zai-org/GLM-Z1-9B-0414

TEXT GENERATIONConcurrency Cost:1Model Size:9BQuant:FP8Ctx Length:32kTool Calling:SupportedPublished:Apr 8, 2025License:mitArchitecture:Transformer0.1K Open Weights Cold

The zai-org/GLM-Z1-9B-0414 is a 9 billion parameter model from the GLM family, developed by zai-org, designed for mathematical reasoning and general tasks. It is a smaller-scale model trained with techniques from the GLM-4-32B-0414 series, including reinforcement learning and specialized training on mathematics, code, and logic. This model offers a balance of efficiency and effectiveness, making it suitable for resource-constrained environments while maintaining strong performance in complex problem-solving.

Loading preview...

GLM-Z1-9B-0414 Overview

GLM-Z1-9B-0414 is a 9 billion parameter model within the GLM family, developed by zai-org. It is a compact yet powerful model that inherits advanced training methodologies from its larger 32 billion parameter counterparts, including GLM-4-32B-0414 and GLM-Z1-32B-0414. The model was specifically trained with a focus on enhancing mathematical reasoning and general task capabilities, leveraging techniques such as cold start, extended reinforcement learning, and specialized training on mathematics, code, and logic tasks.

Key Capabilities

  • Mathematical Reasoning: Exhibits excellent performance in solving complex mathematical problems.
  • General Tasks: Strong capabilities across a range of general language understanding and generation tasks.
  • Efficiency: Designed for lightweight deployment, offering a balance between performance and resource consumption.
  • Agent Task Foundation: Benefits from training enhancements in instruction following, engineering code, and function calling, which are crucial for agent-based applications.

Good For

  • Resource-Constrained Scenarios: Ideal for environments where computational resources are limited but strong reasoning capabilities are still required.
  • Mathematical Problem Solving: Developers needing a model proficient in mathematical tasks.
  • Agent Development: Applications requiring robust instruction following and function calling for agentic workflows.
  • Complex Task Handling: Scenarios demanding deep thinking and logical problem-solving, even at a smaller scale.

Finetunes
3 models