Fine-Tuning Data Preparation
Data volume guidelines, quality checklists, and the complete preparation pipeline for fine-tuning datasets.
Last updated
After this section you can
- Estimate how many examples a task needs from how far it moves the model
- Run a dataset through quality gates and catch leakage, template skew and label noise before training
- Generate synthetic data and filter it with a signal independent of the generator
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Fine-Tuning Data Preparation
A fine-tuned model becomes whatever its examples are, mistakes included. How much data each task needs, the gates every example must pass, and how to format, select and generate it.
Key idea
Fine-tuning is imitation at scale. A thousand clean, varied, correctly labelled examples teach the task. Ten thousand scraped, duplicated, 5%-mislabelled ones teach the mistakes too, faithfully. Time spent on data buys more quality than any hyperparameter.
Five stages from raw examples to a versioned dataset, with gates that reject before training