Compared to abliteration, none of the ablation approaches of this tool make even half a whit of sense if you understand even the most basic aspects of an e.g. Transformer LLM architecture, so my guess is this is BS.
Compared to abliteration, none of the ablation approaches of this tool make even half a whit of sense if you understand even the most basic aspects of an e.g. Transformer LLM architecture, so my guess is this is BS.
Ultimately though, OP is just what you get if you take the idea of abliteration and tell an LLM to fix the core problems: that refusal isn't actually always exactly a rank-1 subspace, nor the same throughout the net, nor nicely isolated to one layer/module, that it damages capabilities, and so on.
The model looks at that list and applies typical AI one-off 'workarounds' to each problem in turn while hyping up the prompter, and you get this slop pile.
[0]: https://www.lesswrong.com/posts/refusal-in-llms-is-mediated-...