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Understanding LLMs and Transformers

INTUITIVE_LLM_AND_TRANSFORMER.md Public

This guide explains the workings of Large Language Models (LLMs) and transformers in simple terms. It covers the step-by-step process from text to numbers, tokenization, and the mathematical operations performed by transformers to generate text.

INTUITIVE LLM AND TRANSFORMER GUIDE (PLAIN ENGLISH)

Big Picture

A Large Language Model (LLM) is a system that turns text into numbers, does a lot of math on those numbers, and then turns the results back into text.

It does not think, reason, or understand like a human. It works by recognizing patterns it learned during training.

The Core Idea

Every message follows this path:

Text → Numbers → Math → Numbers → Text

Let’s walk through it step by step.

1. Text → Tokens (Breaking text into pieces)

The model cannot read words directly.

Your sentence is first broken into small chunks called tokens. A token might be:

  • a whole word
  • part of a word
  • punctuation

Example:

"I love AI" → [40, 582, 921]

Each number is an ID for a token in a vocabulary list.

Is there one standard way to do this?

No. Tokenization is specific to each model. There is no single universal system that all AI models use.

Each model has its own vocabulary and its own rules for how text gets split into tokens. The tokenizer is designed to match the model’s internal number tables. If you use the wrong tokenizer with a model, the numbers won’t line up correctly and the model will behave poorly.

Common styles of tokenization (but still model-specific)

Different models use different general approaches:

  • BPE (Byte Pair Encoding): Breaks words into common chunks
  • SentencePiece: Splits text into statistically useful pieces
  • WordPiece: Similar idea with slightly different rules
  • Byte-level tokenization: Works at the level of raw text bytes

Even if two models use the same type of method (like BPE), their actual token lists and token numbers are different.

Why this matters

Each token number connects directly to:

  • the model’s meaning vectors (embeddings)
  • the list of words the model can predict

So the tokenizer and the model are a matched pair. You always have to use the tokenizer made for that specific model.

This step (turning text into tokens) is done before the transformer math begins.

2. Tokens → Meaning Vectors (Turning IDs into numbers with meaning)

Each token number points to a row in a big table of numbers called an embedding table.

You can imagine this table like a giant spreadsheet where:

  • each row = one token
  • each column = a feature about meaning

Now every token becomes a long list of numbers. These lists are called vectors.

At this point, the system is working only with numbers.

3. Transformer Processing (The Main Math Engine)

This is where the transformer comes in.

The transformer is not a program that understands language. It is a recipe for doing math with matrices (large tables of numbers).

It takes the vectors from step 2 and repeatedly transforms them using many layers of calculations.

Each layer mainly does two things:

A. Attention (Figuring out what matters)

The model compares every word with every other word to decide:

“Which words should influence each other?”

For example, in:

"The dog chased its tail"

the word its needs to connect to dog, not tail.

Attention uses several learned tables of numbers (often called Q, K, and V matrices) to measure how strongly words relate to each other. You don’t need to know the math — just that this step lets words “look at” other words.

B. Expansion and reshaping of meaning

After attention, another set of number tables reshapes the meaning further. This part helps the model build more complex ideas from simpler ones.

These two steps repeat many times (often 20–80 layers). Each pass refines the internal representation of the sentence.

Still: no words, no symbols — only numbers moving through math operations.

4. Final Step Inside the Model → Predicting the next word

At the end, the model has one final vector of numbers.

This is multiplied by another large table to produce one score for every word in the vocabulary (sometimes 50,000+ words).

These scores represent:

“How likely is each possible next word?”

5. Choosing a Word (Sampling)

A small algorithm chooses the next word based on those likelihoods.

This might pick the most likely word, or introduce some randomness for creativity.

Still just numbers.

6. Numbers → Text Again

The chosen token number is converted back into text using the tokenizer’s vocabulary.

Now you see a word on your screen.

This loop repeats again and again to produce a full sentence.

Where the Transformer Fits

The transformer is the system that performs Step 3 and part of Step 4.

It defines:

  • what math steps happen
  • how many layers there are
  • how words influence each other
  • how meaning is reshaped through layers

It does not handle text directly. It only processes numbers.

Final Summary

An LLM is:

  • A giant collection of learned number tables
  • Arranged into a transformer structure
  • Run by a software engine that performs the math

The full cycle is:

Text → Tokenizer → Numbers → Transformer Math → Probabilities → Chosen Token → Text

There are no rules, no logic statements, and no understanding in the human sense — only patterns learned and reproduced through math.