Microsoft has revealed the Phi-3 Mini Model, compact enough to operate on mobile devices.


Although large language models (LLMs) excel at complex tasks, smaller models are valuable for local use on smartphones and PCs. Microsoft’s latest development in this area is the Phi-3 Mini model, boasting 3.8 billion parameters in its training.

Two other models within the Phi-3 family exist: Phi-3 Small (7B) and Phi-3 Medium (14B), but they remain unreleased. However, the Phi-3 Mini model, despite its diminutive size, outperforms Meta’s Llama 3 8B model, Google’s Gemma 7B model, and Mistral 7B model in the MMLU benchmark. Remarkably, it matches the performance of Mixtral 8x7b, despite being significantly smaller.

In HumanEval, the Phi-3 Mini model surpasses Gemma 7B and Mistral 7B by a significant margin. Microsoft’s dedication to crafting potent small models for local use on smartphones and PCs is evident. Additionally, the upcoming Phi-3 Small and Phi-3 Medium models outperform OpenAI’s GPT-3.5 model, Mixtral 8x7b, and Llama 3 8B, showcasing Microsoft’s impressive advancements in the field.

Microsoft attributes Phi-3’s exceptional performance to its clean dataset, comprising heavily filtered web data and synthetic data. Moreover, the model undergoes rigorous checks for safety, harm, and robustness. It appears that Phi-3 is poised to reign supreme among smaller models. I’m eager to see how it fares against Anthropic’s Haiku model, the smallest model in the Claude 3 family. Share your excitement in the comments below! In the meantime, you can explore how to run Google’s Gemma model on your PC locally.


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