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We updated Phi-3 mini in our June release, with the enhancement in instruction following, reasoning in MMLU (70.9)/GPQA(30.6), and better long context. Share with us your feedback on the new models huggingface.co/microsoft/Phi-… huggingface.co/microsoft/Phi-…

Franklin Goodman Cheng Lu NeurIPS Conference Fei-Fei Li Jiahui Yu Ed H. Chi I didn't see the talk, but the images I've seen of the slide seem quite offensive. Such generalizations should have no place in NeurIPS or anywhere else.

+1 on this.

We released Phi-4-mini (3.8B base in LLM), a new SLM excelling in language, vision, and audio through a mixture-of-LoRA, uniting three modalities in one model. I am so impressed with its new audio capability. I hope you can play with it and share with us your feedback. We also

Check out our tech report of the phi4 mini and multimodality.

Happy to see Yiping Wang and the team did some interesting work here.

Glad to see the team used a 3.8B model (Phi-4-mini-reasoning) to achieve 94.6 in Math-500 and 57.5 in AIME-24. arxiv: arxiv.org/pdf/2504.21233 hf: huggingface.co/microsoft/Phi-… Azure: aka.ms/phi4-mini-reas…

Synthesizing challenging problems that current model performs poorly is an important area in RL. Another thing interests me is the self-evolve learning via synthesizing questions/problems that the model can learn continuously. You may check our work here:mastervito.github.io/MasterVito.SwS…

You may check our work of Phi4-mini-flash-Reasoning. What I like the most is the Gated Memory Unit (GMU) design, which can be applied in future model design to achieve quality and long context, as well as the uP++. Liliang Ren ✈️ ICML 2025

See our work in the workshop today. If you are looking for opportunities to work on efficient model architecture or whatever to make the training or inference run much faster with thousands or more gpus, please come to talk to us or dm me. We are hiring.