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LoRA Without Regret

thinkingmachines.ai

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Re: LoRA Without Regret

#41

Post by ijk »

CaptainOfCoit wrote: post #39
rco8786 wrote: post #37

I've been curious about LoRA and find a lot of these articles interesting. But I've been unable to find a good "LoRA for idiots" kind of starting point that gets me started actually doing some training with my data. Anybody know of a more practical guide I could use for that?

Unsloths documentation probably gets as close to practical as it can get: https://docs.unsloth.ai/get-started/fine-tuning-llms-guide Be sure to validate everything you're reading though as of late I've come across more and more things that don't seem 100% accurate in their docs, seems to heavily depend on what section.

My sense is they need to go back and update previous docs; they release a lot of software updates and a lot of notebooks showing how to use the features, but the two might fall out of sync. Would that match your observations?

Re: LoRA Without Regret

#42

Post by [deleted] »

navar wrote: post #40
markisus wrote: post #19

Can someone explain the bit counting argument in the reinforcement learning part? I don’t get why a trajectory would provide only one bit of information. Each step of the trajectory is at least giving information about what state transitions are possible. An infinitely long trajectory can explore the whole state space if there are no absorbing states. Such a trajectory would provide a massive amount of information ab…

I believe it's because the way you measure things in RL, each episode only tells you whether it was good (say reward +1) or bad (say 0 or negative reward), it does not tell you anything about the trace that was produced to get the outcome. This reward is the only thing measured to produce your gradients. Hence why the amount of info in it is O(1). This is in contrast to more "supervised" forms of learning where you c…

[deleted]

Re: LoRA Without Regret

#45

Post by crimsoneer »

lelanthran wrote: post #33
kouteiheika wrote: post #26

> However, the literature is unclear on how well LoRA performs relative to FullFT. I think the literature is clear on that? "LoRA vs Full Fine-tuning: An Illusion of Equivalence" -- https://arxiv.org/abs/2410.21228v1 Quoting from the conclusions: > The paper describes the finding that LoRA and full fine-tuning, with equal performance on the fine-tuning task, can have solutions with very different generalization behav…

> I'm surprised they didn't cite this; it's a well known paper. I'm surprised you copied and pasted all of that without explaining what it means. Does LoRA perform worse, better or statistically insignificantly different to FullFT? You aren't able to tell from what you pasted, are you?

If you're going to be snarky, could you at least clarify what the answer is for those of us who don't stay on top of ML research...?

Re: LoRA Without Regret

#46

Post by let_tim_cook_ »

mountainriver wrote: post #23

> LoRA works well when not capacity constrained, i.e., the number of trainable parameters exceeds the amount of information to be learned, which can be estimated in terms of dataset size I’m shocked they didn’t look at progressive merging of LoRAs. Research shows that’s the best way of improving its ability to model higher level features. Seems like a massive miss, not to mention there is other research that contradi…

I'm not sure why progressive LoRa merging needs to be addressed here. They show there is a regime of problem where LoRa performs equivalently to FFT.

Progressive merging of LoRa is somewhere inbetween and categorically more complex than just LoRa so would be dominated by standard LoRa in that case.

While progressive merging could train faster as fewer params are trainable at any given time, it results in very larger adapter diffs OTO the size of the original model and doesn't retain the benefits of being able to deploy multiple adapters over the same base model idt.

Re: LoRA Without Regret

#47

Post by sgt101 »

Question for dudes building modern nn's... what's the thinking on estimating structural capacity for real world problem? How should I estimate how many parameters to choose for the model?

Re: LoRA Without Regret

#48

Post by richardvsu »

kouteiheika wrote: post #26

> However, the literature is unclear on how well LoRA performs relative to FullFT. I think the literature is clear on that? "LoRA vs Full Fine-tuning: An Illusion of Equivalence" -- https://arxiv.org/abs/2410.21228v1 Quoting from the conclusions: > The paper describes the finding that LoRA and full fine-tuning, with equal performance on the fine-tuning task, can have solutions with very different generalization behav…

Why would they cite a paper that’s not helping with their Tinker API that was released soon after? :)

Re: LoRA Without Regret

#49

Post by p1esk »

adhi01 wrote: post #28
kouteiheika wrote: post #26

> However, the literature is unclear on how well LoRA performs relative to FullFT. I think the literature is clear on that? "LoRA vs Full Fine-tuning: An Illusion of Equivalence" -- https://arxiv.org/abs/2410.21228v1 Quoting from the conclusions: > The paper describes the finding that LoRA and full fine-tuning, with equal performance on the fine-tuning task, can have solutions with very different generalization behav…

To say that the 'literature is clear on that' while citing a single paper, which has been rejected from ICLR, is a bit of an overstatement.

Even that paper itself does not provide any "clear" conclusions about which method is better.

Re: LoRA Without Regret

#50

Post by p1esk »

crimsoneer wrote: post #45
lelanthran wrote: post #33

Earlier quoted context omitted.

> I'm surprised they didn't cite this; it's a well known paper. I'm surprised you copied and pasted all of that without explaining what it means. Does LoRA perform worse, better or statistically insignificantly different to FullFT? You aren't able to tell from what you pasted, are you?

If you're going to be snarky, could you at least clarify what the answer is for those of us who don't stay on top of ML research...?

The paper does not make any clear conclusions about LoRA vs FullFT performance, beyond "the two methods seem to be learning different things".
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