Phi-3 Technical Report: A Highly Capable Language Model Locally on Your
  Phone
Authors: name: Marah Abdin, name: Sam Ade Jacobs, name: Ammar Ahmad Awan, name: Jyoti Aneja, name: Ahmed Awadallah, name: Hany Awadalla, name: Nguyen Bach, name: Amit Bahree, name: Arash Bakhtiari, name: Harkirat Behl, name: Alon Benhaim, name: Misha Bilenko, name: Johan Bjorck, name: Sébastien Bubeck, name: Martin Cai, name: Caio César Teodoro Mendes, name: Weizhu Chen, name: Vishrav Chaudhary, name: Parul Chopra, name: Allie Del Giorno, name: Gustavo de Rosa, name: Matthew Dixon, name: Ronen Eldan, name: Dan Iter, name: Abhishek Goswami, name: Suriya Gunasekar, name: Emman Haider, name: Junheng Hao, name: Russell J. Hewett, name: Jamie Huynh, name: Mojan Javaheripi, name: Xin Jin, name: Piero Kauffmann, name: Nikos Karampatziakis, name: Dongwoo Kim, name: Mahoud Khademi, name: Lev Kurilenko, name: James R. Lee, name: Yin Tat Lee, name: Yuanzhi Li, name: Chen Liang, name: Weishung Liu, name: Eric Lin, name: Zeqi Lin, name: Piyush Madan, name: Arindam Mitra, name: Hardik Modi, name: Anh Nguyen, name: Brandon Norick, name: Barun Patra, name: Daniel Perez-Becker, name: Thomas Portet, name: Reid Pryzant, name: Heyang Qin, name: Marko Radmilac, name: Corby Rosset, name: Sambudha Roy, name: Olli Saarikivi, name: Amin Saied, name: Adil Salim, name: Michael Santacroce, name: Shital Shah, name: Ning Shang, name: Hiteshi Sharma, name: Xia Song, name: Olatunji Ruwase, name: Xin Wang, name: Rachel Ward, name: Guanhua Wang, name: Philipp Witte, name: Michael Wyatt, name: Can Xu, name: Jiahang Xu, name: Sonali Yadav, name: Fan Yang, name: Ziyi Yang, name: Donghan Yu, name: Chengruidong Zhang, name: Cyril Zhang, name: Jianwen Zhang, name: Li Lyna Zhang, name: Yi Zhang, name: Yunan Zhang, name: Xiren Zhou
Organization: Microsoft

We introduce phi-3-mini, a 3.8 billion parameter language model trained on
3.3 trillion tokens, whose overall performance, as measured by both academic
benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and
GPT-3.5 (e.g., phi-3-mini achieves 69% on MMLU and 8.38 on MT-bench), despite
being small enough to be deployed on a phone. The innovation lies entirely in
our dataset for training, a scaled-up version of the one used for phi-2,
composed of heavily filtered web data and synthetic data. The model is also
further aligned for robustness, safety, and chat format. We also provide some
initial parameter-scaling results with a 7B and 14B models trained for 4.8T
tokens, called phi-3-small and phi-3-medium, both significantly more capable
than phi-3-mini (e.g., respectively 75% and 78% on MMLU, and 8.7 and 8.9 on
MT-bench).