Today, the world is abuzz with LLMs, short for Large Language models. Not a day passes without the announcement of a new language model, fueling the fear of missing out in the AI space. Yet, many still struggle with the basic concepts of LLMs, making it challenging to keep pace with the advancements. This article is aimed at those who would like to dive into the inner workings of such AI models to have a solid grasp of the subject. With this in mind, I present a few tools and articles that can help solidify the concepts and break down the concepts of LLMs so they can 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. Tokenizer tool by OpenAI
· 5. Understanding GPT Tokenizers by Simon Wilson
· 6. Do Machine Learning Models Memorize or Generalize? -An explorable by PAIR
I’m sure many of you are already familiar with this iconic article. Jay was one of the earliest pioneers in writing technical articles with powerful visualizations. A quick run through this blog site will make you understand what I’m trying to imply. Over the years, he has inspired many writers to follow suit, and the idea of tutorials changed from simple text and code to immersive visualizations. Anyway, back to the illustrated Transformer. The transformer architecture is the fundamental building block of all Language Models with Transformers (LLMs). Hence, it is essential to understand the basics of it, which is what Jay does beautifully. The blog covers crucial concepts like:
- A High-Level Look at The Transformer Model
- Exploring The Transformer’s…
