---
title: Training, in four stages — How a model works, lesson 7
description: Training nudges random numbers after every guess of the next token, and four stages, from pretraining to reasoning training, make the chat model you use.
url: https://instibrain.ai/guides/how-a-model-works/training
updated: 2026-11-08
site: https://instibrain.ai/llms.txt
---

lesson 7 of 9 · how a model works

# Training, in four stages.

Training starts from random numbers and nudges every weight after each guess of the next token. Four stages turn that into the chat model you use.

## new words

**Loss**
One number for how wrong a guess was. Training makes it smaller.

**Backpropagation**
The way the model works out, for every weight, which direction makes the loss smaller.

**Gradient descent**
Moving every weight a tiny step in that direction, after every batch of text.

**Base model**
A model after pretraining alone. It continues text; it does not answer questions.

## the lesson

People design the shape of lesson 5 and the rule. Nobody sets the numbers: they start random. The model reads a piece of text, guesses each next token, and measures how wrong it was. Backpropagation finds which way each weight should move, and gradient descent moves all of them a tiny step. Then the next piece of text.

| Stage | Learns from | Makes |
| Pretraining | Trillions of tokens of text: guess the next one | A base model, which continues text |
| Supervised fine-tuning | A prompt and an ideal answer, written by people | An instruct model, which answers |
| Preference training | Two answers; a person, or a model following written principles, picks the better | A chat model, helpful and declining harm |
| Reasoning training | Problems whose answer can be checked; the try that gets it right is rewarded | A reasoning model, which works it out first |

Pretraining is the big one. Meta reports that Llama 3 read over 15 trillion tokens. A common rule of thumb counts the work as 6 × weights × tokens:

```
6 × 8,030,000,000 × 15,000,000,000,000
= 722,700,000,000,000,000,000,000
≈ 7.2 × 10²³ operations
```

Meta reports 1.3 million hours of graphics-card time for Llama 3 8B and 6.4 million for 70B. 1.3 million hours on one card is 148 years; on 16,000 cards at once, 3.4 days.

"We will train our own model on our documents" can only mean stage 2: no company has trillions of tokens of its own. Stage 2 teaches a way of answering. A document is not a prompt with an ideal answer, so its facts come out vague, go stale on the first change, and name no source. Facts belong on pages the model reads: AI basics, lesson 8.

The pretraining loop. It runs until the text is read; the three later stages run the same loop on smaller, chosen examples.

## try it

Type this and press Enter:

```
We want to train our own model on our company's documents. Which of the four training stages would that be, and what would it teach the model?
```

## you should see

- Stage 2, fine-tuning: pretraining needs trillions of tokens no company has.
- It teaches format and tone, not reliable facts. The facts belong in pages the model reads, which change on save and name their source.

---

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