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We are currently running a controlled experiment on a model we’ve trained, but need to start the process of trying to train a custom model on a larger corpus of repositories at the same time. We can’t have the newer trained model automatically deploy since it will affect our running experiment.
Do you have any way we can accomplish these things today?
Describe the bug
There are no controls for selecting a deployed model.
To Reproduce
Steps to reproduce the behavior:
Also, there is no UI to select the default public model other than deleting all custom models.
Expected behavior
It should be possible to select a previously trained model. It should also be possible to select the public model.
It should not be necessary to delete models to change what is deployed.
Screenshots
Additional context
Add any other context about the problem here.
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