DeepSeek-OCR

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<p align="center">
  <a href="https://huggingface.co/deepseek-ai/DeepSeek-OCR"><b>📥 Model Download</b></a> |
  <a href="https://github.com/deepseek-ai/DeepSeek-OCR/blob/main/DeepSeek_OCR_paper.pdf"><b>📄 Paper Link</b></a> |
  <a href="https://arxiv.org/abs/2510.18234"><b>📄 Arxiv Paper Link</b></a> |
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<h2>
<p align="center">
  <a href="">DeepSeek-OCR: Contexts Optical Compression</a>
</p>
</h2>

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<img src="assets/fig1.png" style="width: 1000px" align=center>
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<p align="center">
<a href="">Explore the boundaries of visual-text compression.</a>       
</p>

## 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 installat