Part 12
Completed
Pretraining Data Pipeline
Built a complete data pipeline for LLM pretraining: web crawling, deduplication, quality filtering, toxicity detection, PII removal, and tokenization. Implemented distributed processing with Dask/Spark and evaluated data quality impact on downstream performance.
What I Built
Built a complete data pipeline for LLM pretraining: web crawling, deduplication, quality filtering, toxicity detection, PII removal, and tokenization. Implemented distributed processing with Dask/Spark and evaluated data quality impact on downstream performance.
Key Concepts
Data PipelineDeduplicationQuality FilteringToxicity DetectionPII RemovalDistributed Processing
Architecture
1
Crawler2
Deduplicator3
Quality Filter4
Toxicity Detector5
PII Scrubber6
Tokenizer7
Data LoaderResults
Processed 500GB raw text to 100GB high-quality corpus. Deduplication improves perplexity by 8%. Quality filtering improves downstream accuracy by 12%.
Key Learnings
- Data quality matters more than data quantity
- Deduplication is essential—web data is heavily duplicated
- Quality filtering has massive impact on model behavior
Challenges
- Scaling deduplication to terabyte-scale datasets
- Defining 'quality' in a generalizable way
- Balancing filtering with diversity preservation