---
title: DeepSeek-OCR-2
canonical_url: "https://www.modelscope.cn/models/deepseek-ai/DeepSeek-OCR-2"
md_url: "https://www.modelscope.cn/models/deepseek-ai/DeepSeek-OCR-2.md"
repository: deepseek-ai/DeepSeek-OCR-2
last_updated: 2026-02-03
license: apache-2.0
pipeline_tag: image-text-to-text
tasks:
  - image-text-to-text
model_type:
  - deepseek_vl_v2
architectures:
  - DeepseekOCR2ForCausalLM
parameters: 3.4B
tensor_type:
  - BF16
library_name:
  - pytorch
  - transformer
  - safetensors
frameworks:
  - pytorch
language:
  - multilingual
downloads: 222909
stars: 86
tags:
  - deepseek
  - vision-language
  - ocr
  - custom_code
---

# DeepSeek-OCR-2

> DeepSeek-OCR-2 - deepseek-ai 在 ModelScope 开源的模型。🌟 Github | 📥 Model Download | 📄 Paper Link | 📄 Arxiv Paper Link | DeepSeek-OCR 2: Visual Causal Flow Explore more human-like visual encoding.

deepseek-ai/DeepSeek-OCR-2 是 ModelScope 魔搭社区上的 3.4B 参数image-text-to-text模型，采用 apache-2.0 许可。

- **Repository**: deepseek-ai/DeepSeek-OCR-2
- **License**: apache-2.0
- **Tasks**: image-text-to-text
- **Parameters**: 3.4B
- **Tags**: deepseek, vision-language, ocr, custom_code
- **Downloads**: 222909
- **Stars**: 86
- **Last updated**: 2026-02-03

Source: https://www.modelscope.cn/models/deepseek-ai/DeepSeek-OCR-2

---

<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" />
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    <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" />
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<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：

```
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](https://github.com/deepseek-ai/DeepSeek-OCR-2/) 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](https://github.com/deepseek-ai/DeepSeek-OCR/), [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) for their valuable models and ideas.

We also appreciate the benchmark [OmniDocBench](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}
}
@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}
}