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| Developers | Microsoft Research |
| Description | phi-4 is a state-of-the-art open model built upon a blend of synthetic datasets, data from filtered public domain websites, and acquired academic books and Q&A datasets. The goal of this approach was to ensure that small capable models were trained with data focused on high quality and advanced reasoning.phi-4 underwent a rigorous enhancement and alignment process, incorporating both supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures |
| Architecture | 14B parameters, dense decoder-only Transformer model |
| Inputs | Text, best suited for prompts in the chat format |
| Context length | 16K tokens |
| GPUs | 1920 H100-80G |
| Training time | 21 days |
| Training data | 9.8T tokens |
| Outputs | Generated text in response to input |
| Dates | October 2024 – November 2024 |
| Status | Static model trained on an offline dataset with cutoff dates of June 2024 and earlier for publicly available data |
| Release date | December 12, 2024 |
| License | MIT |
phi-4 with a set of models over OpenAI’s SimpleEval benchmark; at the high-level overview of the model quality on representative benchmarks; for the table below, higher numbers indicate better performance:| Category | Benchmark | phi-4 (14B) | phi-3 (14B) | Qwen 2.5 (14B instruct) | GPT-4o-mini | Llama-3.3 (70B instruct) | Qwen 2.5 (72B instruct) | GPT-4o |
|---|---|---|---|---|---|---|---|---|
| Popular Aggregated Benchmark | MMLU | 84.8 | 77.9 | 79.9 | 81.8 | 86.3 | 85.3 | 88.1 |
| Science | GPQA | 56.1 | 31.2 | 42.9 | 40.9 | 49.1 | 49.0 | 50.6 |
| Math | MGSM MATH | 80.6 80.4 | 53.5 44.6 | 79.6 75.6 | 86.5 73.0 | 89.1 66.3* | 87.3 80.0 | 90.4 74.6 |
| Code Generation | HumanEval | 82.6 | 67.8 | 72.1 | 86.2 | 78.9* | 80.4 | 90.6 |
| Factual Knowledge | SimpleQA | 3.0 | 7.6 | 5.4 | 9.9 | 20.9 | 10.2 | 39.4 |
| Reasoning | DROP | 75.5 | 68.3 | 85.5 | 79.3 | 90.2 | 76.7 | 80.9 |