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transformer-from-scratch

Here are 22 public repositories matching this topic...

A complete implementation of the "Attention Is All You Need" Transformer model from scratch using PyTorch. This project focuses on building and training a Transformer for neural machine translation (English-to-Italian) on the OpusBooks dataset.

  • Updated Nov 8, 2025
  • Python

A showcase repository documenting the complete journey of building a Transformer and Neural Machine Translation framework in pure C—from tensor operations to automatic differentiation, training infrastructure, and end-to-end machine translation.

  • Updated Jul 21, 2026
  • Mermaid

PyTorch Transformer for neural machine translation (NMT), inspired by Attention Is All You Need. German→English on OPUS Books: training, inference, and attention visualization.

  • Updated May 14, 2026
  • Jupyter Notebook

A Transformer encoder built from first principles, implementing the core architecture from mathematical foundations to working PyTorch code, including tokenization, embeddings, positional encoding, self-attention, multi-head attention, LayerNorm, feed-forward networks, training, and evaluation.

  • Updated Sep 22, 2026
  • Python

From-scratch ~100k-parameter LLaMA decoder (RMSNorm, RoPE, SwiGLU, GQA), plus an equal-parameter ablation of each choice, a train-short/test-long probe of RoPE against learned absolute positions, and a parameter-free sweep of RoPE's rotation base. CPU-only, multi-seed; every README number renders from a committed artifact and CI byte-compares it.

  • Updated Sep 26, 2026
  • Python

An educational implementation of core Transformer architecture concepts built from scratch using Python. This project explores how modern NLP transformer models work internally by implementing attention mechanisms, embeddings, positional encoding, and next-word prediction logic step-by-step.

  • Updated Jul 9, 2026
  • Jupyter Notebook

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