zai-org/GLM-4-32B-0414

Hugging Face
TEXT GENERATIONConcurrency Cost:2Model Size:32BQuant:FP8Ctx Length:32kTool Calling:SupportedPublished:Apr 7, 2025License:mitArchitecture:Transformer0.5K Open Weights Warm

The GLM-4-32B-0414 is a 32 billion parameter model from the GLM family, developed by zai-org, pre-trained on 15T high-quality data including reasoning-type synthetic data. It excels in instruction following, engineering code generation, function calling, search-based Q&A, and report generation, with performance comparable to larger models like GPT-4o and DeepSeek-V3-0324 on specific benchmarks. This model supports a 32K context length and is optimized for local deployment and agent tasks.

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GLM-4-32B-0414 Overview

The GLM-4-32B-0414 is a 32 billion parameter model from the GLM family, developed by zai-org, pre-trained on 15 trillion tokens of high-quality data, including significant reasoning-type synthetic data. It features a 32K context length and is designed for robust performance in various applications, with a focus on local deployment.

Key Capabilities & Differentiators

  • Enhanced Instruction Following: Improved through human preference alignment, rejection sampling, and reinforcement learning.
  • Strong Engineering Code & Function Calling: Excels in generating engineering code and performing function calls, crucial for agent tasks.
  • Advanced Reasoning: The base model lays the foundation for specialized reasoning variants like GLM-Z1-32B-0414 (deep thinking, math, complex tasks) and GLM-Z1-Rumination-32B-0414 (deeper, longer thinking with search tools).
  • Competitive Performance: Achieves strong results in engineering code, artifact generation, search-based Q&A, and report generation, with benchmarks showing comparable performance to larger models such as GPT-4o and DeepSeek-V3-0324 in specific areas.
  • Tool Use: Supports external tool calling in JSON format, demonstrated with examples for HuggingFace Transformers, vLLM, and sgLang.

Ideal Use Cases

  • Agent Task Development: Its atomic capabilities in instruction following and function calling make it suitable for building AI agents.
  • Code Generation: Particularly strong in engineering code generation.
  • Complex Q&A and Report Generation: Excels in search-based question answering and detailed report creation.
  • Resource-Constrained Environments: The GLM-Z1-9B-0414 variant offers excellent performance for its size, balancing efficiency and effectiveness for lightweight deployment.