In this paper, we presented efficient methods for packing and organizing large-scale CLIP data collections. Our approach, which combines DSLaF, hierarchical data organization, and efficient data packing, provides a better understanding of how to manage and utilize these collections for improved model performance. We hope that this work will contribute to the development of more efficient and effective machine learning models.