Update README.md

README.md

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 - Reasoner
 - Qwen-Base
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 ![omni.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Ai2-yZdpYmAhiU9HBu6gr.png)
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+Omni-Reasoner-2B is based on Qwen2VL and is designed for mathematical and content-based explanations. It excels in providing detailed reasoning about content and solving math problems with proper content formatting. This model integrates a conversational approach with visual and textual understanding to handle multi-modal tasks effectively.
+
+# **Key Enhancements**
+1. **Advanced Reasoning Capabilities**: 
+   - Enhanced ability to perform long-form reasoning for complex mathematical and content-based queries.
+   - Supports detailed step-by-step explanations for problem-solving and content formatting.
+
+2. **Multi-Modal Integration**: 
+   - Combines visual and textual understanding to interpret and analyze diverse input formats (images, text, and mathematical expressions).
+
+3. **Conversational Workflow**: 
+   - Offers a natural conversational interface for interactive problem-solving and explanations.
+
+4. **Content Formatting**: 
+   - Improves content presentation with structured formatting for better readability and understanding.
+
+# **Intended Use**
+1. **Educational Assistance**: 
+   - Ideal for students and educators for solving mathematical problems, creating structured explanations, and formatting educational content.
+
+2. **Research Support**: 
+   - Assists researchers in generating in-depth explanations and interpreting complex visual and textual data.
+
+3. **Content Creation**: 
+   - Enhances the generation of well-formatted documents, reports, and presentations.
+
+4. **General Purpose Assistance**: 
+   - Useful for applications requiring long-form reasoning and conversational AI in domains like tutoring, customer support, and technical writing.
+
+# **Limitations**
+1. **Domain-Specific Expertise**: 
+   - May struggle with niche or highly specialized topics outside its training domain.
+
+2. **Error in Long-Chain Reasoning**: 
+   - In rare cases, it might generate incorrect or inconsistent solutions for highly complex problems.
+
+3. **Visual Data Limitations**: 
+   - Performance may depend on the quality and clarity of visual inputs (e.g., low-resolution images may reduce accuracy).
+
+4. **Formatting Constraints**: 
+   - While effective, complex or heavily customized formatting tasks may require manual adjustments.
+
+5. **Dependence on Context**: 
+   - The model relies on well-structured input to produce accurate and coherent outputs; ambiguous or incomplete prompts may lead to suboptimal results.