The Transformer Model

The Transformer Model is a type of neural network architecture that processes sequential data.

To better understand it, consider the following examples.

1. In a Transformer, All Words Are Read Simultaneously in Parallel

The Transformer has no "step 1, step 2, step 3…" like a Recurrent Neural Network (RNN).
All words are encoded together at once:

1. "I"
2. "eat"
3. "fried"
4. "rice"

The model then calculates the relationships between all words, for example:

• how important "eat" is to "I"
• how important "fried" is to "rice"
• how important "eat" is to "fried"

All of this is computed in a single large matrix operation, not step by step.

How Older Models (RNN) Work

In older models like Recurrent Neural Networks (RNN), processing had to be done sequentially:

1 → "I"
2 → "eat"
3 → "fried"
4 → "rice"

Because older models could only process one token per step, and each step depended on the result of the previous one.

This means:
To understand the meaning of "rice", the model had to wait for "I", "eat", and "fried" to finish processing first.

Why Transformer Architecture Is Superior

The Transformer architecture outperforms older architectures like RNN because it is:

• Much faster
• Can see global context (long-distance word relationships)
• Not trapped in "short-term memory" like RNN
• Uses Self-Attention

The Transformer Model is also the foundation of modern LLM models such as GPT, Gemini, and Claude.

2. Self-Attention

2.1 Purpose of Self-Attention

Self-attention is responsible for finding relationships between tokens in a single sequence.

Example sentence:
"The cat ate the fish because it was hungry."

The model must know:

• The word "hungry" relates to "cat"
• Not to "fish"

Self-attention calculates these relationships mathematically, not through manual rules.

2.2 Where Does Self-Attention Come From?

Self-attention originates from this idea:

"To understand each token, we must look at other tokens and determine which ones are important."

How this is achieved:
By multiplying the token representation (embedding) with learned weights to produce Query, Key, and Value.

So self-attention doesn't come from outside — it is generated by parameters (weight matrices) learned during training, specifically:

• W_Q (Query matrix)
• W_K (Key matrix)
• W_V (Value matrix)

Key Concepts

Embedding

Embedding is the process of converting raw data (text, positions, categories) into numerical vector representations.

Training

• Training the model so it can understand patterns and produce correct outputs.
• The model learns and updates its weights.

Inference

• Using an already-trained model to produce output.
• The model does not learn; it only uses the weights that already exist.

The Transformer architecture revolutionized NLP by enabling parallel processing and long-range dependency modeling, making it the backbone of all modern large language models.