THE MINIMALIST TRANSFORMER
Transformer Explained in 100 Words
Ravindra Dastikop
Understand less. Accomplish more.
Why This Book?
Transformers can sound complicated.
Tokens.
Embeddings.
Position.
Attention.
Queries.
Keys.
Values.
Heads.
Layers.
Relationships.
But understanding Transformers does not have to begin with complexity.
This book strips Transformers down to what matters.
10 ideas.
10 words each.
100 words.
Enough to begin.
How to Read This Book
Read one idea.
Stop.
Think about it.
Try it with AI.
Then move to the next.
The purpose is not to know everything about Transformers.
The purpose is to understand enough to accomplish something with Transformers.
01 — TRANSFORMER
Transformers process sequences using attention to model relationships between tokens.
Before Transformers, sequence models commonly relied on recurrent architectures that processed sequences step by step.
The Transformer introduced a different approach.
It uses attention to establish relationships among elements in a sequence.
For language, those elements are tokens.
Consider:
“The animal didn't cross the road because it was tired.”
Understanding it requires relating that token to other tokens.
Attention helps the model build representations using such relationships.
The original Transformer architecture replaced recurrence and convolution with attention mechanisms for sequence transduction.
02 — TOKEN
Tokens provide sequence elements that Transformers represent, relate, and process.
Transformers do not directly process sentences as humans see them.
Text is divided into tokens.
A token might represent:
A word.
Part of a word.
Punctuation.
Or another textual unit.
Each token becomes an element in the sequence processed by the model.
Consider:
AI transforms work.
The model operates on token representations rather than experiencing the sentence as one indivisible object.
This connects directly with The Minimalist LLM.
03 — EMBEDDING
Embeddings convert tokens into numerical representations Transformers can process mathematically.
A token itself is not enough.
The Transformer needs a numerical representation.
Embeddings provide that representation.
Each token becomes a vector—a collection of numbers.
These vectors give the neural network something it can mathematically transform.
As information passes through Transformer layers, token representations become contextualized by information from other tokens.
So the internal representation associated with a token can reflect its surrounding context.
This connects directly with The Minimalist Embeddings.
04 — POSITION
Position information helps Transformers distinguish token order within input sequences.
Attention alone does not inherently encode the order of tokens.
But order matters.
Compare:
Dog bites man.
and
Man bites dog.
The words are similar.
The meaning is not.
Transformers therefore need information about token position.
The original Transformer added positional encodings to input embeddings.
Other Transformer architectures can use different approaches to representing positional information.
Position helps the model distinguish where elements occur in a sequence.
05 — ATTENTION
Attention weights relationships between tokens according to their contextual relevance.
Attention is the central idea.
When processing one token, not every other token contributes equally.
Consider:
“The bank approved the loan.”
To build a contextual representation of bank, a word such as loan can provide useful information.
Attention allows the model to assign different weights to information from different token positions.
Some relationships receive greater weight.
Others receive less.
The resulting representation incorporates information according to those attention weights.
06 — QUERY
Queries represent what each token seeks from other token representations.
Inside attention, token representations are transformed into several learned representations.
One is the query.
A useful conceptual question is:
What information is this token looking for?
The query is compared with keys associated with tokens.
Those comparisons contribute to attention scores.
A query is not a natural-language question being consciously asked by the model.
It is a learned numerical representation used in the attention calculation.
But the metaphor helps us understand its role.
07 — KEY
Keys represent information queries compare against when calculating attention relevance.
If a query represents what information is being sought, a key provides something against which that query can be compared.
The Transformer compares queries with keys.
Greater compatibility contributes to greater attention.
Conceptually:
A query asks:
What is relevant to me?
A key helps determine:
How relevant might this position be?
The mathematical relationship between queries and keys contributes to the attention weights.
08 — VALUE
Values carry information combined according to calculated token attention weights.
Queries and keys help determine where attention should be allocated.
Values contain the information that will be combined.
A useful simplification is:
Query:
What am I looking for?
Key:
Where should I look?
Value:
What information should I take from there?
Attention weights determine how strongly the different value representations contribute to the resulting output.
So attention does more than identify relationships.
It uses those relationships to construct contextual representations.
09 — HEAD
Multiple attention heads capture different token relationships through parallel projections.
One attention operation provides one learned way of relating information.
But sequences can contain many kinds of relationships.
Grammar.
Reference.
Position.
Meaning.
Dependencies.
Transformers therefore use multi-head attention.
Each head uses learned projections of queries, keys, and values.
The heads operate in parallel, and their outputs are combined.
This allows the model to jointly attend to information from different representation subspaces.
A useful intuition is:
One head can capture one useful pattern of relationships.
Another can capture another.
Together they provide richer representations.
10 — LAYER
Layers repeatedly transform contextual representations into increasingly useful internal representations.
A Transformer is not usually one attention operation.
Transformer blocks are stacked.
Information passes through layer after layer.
Each layer further transforms representations produced by earlier processing.
The original Transformer combined attention mechanisms with position-wise feed-forward networks, residual connections, and normalization.
Modern Transformer architectures vary, but repeated layers remain fundamental.
This repeated transformation lets the network build progressively useful internal representations.
The Entire Book in 100 Words
TRANSFORMER
Transformers process sequences using attention to model relationships between tokens.
TOKEN
Tokens provide sequence elements that Transformers represent, relate, and process.
EMBEDDING
Embeddings convert tokens into numerical representations Transformers can process mathematically.
POSITION
Position information helps Transformers distinguish token order within input sequences.
ATTENTION
Attention weights relationships between tokens according to their contextual relevance.
QUERY
Queries represent what each token seeks from other token representations.
KEY
Keys represent information queries compare against when calculating attention relevance.
VALUE
Values carry information combined according to calculated token attention weights.
HEAD
Multiple attention heads capture different token relationships through parallel projections.
LAYER
Layers repeatedly transform contextual representations into increasingly useful internal representations.
The Minimalist Transformer
You do not need to understand everything about Transformers before you begin.
Remember ten things:
Transformer.
Token.
Embedding.
Position.
Attention.
Query.
Key.
Value.
Head.
Layer.
Everything else can come later.
Break input into tokens.
Represent them numerically.
Preserve position.
Apply attention.
Create queries.
Compare keys.
Combine values.
Use multiple heads.
Stack layers.
Build contextual representations.
Then understand how modern AI models process information.
One Final Question
The next time you use an LLM, don't begin with:
“How does it understand this sentence?”
Begin with:
“Which tokens need information from which other tokens?”
Then think attention.
