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A family of small language models optimized for cost-efficiency, reasoning, and instruction following using high-quality training data.
DESCRIPTION
Phi-3 prioritizes data quality and compact architectures to deliver strong performance at low cost. It scales up to around tens of billions of parameters while targeting efficient inference. The models are well-suited for on-device or constrained environments. Microsoft emphasizes curated, synthetic, and textbook-style data to teach reasoning. Phi-3 powers copilots, content generation, and task automation with guardrails. It competes with compact open-weight models while integrating tightly with Azure offerings. Organizations adopt Phi-3 where budget, latency, or privacy drives deployment.
ALTERNATIVE DEFINITIONS
arXiv
We present Phi-3, a family of small language models (SLMs) with up to 14B parameters, trained on high-quality data and capable of strong reasoning and coding abilities.
Defines Phi-3 as SLMs with focus on quality and capability.
Microsoft Learn
Phi-3 is a family of small language models optimized for cost and performance across a range of tasks.
Highlights optimization for cost and broad task performance.
RELATED TERMS
Small Language Model Transformer Model Instruction Tuning Data Curation Safety Alignment Quantization Efficient Inference Retrieval-Augmented Generation Fine-Tuning Evaluation
COMPETING TERMS
Gemma 2 Mistral 7B Llama 3 8B
KNOWLEDGE AREAS
TARGET AUDIENCE
AI Engineer Data Scientist MLOps Engineer AI Product Manager
EXTERNAL SOURCES
TOP QUESTIONS
Why consider Phi-3 over larger LLMs?
Phi-3 delivers strong quality at lower cost and latency, ideal for edge devices and budget-sensitive deployments.
Category: Relevancy
What is distinctive about Phi-3 training data?
It emphasizes high-quality, curated and synthetic, textbook-style content to improve reasoning.
Category: Data
What are typical Phi-3 use cases?
Copilots, chat, summarization, and code assistance where efficiency matters are common.
Category: UseCases
How does Phi-3 compare to Mistral 7B?
Phi-3 targets similar efficiency goals; relative performance varies by task, dataset, and tuning.
Category: Comparison
What are common mistakes with Phi-3?
Underestimating context limits, skipping guardrails, and insufficient evaluation can lead to poor outcomes.
Category: Mistakes
How to deploy Phi-3 on resource-constrained hardware?
Use quantization, optimized inference runtimes, and retrieval to reduce compute burden.
Category: How-To
What are the risks of using Phi-3?
Hallucinations and domain gaps can occur; add retrieval, validation, and monitoring to mitigate risks.
Category: Risk
How is Phi-3 licensed and accessed?
Availability and licensing depend on specific variants; Azure model catalog provides access and terms.
Category: Licensing
Where to learn more about Phi-3?
Read the technical report and Microsoft Learn model catalog pages, then prototype via Azure AI Studio.
Category: GettingStarted