Raft Develops Multilabel Guardrail Classifier Detecting 15 Risk Categories with 3x Speed and Cost Gains

Raft has published a detailed technical account of how it built and production-optimized a multilabel guardrail classifier capable of simultaneously evaluating 15 risk categories on both user prompts and model generations.

The classifier addresses regulatory, reputational and operational risks that arise when generative systems produce or receive disallowed content such as child exploitation material, violent crime instructions, weapons, narcotics, self-harm, discrimination, Nazism, financial crime, cybercrime and adult content, as well as thematic categories including profanity, religious offences and LGBT-related material governed by specific Russian legislation.

Unlike binary toxicity detectors or keyword filters, the model outputs 15 independent probability scores, each with its own calibrated threshold. Deploying organisations can therefore enable or disable any subset of categories without retraining.

Performance and Economics

On an NVIDIA RTX 3090, the TensorRT backend delivered 5.9 ms P95 latency for batch-1 requests compared with 17.4 ms for the PyTorch + FlashAttention2 baseline, a 2.95× improvement. At the optimal batch size of 16, throughput reached 1 345 texts per second and cost fell to $0.062 per million requests, 2.9× cheaper than a naïve PyTorch batch-8 configuration.

The model was trained with asymmetric loss to prioritise recall on rare but critical classes and uses a two-layer transformer to model statistical dependencies between categories such as armament and violent actions.

Benchmark Results

In a balanced evaluation of 7 480 examples, HiveTrace Multilabel achieved binary F1 of 0.9588 and macro-F1 of 0.7026 across 14 labels, outperforming Qwen3Guard variants and other open toxicity models on the macro metric that better reflects per-category utility for real moderation workflows.

A second open model, gliner_guard_omni, was also released by the team. Both models are positioned as fast, on-premise ML filters that sit between regex checks and slower external LLM judges in production guardrail pipelines.