Comprehensive technical survey of the transformer architecture family

This extensive technical guide covers the evolution of transformer models from the original scaled dot-product attention mechanism through modern variants designed for improved efficiency and capability. The article examines how self-attention enables neural networks to selectively focus on input components through learned weights. The transformer architecture's multi-head self-attention mechanism is explored as a core component allowing parallel attention computations. The survey discusses extensions addressing key limitations of vanilla transformers including memory requirements for handling long sequences, computational complexity, and performance on specialized tasks like reinforcement learning. Multiple architectural improvements are reviewed, representing nearly three years of model development since the survey's original publication. The guide serves as reference material for understanding the diverse transformer implementations prevalent in contemporary language models.