating natural and fluent text, and supports multiple language styles and scenarios, such as dialogue, writing, and translation.
  • Code generation and optimizationIt supports code generation in languages such as HTML, CSS, JavaScript, and SVG, and allows real-time display of code execution results in the chat, making it convenient for users to modify and adjust.
  • Reasoning and Logic TasksIt performs well in mathematical and logical reasoning tasks, and supports the handling of complex reasoning problems.
  • Multimodal supportIt supports generating and parsing content in various formats, such as HTML pages and SVG graphics, to meet diverse application scenarios.

Technical Principles of GLM-4-32B

  • Large-scale pre-trainingThe model is based on 32 billion parameters and pre-trained on 15T of high-quality data, including text, code, and reasoning data, providing a broad knowledge base for the model.
  • Reinforcement learning optimizationBased on pre-training, the performance of the model is further optimized using reinforcement learning techniques, especially in instruction following, code generation, and inference tasks.
  • Reject Sampling and AlignmentBased on rejection sampling techniques to remove low-quality generated results, and combined with human preference alignment, the model's output conforms to human language habits and logical thinking.
  • High-efficiency reasoning frameworkOptimizes inference speed and efficiency by using techniques such as quantization and speculative sampling to reduce memory pressure, improve inference speed, and achieve an ultra-fast response of 200 tokens per second.
  • Multi-task learningThe model learns multiple tasks simultaneously during training, including language generation, code generation, and reasoning, and has broad versatility and adaptability.

Project address for GLM-4-32B

Application scenarios of GLM-4-32B

  • Intelligent ProgrammingGenerates and optimizes code, supports multiple programming languages, and helps developers quickly complete programming tasks.
  • Content creationGenerate multimodal content such as text, web pages, and SVG graphics to support creative writing and design.
  • Smart OfficeIt can automatically generate reports and scripts, automate tasks, and improve work efficiency.
  • Education and LearningIt provides programming examples and answers to questions to assist in teaching and learning.
  • Enterprise ApplicationsUsed in intelligent customer service and data analysis to support enterprise decision-making and service optimization.
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