Phi-4 Technical Report
- Marah I Abdin ,
- Jyoti Aneja ,
- Harkirat Behl ,
- Sébastien Bubeck ,
- Ronen Eldan ,
- Suriya Gunasekar ,
- Michael Harrison ,
- Russell J. Hewett ,
- Mojan Javaheripi ,
- Piero Kauffmann ,
- James R. Lee ,
- Yin Tat Lee ,
- Yuanzhi Li ,
- Weishung Liu ,
- Caio CT Mendes ,
- Anh Nguyen ,
- Eric Price ,
- Gustavo de Rosa ,
- Olli Saarikivi ,
- Adil Salim ,
- Shital Shah ,
- Xin Wang ,
- Rachel Ward ,
- Yue Wu ,
- Dingli Yu ,
- Cyril Zhang ,
- Yi Zhang
MSR-TR-2024-57 |
Published by Microsoft
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.
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Phi-4
June 18, 2025
Phi-4-multimodal and Phi-4-mini, the newest models in Microsoft’s Phi family of small language models (SLMs) are now available. These models are designed to empower developers with advanced AI capabilities. Phi-4-multimodal, with its ability to process speech, vision, and text simultaneously, opens new possibilities for creating innovative and context-aware applications. Phi-4-mini, on the other hand, excels in text-based tasks, providing high accuracy and scalability in a compact form.