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Fine-Tuning Data Preparation

Data volume guidelines, quality checklists, and the complete preparation pipeline for fine-tuning datasets.

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Fine-Tuning Data Preparation

The dataset is the model. Volume guidelines by task, the six-stage preparation pipeline, quality gates that catch silent failures, and the checklists that separate a clean fine-tune from an expensive mistake.

THE CENTRAL IDEA

Fine-tuning is supervised imitation: the model becomes whatever your examples are. Feed it 1,000 clean, diverse, correctly-labeled examples and it learns your task. Feed it 10,000 scraped, duplicated, 5%-mislabeled examples and it learns your mistakes — faithfully, at scale, and permanently. Every hour spent on data preparation buys more quality than any hyperparameter you will ever touch. Data beats hyperparameters. Always.

The LIMA paper (Meta, 2023) made this concrete: a 65B model fine-tuned on just 1,000 hand-curated examples matched or beat models trained on hundreds of thousands of automatically generated ones. The finding has been replicated so many times it is now the field’s default assumption: past a surprisingly small floor, quality and diversity dominate quantity. That inverts where most teams spend their time — and it is exactly what an interviewer probes when they ask “how much data do you need?”

The data preparation pipeline — six stages, with quality gates that reject before training

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