S7351CUN/DeepSeek-OCR-2

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<p align="center"> <a href="https://github.com/deepseek-ai/DeepSeek-OCR-2"><b>🌟 Github</b></a> | <a href="https://huggingface.co/deepseek-ai/DeepSeek-OCR-2"><b>📥 Model Download</b></a> | <a href="https://github.com/deepseek-ai/DeepSeek-OCR-2/blob/main/DeepSeekOCR2paper.pdf"><b>📄 Paper Link</b></a> | <a href="https://arxiv.org/abs/2601.20552"><b>📄 Arxiv Paper Link</b></a> | </p> <h2> <p align="center"> <a href="">DeepSeek-OCR 2: Visual Causal Flow</a> </p> </h2> <p align="center"> <img src="assets/fig1.png" style="width: 900px" align=center> </p> <p align="center"> <a href="">Explore more human-like visual encoding.</a> </p>

Usage

Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.12.9 + CUDA11.8：

torch==2.6.0
transformers==4.46.3
tokenizers==0.20.3
einops
addict 
easydict
pip install flash-attn==2.7.3 --no-build-isolation

from transformers import AutoModel, AutoTokenizer
import torch
import os
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
model_name = 'deepseek-ai/DeepSeek-OCR-2'

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 = 768, crop_mode=True, save_results = True)

vLLM

Refer to 🌟GitHub for guidance on model inference acceleration and PDF processing, etc.<!-- -->

Support-Modes

• —Dynamic resolution
• —Default: (0-6)×768×768 + 1×1024×1024 — (0-6)×144 + 256 visual tokens ✅

Main Prompts

# document: <image>\n<|grounding|>Convert the document to markdown.
# without layouts: <image>\nFree OCR.

Acknowledgement

We would like to thank DeepSeek-OCR, Vary, GOT-OCR2.0, MinerU, PaddleOCR for their valuable models and ideas.

We also appreciate the benchmark OmniDocBench.

Citation

@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}
}
@article{wei2026deepseek,
  title={DeepSeek-OCR 2: Visual Causal Flow},
  author={Wei, Haoran and Sun, Yaofeng and Li, Yukun},
  journal={arXiv preprint arXiv:2601.20552},
  year={2026}
}