---pipeline_tag: image-text-to-texthf_language:- multilingualtags:- deepseek- vision-language- ocr- custom_codelicense: MIT License---<div align="center"> <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek AI" /></div><hr><div align="center"> <a href="https://www.deepseek.com/" target="_blank"> <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" /> </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><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></div><p align="center"> <a href="https://github.com/deepseek-ai/DeepSeek-OCR"><b>🌟 Github</b></a> | <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>## UsageInference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.12.9 + CUDA11.8：```torch==2.6.0transformers==4.46.3tokenizers==0.20.3einopsaddict easydictpip install flash-attn==2.7.3 --no-build-isolation``````pythonfrom transformers import AutoModel, AutoTokenizerimport torchimport osos.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'# infer(self, tokenizer, prompt='', image_file='', output_path = ' ', base_size = 1024, image_size = 640, crop_mode = True, test_compress = False, save_results = False):# Tiny: base_size = 512, image_size = 512, crop_mode = False# Small: base_size = 640, image_size = 640, crop_mode = False# Base: base_size = 1024, image_size = 1024, crop_mode = False# Large: base_size = 1280, image_size = 1280, crop_mode = False# Gundam: base_size = 1024, image_size = 640, crop_mode = Trueres = 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)```## vLLMRefer to [🌟GitHub](https://github.com/deepseek-ai/DeepSeek-OCR/) for guidance on model inference acceleration and PDF processing, etc.<!-- -->[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).```shelluv venvsource .venv/bin/activate# Until v0.11.1 release, you need to install vLLM from nightly builduv pip install -U vllm --pre --extra-index-url https://wheels.vllm.ai/nightly``````pythonfrom vllm import LLM, SamplingParamsfrom vllm.model_executor.models.deepseek_ocr import NGramPerReqLogitsProcessorfrom PIL import Image# Create model instancellm = LLM( model="deepseek-ai/DeepSeek-OCR", enable_prefix_caching=False, mm_processor_cache_gb=0, logits_processors=[NGramPerReqLogitsProcessor])# Prepare batched input with your image fileimage_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 outputmodel_outputs = llm.generate(model_input, sampling_param)# Print outputfor output in model_outputs: print(output.outputs[0].text)```## 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>## AcknowledgementWe 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}}