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• Decoding the Decoder LLM without de code; Ishan Anand

Decoding the Decoder LLM without de code

Abstract

Spreadsheets are all you need: Decoding the Decoder LLM without de code The struggle to grasp the inner workings of AI models can leave even experienced engineers from non-ML backgrounds feeling lost in a sea of terminology and new concepts. What if the key to understanding the intricate mechanics of LLMs didn’t require a Ph.D.? This session offers an innovative approach, employing spreadsheets to dissect and demystify the architecture of decoder-based LLMs using a fully working implementation of GPT-2 entirely in Excel. Attendees will tour through GPT-2’s architecture from tokenization, embeddings, attention, multi-layer perceptron, all translated into the accessible format of spreadsheets with minimal abstractions to get in the way. By the end, you’ll gain unparalleled insights into AI’s backbone, transforming abstract concepts into tangible, understandable processes, without ever touching code.