MonkeyOCR-pro-1.2B-Vision-GGUF

MonkeyOCR-pro-1.2B Vision GGUF

A high-performance vision-language model specialized for Optical Character Recognition (OCR) and document analysis. This repository contains GGUF format models optimized for both general use and LM Studio compatibility.

🎯 Model Capabilities

• ✅ Vision-Language Processing: Understand and process images with text
• ✅ OCR (Optical Character Recognition): Extract text from images and documents
• ✅ Document Structure Analysis: Analyze layout, tables, and formatting
• ✅ Multi-turn Conversations: Chat about images and documents
• ✅ Multiple Languages: Support for English, Chinese, and more

📁 Files Included

Standard GGUF Models

• MonkeyOCR-pro-1.2B-Recognition.gguf (1.26 GB) - Complete vision-enabled model
• MonkeyOCR-pro-1.2B.gguf (1.26 GB) - Alternative version

LM Studio Optimized

• MonkeyOCR-pro-1.2B-Text-LMStudio.gguf (1.23 GB) - Text model for LM Studio
• mmproj-MonkeyOCR-pro-1.2B-Vision-LMStudio.gguf (1.25 GB) - Vision projection for LM Studio

🎨 LM Studio Setup (Recommended)

Step 1: Download Files

Download both LM Studio files:

• MonkeyOCR-pro-1.2B-Text-LMStudio.gguf
• mmproj-MonkeyOCR-pro-1.2B-Vision-LMStudio.gguf

Step 2: Install in LM Studio

1. Copy both files to your LM Studio models directory
2. Keep them in the same folder
3. Open LM Studio and load the text model: MonkeyOCR-pro-1.2B-Text-LMStudio.gguf
4. LM Studio will automatically detect and load the vision projection

Step 3: Enable Vision

1. Start a new chat session
2. Look for the camera/image icon in the chat interface
3. Upload an image and start asking questions!

🔧 Usage Examples

OCR Tasks

User: [uploads image] Extract all text from this document
Assistant: I can see text in the image. Here's what I extracted:
[Provides accurate OCR results]

Document Analysis

User: [uploads form] Analyze the structure of this form
Assistant: This appears to be a [form type] with the following structure:
- Header section with title
- Main content area with fields for...
- Footer with signature area

Vision Q&A

User: [uploads chart] What does this chart show?
Assistant: This chart displays [detailed analysis of the visual content]

⚙️ Technical Specifications

• Architecture: Qwen2.5-VL (qwen2vl)
• Model Size: 1.2B parameters
• Quantization: Q8_0 (8-bit)
• Context Length: 8,196 tokens
• Vision Encoder: CLIP-based projection
• Input Support: Images, text, multimodal conversations

🔧 Configuration Details

The model includes proper vision token configuration:

• Image Token ID: 151655 (<|image_pad|>)
• Video Token ID: 151656 (<|video_pad|>)
• Vision Start: 151652 (<|vision_start|>)
• Vision End: 151653 (<|vision_end|>)

🚀 Performance

• Fast inference with Q8_0 quantization
• High accuracy OCR capabilities
• Efficient memory usage (~1.2-1.3 GB VRAM)
• Multi-language support for global documents

📝 License

Apache 2.0 - Free for commercial and research use

🙏 Credits

Based on the MonkeyOCR model architecture with optimizations for GGUF format and LM Studio compatibility.

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For best vision performance, use the LM Studio optimized files! 🎯