Preparing data for supervised fine-tuning Part 1: Formatting and quality
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What happened
Data preparation determines the ceiling of any supervised fine-tuning project. This first post in a two-part series covers the foundations of SFT data prep: quality checks, conversational (JSONL) formatting, reasoning and tool-calling schemas, and a representative train/evaluation split.
Summary assembled by rule from the sources below
Why it's spreading
Timeline
- First appeared on AWS Machine LearningAWS Machine Learning
- AWS Machine Learning posted a follow-upAWS Machine Learning
Sources
Official
- Preparing data for supervised fine-tuning Part 2: Advanced data strategiesAWS Machine Learning
- Preparing data for supervised fine-tuning Part 1: Formatting and qualityAWS Machine Learning