title: Phi-3 Technical Report: A Highly Capable Language Model Locally on Your
    Phone
summary: 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).
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paper_title: Phi-3 Technical Report: A Highly Capable Language Model Locally on Your
    Phone
paper_summary: 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).