zai-org/GLM-Z1-Rumination-32B-0414
TEXT GENERATIONConcurrency Cost:2Model Size:32BQuant:FP8Ctx Length:32kPublished:Apr 13, 2025License:mitArchitecture:Transformer0.1K Open Weights Cold

THUDM/GLM-Z1-Rumination-32B-0414 is a 32 billion parameter model from the GLM-4 family, developed by THUDM, designed for deep reasoning and complex problem-solving. It is specifically trained with "rumination capabilities" to handle open-ended tasks, integrating search tools during its thought process. This model excels in research-style writing, complex retrieval tasks, and function calling, building upon a base pre-trained on 15T high-quality data.

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Overview

THUDM/GLM-Z1-Rumination-32B-0414 is a 32 billion parameter model from the GLM-4 series, developed by THUDM. It is an advanced reasoning model with "rumination capabilities," distinguishing it from typical deep thinking models by employing longer periods of deep thought to tackle open-ended and complex problems. This model integrates search tools during its deep thinking process and is trained using multiple rule-based rewards to guide and extend end-to-end reinforcement learning.

Key Capabilities

  • Deep Reasoning and Rumination: Designed for complex, open-ended problems, such as comparative analysis and future development plans, by simulating extended deep thought processes.
  • Function Calling: Supports built-in functions like search, click, open, and finish to facilitate information gathering and task completion.
  • Enhanced Performance: Shows significant improvements in research-style writing and complex retrieval tasks, with some benchmarks rivaling larger models like GPT-4o and DeepSeek-V3-0324.
  • Robust Training: Built upon GLM-4-32B-0414, which was pre-trained on 15T of high-quality data, including extensive reasoning-type synthetic data. Further enhanced with reinforcement learning for mathematics, code, and logic tasks.

Good For

  • Research-style writing: Generating comprehensive analyses and reports.
  • Complex retrieval tasks: Utilizing integrated search tools to gather and synthesize information.
  • Agent tasks: Leveraging enhanced instruction following, engineering code, and function calling capabilities.
  • Problem-solving: Addressing open-ended and intricate challenges requiring deep thought.