Microsoft’s 2.7 billion-parameter model Phi-2 showcases outstanding reasoning and language understanding capabilities, setting a new standard for performance among base language models with less than 13 billion parameters.
Phi-2 builds upon the success of its predecessors, Phi-1 and Phi-1.5, by matching or surpassing models up to 25 times larger—thanks to innovations in model scaling and training data curation.
The compact size of Phi-2 makes it an ideal playground for researchers, facilitating exploration in mechanistic interpretability, safety improvements, and fine-tuning experimentation across various tasks.
Phi-2’s achievements are underpinned by two key aspects:
- Training data quality: Microsoft emphasises the critical role of training data quality in model performance. Phi-2 leverages “textbook-quality” data, focusing on synthetic datasets designed to impart common sense reasoning and general knowledge. The training corpus is augmented with carefully selected web data, filtered based on educational value and content quality.
- Innovative scaling techniques: Microsoft adopts innovative techniques to scale up Phi-2 from its predecessor, Phi-1.5. Knowledge transfer from the 1.3 billion parameter model accelerates training convergence, leading to a clear boost in benchmark scores.
Performance evaluation
Phi-2 has undergone rigorous evaluation across various benchmarks, including Big Bench Hard, commonsense reasoning, language understanding, math, and coding.
With only 2.7 billion parameters, Phi-2 outperforms larger models – including Mistral and Llama-2 – and matches or outperforms Google’s recently-announced Gemini Nano 2:
Beyond benchmarks, Phi-2 showcases its capabilities in real-world scenarios. Tests involving prompts commonly used in the research community reveal Phi-2’s prowess in solving physics problems and correcting student mistakes, showcasing its versatility beyond standard evaluations:
Phi-2 is a Transformer-based model with a next-word prediction objective, trained on 1.4 trillion tokens from synthetic and web datasets. The training process – conducted on 96 A100 GPUs over 14 days – focuses on maintaining a high level of safety and claims to surpass open-source models in terms of toxicity and bias.
With the announcement of Phi-2, Microsoft continues to push the boundaries of what smaller base language models can achieve.
(Image Credit: Microsoft)
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