The research paper “Attention is All You Need” is regarded as one of the most important & groundbreaking publications in the realm of ML. The paper introduces the transformer architecture and the attention mechanism, yet many still struggle to wrap their head around it.
When I posted my progress update on my encoder block written in CUDA (Python + Numba), a lot of responses echoed a similar theme:** “I want to understand how transformers work from the ground up.”**
This got me thinking. What really helped ME understand the transformer? It was story-telling & illustrations. Every model in the history of language modeling was built to fix a problem the last one could not solve (which evolved into the transformer). I’ve also always learned best by looking at illustrations and dissecting complex ideas into visuals I can follow (Jay Allamar’s Illustrated Transformer does a good job of this). So why not put these 2 ideas together, and tell the story of how we got to the transformer, and an illustrated break-down of the transformer architecture itself.
So, this article isn’t a tutorial. It’s a guide I wish I had when I first started out - a visual story of how transformers came to life. You’ll find personal illustrations, simplified explanations, and links to the resources that helped me most. Whether you’re here to build a transformer model from scratch, or fell curious how we got from neural nets to today’s GPTs, I hope this gives you a place to start.
A History Lesson
A language model is a machine learning model that is trained to understand, predict and generate human** language.** Early attempts used simple feedforward networks which were good at recognizing fixed patterns, but couldn’t handle sequences. They had NO memory of* order or *context.
This gap led to the creation of recurrent neural networks (RNNs). RNNs were the first real step toward giving models memory for sequential data. From there, researchers kept building new model variations (with tweaks), so that each one was created to fix the limitation of the last.
This progression of ideas eventually gave rise to the transformer in 2017. The timeline and chart below outline why each model was introduced, how it worked, and the drawbacks that led to the next improvement.
Timeline of Key Language Model Architectures
Evolution of language models: what each introduced and WHY the next was needed
TL;DR — Why Did Transformers Matter?
To summarize the above chart, what were old models missing exactly?