deep-div/Custom-Transformer-Pytorch
A clean, ground-up implementation of the Transformer architecture in PyTorch, including positional encoding, multi-head attention, encoder-decoder layers, and masking. Great for learning or building upon the core model.
This project provides a clear, detailed implementation of the Transformer deep learning architecture in PyTorch. It helps users understand how text inputs are converted into numerical representations, how positional information is added, and how attention mechanisms process language. Researchers, students, or machine learning engineers who want to grasp the foundational components of modern natural language processing models will find this useful.
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Use this if you need to learn or teach the core concepts of the Transformer model, understand its components without high-level abstractions, or build custom variants from scratch.
Not ideal if you want to apply a pre-built Transformer model for a specific task like text generation or sentiment analysis, as this project focuses on the underlying architecture rather than practical application.
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Jupyter Notebook
License
Apache-2.0
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Last pushed
Aug 30, 2025
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