github.com/deepseek-ai/DeepSeek-OCR @main sqlite

📥 Model Download | 📄 Paper Link | 📄 Arxiv Paper Link |

DeepSeek-OCR: Contexts Optical Compression

Explore the boundaries of visual-text compression.

Release

• [2026/01/27]🚀🚀🚀🚀🚀🚀 We present DeepSeek-OCR2
• [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.

Contents

• Install
• vLLM Inference
• Transformers Inference

Install

Our environment is cuda11.8+torch2.6.0. 1. Clone this repository and navigate to the DeepSeek-OCR folder

git clone https://github.com/deepseek-ai/DeepSeek-OCR.git

1. Conda

conda create -n deepseek-ocr python=3.12.9 -y
conda activate deepseek-ocr

1. Packages

2. 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-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

cd DeepSeek-OCR-master/DeepSeek-OCR-vllm

1. image: streaming output

python run_dpsk_ocr_image.py

1. pdf: concurrency ~2500tokens/s(an A100-40G)

python run_dpsk_ocr_pdf.py

1. 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/nightly

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

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

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

# 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

Acknowledgement

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.

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} }