Breeze648/Transformer-from-Scratch
📦 Open Source ProjectBreeze648
A clean, modular implementation of the Transformer architecture from scratch for deep learning education.
Transformer-from-Scratch is a pedagogical project that demystifies the Transformer architecture by implementing it from the ground up using Python. The repository strictly adheres to the modular design outlined in the seminal 'Attention Is All You Need' paper. It includes comprehensive implementations of core components, including scaled dot-product attention, multi-head attention mechanisms, position-wise feed-forward networks, and positional encoding.
Beyond just the code, the project is structured to be highly accessible for developers and students. Each module is logically separated, mirroring the original paper's architecture, which makes it an excellent resource for those looking to understand the internal data flow of LLMs. The repository is enriched with English-annotated code and detailed documentation, allowing users to experiment with, modify, and extend the architecture for their own research or application needs. It is an ideal starting point for anyone transitioning from high-level API usage to understanding the fundamental mathematical and structural building blocks of deep learning.
💡Highlights
- ├─Modular encoder-decoder design
- ├─Full multi-head attention logic
- └─Includes positional encoding
🎯For
- ├─AI researchers
- ├─Deep learning students
- └─Software engineers