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Phi-2 Model
Manage episode 398886754 series 3448051
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-model
To learn more about ML observability, join the Arize AI Slack community or get the latest on our LinkedIn and Twitter.
34 episodes
Manage episode 398886754 series 3448051
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-model
To learn more about ML observability, join the Arize AI Slack community or get the latest on our LinkedIn and Twitter.
34 episodes
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