Microsoft recently launched the Phi-3 family, its smallest AI model yet
Recently Microsoft has launched its smallest AI model for small enterprises to provide them with a more cost-effective solution. Microsoft launched their new model a family of Phi-3 in which Phi-3 is the first model among the three. Phi-3 mini is available in two contexts- 4K and 128K tokens, which is an effective solution for small enterprises.
“Some customers may only need small models, some will need big models and many are going to want to combine both in a variety of ways,” said Luis Vargas, vice president of AI at Microsoft.
In their blog post, Microsoft announced that the “Phi-3 family is the most capable and cost-effective small language models (SLMs) available, outperforming models of the same size and next size up across a variety of language, reasoning, coding, and math benchmarks.”
The more interesting fact about the new Microsoft Phi-3 model is the story behind this. This model came into existence from the storybook of a child. Well, that is surprising but it’s true. The developers were inspired by how the children learn words from their stories and they connect them to make sentences. They followed the same approach to build the smallest LLM model Phi-3.
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“There aren’t enough children’s books out there, so we took a list of more than 3,000 words and asked an LLM to make ‘children’s books’ to teach Phi,” Boyd says.
Microsoft’s new model Phi-3 is available on Microsoft Azure AI Model Catalog, Hugging Face, Ollama and soon it will be available on NVIDIA NIM. Microsoft has also announced that in the coming weeks, they will be launching other Phi-3-small (7 billion parameters) and Phi-3-medium (14 billion parameters) to this family.
“What we’re going to start to see is not a shift from large to small, but a shift from a singular category of models to a portfolio of models where customers get the ability to decide on what is the best model for their scenario,” said Sonali Yadav, principal product manager for Generative AI at Microsoft.
