Update README.md

README.md

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 # Tulu-MathLingo-8B Model Files
 
+The **Tulu-MathLingo-8B** model is a fine-tuned version of **meta-llama/Llama-3.1-8B**, optimized for solving mathematical word problems and reasoning tasks in English and the Tulu language. The model integrates advanced language understanding and reasoning capabilities with a focus on providing solutions to math-related queries.
+
 | **File Name**                     | **Size**     | **Description**                                 | **Upload Status** |
 |-----------------------------------|--------------|------------------------------------------------|-------------------|
 | `.gitattributes`                  | 1.57 kB     | Configures LFS tracking for large files.        | Updated           |
@@ -35,4 +37,87 @@ tags:
 | `tokenizer.json`                  | 17.2 MB     | Full tokenizer configuration.                   | Uploaded (LFS)    |
 | `tokenizer_config.json`           | 57.6 kB     | Metadata for tokenizer usage.                   | Uploaded          |
 
+### **Key Features**
+
+1. **Multilingual Math Reasoning:**  
+   - Designed for solving complex math problems in **English** and **Tulu**.  
+
+2. **Text Generation:**  
+   - Generates detailed and contextually accurate text responses.
+
+3. **Fine-Tuned Specializations:**  
+   - Trained on the **microsoft/orca-math-word-problems-200k** dataset for word problem-solving.  
+
+4. **Special Token Mapping:**  
+   - Configured to use tokens for specific functions such as `<PAD>` and `<EOS>` effectively.
+
+5. **Secure and Efficient Storage:**  
+   - Model weights are stored in the **Safetensors** format for secure and faster inference.
+
+6. **Large Parameter Size:**  
+   - 8.03 billion parameters enable handling complex queries and multi-turn conversations.
+
 ---
+
+### **Training Details**
+
+- **Base Model:** [meta-llama/Llama-3.1-8B](#)  
+- **Fine-Tuned:**  
+   - Through multiple stages: **SFT (Supervised Fine-Tuning)** and **DPO (Direct Preference Optimization)**.
+
+- **Dataset:**  
+   - Trained on **200k word problems** from the **Microsoft Orca Math Word Problems Dataset**.
+
+- **Model Size:**  
+   - 8.03B parameters, optimized for **FP16** tensor type.
+
+---
+
+### **Applications**
+
+1. **Mathematical Word Problems:**  
+   - Solve structured or unstructured math problems in natural language.
+
+2. **Conversational AI for Math:**  
+   - Engage users in interactive dialogues focused on math and logic reasoning.
+
+3. **Multilingual Support:**  
+   - Supports queries in **Tulu** and **English**, enhancing accessibility.
+
+4. **Education Tools:**  
+   - Useful in tutoring systems for math, helping students with problem-solving.
+
+---
+
+### **Usage**
+
+#### **Loading the Model**
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Tulu-MathLingo-8B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="fp16")
+```
+
+---
+
+##### **Math Word Problem**
+```python
+query = "If a train travels 60 miles in 2 hours, what is its average speed?"
+inputs = tokenizer(query, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=100)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print("Answer:", response)
+```
+### **Performance Requirements**
+
+- **Hardware:**  
+   - Requires a GPU with at least **24GB VRAM** for optimal performance due to model size and FP16 usage.
+
+- **Optimization:**  
+   - Use mixed precision (`fp16`) for reduced memory footprint.  
+   - Split inference across multiple GPUs if necessary.
+
+---