No Libraries, No Shortcuts: LLM from Scratch with PyTorch

Author(s): Ashish Abraham

Originally published on Towards AI.

The no BS guide to build, train, and fine-tune a Transformer architecture from scratch

OpenAI has recently launched its highly anticipated open-source GPT-OSS models, a moment that invites a minute of reflection on just how far we’ve come. Years back, even before ChatGPT, I remember reading an article on a GPT model, probably GPT-2, that writes its own essays and poems, and they were just experiments. Fast forward to today, it has already become an integral part of my daily life. And it all started with the landmark “Attention is All You Need” publication in 2017 by Google Research. The Transformer architecture was proposed, which soon powered the very first GPT — GPT-1 (Generative Pretrained Transformer) in 2018.

The article discusses the evolution of Large Language Models (LLMs) from the introduction of the Transformer architecture to the recent developments in open-source GPT models. It provides a comprehensive breakdown of how to build and train an LLM using PyTorch, covering various components of the Transformer framework, including tokenization, attention mechanisms, and training strategies. The author emphasizes the significance of fine-tuning LLMs for specific tasks and the impact of these technologies on modern AI applications.

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Published via Towards AI