README_final.md

# LLaMA Model Deployment and Local Testing

**Description:**  
This project provides a comprehensive framework for working with machine-learning models, with a focus on deploying and testing local models and experimenting with advanced AI architectures like LLaMA. The project is split into two main notebooks, each addressing distinct tasks:

1. **Local Model Deployment and Testing:**  
   The first notebook demonstrates how to set up and evaluate machine-learning models on a local machine. It includes:
   - Preprocessing datasets.
   - Configuring and training models.
   - Evaluating performance using standard metrics.

2. **LLaMA-Based Project Implementation:**  
   The second notebook builds on the capabilities of the LLaMA architecture (or a similar model). It covers:
   - Fine-tuning pre-trained AI models.
   - Generating predictions or performing specific tasks (e.g., text generation, classification).
   - Utilizing advanced features for optimization and deployment.

---

## Files Included

1. `Run_Local_Model_6604.ipynb`  
   - **Purpose:** This notebook is designed for testing machine-learning models locally.  
   - **Detailed Explanation:**  
     - **Dataset Preparation:** The notebook includes steps for cleaning, normalizing, or splitting datasets into training and testing sets.  
     - **Model Configuration:** Set up model parameters such as number of layers, learning rate, or optimization algorithms.  
     - **Training Process:** Train models on provided datasets using iterative learning to minimize errors.  
     - **Evaluation Metrics:** Metrics such as accuracy, precision, recall, and F1-score are computed to assess model performance.  
   - **Usage Instructions:**  
     1. Set up your Python environment and install dependencies.
     2. Configure your dataset path.
     3. Open the notebook in Jupyter Notebook.
     4. Execute each cell sequentially to preprocess, train, and evaluate the model.
   - **Requirements:** Ensure dependencies like NumPy, Pandas, Scikit-learn, and PyTorch are installed.

2. `Final_pro_llma3B.ipynb`  
   - **Purpose:** This notebook serves as the final project implementation, focusing on fine-tuning and using the LLaMA model.  
   - **Detailed Explanation:**  
     - **Pre-trained Model Usage:** Uses pre-trained LLaMA AI models to generate predictions.  
     - **Fine-Tuning:** Adapts the LLaMA model to custom datasets for specific NLP tasks such as text classification, analysis, or prediction.
     - **Task Execution:** Includes processes for inference, fine-tuning, or generating outputs using LLaMA's capabilities.  
   - **Usage Instructions:**  
     1. Download required pre-trained models and save them to the designated directory.  
     2. Ensure all dependencies like Hugging Face Transformers, PyTorch, and other necessary libraries are installed.  
     3. Run the Jupyter Notebook sequentially, following each instruction in the cells.
   - **Requirements:** Pre-trained model weights must be downloaded and saved correctly.

---

## Author

**Mahesh Potu**  
Master's Student in Data Science  
University of New Haven  

---

## Requirements

- Python 3.8 or later  
- Jupyter Notebook or JupyterLab  
- Libraries:  
  ```plaintext
  numpy, pandas, matplotlib, scikit-learn, torch, transformers
  ```

---

## Getting Started

1. Clone the repository:  
   ```bash
   git clone https://github.com/username/projectname.git
   ```  

2. Navigate to the project folder:  
   ```bash
   cd projectname
   ```  

3. Create a virtual environment and activate it:  
   ```bash
   python -m venv env  
   source env/bin/activate   # For Linux/Mac  
   env\Scripts\activate      # For Windows  
   ```  

4. Install the required libraries:  
   ```bash
   pip install -r requirements.txt
   ```  

5. Open the Jupyter Notebook:  
   ```bash
   jupyter notebook
   ```  

6. Run the cells in the notebooks sequentially to complete the tasks.

---

## License

This project is licensed under the MIT License. See `LICENSE` for more details.