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This article traces the path from RNNs to transformers, explaining why attention-based, non-recurrent architectures replaced older models. It then breaks down encoder vs decoder designs and shows how GPT’s decoder-only approach and tokenization power today’s large language models.
This gist provides a single-file, dependency-free implementation of a GPT-style transformer, complete with a custom autograd engine, training loop using Adam, and inference routine. It trains on a list of names, demonstrating both the core algorithm and a brief benchmark discussion for a GPU-based microgpt.cu variant.