Skip to content

This is a Phi-3 book for getting started with Phi-3. Phi-3, a family of open sourced AI models developed by Microsoft. 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.

License

Notifications You must be signed in to change notification settings

microsoft/Phi-3CookBook

Repository files navigation

Phi-3 Cookbook: Hands-On Examples with Microsoft's Phi-3 Models

Open and use the samples in GitHub Codespaces Open in Dev Containers

GitHub contributors GitHub issues GitHub pull-requests PRs Welcome

GitHub watchers GitHub forks GitHub stars

Azure AI Community Discord

Phi, is a family of open AI models developed by Microsoft. Phi 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. The Phi-3 Family includes mini, small, medium and vision versions, trained based on different parameter amounts to serve various application scenarios. For more detailed information about Microsoft's Phi family, please visit the Welcome to the Phi Family page.

Follow these steps:

  1. Fork the Repository: Click on the "Fork" button at the top-right corner of this page.
  2. Clone the Repository: git clone https://github.com/microsoft/Phi-3CookBook.git

Phi3Family

Table of Contents

Using Phi-3 Models

Phi-3 on Azure AI Studio

You can learn how to use Microsoft Phi-3 and how to build E2E solutions in your different hardware devices. To experience Phi-3 for yourself, start by playing with the model and customizing Phi-3 for your scenarios using the Azure AI Studio, Azure AI Model Catalog you can learn more at Getting Started with Azure AI Studio

Playground Each model has a dedicated playground to test the model Azure AI Playground.

Phi-3 on GitHub Models

You can learn how to use Microsoft Phi-3 and how to build E2E solutions in your different hardware devices. To experience Phi-3 for yourself, start by playing with the model and customizing Phi-3 for your scenarios using the GitHub Model Catalog you can learn more at Getting Started with GitHub Model Catalog

Playground Each model has a dedicated playground to test the model.

Phi-3 on Hugging Face

You can also find the model on the Hugging Face

Playground Hugging Chat playground

🌐 Multi-Language Support

Note: These translations were automatically generated using the open-source co-op-translator and may contain errors or inaccuracies. For critical information, it is recommended to refer to the original or consult a professional human translation. If you'd like to add or update a translation, please refer to the co-op-translator repository, where you can easily contribute using simple commands.

Language Code Link to Translated README Last Updated
Chinese (Simplified) zh Chinese Translation 2024-10-04
Chinese (Traditional) tw Chinese Translation 2024-10-04
French fr French Translation 2024-10-04
Japanese ja Japanese Translation 2024-10-04
Korean ko Korean Translation 2024-10-04
Spanish es Spanish Translation 2024-10-04

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

About

This is a Phi-3 book for getting started with Phi-3. Phi-3, a family of open sourced AI models developed by Microsoft. 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.

Topics

Resources

License

Code of conduct

Security policy

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published