We dive into Phi-2 and some of the major differences and use cases for a small language model (SLM) versus an LLM.With only 2.7 billion parameters, Phi-2 surpasses the performance of Mistral and Llama-2 models at 7B and 13B parameters on various aggregated benchmarks. Notably, it achieves better performance compared to 25x larger Llama-2-70B model on multi-step reasoning tasks, i.e., coding and math. Furthermore, Phi-2 matches or outperforms the recently-announced Google Gemini Nano 2, despite being smaller in size. Find the transcript and live recording: https://arize.com/blog/phi-2-modelLearn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

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