LLM From Scratch

To deepen my practical understanding of Large Language Models, I recently worked through the hands-on coding exercises from the book “Build a Large Language Model (From Scratch)”. These exercises provided a great opportunity to implement the key components of a modern LLM using PyTorch, from the intricacies of the self-attention mechanism to the data tokenization and embedding pipelines. A key part of this process was also building an evaluation framework to benchmark the model’s performance against a model like Llama 3.

This was an excellent exercise for fine-tuning my existing knowledge and bridging the gap between the theoretical concepts from my Master’s degree and the practical engineering of today’s language models. Working through the code provided a valuable, up-to-date perspective on the nuances of modern LLM architecture.

For others with a similar background looking to get a concrete, code-level understanding of these systems, I found this to be a highly effective and relevant exercise. The repository with the completed exercises is available on my GitHub, and a reference to the book is included below.

Raschka, S. Build A Large Language Model (From Scratch). Manning, 2024. ISBN: 978-1633437166.