Update README.md

README.md

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 *QvQ-Step-Tiny* is a step-by-step context explainer Vision-Language model based on the Qwen2-VL architecture, fine-tuned using the VCR datasets for systematic step-by-step explanations. It is built on the Qwen2VLForConditionalGeneration framework with 2.21 billion parameters and uses BF16 (Brain Floating Point 16) precision.
 
+# **Quickstart with Transformers**
 
 # **Key Enhancements of QvQ-Step-Tiny**  
 
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    - QvQ-Step-Tiny supports multilingual text recognition within images, handling English, Chinese, and a wide range of languages, including most European languages, Japanese, Korean, Arabic, and Vietnamese.  
    - This makes it an effective model for global applications, from document analysis to multi-language accessibility solutions.  
 
+# **Intended Use**  
+1. **Step-by-Step Context Explanation**: Designed to provide detailed and systematic explanations for images and videos, making it ideal for educational, analytical, and instructional tasks.  
+2. **Visual Content Understanding**: Effective for analyzing visual content across diverse resolutions, aspect ratios, and formats, including documents (DocVQA) and mathematical visuals (MathVista).  
+3. **Video-based Reasoning**: Supports comprehension of long-form videos (20+ minutes) for tasks like video question answering, dialog generation, and instructional content creation.  
+4. **Device Integration**: Can act as an intelligent agent to automate device operations (e.g., mobile phones, robots) by understanding visual environments and processing text-based instructions.  
+5. **Multilingual Visual Text Support**: Recognizes and processes multilingual text within images, making it suitable for global applications like document processing and accessibility tools.  
+6. **Advanced Question Answering**: Excels in question-answering tasks involving images, videos, and multimodal data, serving as a robust tool for interactive systems.  
+7. **Accessibility Enhancements**: Assists visually impaired users by explaining visual and textual content in a clear, step-by-step manner.  
+
+# **Limitations**  
+1. **Model Size Constraints**: At 2.21 billion parameters, it may not perform as well as larger models for highly complex or nuanced tasks.  
+2. **Accuracy with Low-Quality Inputs**: Performance may degrade when dealing with low-resolution images, poor lighting conditions, or noisy video/audio inputs.  
+3. **Specialized Training Gaps**: While strong on general benchmarks, it might struggle with niche or highly specialized domains that require additional fine-tuning.  
+4. **Multilingual Text Variability**: While multilingual text recognition is supported, performance may vary across less common or highly complex languages.  
+5. **Context Length Tradeoffs**: Processing very long videos (e.g., over 20 minutes) or highly dense visual data might challenge its coherence or explanation accuracy.  
+6. **Device Integration Complexity**: Deploying the model for operating devices or robots may require significant engineering efforts and robust integration pipelines.  
+7. **Resource-Intensive for Long Contexts**: Despite BF16 precision, tasks with extended context lengths or high-resolution inputs could demand substantial computational resources.  
+8. **Ambiguity in Prompts**: Ambiguously phrased or poorly structured input prompts may lead to incomplete or inaccurate explanations.  
+9. **Static Model**: The model cannot learn dynamically from user interactions or adapt its behavior without retraining.  
+
 # **Applications**  
 - **Education**: Step-by-step explanations for visual and textual content in learning materials, including images and videos.  
 - **Automation**: Integrating with robotics or smart devices for performing tasks based on visual and textual data.