When we start learning about Large Language Models (LLMs), it is but natural to become quite interested in how the various parameters, training data size, context size, tokens, etc. affect the performance of the model. And how the existing models out there in the wild; both open and closed source; use the different parameters, what are their strengths and weaknesses, etc. It is also important to know and compare the training data sizes used in such models so one can understand how much resources would a relative model need in order to be trained from scratch.

Building Large Language Models (LLMs) from scratch is a complex and challenging task. It requires a deep understanding of the underlying mathematics and a strong foundation in computer science. In this post, we will explore the process of building a LLM from scratch and provide a step-by-step guide to help anyone get started.

LLMs are incredibly versatile, aiding in tasks such as checking grammar, composing emails, summarizing lengthy documents, and much more. They are “large”—very large—encompassing millions to billions of parameters. LLMs are a unique subset of AI. There is a very nice book Build LLMs from Scratch by Sebastian Raschka which shows a practical approach to building your own LLM.