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
.ipynbfiles (optional).
- Clone the repository:
git clone https://github.com/your-username/ai-llm-project.git
- Navigate to the project directory:
cd ai-llm-project - Create a virtual environment and activate it:
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
- Install dependencies:
pip install -r requirements.txt
- Data Preparation: Place raw datasets in the
data/rawdirectory. - Preprocessing: Use
src/data/data_preprocessor.pyto preprocess the data. - Training: Run
scripts/train.pyto train or fine-tune the model. - Evaluation: Use
scripts/evaluate.pyto evaluate model performance. - 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].