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Comment by radlad | Hacker News Reader

> What makes this build different is the word before FP8: uncensored. We applied abliteration — orthogonalizing the refusal direction out of the residual stream — to remove the model's safety-alignment refusals. The result is a model that will comply with requests the original would refuse.

Surely this has unintended side effects on output quality?

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> > What makes this build different is the word before FP8: uncensored. We applied abliteration — orthogonalizing the refusal direction out of the residual stream — to remove the model's safety-alignment refusals. The result is a model that will comply with requests the original would refuse.

> Surely this has unintended side effects on output quality?

Can you help me understand why that's the case?

Because deleting model weights after training is likely to cause knock-on effects in model knowledge and/or behavior. Targetting it might mitigate this but it’s

a) not guaranteed that only censor-ey parameters get removed, and b) likely that removing those parameters still has effects on the effectiveness of related parameters.

The weights aren't deleted, it's just additional fine tuning, is my understanding.

It does depending on the technique.

Early attempts at this sort of thing definitely did, but these days the impact is minimal

A bit worse quality is a fine trade off when the alternative is no output (zero quality).

On censored inputs only.

Censorship and bias are so subtle so you don't even know if your answer if censored if you use mainstream Mosad controlled AI.

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