Qwen3-VL: How a 256K-Token Vision Model Masters 500-Page Documents
24 days ago 高效码农Inside Qwen3-VL: How a 256K-Token Vision-Language Model Learns to Read 500-Page Documents and 2-Hour Videos Without Breaking a Sweat A plain-language walk-through of the technical report that introduced Qwen3-VL—no hype, no jargon, and no external facts beyond the original paper. Table of Contents The 30-Second Takeaway Model Family at a Glance Three Architectural Tweaks That Actually Matter Four-Stage Training From Scratch What the Model Was Fed (Data Ingredients) Post-Training: SFT, Distillation, and Reinforcement Learning “Thinking Mode” Explained Benchmark Scores in One Sitting Hardware-Friendly Deployment Answers to the Most-Asked Questions Key Limits and Next Steps 1. The 30-Second Takeaway Qwen3-VL is …
DeepSeek-OCR 3B Vision Language Model Deployment Guide | Fine-tuning Vision Transformer for Document AI
1 months ago 高效码农DeepSeek-OCR: How to Run & Fine-tune for Real-World Document Intelligence How can you effectively deploy and customize DeepSeek-OCR, a 3B-parameter vision model, to achieve production-grade document understanding with minimal resource overhead? The answer lies in understanding its unique architecture—contextual optical compression that converts 2D layouts into efficient vision tokens—and leveraging two distinct but complementary deployment paths: vLLM for service-oriented stability and Unsloth for performance-optimized inference. This guide walks through both approaches, then demonstrates how just 60 training steps on a domain-specific dataset can slash error rates by 88%, turning a capable generalist into a highly accurate specialist. What Makes DeepSeek-OCR …
IBM Granite-Docling-258M: The Open-Source Document AI Model Revolutionizing Enterprise Document Processing
3 months ago 高效码农Granite Docling Logo Introduction: The Challenge of Document Understanding in the Digital Age In today’s enterprise environments, organizations process countless documents daily—contracts, reports, academic papers, technical manuals, and more. While traditional optical character recognition (OCR) technologies can extract text from these documents, they often fail to preserve the underlying structure: tables become disorganized, mathematical formulas render incorrectly, code snippets lose their formatting, and even paragraph sequencing can become disrupted. This structural loss significantly reduces information retrieval efficiency and creates substantial challenges for automated document processing pipelines. IBM’s recently released Granite-Docling-258M represents a transformative approach to these challenges. This completely open-source, …