GitHub enables developers with new GitHub Model

GitHub has announced the launch of GitHub Models, enabling more than 100 million developers to become AI engineers and build with industry-leading AI models. From Llama 3.1, to GPT-4o and GPT-4o mini, to Phi 3 or Mistral Large 2, developers can access each model via a built-in playground that lets them test different prompts and model parameters, for free right in GitHub. 

In the new interactive model playground, students, hobbyists, startups, and more can explore the most popular private and open models from Meta, Mistral, Azure OpenAI Service, Microsoft, and others with just a few clicks and keystrokes. Developers can experiment, compare, test, and deploy AI applications right where they manage their source code. 

In alignment with GitHub and Microsoft’s continued commitment to privacy and security, no prompts or outputs in GitHub Models will be shared with model providers, nor used to train or improve the models. 

“Today, we democratise AI for the many. With GitHub Models, more than 100 million developers can now access and experiment with new AI models where their workflow is—directly on GitHub,” said GitHub CEO, Thomas Dohmke. “This means that every developer in India on GitHub can become an AI engineer, generating a new wave of AI applications that stand to accelerate India’s competitive advantage in the age of AI.

GitHub has also created a glide path to bring the models to a developer’s environment in Codespaces and VS Code. Once a developer is ready to go to production, Azure AI offers built-in responsible AI, enterprise-grade security & data privacy, and global availability, with provisioned throughput and availability in over 25 Azure regions for some models.

With the suite of models, developers will have all the options they need to stay in the flow, experiment more, and learn faster than ever before. And this is just the first wave. In the months ahead, as the general availability of GitHub Models approaches, GitHub will continue to add more language, vision, and other models to the platform. 

With the power of Codespaces, GitHub has created a zero-friction path for users to experiment with the model inference code before dropping it into the project. With sample code for a variety of languages and frameworks of all types ready to go, developers can try out various scenarios without ever hitting “works on my machine” problems.