GLM-OCRZ.ai
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MonoQwen-VisionLightOn
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About
GLM-OCR is a multimodal optical character recognition model and open source repository that provides accurate, efficient, and comprehensive document understanding by combining text and visual modalities into a unified encoder–decoder architecture derived from the GLM-V family. Built with a visual encoder pre-trained on large-scale image–text data and a lightweight cross-modal connector feeding into a GLM-0.5B language decoder, the model supports layout detection, parallel region recognition, and structured output for text, tables, formulas, and complicated real-world document formats. It introduces Multi-Token Prediction (MTP) loss and stable full-task reinforcement learning to improve training efficiency, recognition accuracy, and generalization, achieving state-of-the-art benchmarks on major document understanding tasks.
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About
MonoQwen2-VL-v0.1 is the first visual document reranker designed to enhance the quality of retrieved visual documents in Retrieval-Augmented Generation (RAG) pipelines. Traditional RAG approaches rely on converting documents into text using Optical Character Recognition (OCR), which can be time-consuming and may result in loss of information, especially for non-textual elements like graphs and tables. MonoQwen2-VL-v0.1 addresses these limitations by leveraging Visual Language Models (VLMs) that process images directly, eliminating the need for OCR and preserving the integrity of visual content. This reranker operates in a two-stage pipeline, initially, it uses separate encoding to generate a pool of candidate documents, followed by a cross-encoding model that reranks these candidates based on their relevance to the query. By training a Low-Rank Adaptation (LoRA) on top of the Qwen2-VL-2B-Instruct model, MonoQwen2-VL-v0.1 achieves high performance without significant memory overhead.
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
Developers, researchers, and engineers wanting a tool to accurately parse and understand complex documents, layouts, and visual-text content at scale
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Audience
Companies working with visually rich documents requiring a solution to enhance retrieval accuracy and efficiency in OCR-free RAG systems
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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API
Offers API
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API
Offers API
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Pricing
Free
Free Version
Free Trial
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Pricing
No information available.
Free Version
Free Trial
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Reviews/
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Reviews/
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Company InformationZ.ai
Founded: 2019
China
github.com/zai-org/GLM-OCR
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Company InformationLightOn
Founded: 2016
France
www.lighton.ai/lighton-blogs/monoqwen-vision
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Integrations
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Integrations
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