I think you’re misunderstanding what they mean by adapting to use cases. See this passage:
> The adapter models can be dynamically loaded, temporarily cached in memory, and swapped — giving our foundation model the ability to specialize itself on the fly for the task at hand
This along with other statements in the article about keeping the base model weights unchanged says to me that they are simply swapping out adapters on a per app or per task basis. I highly doubt they will fine tune adapters on user data since they have taken a position against this. I wonder how successful this approach will be vs merging the adapters with the base model. I can see the benefits but there are also downsides.
> The adapter models can be dynamically loaded, temporarily cached in memory, and swapped — giving our foundation model the ability to specialize itself on the fly for the task at hand
This along with other statements in the article about keeping the base model weights unchanged says to me that they are simply swapping out adapters on a per app or per task basis. I highly doubt they will fine tune adapters on user data since they have taken a position against this. I wonder how successful this approach will be vs merging the adapters with the base model. I can see the benefits but there are also downsides.