epsilon-ocr-d.markdown-post3.0.m

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.

Key Enhancements

• Dynamic Markdown and Layout Reconstruction Converts multi page and complex layout documents into structured Markdown or HTML-Markdown with preserved hierarchy, formatting, headings, and semantic reading order.

• Inline Programming Language Support Automatically embeds LaTeX, Python, JavaScript, and shell code blocks within reconstructed documentation for research and technical writing.

• High Accuracy OCR and Visual Parsing Extracts text from structured, semi structured, and unstructured formats. Supports multi page input and contextual alignment.

• Complex Structure Understanding Parses tables, forms, graphs, diagrams, multi column layouts, and mathematical expressions without structural loss.

• Document Retrieval and Semantic Linking Performs cross page reasoning and content referencing for enterprise document workflows.

• Multimodal Long Document Reasoning Supports long content comprehension for slides, scanned books, handwritten pages, and research papers.

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👉 This model is a stage progression model, and it may currently contain artifacts.

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Quick Start with Transformers

from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "prithivMLmods/epsilon-ocr-d.markdown-post3.0.m", torch_dtype="auto", device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/epsilon-ocr-d.markdown-post3.0.m")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {"type": "text", "text": "Convert to Markdown."},
        ],
    }
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=2048)
generated_ids_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)

Intended Use

• OCR to Markdown or HTML Markdown conversion
• Document reconstruction for manuals, books, and research materials
• Table extraction and structural transformation
• Multi page document retrieval and question answering
• Mathematical OCR and LaTeX generation
• Form extraction and structured entity mapping
• Documentation rebuilding for enterprise knowledge systems
• Automation of digitization and archival systems

Limitations

• Accuracy may drop on highly damaged or extremely low resolution images
• Limited performance compared to larger VL models in very large document reasoning
• Language coverage varies for low resource scripts
• Very complex forms may require secondary refinement

References

• Qwen2.5 VL https://huggingface.co/papers/2502.13923

• DocVLM Efficient Reader https://arxiv.org/pdf/2412.08746v1

• YaRN Efficient Context Window Extension https://arxiv.org/pdf/2309.00071

• Qwen2 VL High Resolution Perception https://arxiv.org/pdf/2409.12191

• Qwen VL Vision Language and OCR https://arxiv.org/pdf/2308.12966

• OCR Benchmark for Multimodal Models https://arxiv.org/pdf/2412.02210