Fine-Tuning Dataset Preparation — Clean & Format Training Data

I will prepare your raw data for fine-tuning — cleaning, deduplicating, formatting into the instruction-following schema your training framework requires, splitting into train/validation/test sets, and delivering a quality-assessed dataset ready for training. Fine-tuning datasets prepared without systematic quality assessment produce models that learn from noise as reliably as from signal — duplicate examples that appear in both train and test sets inflate evaluation metrics, poorly formatted examples teach the model incorrect output structure, and data imbalances bias the model toward overrepresented patterns.

The preparation covers data cleaning and normalisation, deduplication, instruction-output pair formatting (JSONL for OpenAI, Alpaca, or ShareGPT format depending on your framework), train/validation/test split with stratification, quality metrics (length distribution, format compliance, diversity), and a dataset quality report.