Hugging Face 热门模型第 15 名：baidu/Unlimited-OCR-2，228 赞，近期下载 513 次。
标签：transformers, safetensors, unlimited-ocr, feature-extraction, baidu, vision-language, ocr, custom_code, image-text-to-text, multilingual, arxiv:2606.23050, license:mit
README：
Unlimited OCR Works 2
Pioneering the OCR-3.0 Era: From Long- to Unlimited-Horizon Parsing.
Release
- [2026/10/27] 📊 We release MarathonDocBench, a new benchmark for ultra-long document parsing
- [2026/10/27] 🤝 Thanks to the ModelScope community for their support. Our model is now available at ModelScope.
- [2026/10/27] 🤝 Thanks to the Baidu Cloud team for their support. Our model is now available on Baidu Cloud.
- [2026/10/27] 🤝 Thanks to AK for creating a new demo for us. Try it at Unlimited-OCR-2.
- [2026/10/27] 🚀 We release Unlimited OCR 2, achieving unlimited-horizon parsing with SR-SWA.
- [2026/07/21] 🤝 Thanks to the ms-swift community for their support, our model now supports training with ms-swift.
- [2026/07/03] 🤝 Thanks to the Baidu Cloud team for their support. Our model is now available on Baidu Cloud.
- [2026/06/28] 🤝 Thanks to the vLLM community and Tianyu Guo for their support, our model now supports vLLM inference.
- [2026/06/24] 🤝 Thanks to AK for creating a demo for us. It is now available at Hugging Face Spaces.
- [2026/06/23] 📄 Our paper is now available on arXiv.
- [2026/06/23] 🤝 Thanks to the ModelScope community for their support. Our model is now available at ModelScope.
- [2026/06/22] 🚀 We present Unlimited-OCR, aiming to push Deepseek-OCR one step further.
What's New in 2
- Sparse Reference Sliding Window Attention (SR-SWA): Compresses text-visual attention interactions from ultra-long sequences down to a fixed-size set of working references (e.g., 512 tokens). This bounds computational complexity, keeping per-step decoding latency and memory footprint nearly constant regardless of document length.
- From Long- to Unlimited-Horizon Parsing: Powered by SR-SWA, Unlimited-OCR 2 achieves true, unbounded document parsing within a finite context window—effectively eliminating quadratic attention scaling and memory bottlenecks as documents grow.
- Ultra-Long Document Parsing SOTA: Achieves state-of-the-art performance across both dozens-of-pages benchmarks (e.g., MPDocBench) and extreme hundred-page benchmarks (e.g., MarathonDocBench).
Inference
Unlimited OCR 2 supports Transformers (native, for quick tests) and SGLang (recommended for serving).
Transformers
Tested with Python 3.10, torch==2.13.0, transformers==4.52.1, huggingface-hub==0.36.0, tokenizers==0.21.4, on an NVIDIA A800-80GB:
pip install torch==2.13.0 torchvision pillow transformers==4.52.1 huggingface-hub==0.36.0 \
tokenizers==0.21.4 addict==2.4.0 einops==0.8.2 easydict==1.13
Load the model once:
import torch
from transformers import AutoModel, AutoTokenizer
model_name = "baidu/Unlimited-OCR-2"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModel.from_pretrained(model_name, trust_remote_code=True, use_safetensors=True,
torch_dtype=torch.bfloat16).eval().cuda()
Multi-page inference (infer_multi). All pages of a document are parsed in one request, so the model can use cross-page context. Pass the page images in page order:
pages = [f"your_doc/page_{i}.jpg" for i in range(1, 8)]
text, num_tokens = model.infer_multi(tokenizer, image_files=pages,
output_path="output/multi", save_results=True)
The prompt is built automatically as "" * num_pages + "Multi page parsing.", and each page is encoded as one global view (base mode). The output contains one `` per input page, in the same order as image_files.
Single-page inference (infer). A single page is encoded with dynamic tiling (gundam mode) by default:
text = model.infer(tokenizer, image_file="your_doc/page_1.jpg",
output_path="output/
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