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-<div align="center">
-  <img src="assets/logo.svg" width="60%" alt="DeepSeek AI" />
-</div>
-
-
-<hr>
-<div align="center">
-  <a href="https://www.deepseek.com/" target="_blank">
-    <img alt="Homepage" src="assets/badge.svg" />
-  </a>
-  <a href="https://huggingface.co/deepseek-ai/DeepSeek-OCR" target="_blank">
-    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" />
-  </a>
-
-</div>
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-<div align="center">
-
-  <a href="https://discord.gg/Tc7c45Zzu5" target="_blank">
-    <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" />
-  </a>
-  <a href="https://twitter.com/deepseek_ai" target="_blank">
-    <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" />
-  </a>
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-</div>
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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> |
-</p>
-
-<h2>
-<p align="center">
-  <a href="">DeepSeek-OCR: Contexts Optical Compression</a>
-</p>
-</h2>
-
-<p align="center">
-<img src="assets/fig1.png" style="width: 1000px" align=center>
-</p>
-<p align="center">
-<a href="">Explore the boundaries of visual-text compression.</a>       
-</p>
-
-## Release
-- [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
-```
-
-```python
-from vllm import LLM, SamplingParams
-from vllm.model_executor.models.deepseek_ocr import NGramPerReqLogitsProcessor
-from PIL import Image
-
-# Create model instance
-llm = LLM(
-    model="deepseek-ai/DeepSeek-OCR",
-    enable_prefix_caching=False,
-    mm_processor_cache_gb=0,
-    logits_processors=[NGramPerReqLogitsProcessor]
-)
-
-# Prepare batched input with your image file
-image_1 = Image.open("path/to/your/image_1.png").convert("RGB")
-image_2 = Image.open("path/to/your/image_2.png").convert("RGB")
-prompt = "<image>\nFree OCR."
-
-model_input = [
-    {
-        "prompt": prompt,
-        "multi_modal_data": {"image": image_1}
-    },
-    {
-        "prompt": prompt,
-        "multi_modal_data": {"image": image_2}
-    }
-]
-
-sampling_param = SamplingParams(
-            temperature=0.0,
-            max_tokens=8192,
-            # ngram logit processor args
-            extra_args=dict(
-                ngram_size=30,
-                window_size=90,
-                whitelist_token_ids={128821, 128822},  # whitelist: <td>, </td>
-            ),
-            skip_special_tokens=False,
-        )
-# Generate output
-model_outputs = llm.generate(model_input, sampling_param)
-
-# Print output
-for output in model_outputs:
-    print(output.outputs[0].text)
-```
-## Transformers-Inference
-- Transformers
-```python
-from transformers import AutoModel, AutoTokenizer
-import torch
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = '0'
-model_name = 'deepseek-ai/DeepSeek-OCR'
-
-tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
-model = AutoModel.from_pretrained(model_name, _attn_implementation='flash_attention_2', trust_remote_code=True, use_safetensors=True)
-model = model.eval().cuda().to(torch.bfloat16)
-
-# prompt = "<image>\nFree OCR. "
-prompt = "<image>\n<|grounding|>Convert the document to markdown. "
-image_file = 'your_image.jpg'
-output_path = 'your/output/dir'
-
-res = model.infer(tokenizer, prompt=prompt, image_file=image_file, output_path = output_path, base_size = 1024, image_size = 640, crop_mode=True, save_results = True, test_compress = True)
-```
-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: <image>\n<|grounding|>Convert the document to markdown.
-# other image: <image>\n<|grounding|>OCR this image.
-# without layouts: <image>\nFree OCR.
-# figures in document: <image>\nParse the figure.
-# general: <image>\nDescribe this image in detail.
-# rec: <image>\nLocate <|ref|>xxxx<|/ref|> in the image.
-# '先天下之忧而忧'
-```
-
-
-## Visualizations
-<table>
-<tr>
-<td><img src="assets/show1.jpg" style="width: 500px"></td>
-<td><img src="assets/show2.jpg" style="width: 500px"></td>
-</tr>
-<tr>
-<td><img src="assets/show3.jpg" style="width: 500px"></td>
-<td><img src="assets/show4.jpg" style="width: 500px"></td>
-</tr>
-</table>
-
-
-## 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}
-}