Build fluency in discovering and integrating models via GitHub Models.
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1 / 5
At standup, a dev browses available AI models on GitHub. What is this listing called?
GitHub Models provides a model catalog where you browse and compare available AI models. It is the entry point for discovering models to try. From there you can experiment and integrate.
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During a design review, a dev wants to test prompts interactively before coding. Which GitHub Models feature fits?
The GitHub Models playground lets you experiment with prompts and parameters in the browser without writing code. It is ideal for quick iteration and comparison. You can then export sample code to integrate.
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In a code review, a dev integrates a catalog model into an app. Which SDK does GitHub Models point to?
GitHub Models exposes an inference endpoint commonly accessed via the Azure AI Inference SDK using a GitHub token. The SDK provides a consistent client across catalog models. This is the documented integration path.
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An incident report shows requests rejected after heavy testing. Which constraint of the free tier applies?
GitHub Models enforces rate limits on the free experimentation tier, capping requests and tokens per period. Heavy usage hits these limits and is throttled. For production volume you move to a paid Azure deployment.
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During a PR review, a dev compares two catalog models side by side. Where is this best done?
The playground supports trying and comparing models from the model catalog interactively. It is the right place to evaluate before committing to one in code. This shortens the model-selection loop.