Transformer

This repository provides a PyTorch implementation of core Transformer functionalities and optimization techniques. It focuses on building and understanding Transformer models, including key concepts like self-attention and Low-Rank Adaptation (LoRA), while providing practical implementations for educational and experimental purposes.

Key Features

• Self-Attention Implementation: A detailed implementation of the self-attention mechanism, a key component of the Transformer architecture.
• Model Optimization with LoRA: Demonstrates how to optimize Transformers using LoRA (Low-Rank Adaptation), a method to efficiently fine-tune pre-trained models.
• Customizable PyTorch Code: All implementations are in PyTorch, making them easy to extend, experiment with, and adapt to other use cases.

Files in the Repository

• self_attention.ipynb: A notebook containing the implementation and explanation of the self-attention mechanism from scratch.
• LORA.ipynb: A notebook demonstrating how to use LoRA for optimizing Transformer models.
• Model_optimization.ipynb: An exploration of techniques to optimize Transformer models for better performance and efficiency.
• README.md: This documentation file.

Getting Started

Prerequisites

• Python 3.7+
• PyTorch (latest version)
• Jupyter Notebook or Google Colab

Installation

1. Clone this repository: git clone https://github.com/Ruthuvikas/Transformer.git cd Transformer

2. Install dependencies:

3. Open the notebooks:

Alternatively, you can upload the .ipynb files to Google Colab for execution.

Running the Notebooks

• Open any of the provided notebooks (e.g., self_attention.ipynb) and follow the instructions within the notebook to run the cells and explore the implementations.

Use Cases

• Learn how Transformers work under the hood.
• Experiment with self-attention and LoRA optimizations.
• Fine-tune or extend the provided implementations for your own projects.

Contribution

Contributions are welcome! If you have improvements, bug fixes, or new features you'd like to add:

1. Fork the repository.
2. Create a new branch for your feature/bugfix.
3. Submit a pull request.

Acknowledgments

• Based on the seminal Transformer paper: "Attention Is All You Need".
• LoRA technique inspired by "LoRA: Low-Rank Adaptation of Large Language Models".

License

This project is open-source and available under the MIT License.