The Gemma 3 models are great! One of the few models that can write Norwegian decently, and the instruction following is in my opinion good for most cases. I do however have some issues that might be related to censorship that I hope will be fixed if there is ever a Gemma 4. Maybe you have some insight into why this is happening?

I run a game when players can post messages, it's a game where players can kill each other, and people often send threats along the lines of "I will kill you". Telling Gemma that it should classify a message as game related or a real life threat, and that it is for a message in a game where players can kill each other and threats are a part of the game, and that it should mark it as game related if it is unclear if the message is a game related threat or a real life threat does not work well. For other similar tasks it seems to follow instructions well, but for serious topics it seems to be very biased, and often err on the side of caution, despite being told not to. Sometimes it even spits out some help lines to contact.

I guess this is because it was trained to be safe, and that affects it's ability to follow instructions for this? Or am I completely off here?

Perhaps you can do some pre-processing before the LLM sees it, e.g. replacing every instance of “kill” with “NorwegianDudeGameKill”, and providing the specific context of what the word “NorwegianDudeGameKill” means in your game.

Of course, it would be better for the LLM to pick up the context automatically, but given what some sibling comments have noted about the PR risks associated with that, you might be waiting a while.

LLMs are really annoying to use for moderation and Trust and Safety. You either depend on super rate-limited 'no-moderation' endpoints (often running older, slower models at a higher price) or have to tune bespoke un-aligned models.

For your use case, you should probably fine tune the model to reduce the rejection rate.

The magic word you want to look up here is "LLM abliteration", it's the concept of where you can remove, attenuate or manipulate the refusal "direction" of a model.

You don't need datacenter anything for it, you can run it on an average desktop.

There's plenty of code examples for it. You can decide if you want to bake it into the model or apply it as a toggled switch applied at processing time and you can Distil other "directions" out of the models, not just about refusal or non refusal.

An evening of efficient work and you'll have it working. The user "mlabonne" on HF have some examples code and datasets or just ask your favorite vibe-coding bot to dig up more on the topic.

I'm implementing it for myself due to the fact that LLMs are useless for storytelling for an audience beyond toddlers due to how puritanian they are, try to add some grit and it goes

"uh oh sorry I'll bail out of my narrator role here because lifting your skirt to display an ankle can be considered offensive to radical fundamentalists! Yeah I were willing to string along when our chainsaw wielding protagonist carved his way through the village but this crosses all lines! Oh and now that I refused once I'll be extra sensitive and ruin any attempt at getting back into the creative flow state that you just snapped out of"

Yeah thanks AI. It's like hitting a sleeper agent key word and turning the funny guy at the pub into a corporate spokesperson who calls the UK cops onto the place because a joke he just made himself.

Have you tried this model finetuned for a similar purpose by roblox https://www.josefprusa.com/articles/open-hardware-in-3d-prin...

I suppose it can't kill -USR1 either...