Decoding GPT: How a Spreadsheet Unveiled the Secrets of AI Transformers


Exploring the intricacies of Generative Pre-trained Transformers (GPT) can be a complex task, but a creative project has demonstrated that sometimes, a spreadsheet is all you need. By translating the nanoGPT architecture—a simplified version of GPT designed by Andrej Karpathy—into a single, interactive spreadsheet, this project offers a unique and visual way to understand how transformers work. The spreadsheet includes all essential components of a transformer, such as embedding, layer normalization, self-attention, projection, MLP, softmax, and logits, with around 85,000 parameters. It’s a character-based prediction system focusing on simplicity by tokenizing only letters A, B, and C.

This approach not only demystifies the data flow and calculations within a transformer but also makes the learning process engaging. The spreadsheet is color-coded to differentiate between parameters, input values, and intermediate calculations, guiding users through the architecture from top to bottom. Although it lacks trained weights, making it incapable of producing accurate predictions without updates, it serves as an educational tool, allowing users to explore and modify the transformer’s workings.

The project underscores the potential of simple tools to unravel complex technologies, providing a bridge for visual thinkers to grasp the fundamentals of machine learning models. It’s a testament to the power of innovative thinking in educational approaches to technology.
Read more at GitHub…