On October 27 Baidu published Unlimited-OCR-2, its document-parsing model, on Hugging Face, and shipped a companion hundred-page benchmark, MarathonDocBench, the same day. The first generation arrived in June, positioned as standing on DeepSeek-OCR's shoulders to push "parse long documents in one pass" forward; four months later, v2 changes the goal to "unbounded".
SR-SWA: attention squeezed into 512 working references
The first Unlimited-OCR used R-SWA (Reference Sliding Window Attention) to keep the KV cache constant, already easing the "more pages, more memory" problem. The v2 upgrade is SR-SWA (Sparse Reference Sliding Window Attention): instead of attending over the whole history, the model compresses text-visual interactions into a fixed-size set of working references — for example 512 tokens — and decodes on top of that. The inference API provides all-pages-in-one-request parsing: every page of a document goes into a single request rather than page-by-page calls stitched back together.
Per the official model card, this bounds computational complexity: whether the document is 50 or 500 pages, per-step decoding latency and memory stay nearly constant, and quadratic attention scaling is eliminated. With a 131K context window, v2 does unbounded parsing inside a finite context — a hundred-page document goes in as one request, not pre-chunked pieces stitched back together.
MIT license, laptop-friendly footprint
The engineering spec is deployment-friendly: MIT license with no commercial restrictions, 6.7GB BF16 weights, built on the UnlimitedOCRForCausalLM architecture and positioned for multilingual document recognition — invoices, forms and scanned archives in mixed-language enterprise settings. Per theopenweights estimates, BF16 runs on a 12GB RTX 3060/4070 or a 16GB Mac; 8-bit fits 8GB GPUs; 4-bit lands around 3.5GB and runs on any modern laptop with 8GB RAM. Transformers is supported natively for quick tests and SGLang is recommended for serving; the release log records vLLM support since late June and ms-swift training in July, with weights mirrored on ModelScope and Baidu Cloud. The official test bed is a single A800-80GB GPU (torch 2.13, transformers 4.52), and community author AK shipped a fresh online demo alongside the release.
The SOTA claim is self-reported, on a self-released benchmark
The card claims SOTA on both MPDocBench (dozens of pages) and MarathonDocBench (hundred-page scale). Caveat: MarathonDocBench shipped the same day, so the numbers exist only from the vendor for now — a typical self-reported benchmark, and that SOTA deserves quotation marks until third-party replication.
The direction still matters: long-document parsing is the input stage of RAG, invoice and archive digitization pipelines, and chunk-then-stitch workflows inherently lose cross-page context — end-to-end approaches are closing that gap. The track is not Baidu's alone: Cohere's Parse already sells document parsing as a $1.5-per-thousand-pages API, so free open weights and metered closed APIs now meet at the same input port. Demand is visible: on theopenweights' trending board the Unlimited-OCR family (including v1) ranks first with 3.1M cumulative downloads and +1.1M in a week. MIT plus laptop-runnable effectively moves "whole documents into one model" from datacenter GPUs down to consumer hardware. For document-AI teams the next step is direct: run MarathonDocBench on your own corpus before deciding whether to replace the chunking pipeline. That step beats arguing over leaderboard ranks.