Phase 3: LLM Architecture · ~90 minutes · Python
Data Pipelines for Pre-Training
The model is a mirror. It reflects whatever data you feed it. Feed it garbage, it reflects garbage with perfect fluency.
Hiring signal: Understanding of data pipelines for pre-training internals
What you will learn
- Build a streaming data pipeline that tokenizes, chunks, shuffles, and batches terabytes of text without loading it all into memory
- Implement data quality filters (deduplication, language detection, content filtering) used in real pre-training pipelines
- Create fixed-length training sequences with proper attention masks and document boundary handling
- Profile pipeline throughput to ensure the dataloader keeps up with GPU training speed
Introduction
Type: Build Languages: Python Prerequisites: Phase 10, Lessons 01-02 (Tokenizers, Building a Tokenizer) Time: ~90 minutes
Learning Objectives
- Build a streaming data pipeline that tokenizes, chunks, shuffles, and batches terabytes of text without loading it all into memory
- Implement data quality filters (deduplication, language detection, content filtering) used in real pre-training pipelines
- Create fixed-length training sequences with proper attention masks and document boundary handling
- Profile pipeline throughput to ensure the dataloader keeps up with GPU training speed
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You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.
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