Introducing Phi-4: Microsoft’s Newest Small Language Model Specializing in Complex Reasoning
Today we are introducing Phi-4, our 14B parameter state-of-the-art small language model (SLM) that excels at complex reasoning in areas such as math, in addition to conventional language processing. Phi-4 is the latest member of our Phi family of small language models and demonstrates what’s possible as we continue to probe the boundaries of SLMs. Phi-4 is available on Azure AI Foundry and on Hugging Face. Phi-4 Benchmarks Phi-4 outperforms comparable and larger models on math related reasoning due to advancements throughout the processes, including the use of high-quality synthetic datasets, curation of high-quality organic data, and post-training innovations. Phi-4 continues to push the frontier of size vs quality. Phi-4 is particularly good at math problems, for example here are the benchmarks for Phi-4 on math competition problems: Phi-4 performance on math competition problems To see more benchmarks read the newest technical paper released on arxiv. Enabling AI innovation safely and responsibly Building AI solutions responsibly is at the core of AI development at Microsoft. We have made our robust responsible AI capabilities available to customers building with Phi models, including Phi-3.5-mini optimized for Windows Copilot+ PCs. Azure AI Foundry provides users with a robust set of capabilities to help organizations measure, mitigate, and manage AI risks across the AI development lifecycle for traditional machine learning and generative AI applications. Azure AI evaluations in AI Foundry enable developers to iteratively assess the quality and safety of models and applications using built-in and custom metrics to inform mitigations. Additionally, Phi users can use Azure AI Content Safety features such as prompt shields, protected material detection, and groundedness detection. These capabilities can be leveraged as content filters with any language model included in our model catalog and developers can integrate these capabilities into their application easily through a single API. Once in production, developers can monitor their application for quality and safety, adversarial prompt attacks, and data integrity, making timely interventions with the help of real-time alerts. Phi-4 in action One example of the mathematical reasoning Phi-4 is capable of is demonstrated in this problem. Start Exploring Phi-4 is currently available on Azure AI Foundry and Hugging Face, take a look today.Getting Started - Generative AI with Phi-3-mini: A Guide to Inference and Deployment
Getting started with Microsoft Phi-3-mini - Inference Phi-3-mini models, Discover how Phi-3-mini, a new series of models from Microsoft, enables deployment of Large Language Models (LLMs) on edge devices and IoT devices. Learn how to use Semantic Kernel, Ollama/LlamaEdge, and ONNX Runtime to access and infer phi3-mini models, and explore the possibilities of generative AI in various application scenariosGetting Started - Generative AI with Phi-3-mini: Running Phi-3-mini in Intel AI PC
In 2024, with the empowerment of AI, we will enter the era of AI PC. On May 20, Microsoft also released the concept of Copilot + PC, which means that PC can run SLM/LLM more efficiently with the support of NPU. We can use models from different Phi-3 family combined with the new AI PC to build a simple personalized Copilot application for individuals. This content will combine Intel's AI PC, use Intel's OpenVINO, NPU Acceleration Library, and Microsoft's DirectML to create a local Copilot.Fine-Tune and Integrate Custom Phi-3 Models with Prompt Flow: Step-by-Step Guide
In this tutorial, you will learn how to fine-tune the Phi-3 model and integrate it with Prompt Flow. By leveraging Azure Machine Learning, and Prompt flow you will establish a workflow for deploying and utilizing custom AI models.Fine-Tune and Integrate Custom Phi-3 Models with Prompt Flow in Azure AI Studio
Phi-3 is a family of small language models (SLMs) developed by Microsoft that delivers exceptional performance and cost-effectiveness. In this tutorial, you will learn how to fine-tune the Phi-3 model and integrate the custom Phi-3 model with Prompt flow in Azure AI Studio. By leveraging Azure AI / ML Studio, you will establish a workflow for deploying and utilizing custom AI models.Evaluate Fine-tuned Phi-3 / 3.5 Models in Azure AI Studio Focusing on Microsoft's Responsible AI
Fine-tuning a model can sometimes lead to unintended or undesired responses. To ensure that the model remains safe and effective, it's important to evaluate the model's potential to generate harmful content and its ability to produce accurate, relevant, and coherent responses. In this tutorial, you will learn how to evaluate the safety and performance of a fine-tuned Phi-3 / Phi-3.5 model integrated with Prompt flow in Azure AI Studio. Before beginning the technical steps, it's essential to understand Microsoft's Responsible AI Principles, an ethical framework designed to guide the responsible development, deployment, and operation of AI systems. These principles guide the responsible design, development, and deployment of AI systems, ensuring that AI technologies are built in a way that is fair, transparent, and inclusive. These principles are the foundation for evaluating the safety of AI models.Getting started with Microsoft Phi-3-mini - Try running the Phi-3-mini on iPhone with ONNX Runtime
In this article, we explore how to deploy generative AI applications to mobile devices, specifically on iPhone, using ONNX Runtime. We cover the steps to compile ONNX Runtime for iOS and then create an App application in Xcode. We also show you how to copy the ONNX quantized INT4 model to the project and add the C++ API to generate text. This is a preliminary exploration of deploying generative AI on mobile devices, but it provides a good starting point for further development.Building Intelligent Applications with Local RAG in .NET and Phi-3: A Hands-On Guide
Let's learn how to do Retrieval Augmented Generation (RAG) using local resources in .NET! In this post, we’ll show you how to combine the Phi-3 language model, Local Embeddings, and Semantic Kernel to create a RAG scenario.
