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 have recently completed training a model using LoRA (referred to as LoRA-1) with Dataset A. I am now considering how best to proceed with training on a new Dataset B.
My question is: Should I continue training directly on LoRA-1 with Dataset B, or would it be more effective to merge LoRA-1 back into the original model, create a new LoRA layer (LoRA-2), and then proceed with training on Dataset B using LoRA-2?
An additional consideration is the difference in data distribution between Dataset A and Dataset B. If the distributions are significantly different, how might this influence the decision on the best approach to take?
The text was updated successfully, but these errors were encountered:
I have recently completed training a model using LoRA (referred to as LoRA-1) with Dataset A. I am now considering how best to proceed with training on a new Dataset B.
My question is: Should I continue training directly on LoRA-1 with Dataset B, or would it be more effective to merge LoRA-1 back into the original model, create a new LoRA layer (LoRA-2), and then proceed with training on Dataset B using LoRA-2?
An additional consideration is the difference in data distribution between Dataset A and Dataset B. If the distributions are significantly different, how might this influence the decision on the best approach to take?
The text was updated successfully, but these errors were encountered: