Harassment, Hate Speech, Pig Butchering Scams, and Underage users. These are just some of the possible categories of the (very) long tail of harm on Tinder. How can we possibly train, serve, and maintain models for all of these, at global, real-time scale? We build off of pre-trained models and an increasingly mature open-source ecosystem. In this talk, we'll cover how we've dramatically accelerated our modeling pipeline with (1) human-AI hybrid dataset generation for different harm vectors, (2) automated parameter-efficient fine-tuning of open-source large language and multimodal models for violation detection, and (3) serving fine-tuned adapters efficiently in real-time and at scale using LoRAX and cascade classification.