Such managed inference providers have (for now) plausible deniability of behaving ethically (at least enough that they don't get boycotted / scare away investors) due to them being "blind" to what gets run on their systems. They're acting as the inference equivalent of data transit carriers.
But I don't think it would be possible for managed inference providers to publicly expose "runtime activation steering" in the way antirez's DS4 does, without that reading much more explicitly as them inviting unethical workloads.
(Yes, there are other things you can do with runtime steering. But almost all of those things are workload-specific, relying on you privately tuning to the needs of your own dataset. And if you can do that, you can run inference without the help of a managed inference provider. The only time a customer will come along with a pre-made runtime-steering vector file in hand, is if that vector is an alignment-orthogonalization vector.)
I haven't wrapped my mind around this
There's an empirical observation that models often have a single direction in their activation space for "hmm no I shouldn't do this". It forms naturally during pre-training, and is then surfaced during post-training to make the model refuse to engage in certain behaviour.
With a little bit of linear algebra you can zap that direction from the model's activations, and it stops refusing to do things. You can also do the opposite: magnify that direction, and the model refuses to do anything at all.
Also this one was interesting, training the model to give preambles with reasons for the reasons for refusal seems to make it less sensitive to modulating the single refusal direction: https://arxiv.org/html/2505.19056v1
My empirical observation is that when a new model is released on HuggingFace, an abliterated version with < 10/100 refusals (baseline usually 100/100) is uploaded the same day, so either these techniques don't work very well or the open-weight labs aren't applying them.
There's some defense-in-depth, like a lot of the "guardrails" people hit on cloud models are classifiers applied to prompt or output, not a refusal generated by the model. Also closed-weight models obviously try to avoid this by not letting you see or modify the weights.
Distributing the vectors themselves isn't (yet) common practice, because people have gotten used to just putting the full modified weights up on HuggingFace's huge free storage.
Thanks for this information, Q4 seemed fine but they reappeared again in Q5 with an vengeance, I couldn't understand why. Very Strict and I've only found one jail break that barely works around 60% of the time.