Phi-4 Technical Report - Abdin, Marah I; Aneja, Jyoti; Behl, Harkirat; Bubeck, Sébastien; Eldan, Ronen; Gunasekar, Suriya; Harrison, Michael; Hewett, Russell J.; Javaheripi, Mojan; Kauffmann, Piero; Lee, James R.; Lee, Yin Tat; et al. (Microsoft, 2024)
We present phi-4, a 14-billion parameter language model developed with a training recipe that is centrally focused on data quality. Unlike most language models, where pre-training is based primarily on organic data sources such as web content or code, phi-4 strategically incorporates synthetic data throughout the training process. While previous models in the Phi family largely distill the capabilities of a teacher model (specifically GPT-4), phi-4 substantially surpasses its teacher model on STEM-focused QA capabilities, giving evidence that our data-generation and post-training techniques go beyond distillation. Despite minimal changes to the phi-3 architecture, phi-4 achieves strong performance relative to its size– especially on reasoning-focused benchmarks– due to improved data, training curriculum, and innovations in the post-training scheme.