PhiCookBook

PhiCookBook

This is a Phi Family of SLMs book for getting started with Phi Models. Phi a family of open sourced 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

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Phi Cookbook is a repository containing hands-on examples with Microsoft's Phi models, which are a series of open source AI models developed by Microsoft. Phi is currently the most powerful and cost-effective small language model with benchmarks in various scenarios like multi-language, reasoning, text/chat generation, coding, images, audio, and more. Users can deploy Phi to the cloud or edge devices to build generative AI applications with limited computing power.

README:

Phi Cookbook: Hands-On Examples with Microsoft's Phi 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 series of open source AI models developed by Microsoft.

Phi is currently the most powerful and cost-effective small language model (SLM), with very good benchmarks in multi-language, reasoning, text/chat generation,coding, images, audio and other scenarios.

You can deploy Phi to the cloud or to edge devices, and you can easily build generative AI applications with limited computing power.

Follow these steps to get started using these resource :

  1. Fork the Repository: Click GitHub forks
  2. Clone the Repository: git clone https://github.com/microsoft/PhiCookBook.git
  3. Join The Microsoft AI Discord Community and meet experts and fellow developers

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๐ŸŒ Multi-Language Support

French | Spanish | German | Russian | Arabic | Persian (Farsi) | Urdu | Chinese (Simplified) | Chinese (Traditional, Macau) | Chinese (Traditional, Hong Kong) | Chinese (Traditional, Taiwan) | Japanese | Korean | Hindi Bengali | Marathi | Nepali | Punjabi (Gurmukhi) | Portuguese (Portugal) | Portuguese (Brazil) | Italian | Polish | Turkish Greek | Thai | Swedish | Danish | Norwegian | Finnish | Dutch | Hebrew Vietnamese | Indonesian | Malay | Tagalog (Filipino) | Swahili | Hungarian | Czech | Slovak Romanian | Bulgarian | Serbian (Cyrillic) | Croatian | Slovenian

Table of Contents

Using Phi Models

Phi on Azure AI Foundry

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

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

Phi on GitHub Models

You can learn how to use Microsoft Phi and how to build E2E solutions in your different hardware devices. To experience Phi for yourself, start by playing with the model and customizing Phi for your scenarios using theโ€ฏGitHub 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 on Hugging Face

You can also find the model on the Hugging Face

Playground Hugging Chat playground

Responsible AI

Microsoft is committed to helping our customers use our AI products responsibly, sharing our learnings, and building trust-based partnerships through tools like Transparency Notes and Impact Assessments. Many of these resources can be found at https://aka.ms/RAI. Microsoftโ€™s approach to responsible AI is grounded in ourโ€ฏAI principles of fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.

Large-scale natural language, image, and speech models - like the ones used in this sample - can potentially behave in ways that are unfair, unreliable, or offensive, in turn causing harms. Please consult the Azure OpenAI service Transparency note to be informed about risks and limitations.

The recommended approach to mitigating these risks is to include a safety system in your architecture that can detect and prevent harmful behavior. Azure AI Content Safety provides an independent layer of protection, able to detect harmful user-generated and AI-generated content in applications and services. Azure AI Content Safety includes text and image APIs that allow you to detect material that is harmful. Within Azure AI Foundry, the Content Safety service allows you to view, explore and try out sample code for detecting harmful content across different modalities. The following quickstart documentation guides you through making requests to the service.

Another aspect to take into account is the overall application performance. With multi-modal and multi-models applications, we consider performance to mean that the system performs as you and your users expect, including not generating harmful outputs. It's important to assess the performance of your overall application using Performance and Quality and Risk and Safety evaluators. You also have the ability to create and evaluate with custom evaluators.

You can evaluate your AI application in your development environment using the Azure AI Evaluation SDK. Given either a test dataset or a target, your generative AI application generations are quantitatively measured with built-in evaluators or custom evaluators of your choice. To get started with the azure ai evaluation sdk to evaluate your system, you can follow the quickstart guide. Once you execute an evaluation run, you can visualize the results in Azure AI Foundry.

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.

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