If you want the fastest local installation for this model, use standard pip packages.
Just follow the guidelines provided below.
All large files and heavy weights are downloaded automatically by the script.
To guarantee smooth performance, the process auto-selects the best options.
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 |
- Downloader for specialized LoRA styles for local Forge WebUI setups
- GLM-OCR Quantized GGUF
- Script fetching context-extended models with custom ROPE scaling
- Launch GLM-OCR Full Speed NPU Mode FREE
- Script automating visual encoder weight downloads for advanced multi-modal visual parsing tasks
- How to Deploy GLM-OCR Locally (No Cloud) with 1M Context FREE