What is a Transformer? How Transformer models work

A Transformer is a neural-network architecture designed to understand relationships between pieces of data, especially sequences such as text.

Transformers are the foundation of most modern large language models. They are also used in speech, vision, translation, code generation and many other AI systems.

The central idea is attention. Instead of reading a sentence strictly one word after another, a Transformer can look at the relationships between many tokens at once and decide which parts of the context matter most.

In a simplified view, the data moves through a Transformer like this:

1. The text is split into tokens.
2. Each token becomes a vector, called an embedding, and position information is added to it.
3. Transformer layers repeat two steps: attention, then feed-forward processing.
4. The result is a context-aware representation of every token.
5. The model uses it to make a prediction or produce an output.

That basic design changed modern AI.

Where did Transformers come from?

The Transformer architecture was introduced in the 2017 research paper “Attention Is All You Need”.

At the time, many language systems relied heavily on recurrent neural networks, or RNNs. An RNN processes a sequence step by step: the first token, then the second, then the third, and so on.

This approach can work well, but the sequential structure makes it harder to process many positions in parallel and can make long-range relationships difficult to learn.

The Transformer took a different approach. Instead of recurrence, it relied primarily on attention mechanisms to model relationships within a sequence.

This made training much more parallelisable and gave models a powerful way to connect information that may be far apart in the text.

What is attention?

Attention is a mechanism that helps a model decide which parts of the input are most relevant to one another.

Consider the sentence:

“The animal did not cross the street because it was tired.”

To understand what “it” refers to, the model needs context from earlier in the sentence. A useful model should give more importance to the relationship between “it” and “animal” than to unrelated words.

Attention gives the model a way to calculate these relationships. Conceptually, it works in four steps:

1. Take the current token.
2. Compare it with the other tokens.
3. Give each of them an importance score.
4. Combine the most useful context.

The result is a representation of each token that includes information from other relevant tokens.

What is self-attention?

When attention compares elements within the same sequence, it is called self-attention.

Suppose the input is:

“The bank raised interest rates.”

When processing the word “bank,” the model can examine the other tokens and see that a financial institution is meant. Now compare:

“The boat reached the river bank.”

Here the surrounding tokens point toward a different meaning.

Self-attention allows the representation of a token to change depending on the rest of the sequence. This is one reason Transformers are so effective with language: words are not interpreted in isolation.

What are query, key, and value?

Transformer attention is often explained using three terms. The mathematics can become detailed, but the intuition is manageable:

• Query (Q): what information am I looking for?
• Key (K): what information does this token offer?
• Value (V): what information should be passed forward?

For each token, the model creates query, key and value representations.

It compares queries with keys to estimate how much attention one token should give another. Those attention scores are then used to combine the values.

The model learns how to create useful Q, K and V representations during training.

Why use multiple attention heads?

Transformers usually do not perform attention only once. They use multi-head attention: several attention calculations happen in parallel, each with its own learned parameters.

Different heads can learn to capture different types of relationships. For example, one head might become useful for nearby grammatical relationships, while another may help with long-range dependencies, names and references, sentence structure, or semantic relationships.

It is not guaranteed that every head develops one clean human-readable role, but multiple heads give the model several ways to analyse the same sequence.

How does a Transformer know word order?

Self-attention alone does not automatically know that one token came before another. If the same tokens were rearranged, the model needs some way to recognise the difference.

Transformers therefore add positional information to the meaning of each token before the Transformer layers process it.

The original Transformer used positional encodings. Modern models use several approaches. One common method is rotary positional embeddings (RoPE), which helps attention represent relative positions between tokens.

This positional information allows a Transformer to distinguish “Dog bites man” from “Man bites dog,” even though the same three words appear in both.

What is inside a Transformer layer?

A Transformer is built from repeated blocks. A simplified block contains two major parts: an attention step, followed by a feed-forward network.

Real Transformer blocks also include components such as normalisation and residual connections, which help information and gradients move through deep networks reliably.

The attention part mixes information across tokens. The feed-forward part performs additional learned transformations on each position.

Stacking many Transformer blocks allows the model to build increasingly useful representations of the input.

Encoder, decoder, and decoder-only Transformers

The original Transformer was designed for machine translation and used two major components. The encoder read the input sentence and built a context representation. The decoder used that representation to produce the translated sentence.

Encoder

The encoder reads an input sequence and builds context-rich representations.

Encoder-focused models are useful when the goal is mainly to understand or classify input.

Decoder

The decoder generates an output sequence.

When generating text, it normally uses a causal mask, which prevents the model from looking at future tokens that have not been generated yet.

Decoder-only

Many modern LLMs use a decoder-only Transformer. Instead of a separate encoder and decoder, they use a stack of causal Transformer blocks to repeatedly predict the next token.

A simplified LLM pipeline looks like this:

1. The prompt is split into tokens.
2. The tokens pass through the Transformer blocks.
3. The model outputs probabilities for the next token.
4. One token is chosen.
5. The chosen token is added to the text, and the process repeats from step 2.

This is how the Transformer architecture connects directly to the next-token prediction described in our earlier articles on LLMs and tokenization.

Why did Transformers become so important?

Several characteristics made Transformers especially useful.

1. They model long-range relationships

Attention allows information from distant parts of a sequence to interact directly. A token near the end of a paragraph can attend to something much earlier if it is relevant.

2. Training can be highly parallel

Unlike traditional recurrent processing, many token positions can be processed together during Transformer training. This maps well to modern GPUs and AI accelerators.

Generating text is different: a decoder-only model still produces one token at a time, as in the pipeline above.

3. They scale well

Researchers found that Transformer models could benefit strongly from more data, more parameters and more computation. This made the architecture suitable for the rapid growth of large language models.

4. The architecture is flexible

Transformers are no longer limited to translation or even text. The same basic attention idea can be adapted to many types of data.

Are all Transformers LLMs?

No. An LLM is one application of Transformer technology.

Transformers are also used in:

• computer vision: image classification and vision Transformers;
• speech: speech recognition and audio modelling;
• translation: mapping text from one language to another;
• code: code completion and generation;
• multimodal AI: combining text, images, audio or video;
• time-series analysis: modelling relationships across ordered measurements.

So the two terms should not be treated as synonyms:

• Transformer is an architecture.
• LLM is a type of language model that often uses that architecture.

What does this look like in a real NeuronAI model?

Transformer ideas are directly relevant to NeuronAI’s own LLM work.

MustaqiLLM is a decoder-only Transformer that stacks 36 blocks. Its model card on Hugging Face lists Grouped-Query Attention (GQA), with 28 query heads sharing 4 key/value heads, and RoPE for positional information.

GQA is a modern attention variant. By using fewer key/value heads than standard multi-head attention, it reduces the memory the model needs when it generates text.

These are not alternatives to the Transformer. They are modern design choices used inside Transformer-style language models to improve efficiency and handle sequence information. They show how the original 2017 architecture has evolved rather than remaining frozen in its first form.

Are Transformers perfect?

No. Their strengths come with trade-offs.

Standard self-attention compares every token with every other token, so the work grows roughly with the square of the sequence length. As the sequence becomes longer, this can require much more computation and memory.

Large Transformer models can also require:

• enormous training datasets;
• expensive accelerator hardware;
• significant inference memory;
• careful optimisation;
• substantial energy and infrastructure.

Transformers can also produce incorrect or misleading outputs. The architecture is powerful, but it does not guarantee factual accuracy or human-like understanding.

Researchers therefore continue developing more efficient attention methods, memory techniques, sparsity, better positional methods and alternative architectures.

Do Transformers understand language like humans?

Not in the human sense. A Transformer learns statistical and structural relationships in its training data.

Self-attention allows it to build very rich representations of context, but this should not automatically be interpreted as human understanding or reasoning. A Transformer can produce a fluent and convincing answer and still be wrong.

That distinction matters whenever these models are used for important decisions, for example in education or medicine.

The easiest way to remember it

If you remember only one idea, use this:

A Transformer is a neural-network architecture that uses attention to understand how different parts of a sequence relate to one another.

Or more simply: tokens go in, the model looks at how they relate to one another, builds context, and then predicts or generates the output.

The architecture began as a new approach to machine translation. Today, Transformer models sit underneath much of modern AI, from LLMs and chatbots to speech, code, images and multimodal systems.

The name may sound complicated, but the core idea is surprisingly intuitive: do not treat each token alone. Let the model learn what it should pay attention to.