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Thanks! I'm personally skeptical of ablating a separate direction per block, it feels less surgical than a single direction everywhere, and we show that a single direction works fine for LLAMA3 8B and 70B
The transformer lens library does not have a save feature :(
Note that you can just do torch.save(FILE_PATH, model.state_dict()) as with any PyTorch model.
Thanks for making these! How expensive is it?
Makes sense! Sounds like a fairly good fit
It just seems intuitively like a natural fit: Everyone in mech interp needs to inspect models. This tool makes it easier to inspect models.
Another way of framing it: Try to write your paper in such a way that a mech interp researcher reading it says "huh, I want to go and use this library for my research". Eg give examples of things that were previously hard that are now easy.
Looks relevant to me on a skim! I'd probably want to see some arguments in the submission for why this is useful tooling for mech interp people specifically (though being useful to non mech interp people too is a bonus!)
That's awesome, and insanely fast! Thanks so much, I really appreciate it
1Nope to both of those, though I think both could be interesting directions!
Nah I think it's pretty sketchy. I personally prefer mean ablation, especially for residual stream SAEs where zero ablation is super damaging. But even there I agree. Compute efficiency hit would be nice, though it's a pain to get the scaling laws precise enough
For our paper this is irrelevant though IMO because we're comparing gated and normal SAEs, and I think this is just scaling by a constant? It's at least monotonic in CE loss degradation
I don't think we really engaged with that question in this post, so the following is fairly speculative. But I think there's some situations where this would be a superior technique, mostly low resource settings where doing a backwards pass is prohibitive for memory reasons, or with a very tight compute budget. But yeah, this isn't a load bearing claim for me, I still count it as a partial victory to find a novel technique that's a bit worse than fine tuning, and think this is significantly better than prior interp work. Seems reasonable to disagree though, and say you need to be better or bust
+1 to Rohin. I also think "we found a cheaper way to remove safety guardrails from a model's weights than fine tuning" is a real result (albeit the opposite of useful), though I would want to do more actual benchmarking before we claim that it's cheaper too confidently. I don't think it's a qualitative improvement over what fine tuning can do, thus hedging and saying tentative
Idk. This shows that if you wanted to optimally get rid of refusal, you might want to do this. But, really, you want to balance between refusal and not damaging the model. Probably many layers are just kinda irrelevant for refusal. Though really this argues that we're both wrong, and the most surgical intervention is deleting the direction from key layers only.