Building and Training an LLM from Scratch Without a Paid GPU

A developer has successfully built and trained a 10-million parameter decoder-only Transformer model from scratch using only the free tier of Google Colab. By implementing every layer, attention head, and training loop in PyTorch, the project aims to demystify the mechanics of large language models. The model was trained on the 'Tiny Shakespeare' dataset, a character-level corpus that allows for fast iteration and debugging. The author details the architecture, which includes six layers and six attention heads, and explains how to optimize memory usage for the free T4 GPU using mixed-precision training. The project serves as an educational resource for those looking to understand the inner workings of Transformers beyond high-level APIs. The complete code, including training and inference scripts, is available on GitHub, providing a practical, hands-on approach to learning how LLMs process context and generate text.