README

Unlimited OCR Works






  
    
  
  
    
  
  
    
  



  
    
  
    
    
  
  
    
  


Pioneering the OCR-3.0 Era: From Long- to Unlimited-Horizon Parsing.








 Release
- [2026/11/02] 📄 The paper of Unlimited OCR 2 is now available on arXiv.
- [2026/10/28] Thanks to the ms-swift community for supporting training of Unlimited OCR 2.
- [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:
shell
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:
python
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.f