The project provides automated data selection methods for instruction tuning of LLMs, aiming to identify optimal training samples that maximize model performance with minimal data. It includes scorer models (complexity and quality scorers) and complete pipelines that can select a high-quality 6k or 10k subset from larger datasets. The released datasets have been used to train models like Zephyr Gemma.