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--- |
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license: other |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen2.5-VL-3B-Instruct |
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pipeline_tag: image-text-to-text |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- document-ai |
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- table-extraction |
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- layouts |
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- markdown |
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- html-markdown |
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- document-retrieval |
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- visual-grounding |
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- pdf-ocr |
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- layout-analysis |
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- xml |
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- yaml |
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- json |
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--- |
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# **epsilon-ocr-d.markdown-post3.0.m** |
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> **epsilon-ocr-d.markdown-post3.0.m** is an experimental document AI multimodal model fine tuned on top of **Qwen2.5-VL-3B-Instruct**, optimized for OCR driven document reconstruction and dynamic Markdown generation. It converts documents into structured **Markdown**, **HTML-Markdown**, and hybrid technical documentation formats with inline code adaptation. Built for efficient model scaling, it offers strong performance with reduced compute requirements. |
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# Key Enhancements |
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* **Dynamic Markdown and Layout Reconstruction** |
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Converts multi page and complex layout documents into structured Markdown or HTML-Markdown with preserved hierarchy, formatting, headings, and semantic reading order. |
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* **Inline Programming Language Support** |
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Automatically embeds LaTeX, Python, JavaScript, and shell code blocks within reconstructed documentation for research and technical writing. |
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* **High Accuracy OCR and Visual Parsing** |
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Extracts text from structured, semi structured, and unstructured formats. Supports multi page input and contextual alignment. |
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* **Complex Structure Understanding** |
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Parses tables, forms, graphs, diagrams, multi column layouts, and mathematical expressions without structural loss. |
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* **Document Retrieval and Semantic Linking** |
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Performs cross page reasoning and content referencing for enterprise document workflows. |
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* **Multimodal Long Document Reasoning** |
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Supports long content comprehension for slides, scanned books, handwritten pages, and research papers. |
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--- |
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> 👉 This model is a stage progression model, and it may currently contain artifacts. |
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--- |
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# Quick Start with Transformers |
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```python |
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from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor |
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from qwen_vl_utils import process_vision_info |
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model = Qwen2_5_VLForConditionalGeneration.from_pretrained( |
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"prithivMLmods/epsilon-ocr-d.markdown-post3.0.m", torch_dtype="auto", device_map="auto" |
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) |
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processor = AutoProcessor.from_pretrained("prithivMLmods/epsilon-ocr-d.markdown-post3.0.m") |
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messages = [ |
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{ |
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"role": "user", |
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"content": [ |
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{ |
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"type": "image", |
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"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", |
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}, |
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{"type": "text", "text": "Convert to Markdown."}, |
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], |
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} |
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] |
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text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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image_inputs, video_inputs = process_vision_info(messages) |
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inputs = processor( |
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text=[text], |
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images=image_inputs, |
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videos=video_inputs, |
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padding=True, |
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return_tensors="pt", |
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) |
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inputs = inputs.to("cuda") |
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generated_ids = model.generate(**inputs, max_new_tokens=2048) |
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generated_ids_trimmed = [ |
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out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) |
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] |
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output_text = processor.batch_decode( |
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False |
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) |
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print(output_text) |
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``` |
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# Intended Use |
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* OCR to Markdown or HTML Markdown conversion |
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* Document reconstruction for manuals, books, and research materials |
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* Table extraction and structural transformation |
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* Multi page document retrieval and question answering |
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* Mathematical OCR and LaTeX generation |
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* Form extraction and structured entity mapping |
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* Documentation rebuilding for enterprise knowledge systems |
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* Automation of digitization and archival systems |
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# Limitations |
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* Accuracy may drop on highly damaged or extremely low resolution images |
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* Limited performance compared to larger VL models in very large document reasoning |
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* Language coverage varies for low resource scripts |
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* Very complex forms may require secondary refinement |
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## References |
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* Qwen2.5 VL |
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[https://huggingface.co/papers/2502.13923](https://huggingface.co/papers/2502.13923) |
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* DocVLM Efficient Reader |
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[https://arxiv.org/pdf/2412.08746v1](https://arxiv.org/pdf/2412.08746v1) |
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* YaRN Efficient Context Window Extension |
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[https://arxiv.org/pdf/2309.00071](https://arxiv.org/pdf/2309.00071) |
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* Qwen2 VL High Resolution Perception |
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[https://arxiv.org/pdf/2409.12191](https://arxiv.org/pdf/2409.12191) |
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* Qwen VL Vision Language and OCR |
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[https://arxiv.org/pdf/2308.12966](https://arxiv.org/pdf/2308.12966) |
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* OCR Benchmark for Multimodal Models |
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[https://arxiv.org/pdf/2412.02210](https://arxiv.org/pdf/2412.02210) |
