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+*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](https://huggingface.co/papers/2601.20552) 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.
+
+<img src="https://huggingface.co/deepseek-ai/DeepSeek-OCR-2/resolve/main/assets/fig1.png" width="600">
+
+<small> DeepSeek-OCR 2: Visual Causal Flow.</small>
+
+This model was contributed by [thisisiron](https://huggingface.co/thisisiron).
+
+
+## Usage example
+
+### Plain OCR
+
+```python
+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.
+
+```python
+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