Contexts Optical Compression
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DeepSeek-OCR: Contexts Optical Compression
## Release - [2026/01/27]🚀🚀🚀🚀🚀🚀 We present [DeepSeek-OCR2](https://github.com/deepseek-ai/DeepSeek-OCR-2) - [2025/10/23]🚀🚀🚀 DeepSeek-OCR is now officially supported in upstream [vLLM](https://docs.vllm.ai/projects/recipes/en/latest/DeepSeek/DeepSeek-OCR.html#installing-vllm). Thanks to the [vLLM](https://github.com/vllm-project/vllm) team for their help. - [2025/10/20]🚀🚀🚀 We release DeepSeek-OCR, a model to investigate the role of vision encoders from an LLM-centric viewpoint. ## Contents - [Install](#install) - [vLLM Inference](#vllm-inference) - [Transformers Inference](#transformers-inference) ## Install >Our environment is cuda11.8+torch2.6.0. 1. Clone this repository and navigate to the DeepSeek-OCR folder ```bash git clone https://github.com/deepseek-ai/DeepSeek-OCR.git ``` 2. Conda ```Shell conda create -n deepseek-ocr python=3.12.9 -y conda activate deepseek-ocr ``` 3. Packages - download the vllm-0.8.5 [whl](https://github.com/vllm-project/vllm/releases/tag/v0.8.5) ```Shell pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu118 pip install vllm-0.8.5+cu118-cp38-abi3-manylinux1_x86_64.whl pip install -r requirements.txt pip install flash-attn==2.7.3 --no-build-isolation ``` **Note:** if you want vLLM and transformers codes to run in the same environment, you don't need to worry about this installation error like: vllm 0.8.5+cu118 requires transformers>=4.51.1 ## vLLM-Inference - VLLM: >**Note:** change the INPUT_PATH/OUTPUT_PATH and other settings in the DeepSeek-OCR-master/DeepSeek-OCR-vllm/config.py ```Shell cd DeepSeek-OCR-master/DeepSeek-OCR-vllm ``` 1. image: streaming output ```Shell python run_dpsk_ocr_image.py ``` 2. pdf: concurrency ~2500tokens/s(an A100-40G) ```Shell python run_dpsk_ocr_pdf.py ``` 3. batch eval for benchmarks ```Shell python run_dpsk_ocr_eval_batch.py ``` **[2025/10/23] The version of upstream [vLLM](https://docs.vllm.ai/projects/recipes/en/latest/DeepSeek/DeepSeek-OCR.html#installing-vllm):** ```shell uv venv source .venv/bin/activate # Until v0.11.1 release, you need to install vLLM from nightly build uv pip install -U vllm --pre --extra-index-url https://wheels.vllm.ai/nightly ``` ``` … ``` ## Transformers-Inference - Transformers ``` … ``` or you can ```Shell cd DeepSeek-OCR-master/DeepSeek-OCR-hf python run_dpsk_ocr.py ``` ## Support-Modes The current open-source model supports the following modes: - Native resolution: - Tiny: 512×512 （64 vision tokens）✅ - Small: 640×640 （100 vision tokens）✅ - Base: 1024×1024 （256 vision tokens）✅ - Large: 1280×1280 （400 vision tokens）✅ - Dynamic resolution - Gundam: n×640×640 + 1×1024×1024 ✅ ## Prompts examples ```python # document: \n<|grounding|>Convert the document to markdown. # other image: \n<|grounding|>OCR this image. # without layouts: \nFree OCR. # figures in document: \nParse the figure. # general: \nDescribe this image in detail. # rec: \nLocate <|ref|>xxxx<|/ref|> in the image. # '先天下之忧而忧' ``` ## Visualizations ## Acknowledgement We would like to thank [Vary](https://github.com/Ucas-HaoranWei/Vary/), [GOT-OCR2.0](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/), [MinerU](https://github.com/opendatalab/MinerU), [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR), [OneChart](https://github.com/LingyvKong/OneChart), [Slow Perception](https://github.com/Ucas-HaoranWei/Slow-Perception) for their valuable models and ideas. We also appreciate the benchmarks: [Fox](https://github.com/ucaslcl/Fox), [OminiDocBench](https://github.com/opendatalab/OmniDocBench). ## Citation ```bibtex @article{wei2025deepseek, title={DeepSeek-OCR: Contexts Optical Compression}, author={Wei, Haoran and Sun, Yaofeng and Li, Yukun}, journal={arXiv preprint arXiv:2510.18234}, year={2025} }