7.4.1 Pretraining Roadmap: Data, Objective, Engineering

Pretraining is how a model first learns broad language patterns. The useful engineering view is: clean data, choose an objective, train at scale, track risk.

Look at the Pretraining Triangle First

Piece	First question
data	what text enters training and what must be filtered?
objective	what prediction task creates learning signal?
engineering	how are scale, checkpoints, logs, and failures handled?
evaluation	what can the model do, and where does it fail?

Create Next-Token Pairs

tokens = ["AI", "learns", "from", "text"]
pairs = list(zip(tokens[:-1], tokens[1:]))

for source, target in pairs:
    print(f"{source} -> {target}")

Expected output:

AI -> learns
learns -> from
from -> text

This tiny example is the shape of next-token prediction. Real pretraining repeats this over massive text with careful data governance.

Learn in This Order

Order	Read	What to focus on
1	7.4.2 Pretraining Data	sources, filtering, deduplication, contamination
2	7.4.3 Pretraining Methods	next-token prediction, loss, scaling
3	7.4.4 Pretraining Engineering	distributed training, checkpoints, monitoring
4	7.4.5 Rent a GPU and Train a Hand-Built GPT-2	platform choice, environment setup, device: cuda mini GPT-2 walkthrough

Evidence to Keep

Keep this page’s proof of learning as a small evidence card:

Triangle

data, objective, and engineering all matter

Sample Pairs

next-token training pairs from one sentence

Data Risk

contamination, duplication, or low-quality mixture

Objective Note

objective shapes behavior and architecture fit

Engineering Note

sharding, resume, throughput, and monitoring

Hands On Bridge

run a mini GPT-2 training script on free or low-cost GPU compute

Pass Check

You pass this roadmap when you can explain how data, objective, and engineering each affect the final model, why contamination can make evaluation misleading, and why the mini GPT-2 lab treats CPU as smoke testing while device: cuda is the official training evidence.

Check reasoning and explanation

1. A passing answer explains how tokens, context, attention, prompts, and generation behavior connect in one request-response path.
2. The evidence should include at least one reproducible prompt or structured-output test, plus notes on why the output passed or failed.
3. A good self-check separates prompt design, RAG, fine-tuning, and alignment: use the lightest method that fixes the observed problem.