Update README.md

README.md

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 ### SmolLM2-Math-IIO-1.7B-Instruct
 
+The **SmolLM2-Math-IIO-1.7B-Instruct** model is a fine-tuned variant of the **SmolLM2-1.7B** architecture, optimized for mathematical instruction and reasoning tasks. It is particularly suited for applications that require mathematical problem-solving, logical inference, and detailed step-by-step explanations.
+
 | File Name                              | Size       | Description                                    | Upload Status  |
 |----------------------------------------|------------|------------------------------------------------|----------------|
 | `.gitattributes`                       | 1.52 kB    | Git attributes configuration file              | Uploaded       |
@@ -34,4 +36,51 @@ tags:
 | `tokenizer_config.json`                | 3.95 kB   | Tokenizer configuration for loading and usage  | Uploaded       |
 | `vocab.json`                           | 801 kB     | Vocabulary for the tokenizer                   | Uploaded       |
 
+### **Key Features:**
+
+1. **Math-Focused Capabilities:**  
+   This model is fine-tuned to handle a wide range of mathematical queries, from simple arithmetic to complex equations and mathematical proofs.  
+
+2. **Instruction-Tuned:**  
+   Specifically trained to follow structured queries and deliver clear, coherent outputs based on instructions, ensuring high-quality, relevant responses to prompts.
+
+3. **Tokenizer & Custom Tokens:**  
+   Includes a robust tokenizer configuration with support for mathematical notation, custom tokens, and an extended vocabulary for accurate understanding and output generation.
+
+---
+
+### **Training Details:**
+- **Base Model:** [SmolLM2-1.7B](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct)  
+- **Dataset:** Trained on **Math-IIO-68K-Mini**, a dataset focused on mathematical instructions and logic-based queries, with a total of 68.8k examples.
+
 ---
+
+### **File Details:**
+| **File Name**                   | **Size**       | **Description**                                  |
+|-----------------------------------|----------------|--------------------------------------------------|
+| `.gitattributes`                  | 1.52 kB        | Git attributes configuration file.               |
+| `README.md`                       | 287 Bytes      | Updated README file with model details.          |
+| `config.json`                     | 940 Bytes      | Configuration file for model setup.              |
+| `generation_config.json`          | 162 Bytes      | Configuration for generation-specific settings.  |
+| `merges.txt`                      | 515 kB         | Tokenizer merging rules (Byte Pair Encoding).    |
+| `pytorch_model.bin`               | 3.42 GB        | Full model weights in PyTorch format.            |
+| `special_tokens_map.json`         | 572 Bytes      | Special token mappings for the tokenizer.        |
+| `tokenizer.json`                  | 3.77 MB        | Tokenizer configuration and vocabulary.          |
+| `tokenizer_config.json`           | 3.95 kB        | Tokenizer configuration for loading.             |
+| `vocab.json`                      | 801 kB         | Vocabulary file for the tokenizer.               |
+
+---
+
+### **Capabilities:**
+- **Mathematical Problem-Solving:** Solves and explains complex mathematical problems, including algebra, calculus, and more advanced topics.  
+- **Instruction-Following:** Adheres to structured inputs and outputs, making it effective for generating step-by-step solutions.  
+- **Text Generation:** Capable of generating mathematical proofs, explanations, and educational content tailored to various user queries.
+
+---
+
+### **Usage Instructions:**
+1. **Model Setup:** Download all model files and ensure the PyTorch model weights and tokenizer configurations are included.  
+2. **Inference:** Load the model in a Python environment using frameworks like PyTorch or Hugging Face's Transformers.  
+3. **Customization:** Configure the model with the `config.json` and `generation_config.json` files for optimal performance during inference.
+
+---