Today, the world is abuzz with LLMs, short for Large Language models. Not a day passes without the announcement of a brand new language model, fueling the fear of missing out within the AI space. Yet, many still struggle with the fundamental concepts of LLMs, making it difficult to maintain pace with the advancements. This text is geared toward those that would love to dive into the inner workings of such AI models to have a solid grasp of the topic. With this in mind, I present just a few tools and articles that can assist solidify the concepts and break down the concepts of LLMs so that they might be easily understood.

· 1. The Illustrated Transformer by Jay Alammar
· 2. The Illustrated GPT-2 by Jay Alammar
· 3. LLM Visualization by Brendan Bycroft
· 4. Generative AI exists due to the transformer — Financial Times
· 5. Tokenizer tool by OpenAI
· 6. Understanding GPT tokenizers by Simon Wilson
· 7. Chunkviz by Greg Kamradt
· 8. Do Machine Learning Models Memorize or Generalize? -An explorable by PAIR

I’m sure lots of you’re already acquainted with this iconic article. Jay was certainly one of the earliest pioneers in writing technical articles with powerful visualizations. A fast run through this blog site will make you understand what I’m attempting to imply. Over time, he has inspired many writers to follow suit, and the thought of tutorials modified from easy text and code to immersive visualizations. Anyway, back to the illustrated Transformer. The transformer architecture is the basic constructing block of all Language Models with Transformers (LLMs). Hence, it is crucial to grasp the fundamentals of it, which is what Jay does beautifully. The blog…