初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/epsilon-ocr-d.markdown-post3.0.m
Source: Original Platform

README.md

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+---
+license: other
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-VL-3B-Instruct
+pipeline_tag: image-text-to-text
+library_name: transformers
+tags:
+- text-generation-inference
+- document-ai
+- table-extraction
+- layouts
+- markdown
+- html-markdown
+- document-retrieval
+- visual-grounding
+- pdf-ocr
+- layout-analysis
+---
+ 
+![1](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/3BlRVBsY8SFY34bBdwICO.png)
+
+# **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](https://huggingface.co/papers/2502.13923)
+
+* DocVLM Efficient Reader
+  [https://arxiv.org/pdf/2412.08746v1](https://arxiv.org/pdf/2412.08746v1)
+
+* YaRN Efficient Context Window Extension
+  [https://arxiv.org/pdf/2309.00071](https://arxiv.org/pdf/2309.00071)
+
+* Qwen2 VL High Resolution Perception
+  [https://arxiv.org/pdf/2409.12191](https://arxiv.org/pdf/2409.12191)
+
+* Qwen VL Vision Language and OCR
+  [https://arxiv.org/pdf/2308.12966](https://arxiv.org/pdf/2308.12966)
+
+* OCR Benchmark for Multimodal Models
+  [https://arxiv.org/pdf/2412.02210](https://arxiv.org/pdf/2412.02210)