The Principles of Transformer Technology: The Foundation of Large Model Architectures (Part 1)

Transformer is a deep learning model using self-attention layers instead of RNNs to capture long-range dependencies, improving speed and efficiency in processing long sequences.

Welcome to the "Practical Application of AI Large Language Model Systems" Series

Table of Contents

We've laid the groundwork, and now it's time for the main event. If the previous basic knowledge was just appetizers, this lesson on Transformers is the main course.

Recall our last lesson on Seq2Seq, where we used GRU (Gated Recurrent Unit) at the core. We mentioned RNNs but didn't delve deeply. Both GRU and LSTM face issues like vanishing and exploding gradients. RNNs process sequences sequentially, hindering parallel processing and struggling with long dependencies. These problems persisted until Google researchers published "Attention Is All You Need," introducing the Transformer model. This breakthrough seemed to solve these challenges instantly.

Today, we'll explore the details of why Transformers address these issues effectively.