DeepSeek-OCR-2

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>\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) Refer to [🌟GitHub](https://github.com/deepseek-ai/DeepSeek-OCR-2/) for guidance on model inference acceleration and PDF processing, etc.<!-- --> - Dynamic resolution - Default: (0-6)×768×768 + 1×1024×1024 — (0-6)×144 + 256 visual tokens ✅ We would like to thank [DeepSeek-OCR](https://github.com/deepseek-ai/DeepSeek-OCR/), [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) for their valuable models and ideas. We also appreciate the benchmark [OmniDocBench](https://github.com/opendatalab/OmniDocBench). @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} }