Microsoft Introduces Phi-3-mini

April 27, 2024

Microsoft has unveiled Phi-3-mini, the smallest model in its new Phi-3 family of open AI models. Phi-3-mini, with just 3.8 billion parameters, is claimed to be the most capable and cost-effective small language model (SLM) available. Despite its small size, it outperforms models twice its size across various benchmarks in language, reasoning, coding, and math.

Language models form the backbone of AI applications like ChatGPT, Claude, and Gemini. They are trained on existing data to solve common language problems such as text classification, question answering, text generation, and document summarization.
Large Language Models (LLMs) have enormous training data and high parameter counts, while SLMs are more streamlined versions.

Key Features of Phi-3-mini

Available in two variants: 4K and 128K context-length tokens
First model in its class to support a context window of up to 128K tokens
Instruction-tuned, making it ready to use out-of-the-box
Cost-effective to develop and operate compared to LLMs
Performs better on smaller devices like laptops and smartphones

Advantages of SLMs

SLMs have several advantages over LLMs:

Ideal for resource-constrained environments and offline inference scenarios
Faster response times, making them suitable for chatbots and virtual assistants
Cost-effective for simpler tasks
Can be customized for specific tasks through fine-tuning
Quicker processing due to compact size

Phi-3 Model Performance

Microsoft claims that Phi-3 models significantly outperform several models of the same size or even larger ones in key areas. The Phi-3-mini demonstrates strong reasoning and logic capabilities. ITC, a leading Indian conglomerate, is leveraging Phi-3 as part of their collaboration with Microsoft on the copilot for Krishi Mitra, a farmer-facing app that reaches over a million farmers.

Future Developments

Microsoft plans to release two more models in the Phi-3 family: Phi-3-small (7B parameters) and Phi-3-medium (14B parameters). These will be available in the Azure AI Model Catalog and other model libraries shortly, offering customers more flexibility in choosing the right model for their needs.

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