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Use Github Models Github Docs

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GitHub Models helps you go from prompt to production by testing, comparing, evaluating, and integrating AI directly in your repository. Find and experiment with AI models for free. GitHub Models is a suite of developer tools that take you from AI idea to ship, including a model catalog, prompt management, and quantitative evaluations. Run your first model with GitHub Models in minutes. GitHub Models helps you go from prompt to production by testing, comparing, evaluating, and integrating AI directly in your repository. Manage GitHub Models in your enterprise and organizations.

Run your first model with GitHub Models in minutes. GitHub Models is an AI inference API from GitHub that lets you run AI models using just your GitHub credentials. You can choose from many different models—including from OpenAI, Meta, and DeepSeek—and use them in scripts, apps, or even GitHub Actions, with no separate authentication process. This guide helps you try out models quickly in the playground, then shows you how to run your first model via API or workflow. Go to https://github.com/marketplace/models. In the playground, select at least one model from the dropdown menu.

Learn how to use GitHub Models responsibly by understanding its purposes, capabilities, and limitations. With GitHub Models, you build your understanding of AI model capabilities by experimenting with model settings and sending prompts through a chat interface. Additionally, you can directly interact with models through an SDK. Refer to a model's "Getting Started" tab for more information about how to use the SDK. Refer to a model’s "README" tab for more information on the model. Remember when interacting with a model you are experimenting with AI, so content mistakes are possible.

GitHub Models is designed to allow for learning, experimentation and proof-of-concept activities. The feature is subject to various limits (including requests per minute, requests per day, tokens per request, and concurrent requests) and is not designed for production use cases. GitHub Models employs a number of content filters. These filters cannot be turned off as part of the GitHub Models experience. If you decide to employ models through Azure AI or a paid service, please configure your content filters to meet your requirements. Streamline AI development in your enterprise.

Organization owners and enterprise owners GitHub Models for organizations and repositories is in public preview and subject to change. GitHub Models allows your developers to build AI-powered applications at scale while your enterprise maintains control, compliance, and cost efficiency. The following best practices can help you effectively use GitHub Models across your organization. There was an error while loading. Please reload this page.

There was an error while loading. Please reload this page. Models, prompts, evals, and more. Everything you need to go from idea to shipped—without ever leaving GitHub. Run side-by-side evaluations to compare outputs from industry-leading models in real time. No guesswork, just better results.

Version, share, and reuse your prompts across projects. Treat AI inputs as first-class development assets, just like your source code. Control which models your team can use, keep data and prompts private, and ensure everything runs within GitHub and Azure infrastructure. Build, test, and ship AI—right from your GitHub workflow. Make direct API calls or integrate with the Azure AI SDK or any supported model SDK. GitHub Models are AI capabilities built directly into GitHub, acting like an “AI lab” within your repository.

They let you experiment, compare, and run models seamlessly as part of your existing workflow – no switching tools required. This makes it much easier to move from experimentation to production AI without context switching or security headaches. In simple terms, inference is running a trained AI model against new input to get an output. In your workflows, inference means you can feed code, text, or structured data into a model—and instantly use the results to drive automation. The actions/ai-inference GitHub Action gives you a standard way to run inference in a workflow job.

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