Build a tiny GPT. Train it on 1MB of text. Watch it learn to spell "the" correctly.

. This guide outlines the essential steps based on industry-standard practices, such as those found in Sebastian Raschka's Build a Large Language Model (From Scratch) 1. Data Preparation & Preprocessing The foundation of any LLM is the data it learns from. Data Collection:

: Convert tokens into numerical IDs, which are then mapped to high-dimensional vectors (embeddings) that capture semantic meaning. 2. Implementing the Transformer Architecture Modern LLMs almost exclusively use the Transformer architecture. Self-Attention Mechanism