deepseek-ai/DeepSeek-OCR

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

DeepSeek-OCR: Contexts Optical Compression

Explore the boundaries of visual-text compression.

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 ``

```python 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 = "\nFree OCR. "

prompt = "\nConvert 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 = True

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) ```

vLLM

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

[2025/10/23] 🚀🚀🚀 DeepSeek-OCR is now officially supported in upstream vLLM. ``shell 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 ``

```python 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 = "\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: , · ), 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) ```

Visualizations

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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} }