README.md

---
license: apache-2.0
pipeline_tag: image-text-to-text
language:
- en
base_model:
- prithivMLmods/Qwen2-VL-OCR-2B-Instruct
library_name: peft
tags:
- ocr_test
- qwen
- qvq
- kie
- trl
- text-generation-inference
- qwen2_vl
---
# **QvQ KiE [Key Information Extractor] Adapter for Qwen2-VL-OCR-2B-Instruct**

The **QvQ KiE adapter** is a fine-tuned version of the **Qwen/Qwen2-VL-2B-Instruct** model, specifically tailored for tasks involving **Optical Character Recognition (OCR)**, **image-to-text conversion**, and **math problem-solving** with **LaTeX formatting**. This adapter enhances the model’s performance for multi-modal tasks by integrating vision and language capabilities in a conversational framework.

# **Key Features**

### 1. **Vision-Language Integration**  
- Seamlessly combines **image understanding** with **natural language processing**, enabling accurate image-to-text conversion.  
   
### 2. **Optical Character Recognition (OCR)**  
- Extracts and processes textual content from images with high precision, making it ideal for document analysis and information extraction.  

### 3. **Math and LaTeX Support**  
- Efficiently handles complex **math problem-solving**, outputting results in **LaTeX format** for easy integration into scientific and academic workflows.  

### 4. **Conversational Capabilities**  
- Equipped with multi-turn conversational capabilities, providing context-aware responses during interactions. This makes it suitable for tasks requiring ongoing dialogue and clarification.  

### 5. **Image-Text-to-Text Generation**  
- Supports input in various forms:
  - **Images**
  - **Text**
  - **Image + Text (multi-modal)**  
- Outputs include descriptive or problem-solving text, depending on the input type.  

### 6. **Secure Weight Format**  
- Utilizes **Safetensors** for fast and secure model weight loading, ensuring both performance and safety during deployment.

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