Phi 4 reasoning Model Card Phi 4 reasoning Technical Report Model Summary Developers Microsoft Research Description Phi 4 reasoning is a state of the art open weight reasoning model finetuned from Phi 4 using supervised fine tuning on a dataset of chain of thought traces and reinforcement learning. The supervised fine tuning dataset includes a blend of synthetic prompts and high quality filtered data from public domain websites, focused on math, science, and coding skills as well as alignment data for safety and Responsible AI. The goal of this approach was to ensure that small capable models were trained with data focused on high quality and advanced reasoning. Architecture Base model same as previously released Phi 4, 14B parameters, dense decoder only Transformer model Inputs Text, best suited for prompts in the chat format Context length 32k tokens GPUs 32 H100 80G Training time 2.5 days Training data 16B tokens, ~8.3B unique tokens Outputs Generated text in response to the input. Model responses have two sections, namely, a reasoning chain of thought block followed by a summarization block Dates January 2025 – April 2025 Status Static model trained on an offline dataset with c…
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