Understanding Transformers: Core Mechanics Behind Modern AI Models

Introduction

This article explains the core principles of the Transformer model in an easy‑to‑understand way for "large‑model beginners", covering its relationship with large models, self‑attention, multi‑head attention, positional encoding, and the composition of Encoder and Decoder.

Transformer and Large Models

Think of the Transformer as a "super recipe" and a large model as the "feast" created from that recipe. Compared to traditional AI models, the Transformer provides:

High firepower: processes all words in a sentence simultaneously.

Simplified steps: uses attention to automatically find key information.

Scalability: the method can be expanded indefinitely, improving performance as the model grows.

Thus, the Transformer is the methodology that teaches AI how to learn efficiently, while large models are the practical results of applying this methodology.

Self‑Attention Mechanism

Self‑attention assigns dynamic weights to each token in a sequence, similar to highlighting important words with a highlighter while reading. It computes similarity between token vectors using a matrix multiplied by its transpose, normalizes the scores with softmax, and produces weighted sums that capture contextual relationships.

Key Formulas

Attention(Q,K,V)=softmax(QK^T / sqrt(d_k)) V

Q (Query), K (Key), and V (Value) are linear projections of the input matrix X, learned during training to enhance model capacity.

Multi‑Head Attention

Multi‑head attention runs several self‑attention operations in parallel (each called a "head"), allowing the model to capture different types of relationships simultaneously. The outputs of all heads are concatenated and linearly transformed back to the original dimension.

Positional Encoding

Since self‑attention lacks inherent order information, positional encodings are added to token embeddings before they enter the Encoder. The sinusoidal formulas enable the model to handle sequences longer than those seen during training and to compute relative positions efficiently.

Encoder Structure

The Encoder consists of a Multi‑Head Attention block followed by a Feed‑Forward Network (FFN) and an Add & Norm layer (residual connection plus layer normalization). The FFN adds non‑linear transformations, while Add & Norm stabilizes training and accelerates convergence.

Decoder Structure

The Decoder mirrors the Encoder but includes two Multi‑Head Attention blocks:

The first uses a mask to prevent attending to future tokens during training.

The second attends to the Encoder’s output, allowing each decoding step to consider the entire input sequence.

After these blocks, a linear layer projects the Decoder output to a vocabulary‑size vector (logits), which is passed through softmax to obtain probabilities for the next token.

Masking in Decoder

Masking creates an upper‑triangular matrix that zeroes out future positions, ensuring the model only attends to already generated tokens when predicting the next word.

Summary of Key Points

Transformer comprises Encoder, Decoder, and positional encoding modules.

Self‑attention enables parallel processing and captures long‑range dependencies.

Multi‑head attention aggregates information from multiple representation subspaces.

Positional encoding injects order information into token embeddings.

FFN introduces non‑linear transformations, and Add & Norm stabilizes training.

Masking ensures autoregressive generation in the Decoder.

References

"Attention Is All You Need" – https://arxiv.org/pdf/1706.03762

"The Illustrated Transformer" – https://jalammar.github.io/illustrated-transformer/

"Transformer模型详解（图解最完整版）" – https://zhuanlan.zhihu.com/p/338817680

"超详细图解Self‑Attention" – https://zhuanlan.zhihu.com/p/410776234