Understanding Transformer Model Architecture

Transformers are the backbone of many state-of-the-art natural language processing (NLP) systems today, from machine translation to large language models like GPT. But how do they work internally? In this article, we'll break down the transformer model architecture, explore how the encoder and decoder work, and understand key concepts like self-attention, multi-head attention, softmax, and temperature—all in a clear and intuitive way.

🔧 What Is a Transformer?

A transformer is a type of deep learning model that’s really good at understanding and generating text. It was introduced in 2017 in a paper called “Attention Is All You Need.”

Unlike older models like RNNs, which read words one by one, transformers can look at the whole sentence at once using something called self-attention.

Transformers are made of two main parts:

Encoder – reads and understands the input (like a sentence).
Decoder – uses that understanding to generate an output (like a translation).

Think of it like a reader and a writer: the encoder reads and understands, and the decoder writes a response based on that.

🧱 Encoder and Decoder: The Twin Towers

📥 Encoder

The encoder takes the input (e.g., a sentence) and converts it into a rich set of representations. These are not just raw words—they're embedded vectors capturing meaning and context.

Each encoder layer contains:

Self-attention mechanism
Feed-forward neural network
Layer normalization and residual connections

📤 Decoder

The decoder uses the encoder’s output and generates the desired result - like translating “Hello” to “Bonjour.”

Each decoder layer has:

Self-attention (to look at previous outputs)
Encoder-decoder attention (to look at encoder output)
Feed-forward network

🧠 Self-Attention: Looking Inward for Context

Self-attention allows the model to weigh the importance of other words in the input when processing a specific word.

Example:

In the sentence “The animal didn't cross the street because it was too tired”, what does “it” refer to?

Self-attention helps the model realize “it” likely refers to “the animal,” not “the street.”

This gives us a weighted combination of values, determining how much focus each word should have.

🎩 Multi-Head Attention: Diverse Perspectives

Instead of computing self-attention once, multi-head attention does it multiple times in parallel with different projections. This enables the model to capture various types of relationships.

Imagine reading a sentence focusing on grammar with one head and on meaning with another—multi-head attention allows that.

🧂 Softmax: Turning Scores into Probabilities

Once self-attention scores are computed, we apply softmax to turn them into probabilities.

Go Code Example: Softmax in Go

Here’s a simple implementation of softmax in Golang:

package main

import (
    "fmt"
    "math"
)

func softmax(x []float64) []float64 {
    max := x[0]
    for _, v := range x {
        if v > max {
            max = v
        }
    }

    // Subtract max for numerical stability
    var sum float64
    exp := make([]float64, len(x))
    for i, v := range x {
        exp[i] = math.Exp(v - max)
        sum += exp[i]
    }

    for i := range exp {
        exp[i] /= sum
    }
    return exp
}

func main() {
    input := []float64{2.0, 1.0, 0.1}
    result := softmax(input)
    fmt.Println("Softmax:", result)
}

The softmax function:

Finds the maximum value in the input to stabilize the computation.
Applies the exponential function to each input after subtracting the max value.
Normalizes the results so they sum to 1, turning them into a probability distribution.

This is crucial in transformers, where softmax is used in attention mechanisms to decide how much focus each word should receive.

Example output for {2.0, 1.0, 0.1}:
Softmax: [0.659, 0.242, 0.099]
Meaning: the first value (2.0) gets the highest attention weight.

🌡️ Temperature: Controlling Confidence

Temperature is a parameter used during generation (like in text prediction) to control randomness.

Low temperature (e.g., 0.5): More confident, focused choices
High temperature (e.g., 1.5): More diverse, creative outputs

It modifies the softmax:

softmax(x/T)softmax(x / T) softmax(x/T)

Lower T sharpens the probabilities; higher T flattens them.

🧠 Summary: How It All Comes Together

🔄 Flowchart: Transformer Workflow

                +------------------------+
                |    Input Tokens        |
                |   (e.g., a sentence)   |
                +----------+-------------+
                           |
                           v
                +------------------------+
                |   Embedding Layer      |
                | (Word + Positional Enc.)|
                +----------+-------------+
                           |
                           v
                +------------------------+
                |      Encoder           |
                | Self-Attention Layers  |
                | Multi-Head Attention   |
                +----------+-------------+
                           |
                           v
                +------------------------+
                |      Decoder           |
                | Self-Attention         |
                | + Encoder-Decoder Attn |
                +----------+-------------+
                           |
                           v
                +------------------------+
                | Softmax + Temperature  |
                |   (Generates output)   |
                +----------+-------------+
                           |
                           v
                +------------------------+
                |    Final Output        |
                | (e.g., Translated Text)|
                +------------------------+

✅ Explanation of the Flow:

Input Tokens: Words or subwords broken down by tokenization.
Embeddings: Tokens are converted into numerical vectors, with positional encoding added.
Encoder: Learns contextual relationships using self-attention and multi-head attention.
Decoder: Uses its own self-attention and connects to the encoder output for richer understanding.
Softmax + Temperature: Produces a probability distribution over vocabulary. Temperature adjusts randomness.
Final Output: The predicted text, such as a translated sentence or next token.

🚀 Final Thoughts

The transformer architecture revolutionized NLP by allowing models to look at input data holistically rather than sequentially. The encoder-decoder structure, powered by self-attention and softmax, allows for powerful, efficient learning from vast amounts of text.

In the next article, we’ll dig into how text data is prepared for transformers, including embeddings, vectors, positional encoding, and more.

The Foundation of Transformer Models: A Deep Dive into the Architecture

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