The innovation in small models lies in utilizing a scaled-up version of a data set composed of heavily filtered web data and synthetic data for training. It emphasizes that while exploring large models like mixture of experts is crucial, the real progress in model performance stems from the quality and nature of the training data. Microsoft's four billion parameter model showcases two versions with varying context windows, aiming to facilitate open-source developers by aligning the model's structure with llama two for easier adoption. This approach signifies a philosophical shift towards prioritizing data quality and accessibility over sheer model size.