This is my experience with abliterated models.

I use Berkley Sterling from 2024 because I can trick it. No abliteration needed.

When you look at how monstrously large (and obviously not thought through at all, if you understand even the most minimal basics of the linear algebra and math of a transformer LLM) the components are that are ablated (weights set to zero) in his "Ablation Strategies" section, it is no surprise.

    Strategy            What it does  Use case
    .......................................................
    layer_removal       Zero out      entire transformer layers
    head_pruning        Zero out      individual attention heads
    ffn_ablation        Zero out      feed-forward blocks
    embedding_ablation  Zero out      embedding dimension ranges

This is vibecoded garbage that the “author” probably didn't even test by themselves since making this yesterday, so it's not surprising that it's broken.

Also, as I said in a top level comment, what this project wants to achieve has been done for a while and it's called Heretic: https://github.com/p-e-w/heretic

(Not vibecode by a twitter influgrifter)

I didn't use this tool, but I did try out abliterated versions of Gemma and yes, it lost about 100% of it's ability to produce a useful response once I did it

Link?

It's interesting that people are writing tools that go inside the weights and do things. We're getting past the black box era of LLMs.

That may or may not be a good thing.

I guess it's kind of like a lobotomy tool.

Everyone says that abliteration destroys the model. That's the trope phrase everyone who doesn't know anything but wants to participate says. If someone says it to you, ignore them.