Baidu Releases Unlimited OCR, a 3B Model That Keeps the KV Cache Flat for Long-Document Parsing
Most end-to-end OCR models slow down as output grows. Each generated token adds to the KV cache. Memory rises and generation drags. Parsing dozens of pages becomes impractical. Baidu’s Unlimited OCR addresses this directly. It swaps the decoder’s attention for a design that keeps memory constant. TL;DR Unlimited OCR is a 3B-parameter Mixture-of-Experts model, with only 500M parameters active. It replaces decoder attention with Reference Sliding Window Attention (R-SWA), k
An open-source OCR model addresses a fundamental problem: as document-parsing models generate more text, their memory requirements grow, slowing down processing. This new model keeps memory constant by using a modified attention mechanism called Reference Sliding Window Attention, which stores only visual tokens and the most recent output tokens rather than all previous outputs. The model achieves this while maintaining high accuracy on document parsing benchmarks and requiring only 500 million active parameters during inference. This design enables practical processing of entire documents, including dozens of pages, in a single pass rather than page-by-page processing.
Explainer: What Is a Mixture of Experts Model and Why Does It Use Fewer Resources?
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