Name: DeepSeek OCR 2
Author: deepseek-ai

Overview

DeepSeek OCR 2 is a specialized vision-language model engineered to bridge the gap between raw image data and structured text. Unlike traditional OCR engines that rely on rigid layout analysis, this model treats document parsing as a generative task, allowing it to handle complex tables, multi-column layouts, and handwritten notes with higher contextual accuracy. For developers, it serves as a robust backend for automating data extraction pipelines, digitizing legacy archives, or building RAG systems that require precise ingestion of PDF and image-based documents. It integrates easily into existing AI workflows via API, offering a competitive alternative to proprietary vision models by balancing high-fidelity transcription with efficient inference speeds under an Apache-2.0 license.

Highlights

High-accuracy parsing of complex tables and layouts
Native support for multi-column and handwritten text
Permissive Apache-2.0 license for commercial deployment
Optimized for structured data extraction and RAG pipelines

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("deepseek-ai/DeepSeek-OCR-2")
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-OCR-2")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download deepseek-ai/DeepSeek-OCR-2

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download deepseek-ai/DeepSeek-OCR-2 config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('deepseek-ai/DeepSeek-OCR-2')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/deepseek-ai/DeepSeek-OCR-2

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/deepseek-ai/DeepSeek-OCR-2

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('deepseek-ai/DeepSeek-OCR-2')
tokenizer = AutoTokenizer.from_pretrained('deepseek-ai/DeepSeek-OCR-2')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance

pip install modelscope

CLI Download

Download the full repository

modelscope download --model deepseek-ai/DeepSeek-OCR-2

Download a single file to a local folder (e.g. README.md into ./dir)

modelscope download --model deepseek-ai/DeepSeek-OCR-2 README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('deepseek-ai/DeepSeek-OCR-2')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://www.modelscope.cn/deepseek-ai/DeepSeek-OCR-2.git

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/deepseek-ai/DeepSeek-OCR-2.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'deepseek-ai/DeepSeek-OCR-2')

Full Documentation

来源: HuggingFace

---
pipeline_tag: image-text-to-text
language:

multilingual

tags:

deepseek

vision-language

ocr

custom_code

license: apache-2.0
library_name: transformers
---
<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-2" 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>

<p align="center">
<a href="https://github.com/deepseek-ai/DeepSeek-OCR-2"><b>🌟 Github</b></a> |
<a href="https://huggingface.co/deepseek-ai/DeepSeek-OCR-2"><b>📥 Model Download</b></a> |
<a href="https://github.com/deepseek-ai/DeepSeek-OCR-2/blob/main/DeepSeek_OCR2_paper.pdf"><b>📄 Paper Link</b></a> |
<a href="https://arxiv.org/abs/2601.20552"><b>📄 Arxiv Paper Link</b></a> |
</p>
<h2>
<p align="center">
<a href="">DeepSeek-OCR 2: Visual Causal Flow</a>
</p>
</h2>
<p align="center">
<img src="assets/fig1.png" style="width: 900px" align=center>
</p>
<p align="center">
<a href="">Explore more human-like visual encoding.</a>
</p>

Usage

Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.12.9 + CUDA11.8：

code

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-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>\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 = 768, crop_mode=True, save_results = True)

vLLM

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

Support-Modes

Dynamic resolution

- Default: (0-6)×768×768 + 1×1024×1024 — (0-6)×144 + 256 visual tokens ✅

Main Prompts

python

# document: <image>\n<|grounding|>Convert the document to markdown.
without layouts: <image>\nFree OCR.

Acknowledgement

We would like to thank DeepSeek-OCR, Vary, GOT-OCR2.0, MinerU, PaddleOCR for their valuable models and ideas.

We also appreciate the benchmark 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}
}
@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}
}