lora's "catastrophic forgetting" problem about refact

[JegernOUTT]

Hi! We faced this problem, so there are several things you can do:

• make lora finetune params as "small" as they can be, use finetune settings section for that (Lora R, Lora Alpha)
• try different checkpoints (from early steps)

Combining these two, you can find a balance between performance on humaneval / your codebase

However, you can never completely beat that problem while you're using the finetune. There are a couple of methods to prepare data to make the problem less visible though, https://arxiv.org/abs/2312.05934. I guess we'll revisit this in some time, but you're welcome to contribute if you have some ideas

[shatealaboxiaowang]

Hi! We faced this problem, so there are several things you can do:

• make lora finetune params as "small" as they can be, use finetune settings section for that (Lora R, Lora Alpha)
• try different checkpoints (from early steps)

Combining these two, you can find a balance between performance on humaneval / your codebase

However, you can never completely beat that problem while you're using the finetune. There are a couple of methods to prepare data to make the problem less visible though, https://arxiv.org/abs/2312.05934. I guess we'll revisit this in some time, but you're welcome to contribute if you have some ideas

Thx, i will try. I have two questions:
(1) My code-dataset is relatively large(≈1G)，may be better to fine-tune on full parameter? but full parameter fine-tuning is more prone to catastrophic forgetting than lora, is that right ?
(2) I find very few project use FIM to fine-tune codellama, but use instruction, but you use it here.
My current task is to fine-tune and implement performance(Generation && FIM) enhancements based on our internal code, do you have any better suggestions ?
Thx again !