Phi-3: Redefining what's possible with SLMs

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Phi-3 models are the most capable and cost-effective small language models (SLMs) available, outperforming models of the same size and next size up across a variety of language, reasoning, coding, and math benchmarks.

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Updated:2024/10/02

What is Phi-3: Redefining what's possible with SLMs

Introducing Phi-3, a family of small open models developed by Microsoft, designed to redefine what is possible with small language models (SLMs).

Key Features of Phi-3: Redefining what's possible with SLMs

Phi-3 models are the most capable and cost-effective SLMs, outperforming models of the same size and next size up across a variety of language, reasoning, coding, and math benchmarks.

High-quality training data

Phi-3 models are trained using high-quality data, ensuring they are ready to use out-of-the-box.

Instruction-tuned

Phi-3 models are instruction-tuned, meaning they are trained to follow different types of instructions reflecting how people normally communicate.

Safety-first model design

Phi-3 models were developed in accordance with the Microsoft Responsible AI Standard, ensuring they are responsibly developed, tested, and deployed.

Use Cases of Phi-3: Redefining what's possible with SLMs

Resource-constrained environments
Latency-bound scenarios
Cost-constrained use cases, particularly those with simpler tasks

Pros and Cons of Phi-3: Redefining what's possible with SLMs

Pros

High-quality training data
Instruction-tuned
Safety-first model design

Cons

May not perform as well on factual knowledge benchmarks
May require further optimization for specific use cases

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How to Use Phi-3: Redefining what's possible with SLMs

1
Get started with Phi-3 on Microsoft Azure AI Studio
2
Explore the Phi-3 model on Hugging Face and Ollama
3
Use Phi-3 for resource-constrained environments, latency-bound scenarios, and cost-constrained use cases

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Phi-3: Redefining what's possible with SLMs

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Phi-3: Redefining what's possible with SLMs

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What is Phi-3: Redefining what's possible with SLMs