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

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

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

Quick Start with Transformers

python

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