Create README.md

README.md

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+---
+license: apache-2.0
+base_model: ATH-MaaS/OvisOCR2
+language:
+  - multilingual
+library_name: mlx
+pipeline_tag: image-text-to-text
+tags:
+  - ocr
+  - document-parsing
+  - markdown
+  - tables
+  - formulas
+  - multimodal
+  - vision-language
+  - qwen3.5
+  - mlx
+  - apple-silicon
+  - oq
+  - oqe4
+  - quantized
+---
+
+# OvisOCR2-oQe4
+
+> **Apple Silicon Optimized oQe4 MLX Quantized Release**
+
+This repository contains an **oQe4 mixed-precision MLX quantized** version of **OvisOCR2**, optimized for fast and memory-efficient document understanding on Apple Silicon.
+
+The original **OvisOCR2** model was developed by **ATH-MaaS**. This repository provides an optimized **MLX/oQe4 conversion only** and **does not include any additional training or fine-tuning**.
+
+Using **oQe4 sensitivity-aware mixed-precision quantization**, this release preserves the excellent OCR and document parsing capabilities of the original model while significantly reducing memory usage and improving inference efficiency on Apple Silicon. OvisOCR2 is a compact 0.8B end-to-end document parser that converts document images directly into structured Markdown and achieves state-of-the-art results on OmniDocBench v1.6 and PureDocBench. 0
+
+---
+
+# About OvisOCR2
+
+OvisOCR2 is a compact **Vision Language Model (VLM)** specialized for **end-to-end document parsing**.
+
+Unlike traditional OCR pipelines that separately detect layouts, recognize text, and reconstruct documents, OvisOCR2 directly converts a document page into structured Markdown while preserving natural reading order.
+
+The model is capable of extracting:
+
+- 📄 Plain text
+- 📐 Mathematical formulas (LaTeX)
+- 📊 Tables (HTML)
+- 🖼 Images and visual regions
+- 📚 Complex layouts
+- 📰 Multi-column documents
+- 📑 Scientific papers
+- 📋 Forms
+- 📖 Books
+- 📃 Receipts
+- 📜 Scanned documents
+
+The model is based on **Qwen3.5-0.8B** and is trained using a combination of:
+
+- Real-world document data
+- Synthetic document generation
+- Supervised Fine-Tuning (SFT)
+- Reinforcement Learning (RL)
+- On-Policy Distillation (OPD)
+
+OvisOCR2 achieves an **overall score of 96.58 on OmniDocBench v1.6**, becoming the first end-to-end model to top that benchmark, and also leads PureDocBench with an Avg3 score of 75.06. 1
+
+---
+
+# Quantization
+
+This release uses **oQe4 mixed-precision quantization**.
+
+## Specifications
+
+- **Format:** MLX
+- **Quantization:** oQe4
+- **Method:** Sensitivity-Aware Mixed Precision
+- **Target Platform:** Apple Silicon
+- **Inference Engine:** MLX / oMLX
+
+Unlike traditional fixed-bit quantization, **oQe4 automatically assigns precision according to layer sensitivity**, preserving critical components while compressing less sensitive regions.
+
+Benefits include:
+
+- Better OCR accuracy retention
+- Lower memory usage
+- Faster inference
+- Higher throughput
+- Excellent Apple Silicon optimization
+
+---
+
+# Recommended Settings
+
+For the best OCR quality:
+
+```yaml
+temp: 0.0
+top_p: 1.0
+top_k: 1
+max_tokens: 4096
+```
+
+For difficult or noisy scans:
+
+```yaml
+temp: 0.1
+top_p: 0.95
+top_k: 20
+max_tokens: 4096
+```
+
+OCR is generally deterministic, so greedy or near-greedy decoding is recommended for maximum transcription accuracy.
+
+---
+
+# Example Usage
+
+```python
+from mlx_vlm import load, generate
+from PIL import Image
+
+model, processor = load("yugeshkarunamurthy/OvisOCR2-oQe4")
+
+image = Image.open("document.png")
+
+prompt = """
+Extract all readable content from the document.
+Output Markdown preserving reading order.
+Render tables as HTML.
+Render formulas using LaTeX.
+"""
+
+response = generate(
+    model=model,
+    processor=processor,
+    image=image,
+    prompt=prompt,
+    temperature=0.0,
+)
+
+print(response)
+```
+
+---
+
+# Optimized For
+
+This release is optimized for:
+
+- Apple M1
+- Apple M2
+- Apple M3
+- Apple M4
+
+Compatible with:
+
+- MLX
+- MLX-VLM
+- oMLX
+- Local OCR Applications
+- Document Processing Pipelines
+
+---
+
+# Model Highlights
+
+- End-to-End OCR
+- Page-level Document Parsing
+- Markdown Generation
+- HTML Table Extraction
+- LaTeX Formula Recognition
+- Scientific Paper Parsing
+- Forms and Receipts
+- Complex Multi-column Documents
+- Reading Order Preservation
+- High OCR Accuracy
+
+---
+
+# Intended Use
+
+OvisOCR2-oQe4 is well suited for:
+
+- OCR
+- PDF Digitization
+- Scientific Paper Extraction
+- Book Digitization
+- Invoice Processing
+- Receipt Processing
+- Form Parsing
+- Research Automation
+- Knowledge Base Construction
+- RAG Preprocessing
+- Markdown Conversion
+- Digital Archives
+
+---
+
+# Hardware Recommendations
+
+Recommended systems:
+
+- Apple M1 Pro / Max / Ultra
+- Apple M2 Pro / Max / Ultra
+- Apple M3 Series
+- Apple M4 Series
+
+The compact 0.8B model is lightweight and runs comfortably on most Apple Silicon devices while benefiting from additional memory for larger documents and higher throughput.
+
+---
+
+# About oQe4 Quantization
+
+oQe4 is a sensitivity-aware mixed-precision quantization technique designed to preserve model quality while significantly reducing memory requirements.
+
+Rather than assigning identical precision to every layer, oQe4 analyzes the sensitivity of individual modules and allocates higher precision only where it has the greatest impact.
+
+Benefits include:
+
+- Better OCR accuracy retention
+- Improved document layout understanding
+- Lower RAM usage
+- Faster inference
+- Excellent Apple Silicon performance
+
+---
+
+# Original Model
+
+The original **OvisOCR2** is an end-to-end document parsing model built upon **Qwen3.5-0.8B**.
+
+Notable features include:
+
+- State-of-the-art document OCR
+- Markdown document generation
+- HTML table extraction
+- LaTeX formula recognition
+- Natural reading order
+- End-to-end architecture
+- Compact 0.8B model
+- Apache-2.0 license
+
+For benchmark results, technical details, and training methodology, please visit the original repository. 2
+
+---
+
+# Credits
+
+## Original Model
+
+All credit for the original model, datasets, training methodology, evaluation, benchmarks, and research belongs entirely to:
+
+**ATH-MaaS**
+
+Original Repository:
+
+https://huggingface.co/ATH-MaaS/OvisOCR2
+
+Technical Report:
+
+https://arxiv.org/abs/2607.13639
+
+---
+
+## oQe4 MLX Quantized Release
+
+This repository provides an Apple Silicon optimized **oQe4 MLX quantized** version of the original model.
+
+No additional fine-tuning has been performed.
+
+---
+
+# Acknowledgements
+
+- ATH-MaaS
+- Qwen Team
+- Apple MLX
+- Hugging Face
+- Transformers
+- vLLM
+- SGLang
+- MLX-VLM
+- oMLX
+- OptiQ Quantization
+
+---
+
+# Citation
+
+If you use this model in research, please cite the original OvisOCR2 paper:
+
+```bibtex
+@misc{lu2026ovisocr2,
+  title        = {OvisOCR2 Technical Report},
+  author       = {Shiyin Lu and Yinglun Li and Yu Xia and Yuhui Chen and An-Yang Ji and Jun-Peng Jiang and Qing-Guo Chen and Jianshan Zhao and En Lin and Haijun Li and Cheng Qin and Zhao Xu and Weihua Luo},
+  year         = {2026},
+  eprint       = {2607.13639},
+  archivePrefix= {arXiv},
+  primaryClass = {cs.CV}
+}
+```
+
+---
+
+# License
+
+This release inherits the **Apache-2.0** license from the original model.
+
+Please refer to the original repository for complete licensing information.
+
+---
+
+# Disclaimer
+
+This repository contains an optimized **oQe4 MLX quantized conversion** intended for efficient local inference on Apple Silicon.
+
+All original model architecture, datasets, training methodology, benchmarks, evaluations, and research remain entirely the work of the original ATH-MaaS team.
+````3