This repository is a work in progress, and more features and improvements are coming soon!
This repository provides a fully handcrafted implementation of the Transformer model from scratch using only PyTorch, without relying on any external libraries or pre-built modules. It is designed for educational purposes, aiming to offer a clear understanding of how Transformers work under the hood.
- Pure PyTorch: No external deep learning frameworks or utilities, just plain PyTorch.
- From Scratch Implementation: Every component of the Transformer model, including multi-head attention, position-wise feedforward layers, and positional encodings, is built from the ground up.
- Educational Focus: Clear and modular code designed to help learners understand the inner workings of Transformers.
- Extensible: The implementation can be easily extended for experimentation or custom Transformer architectures.
- Full implementation of the Transformer encoder and decoder.
- Examples of training the model on synthetic data.
- Detailed documentation and comments to explain key components.
Transformers are a foundational model in modern machine learning, powering state-of-the-art results in natural language processing and beyond. Understanding how they work internally by building them from scratch can be a powerful learning experience.