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transformers/docs/source/en/model_doc/deepseek_ocr2.md
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first commit
2026-06-05 16:53:03 +08:00

3.9 KiB

This model was published in HF papers on 2026-01-28 and contributed to Hugging Face Transformers on 2026-06-01.

DeepSeek-OCR-2

Overview

The DeepSeek-OCR-2 model was proposed in Visual Causal Flow: A Novel Approach to OCR-Specialized Vision-Language Models by the DeepSeek team.

DeepSeek-OCR-2 is an OCR-specialized vision-language model built on a distinctive architecture: a SAM ViT-B vision encoder feeds into a Qwen2 hybrid attention encoder, which is connected through an MLP projector to a DeepSeek-V2 Mixture-of-Experts (MoE) language model. A key feature of the model is its hybrid attention mechanism, which applies bidirectional attention over image tokens and causal attention over query tokens, enabling efficient and accurate document understanding.

DeepSeek-OCR 2: Visual Causal Flow.

This model was contributed by thisisiron.

Usage example

Plain OCR

from transformers import AutoProcessor, AutoModelForImageTextToText

model = AutoModelForImageTextToText.from_pretrained(
    "deepseek-community/DeepSeek-OCR-2", device_map="auto"
)
processor = AutoProcessor.from_pretrained("deepseek-community/DeepSeek-OCR-2")

image = "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/image_ocr.jpg"
inputs = processor(images=image, text="<image>\nFree OCR.", return_tensors="pt").to(model.device)

generate_ids = model.generate(**inputs, do_sample=False, max_new_tokens=256)
processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
# "R&D QUALITY IMPROVEMENT\nSUGGESTION/SOLUTION FORM\nName/Phone Ext. : (...)"

Grounding with markdown conversion

The <|grounding|> token enables coordinate-aware output with <|ref|> and <|det|> tags.

inputs = processor(
    images=image,
    text="<image>\n<|grounding|>Convert the document to markdown.",
    return_tensors="pt",
).to(model.device)

generate_ids = model.generate(**inputs, do_sample=False, max_new_tokens=256)
processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=False)
# "<|ref|>title<|/ref|><|det|>[[330, 198, 558, 230]]<|/det|>\n# R&D QUALITY (...)"

DeepseekOcr2Config

autodoc DeepseekOcr2Config

DeepseekOcr2VisionConfig

autodoc DeepseekOcr2VisionConfig

DeepseekOcr2SamVisionConfig

autodoc DeepseekOcr2SamVisionConfig

DeepseekOcr2VisionEncoderConfig

autodoc DeepseekOcr2VisionEncoderConfig

DeepseekOcr2TextConfig

autodoc DeepseekOcr2TextConfig

DeepseekOcr2ImageProcessor

autodoc DeepseekOcr2ImageProcessor

DeepseekOcr2ImageProcessorPil

autodoc DeepseekOcr2ImageProcessorPil

DeepseekOcr2Processor

autodoc DeepseekOcr2Processor

DeepseekOcr2TextModel

autodoc DeepseekOcr2TextModel

DeepseekOcr2VisionModel

autodoc DeepseekOcr2VisionModel

DeepseekOcr2Model

autodoc DeepseekOcr2Model

DeepseekOcr2ForConditionalGeneration

autodoc DeepseekOcr2ForConditionalGeneration