zr-wang/AgenticOCR-4B

AgenticOCR-4B Overview

AgenticOCR-4B is a specialized 4 billion parameter vision-language model, built upon the Qwen3-VL-4B-Instruct architecture. Developed by zr-wang, its primary purpose is to facilitate the AgenticOCR project, focusing on advanced document processing.

Key Capabilities

• Query-Driven Visual Evidence Extraction: The model excels at identifying and extracting specific visual information from documents based on user queries.
• On-Demand Information Retrieval: It supports dynamic retrieval of visual evidence, crucial for enhancing Retrieval Augmented Generation (RAG) systems.
• Document RAG Enhancement: By providing precise visual context, AgenticOCR-4B improves the accuracy and relevance of information retrieved for RAG applications.
• Integrated Visual Processing: As a vision-language model, it processes both textual and visual inputs, enabling a deeper understanding of document content.
• Extended Context Length: Features a substantial context length of 32768 tokens, allowing for comprehensive analysis of longer documents.

Good For

• Document Analysis: Ideal for tasks requiring detailed understanding and extraction of information from complex documents.
• RAG Systems: Particularly suited for developers building RAG applications that benefit from visual evidence.
• Information Extraction: Useful for scenarios where specific data points, including those embedded in images or layouts, need to be precisely located and extracted based on queries.