This project is a comprehensive template designed to facilitate the development and fine-tuning of Large Language Models (LLMs). It provides a modular and organized structure for handling all aspects of an AI/LLM project, from data preprocessing to model evaluation and deployment. Whether you are building a model from scratch or fine-tuning an existing pre-trained LLM, this template will serve as a solid foundation for your work.

• Modular Structure: Organized directories and files for clean and maintainable code.
• Data Handling: Modules for loading, preprocessing, and managing datasets.
• Model Training and Fine-Tuning: Scripts for training and fine-tuning LLMs.
• Evaluation: Utilities to measure model performance using custom metrics.
• Scalability: Ready-to-use configuration files for scaling from local development to production.
• Automation: Scripts for automating repetitive tasks like training and evaluation.

ai-llm-project/ ├── README.md # Project overview and instructions ├── LICENSE # Licensing information ├── .gitignore # Files and directories to be ignored by Git ├── requirements.txt # Python dependencies ├── setup.py # Packaging and distribution script ├── pyproject.toml # Project configuration ├── config.yaml # Default configuration file ├── src/ # Source code │ ├── __init__.py # Initializes the src package │ ├── data/ # Data handling modules │ │ ├── __init__.py │ │ ├── data_loader.py # Data loading logic │ │ ├── data_preprocessor.py # Data preprocessing steps │ ├── models/ # Model modules │ │ ├── __init__.py │ │ ├── base_model.py # Base model architecture │ │ ├── fine_tune.py # Fine-tuning logic │ ├── utils/ # Utility functions │ │ ├── __init__.py │ │ ├── file_utils.py # File operation helpers │ │ ├── logger.py # Logging utilities │ ├── evaluation/ # Evaluation modules │ ├── __init__.py │ ├── metrics.py # Evaluation metrics │ ├── evaluate.py # Evaluation scripts ├── tests/ # Unit tests │ ├── test_data_loader.py # Tests for data loading │ ├── test_fine_tune.py # Tests for fine-tuning │ ├── test_metrics.py # Tests for evaluation metrics ├── notebooks/ # Jupyter notebooks │ ├── data_exploration.ipynb # Dataset exploration and visualization │ ├── model_training.ipynb # Model training workflow ├── data/ # Dataset storage │ ├── raw/ # Raw datasets │ ├── processed/ # Processed datasets ├── scripts/ # Standalone scripts │ ├── train.py # Script for training models │ ├── predict.py # Script for generating predictions ├── docs/ # Documentation │ ├── index.md # Documentation index │ ├── api_reference.md # API reference documentation ├── configs/ # Configuration files │ ├── default_config.yaml # Default configuration settings │ ├── dev_config.yaml # Development configuration settings ├── logs/ # Log files ├── checkpoints/ # Saved model checkpoints 

• Python: Version 3.8 or higher.
• Libraries: Listed in requirements.txt.
• Git: Version control system.
• Jupyter Notebook: For running .ipynb files (optional).

1. Clone the repository:

   git clone https://github.com/your-username/ai-llm-project.git

2. Navigate to the project directory:

   cd ai-llm-project

3. Create a virtual environment and activate it:

   python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate

4. Install dependencies:

   pip install -r requirements.txt

1. Data Preparation: Place raw datasets in the data/raw directory.
2. Preprocessing: Use src/data/data_preprocessor.py to preprocess the data.
3. Training: Run scripts/train.py to train or fine-tune the model.
4. Evaluation: Use scripts/evaluate.py to evaluate model performance.
5. Predictions: Generate predictions using scripts/predict.py.

Contributions are welcome! Please fork the repository and submit a pull request with your changes.

This project is licensed under the terms specified in the LICENSE file.

For questions or feedback, please contact [Your Name] at [your-email@example.com].