Update README.md

README.md

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 ### Neumind-Math-7B-Instruct Model Files 
 
+The **Neumind-Math-7B-Instruct** is a fine-tuned model based on **Qwen2.5-7B-Instruct**, optimized for mathematical reasoning, step-by-step problem-solving, and instruction-based tasks in the mathematics domain. The model is designed for applications requiring structured reasoning, numerical computations, and mathematical proof generation.
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 | File Name                          | Size       | Description                              | Upload Status  |
 |------------------------------------|------------|------------------------------------------|----------------|
 | `.gitattributes`                   | 1.57 kB    | Git attributes configuration file        | Uploaded       |
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 | `vocab.json`                       | 2.78 MB    | Vocabulary for tokenization              | Uploaded       |
 
 ---
+
+### **Key Features:**
+
+1. **Mathematical Reasoning:**  
+   Specifically fine-tuned for solving mathematical problems, including arithmetic, algebra, calculus, and geometry.
+
+2. **Step-by-Step Problem Solving:**  
+   Provides detailed, logical solutions for complex mathematical tasks and demonstrates problem-solving methodologies.
+
+3. **Instructional Applications:**  
+   Tailored for use in educational settings, such as tutoring systems, math content creation, and interactive learning tools.
+
+---
+
+### **Training Details:**
+- **Base Model:** [Qwen2.5-7B-Instruct](#)  
+- **Dataset:** Trained on **AI-MO/NuminaMath-CoT**, a large dataset of mathematical problems and chain-of-thought (CoT) reasoning. The dataset contains **860k problems** across various difficulty levels, enabling the model to tackle a wide spectrum of mathematical tasks.
+
+---
+
+### **Capabilities:**
+
+- **Complex Problem Solving:**  
+   Solves a wide range of mathematical problems, from basic arithmetic to advanced calculus and algebraic equations.
+
+- **Chain-of-Thought Reasoning:**  
+   Excels in step-by-step logical reasoning, making it suitable for tasks requiring detailed explanations.
+
+- **Instruction-Based Generation:**  
+   Ideal for generating educational content, such as worked examples, quizzes, and tutorials.
+
+---
+
+### **Usage Instructions:**
+
+1. **Model Setup:**  
+   Download all model shards and the associated configuration files. Ensure the files are correctly placed for seamless loading.
+
+2. **Inference:**  
+   Load the model using frameworks like PyTorch and Hugging Face Transformers. Ensure the `pytorch_model.bin.index.json` file is in the same directory for shard-based loading.
+
+3. **Customization:**  
+   Adjust generation parameters using `generation_config.json` to optimize outputs for your specific application.
+---
+
+### **Applications:**
+
+- **Education:**  
+   Interactive math tutoring, content creation, and step-by-step problem-solving tools.
+- **Research:**  
+   Automated theorem proving and symbolic mathematics.
+- **General Use:**  
+   Solving everyday mathematical queries and generating numerical datasets.
+---