Microsoft Research has recently released Phi-2, a 2.7 billion-parameter language model that demonstrates outstanding reasoning and language understanding capabilities. Phi-2 is part of Microsoft’s Phi project, which aims to create small but powerful language models that can achieve performance on par with models of much higher scale.
It is trained on a mixture of synthetic and web data, including data generated by GPT-3.5, a large language model developed by OpenAI. Phi-2 leverages the knowledge and diversity of GPT-3.5, but also focuses on “textbook-quality” data, which contains high-quality information and explanations on various topics, such as science, common sense, and theory of mind. By using this data, Phi-2 can learn to reason and understand language better than existing models of similar size.
Phi-2 outperforms models up to 25 times larger on a variety of benchmarks, such as common sense reasoning, natural language inference, and coding. For example, Phi-2 defeats the 70 billion-parameter Llama 2 at coding, and even surpasses Gemini Nano, a small version of Google’s latest foundation model. Phi-2 also shows better behavior in terms of toxicity and bias compared to some existing models, despite not undergoing alignment through reinforcement learning from human feedback.
Phi-2 is available through the Azure AI Studio model catalog and Hugging Face, and can be used for research and development of language models. However, Phi-2 cannot be used for commercial purposes, as it is licensed under the Microsoft Research license, which only allows non-commercial, research-oriented use.
With its compact size and impressive performance, Phi-2 is an ideal playground for researchers who want to explore the potential and limitations of small language models, and to experiment with different tasks and applications. Phi-2 also showcases the importance of data quality and selection for language model training, and the possibility of breaking the conventional language model scaling laws.
Microsoft Research has released Phi-2, a 2.7 billion-parameter language model that can reason and understand language better than models up to 25 times larger. Phi-2 is trained on a mixture of synthetic and web data, including data generated by GPT-3.5 and “textbook-quality” data. \
The outperforms models of similar size on various benchmarks, such as common sense reasoning, natural language inference, and coding. Phi-2 is available for non-commercial, research-oriented use through the Azure AI Studio model catalog and Hugging Face.
The Phi project is an initiative by Microsoft Research to create small but powerful language models that can achieve performance on par with models of much higher scale. This project aims to explore the potential and limitations of small language models, and to experiment with different tasks and applications. The Phi project also showcases the importance of data quality and selection for language model training, and the possibility of breaking the conventional language model scaling laws.
One of the outcomes of the Phi project is Phi-2, a 2.7 billion-parameter language model that demonstrates outstanding reasoning and language understanding capabilities. You can learn more about the Phi project and Phi-2 by reading this article or watching this video.
Microsoft Research is a division of Microsoft that conducts cutting-edge research and innovation in various fields of computer science and technology. Some of the other projects by Microsoft Research are:Project Natick: An experiment to deploy underwater data centers that are powered by renewable energy and offer low latency and high reliability.
Project Premonition a system that uses drones, robots, and cloud computing to detect and monitor pathogens and diseases in the environment.Project Silica: A technology that uses femtosecond lasers to store data in quartz glass, offering high durability and density.Project Freta: A cloud-based service that provides automated and comprehensive malware detection for virtual machines.Project Alexandria: A large-scale language model that aims to achieve human-like understanding and reasoning across natural language, images, and knowledge bases.
Project Silica is a research project by Microsoft that aims to create a new storage technology that uses quartz glass and femtosecond lasers to store data. The project claims that this technology can offer high durability, density, and security for long-term data storage.