Update README.md

README.md

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 ![aaa.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/faLfR-doaWP_BLUkOQrbq.png)
 
-#### **Math IIO 7B Instruct Model Files Upload Details:**
+### **Math IIO 7B Instruct**  
+
+### **Key Features:**
+
+1. **Math-Optimized Capabilities:**  
+   The model is designed to handle complex mathematical problems, step-by-step calculations, and reasoning tasks.  
+
+2. **Instruction-Tuned:**  
+   Fine-tuned for better adherence to structured queries and task-oriented prompts, enabling clear and concise outputs.
+
+3. **Large Vocabulary:**  
+   Equipped with an extensive tokenizer configuration and custom tokens to ensure precise mathematical notation support.
 
 | File Name                          | Size       | Description                                   | Upload Status  |
 |------------------------------------|------------|-----------------------------------------------|----------------|
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 | `tokenizer_config.json`            | 7.73 kB    | Configuration for tokenizer                   | Uploaded       |
 | `vocab.json`                       | 2.78 MB    | Vocabulary for tokenizer                      | Uploaded       |
 
+
+The **Math IIO 7B Instruct** is a fine-tuned language model based on the robust **Qwen2.5-7B-Instruct** architecture. This model has been specifically trained to excel in mathematical reasoning and instruction-based tasks, making it a reliable choice for educational, analytical, and problem-solving applications.
+
+### **Training Details:**
+- **Base Model:** [Qwen/Qwen2.5-7B-Instruct](#)  
+- **Dataset:** Trained on **Math-IIO-68K-Mini**, a curated dataset with 68.8k high-quality examples focusing on mathematical instructions, equations, and logic-based queries.
+
+### **Capabilities:**
+- **Problem-Solving:** Solves mathematical problems ranging from basic arithmetic to advanced calculus and linear algebra.  
+- **Educational Use:** Explains solutions step-by-step, making it a valuable teaching assistant.  
+- **Analysis & Reasoning:** Handles logical reasoning tasks and computational queries effectively.
+
+### **How to Use:**
+1. Download all model files, ensuring the PyTorch weights and tokenizer configurations are included.  
+2. Load the model in your Python environment using frameworks like PyTorch or Hugging Face Transformers.  
+3. Use the provided configurations (`config.json` and `generation_config.json`) for optimal inference.