DeepSeek-OCR

In plain words

DeepSeek-OCR is an open-source model that converts documents into images for processing, reducing the number of vision tokens needed for long-context tasks. It combines optical character recognition with text compression, enabling more efficient document analysis. The tool is designed for developers and researchers working with large volumes of text who need faster, token-efficient processing of lengthy documents.

written from the facts on this page · September 2026

From the sources

DeepSeek-OCR is a model that compresses long text by treating it as an image. This optical compression uses far fewer vision tokens to represent documents, unlocking new levels of efficiency for long-context tasks while delivering powerful OCR capabilities.

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

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 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 =…from huggingface.co

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