Docker offers the quickest path to setting up this model locally.

Please follow the instructions listed below to get started.

The setup auto-streams the model assets (expect a multi-GB download).

The deployment tool scans your environment and automatically chooses the ideal parameters for your OS.

? Hash: f073c6b28d27e05b3b0bde1adc7030c8 • Last Updated: 2026-06-25 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: 64 GB to avoid OOM crashes on large contexts Disk Space: at least 100 GB for multiple local LLM variants GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

GLM-OCR is a lightweight vision-language model tailored specifically for advanced document understanding and structure preservation. The architecture integrates a 400M parameter CogViT visual encoder alongside a compact 500M parameter GLM language decoder to maximize layout analysis precision. Unlike classic character recognition engines, this framework introduces an innovative Multi-Token Prediction (MTP) loss mechanism to increase decoding throughput substantially while lowering system memory demands. It effortlessly reconstructs intricate multilingual tables, LaTeX formulas, and handwritten text into semantic Markdown or structured JSON outputs. The compact blueprint allows for highly accurate, state-of-the-art multi-page processing directly within resource-constrained edge computing environments.

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