Qv Q Ki E

Introduction

The QvQ KiE (Key Information Extractor) adapter is a specialized enhancement of the Qwen2-VL-2B-Instruct model. It is designed for tasks involving Optical Character Recognition (OCR), image-to-text conversion, and solving mathematical problems with LaTeX formatting. This adapter improves the model's performance in multi-modal tasks by integrating visual and linguistic capabilities within a conversational framework.

Architecture

The QvQ KiE adapter features several key components:

• Vision-Language Integration: Combines image understanding with natural language processing for accurate image-to-text conversion.
• Optical Character Recognition (OCR): Extracts and processes text from images with high precision, ideal for document analysis and information extraction.
• Math and LaTeX Support: Solves complex math problems and outputs results in LaTeX format for scientific and academic use.
• Conversational Capabilities: Supports multi-turn conversations and context-aware responses, suitable for tasks requiring dialogue and clarification.
• Image-Text-to-Text Generation: Handles various input forms, including images, text, or a combination, and generates descriptive or problem-solving text.
• Secure Weight Format: Uses Safetensors for secure and efficient model weight loading.

Training

The QvQ KiE adapter is a fine-tuned model based on the Qwen2-VL-2B-Instruct architecture. The training process involves enhancing the model's capabilities in OCR, image-to-text conversion, and math problem-solving, specifically for multi-modal tasks.

Guide: Running Locally

1. Installation:

   • Clone the repository from Hugging Face.
   • Install the required dependencies using a package manager like pip.

2. Setup:

   • Load the QvQ KiE model using the Hugging Face Transformers library.

3. Execution:

   • Prepare your input data (images, text, or both).
   • Run the model to obtain the desired output.

4. Cloud GPUs:

   • For optimal performance, consider using cloud GPU services such as AWS, Google Cloud, or Azure.

License

This model is licensed under the Apache-2.0 License, allowing for both commercial and non-commercial use under specified conditions.