help me better understand how transformers work in AI LLMs
I would love to help you unpack how Transformers work. Before we dive into the mechanics of these models, it helps me to know a bit about your background so we can explore this at the perfect level for you.
Are you already familiar with some concepts in programming, neural networks, or machine learning, or are you looking at this from a non-technical perspective? Let me know so we can tailor this journey together.
I have some basic tech/programming context
Since you have some programming and tech background, we can skip the extreme basics and look at what makes the architecture such a massive leap forward. Let's start with its core definition.
What is a Transformer?
At its heart, a Transformer is a neural network architecture designed to process sequential data, like human language, by treating the entire input sequence simultaneously. Because it doesn't process data step-by-step, it relies on a mathematical mapping to keep track of where words are in a sentence.
Instead of reading a sentence from left to right like we do, it ingests all the tokens at the exact same time and uses mathematical vectors to preserve the structure and meaning of the text.
Your path
Key Components
The Power of Self-Attention
How LLMs Learn and Generate