📥 Model Download | 📄 Paper Link | 📄 Arxiv Paper Link |
Explore the boundaries of visual-text compression.
- [2025/10/23]🚀🚀🚀 DeepSeek-OCR is now officially supported in upstream vLLM. Thanks to the 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.
Our environment is cuda11.8+torch2.6.0.
- Clone this repository and navigate to the DeepSeek-OCR folder
git clone https://github.com/deepseek-ai/DeepSeek-OCR.git- Conda
conda create -n deepseek-ocr python=3.12.9 -y conda activate deepseek-ocr- Packages
- download the vllm-0.8.5 whl
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-isolationNote: 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:
Note: change the INPUT_PATH/OUTPUT_PATH and other settings in the DeepSeek-OCR-master/DeepSeek-OCR-vllm/config.py
cd DeepSeek-OCR-master/DeepSeek-OCR-vllm- image: streaming output
python run_dpsk_ocr_image.py- pdf: concurrency ~2500tokens/s(an A100-40G)
python run_dpsk_ocr_pdf.py- batch eval for benchmarks
python run_dpsk_ocr_eval_batch.py[2025/10/23] The version of upstream vLLM:
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/nightlyfromvllmimportLLM, SamplingParamsfromvllm.model_executor.models.deepseek_ocrimportNGramPerReqLogitsProcessorfromPILimportImage# 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 argsextra_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 outputforoutputinmodel_outputs: print(output.outputs[0].text)- Transformers
fromtransformersimportAutoModel, AutoTokenizerimporttorchimportosos.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
cd DeepSeek-OCR-master/DeepSeek-OCR-hf python run_dpsk_ocr.pyThe 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 ✅
# 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' |  |
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We would like to thank Vary, GOT-OCR2.0, MinerU, PaddleOCR, OneChart, Slow Perception for their valuable models and ideas.
We also appreciate the benchmarks: Fox, OminiDocBench.
@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} }