Comment by CMay

Did y'all do any particular work that makes these models great for moderation? One of the issues between cloud models vs local is that often people are trying to push the limit of what their hardware can do and it doesn't leave too much headroom for moderation to make the models a little more constrained for kids or schools to use locally, or just for family in general who don't want accidental unhinged results.

In the past I've noticed that some LFM were way better at moderation than much larger models. Not necessarily prejudging what is acceptable, but allowing you to interrogate content with a series of questions to customize the moderation. Other models I tested were far worse.

Decision models are like the next optimization in that direction, but none of the benchmarks for the popular models ever show how good they are at tasks like content moderation. The smaller and more efficient we can get that workload, the more that it can simply be on by default in a lot of use cases without feeling like you're giving up a lot of performance, creative freedom or accuracy. You can't have a high number of false positives or people would rather take control back.

We need local content moderation to be cool, efficient, accurate and configurable so that people can get the higher quality results which unsafe models tend to produce, while filtering out the unsafe results fast enough that it can even be done in an agentic way and fed back into the model to steer its results.