The Sequence Research #466: Small but Migthy, Diving Into Microsoft Phi-4
Some architecture details about Microsoft's famous SLM.
Given the recent news about Microsoft open sourcing Phi-4, I thought it would be a good timing to dive into some of its technical details.
Microsoft Phi was been credited with starting the small language model(SLM) movement as an alternative to the “intelligence by scale” approach followed by the large AI labs. Released a couple of years ago as part of the famous paper “Textbooks is All You Need”, every release of Phi brings new innovations in terms of data quality and training. Phi-4 is the latest addition to Microsoft’s marquee SLM and it does not disappoint. Today, I would like to dive into some of the details behind Phi-4.
Not so small anymore, Phi-4 is a 14-billion parameter language model that emphasizes the importance of data quality in achieving performance comparable to, or even exceeding, much larger models. It builds on the success of the Phi family of models, which have consistently demonstrated that improvements in data can rival the benefits of scaling model size. The innovations of Phi-4 rely on its unique pre-training, midtrainign and post-training approaches.
