周红伟 DeepSeek-OCR v2技术原理和架构，部署案例实操

DeepSeek-OCR 发布于25年10月，而这次DeepSeek-OCR 2发布仅隔了三个月。这会不会是DeepSeek V4发布前上的前菜呢？让我们一起尝尝鲜吧。

DeepSeek发布全新DeepSeek-OCR 2模型，采用创新的DeepEncoder V2方法，让AI能够根据图像的含义动态重排图像的各个部分，而不再只是机械地从左到右扫描。这种方式模拟了人类在观看场景时所遵循的逻辑流程。最终，该模型在处理布局复杂的图片时，表现优于传统的视觉-语言模型，实现了更智能、更具因果推理能力的视觉理解。

github地址：https://github.com/deepseek-ai/DeepSeek-OCR-2

DeepSeek-OCR 2: Visual Causal Flow

Explore more human-like visual encoding.

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-2 folder

git clone https://github.com/deepseek-ai/DeepSeek-OCR-2.git

1. Conda

conda create -n deepseek-ocr2 python=3.12.9 -y
conda activate deepseek-ocr2

1. 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-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-OCR2-master/DeepSeek-OCR2-vllm/config.py

cd DeepSeek-OCR2-master/DeepSeek-OCR2-vllm

1. image: streaming output

python run_dpsk_ocr2_image.py

1. pdf: concurrency (on-par speed with DeepSeek-OCR)

python run_dpsk_ocr2_pdf.py

1. batch eval for benchmarks (i.e., OmniDocBench v1.5)

python run_dpsk_ocr2_eval_batch.py

Transformers-Inference

• Transformers

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)

or you can

cd DeepSeek-OCR2-master/DeepSeek-OCR2-hf
python run_dpsk_ocr2.py

Support-Modes

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