The GPT Architecture, on a Napkin (2022) (dugas.ch)

A new detailed post explores the architecture of GPT-3, aiming to simplify its complex structure for those interested in understanding how it processes language. The key insight is that GPT-3 operates on a straightforward input-output mechanism: it takes a sequence of tokens (up to 2048 words) and predicts the next word based on learned probabilities across its vast vocabulary of 50,257 words. To represent words numerically, the model uses a combination of one-hot encoding and an embedding function, transforming each word into a more manageable dimensional space of 12,288 dimensions. This breakdown not only highlights the importance of tokenization and embedding but also delves into the mechanics of multi-head attention, allowing the model to focus on relevant tokens within the input sequence. Each output vector is formed by combining the influence of all other tokens, significantly enhancing the model's ability to generate coherent text. Furthermore, the architecture introduces optimizations like sparse attention, offering efficiency improvements. Overall, this analysis contributes valuable insights into GPT-3’s functionalities, benefitting researchers and practitioners in the AI/ML community by deepening their understanding of one of the leading models in natural language processing.