The shortest path to running this model is by activating Hyper-V features.
Go through the configuration rules shown below.
All large files and heavy weights are downloaded automatically by the script.
Once launched, the wizard detects your specs to configure the model for maximum efficiency.
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.
| Specification | Detail |
|---|---|
| Total Parameters | 0.9 Billion |
| Visual Encoder | CogViT (400M) |
| Language Decoder | GLM-0.5B (500M) |
| Output Formats | Markdown, JSON, LaTeX |
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