zai-org/GLM-V

GLM-4.6V/4.5V/4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

What it solves

GLM-V is a series of vision-language models (VLMs) designed to move beyond basic multimodal perception toward complex reasoning. It addresses the need for AI systems that can accurately solve complex problems, understand long-context visual information, and act as intelligent multimodal agents capable of interacting with digital interfaces.

How it works

The project provides several model versions, including the high-performance GLM-4.6V (106B) and the lightweight GLM-4.6V-Flash (9B), as well as reasoning-focused models like GLM-4.1V-9B-Thinking. These models utilize a Chain-of-Thought reasoning mechanism and Reinforcement Learning with Curriculum Sampling (RLCS) to improve accuracy and interpretability. They are integrated into the Hugging Face Transformers library and support deployment via SGLang, vLLM, and Transformers.

Who it’s for

This project is for developers and researchers building multimodal applications, such as GUI agents, desktop assistants, and tools for complex document analysis or visual grounding.

Highlights

  • Native Multimodal Function Calling: Allows models to use tools directly with images and screenshots as inputs without converting them to text.
  • Interleaved Image-Text Generation: Synthesizes coherent content combining both text and visuals based on multimodal context.
  • Visual Grounding: Precisely identifies and locates objects within an image using bounding boxes.
  • Multimodal Document Understanding: Processes up to 128K tokens of multi-document input, interpreting layout, charts, and tables as images.
  • Frontend Replication: Reconstructs pixel-accurate HTML/CSS from UI screenshots and supports natural-language edits.

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