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Over the past few months, Microsoft Research has been releasing a suite of small language models (SLMs) called “Phi”. The Phi-1 was released first with 1.3 billion parameters and it was specialized for basic Python coding. In September, Microsoft Research released Phi-1.5 model with 1.3 billion parameters, but it was trained with a new data source that included various NLP synthetic texts. Despite its small size, phi-1.5 was delivering a nearly state-of-the-art performance when comparable to other similarly sized models.
Today, Microsoft announced the release of Phi-2 model with 2.7 billion parameters. Microsoft Research claims that this new SLM delivers state-of-the-art performance among base language models with less than 13 billion parameters. On some complex benchmarks, Phi-2 matches or outperforms models up to 25x larger.
Last week, Google announced Gemini suite of language models. The Gemini Nano is Google’s most efficient model built for on-device tasks and it can run directly on mobile silicon. Gemini Nano-like small language model enables features such as text summarization, contextual smart replies, and advanced proofreading and grammar correction.
According to Microsoft, the new Phi-2 model matches or outperforms the new Google Gemini Nano-2, despite being smaller in size. You can find the benchmarks comparison between Google Gemini Nano-2 and Phi-2 models below.
|Gemini Nano 2
In addition to outperforming Gemini Nano-2, Phi-2 also surpasses the performance of Mistral and Llama-2 models at 7B and 13B parameters on various benchmarks. Find the details below.