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# Decoder-Only Transformers From Scratch: What Building ChessGPT Taught Me

**[Medium](https://daily.dev/sources/medium_js)** · 12 min read · 0 upvotes · 0 comments

## Summary

A personal learning journal documenting the journey of building a decoder-only transformer model from scratch, culminating in ChessGPT — a chess move prediction model. Covers the author's path from Stanford CS224N through hands-on implementation, including decisions around chess notation formats (PGN vs UCI), vocabulary design, tokenization, masked self-attention, positional embeddings, and training on ~10,000–30,000 chess games with an RTX 3060. Key lessons include PyTorch's automatic backpropagation, the importance of positional embeddings, the auto-regressive vs non-auto-regressive behavior of decoders at inference vs training, and the limitations of overfitting on small datasets.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://medium.com/@iamlyncan/decoder-only-transformers-from-scratch-what-building-chessgpt-taught-me-8c5a864e3047>

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Tags: [#deep-learning](https://daily.dev/tags/deep-learning), [#nlp](https://daily.dev/tags/nlp), [#pytorch](https://daily.dev/tags/pytorch)

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