Microsoft Research Debuts Phi-2, New Small Language Design

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Phi-2 is a generative AI design with 2.7 billion-parameters utilized for research study and advancement of language designs. While large language designs can reach numerous billions of parameters, Microsoft Research study is explore small language designs in order to achieve similar efficiency at a smaller sized scale. On Dec. 12, Microsoft Research revealed Phi-2, a 2.7 billion-parameter language model for natural language and coding. Phi-2 carried out better than some bigger language designs, including Google’s Gemini Nano 2, on certain tests.

Phi-2 is readily available in the Azure AI Studio design catalog. Microsoft plans for it to be used only by researchers; however, Phi-2 might ultimately result in the advancement of smaller sized, more effective models that can be utilized by businesses and can take on huge designs.

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What is Phi-2?

Phi-2 is a language design utilized for research and development of other language models, commonly known as expert system.

And, Phi-2 is the follower to Phi-1, a 1.3 billion specification small language design that was launched in September 2023. Phi-1 revealed outstanding performance on the HumanEval and MBPP standards, which grade a model’s capability to code in Python. In November 2023, Microsoft Research study launched Phi-1.5, which added more good sense thinking and language understanding to Phi-1. Satya Nadella revealed Phi-2 throughout Microsoft Ignite in November 2023 (Figure A).

Figure A

Satya Nadella announcing Phi-2 at Microsoft Ignite 2023. Satya Nadella announcing Phi-2 at Microsoft Spark 2023. Image: Microsoft “With its compact size, Phi-2 is an ideal play area for researchers, including for exploration around mechanistic interpretability, safety improvements or tweak experimentation on a range of jobs,”Microsoft Senior Researcher Mojan Javaheripi and Microsoft Partner Research Study Supervisor S├ębastien Bubeck composed in an article on Dec. 12.

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“We are finding ways to make models cheaper, more efficient, and easier to train, and we feel it is essential to share what we’re discovering so that the entire community advantages,” Bubeck informed TechRepublic in an email. “… The size of Phi-2 makes it an ideal playground for their use, including for expedition around mechanistic interpretability, security improvements, or tweak experimentation on a range of jobs.”

Phi-2 is outshining larger language models

Microsoft Research study states Phi-2 surpasses Mistral AI’s 7B (7 billion parameter) design and Llama-2 (which has 13 billion criteria) on basic benchmarks like Big Bench Hard and other language, mathematics multi-step thinking and coding tests. Microsoft Research tested Phi-2 against Google’s just recently released Gemini Nano 2 and found that it carried out better on the BBH, BoolQ, MBPP and MMLU tests.

How to make a smaller sized language model work like a big one

Microsoft Research study discovered that smaller models can carry out along with bigger ones if particular choices are made during training. One way Microsoft Research study makes smaller language designs perform in addition to big ones is by using “textbook-quality data.”

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“Our training data mix includes synthetic datasets specifically developed to teach the design good sense reasoning and basic understanding, consisting of science, daily activities and theory of mind, to name a few,” Javaheripi and Bubeck wrote. “We even more enhance our training corpus with carefully chosen web information that is filtered based upon instructional value and content quality.”

Another way to make a smaller sized language design perform along with a large one is by scaling up. For instance, the research study team embedded the understanding of the 1.3 billion criterion Phi-1.5 design into the 2.7 billion parameter Phi-2 model.

“This scaled knowledge transfer not just speeds up training merging but reveals clear boost in Phi-2 standard scores,” composed Javaheripi and Bubeck.

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