Handling of additional trainable params from Hub utils

[sayakpaul]

@younesbelkada

Now that #39 has been merged, I wanted to focus on #44 and #45.

As you can see here, after wrapping a base model with LoraModel for image classification fine-tuning, I am still having to

for param in lora_model.classifier.parameters():
    param.requires_grad = True

I am aware that if we fix the internal task types of LoraConfig, we wouldn't need to do this. But on the other hand, this goes on to show the flexibility of PEFT, isn't it?

In this case, would the Hub utilities introduced in #39 take care of the additional trainable parameters?

[pacman100]

Hello Sayak, that case isn't handled yet, it would require changes to the LoraConfig to accept modules_to_save param in which users can specify additional trainable layers apart from lora layers. Will raise PR for same in sometime

[sayakpaul]

Yes, let's do that!

[pacman100]

Hello, the above PR should resolve this issue.

[sayakpaul]

Let me try to rework my example and see if everything works as expected.

[pacman100]

Shared the example notebook offline with you