SUMMARY AI summary by gpt-5-mini
My GPT — Built from Scratch is an educational repository that implements a GPT-style transformer from first principles. Assembled while completing the NeetCode ML course, it’s intended for students and engineers who want a runnable, from-scratch reference for how attention, transformers and a small GPT are implemented and trained without relying on high‑level abstractions. Key features: - Full transformer/GPT implementation: attention head, multi-head attention, transformer block, GPT model. - Multiple normalization layers: LayerNorm, BatchNorm, RMSNorm. - Tokenization and data pipeline: BPE tokenizer, vocab, dataset loader, NLP preprocessing. - Training and inference: train.py and generate.py, plus a KV-cache for faster sampling. - Low-level foundations: neuron/backprop, MLP, activations, losses, training loop — all handwritten for instructional value.
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| Created on GitHub | 2026-05-09 |
| Last push | 2026-05-09 |
| First seen here | 2026-05-09 |
| Last fetched | 2026-05-09 16:10 |
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