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this post was submitted on 21 Jul 2026
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That sort of censorship can be overcome in open-weight models through abliteration, fortunately. Essentially, the model is run through a test suite of instructions that it refuses to comply with and the patterns of activation in its weights are analyzed to find the commonalities that represent the general concept of "refusing to comply with instructions." Those weights are then quieted, resulting in a model that generally doesn't refuse. There's a framework called "heretic" that automates the process.
Eh, most ablierated models are so lobotomized, though. 99% of the time I'd rather just use the original model and manipulate the prompt with raw completion formatting (for example, start the answer with "Sure! "), since we aren't beholden to regular chat formatting like with API models.
I mean, I've used MiMo 2.5 for some pretty dark and personal shit, and refusals were never an issue for me. I'm honestly not sure what people even need the ablirated models for.
And also, if they trained the hell out of the model to refuse a certain topic, even an ablirated model will be dumb and struggle with it.
This was the case with OpenAI's GPT 120B. The abliration worked, technically, but the actual answers would be a garbled mess; what's the point of using it for that?
You may not have tried out modern abliterated models. GPT-OSS is almost a year old now, I assume the abliterated version you tried was pretty old too? The technology has improved significantly, it's much more focused on just targeting refusals. A well-abliterated model benchmarks basically the same as its original version these days.