Diving Into the Transformer Attention Mechanism: Building a Minimal Transformer in Pure Python

Overview

In this project, we’ll implement the attention mechanism from the Transformer architecture using pure Python, without any frameworks.

The attention mechanism is essentially a set of matrix operations that form the mathematical foundation of the model.

There are two scenarios where building from scratch makes sense: either to customize the architecture or for educational purposes.

In custom setups, you’re required to modify the model’s mathematical and statistical core — a process that is both advanced and time-consuming.

With frameworks like PyTorch and TensorFlow, you simply use pre-built architectures, adjusting parameters as needed.

Since the goal here is to learn, we won’t build the entire Transformer architecture — just the attention mechanism.

While the example provided is simple and not optimized for performance, it will serve as a foundation to understand the mathematical workings before moving on to more complex frameworks in future projects.

Defining the Transformer Architecture