You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
I am curious why the train loss drops after each epoch and tends to converge within one epoch. @artidoro
The problem is that the train loss tends to always drop and never converge.
I am running 4-bit qlora fine-tuning on alpaca and about 3000 for one epoch.
Though authors have explained that pre-training/evaluation loss is not important while the downstream task performance means more, it is common sense to get the pre-trained well converged. Does anyone have this problem?
The text was updated successfully, but these errors were encountered:
I am curious why the train loss drops after each epoch and tends to converge within one epoch. @artidoro
The problem is that the train loss tends to always drop and never converge.
I am running 4-bit qlora fine-tuning on alpaca and about 3000 for one epoch.
Though authors have explained that pre-training/evaluation loss is not important while the downstream task performance means more, it is common sense to get the pre-trained well converged. Does anyone have this problem?
The text was updated successfully, but these errors were encountered: