๐Ÿงญ Roadmap โ€บ 04 ยท Build Language Models โ€บ Pretraining โ€บ Dataset Preparation
โ† Pretraining

Dataset Preparation

๐ŸŸก Helpful
Area04 ยท Build Language Models
TopicPretraining
Topic priority๐Ÿ”ด MUST
Subtopic2 of 11
Study this using 6 phases
๐Ÿง  01 Understand

What is Dataset Preparation? Why does it exist? Where does it appear in Pretraining?

๐Ÿ‘๏ธ 02 Visualize

Draw or describe a visual model of Dataset Preparation. What does it look like conceptually?

โœ๏ธ 03 Calculate

Work through a concrete manual example of Dataset Preparation with real numbers or pseudocode.

๐Ÿ’ป 04 Code

Implement Dataset Preparation from scratch in Python. Keep it minimal โ€” just enough to see it work.

โš–๏ธ 06 Compare

How is Dataset Preparation different from related concepts in Pretraining? What are the trade-offs?

๐Ÿ—ฃ๏ธ 07 Explain

Explain Dataset Preparation in plain language using an analogy. Could a non-expert follow it?

โ† Training Data Tokenization โ†’
๐Ÿงช
RASL Pull ยท S-05 Pulled because: how do checkpoints work โ€” what does save/restore mean?
๐Ÿ”จ M-06 train.py checkpoints ๐Ÿ” checkpoint pattern = RASL state snapshots โ†’ return to train.py ๐Ÿ“‹ full plan โ†’
โ†‘ All subtopics in Pretraining
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