Enjoy everyone! (And remember it's a base model so you might have to play around with your prompts; if you want it to follow instructions you can try the format "Instruct:... Ouput:") huggingface.co/microsoft/phi-2

I'm excited for our NeurIPS LLM Efficiency Competition workshop tomorrow: 1LLM + 1GPU + 1Day! Stop by 1:30 CT to see Weiwei Yang (MSR), Mark Saroufim , Jeremy Howard , Sebastian Raschka , Ao Liu, Tim Dettmers , @sourab_m , Keming (Luke) Lu , Mojan Javaheripi , Leshem (Legend) Choshen 🤖🤗 @ACL , Vicki Boykis, Christian Puhrsch

phi-3 is here, and it's ... good :-). I made a quick short demo to give you a feel of what phi-3-mini (3.8B) can do. Stay tuned for the open weights release and more announcements tomorrow morning! (And ofc this wouldn't be complete without the usual table of benchmarks!)

🚀 Phi-4 is here! A small language model that performs as well as (and often better than) large models on certain types of complex reasoning tasks such as math. Useful for us in Microsoft Research, and available now for all researcher on the Azure AI Foundry! aka.ms/phi4blog

Surprise #NeurIPS2024 drop for y'all: phi-4 available open weights and with amazing results!!! Tl;dr: phi-4 is in Llama 3.3-70B category (win some lose some) with 5x fewer parameters, and notably outperforms on pure reasoning like GPQA (56%) and MATH (80%).

Are you ready for an early Christmas present from our team at Microsoft Research? Introducing the most powerful smol model ever built in the world! Welcome to Phi-4! 👇

Excited to see our SLM work, Phi, mentioned in MIT Technology Review as top 10 breakthrough technologies! 😊 technologyreview.com/2025/01/03/110…

Introducing Phi-4-reasoning, adding reasoning models to the Phi family of SLMs. The model is trained with both supervised finetuning (using a carefully curated dataset of reasoning demonstration) and Reinforcement Learning. 📌Competitive results on reasoning benchmarks with

In all, we SFT’ed on ~1.4M reasoning traces on select prompts and further RL'd on a small ~6k sample. Despite the relatively long SFT on select domains, we see broad generalization across domains and no degradation in general purpose performance. On the contrary....🔁📚

wow phi-4-reasoning with its mere 14B parameters beats deepseek-R1 and its 671B parameters (on AIME25). So data quality matters you tell me? 😁

Excited to share our latest Phi model, Phi4-reasoning, a small but powerful model that match the performance of much larger reasoning models up to DeepSeek R1. Here is the report for new insights into training reasoning models and evaluating them: lnkd.in/g_Pz5JQA

Nice summary of more cool results for Phi-4-Reasoning by Dimitris Papailiopoulos

Great to see the additive dataset methodology we proposed in Phi-4-reasoning adopted in open-r1. Tldr: optimize data mixture per reasoning domain, and combine in final run for generalized performance. This is a game changer for reducing data ablation costs.