Fastino Labs Open-Sources GLiGuard: A 300M Parameter Safety Moderation Model That Matches or Exceeds Accuracy of Models 23–90x Its Size
300M parameters. 16x faster. Same accuracy as models 90x larger. Fastino Labs just open-sourced the safety guardrail that changes the economics of content moderation.
Why it matters
GLiGuard demonstrates that safety moderation—a critical infrastructure layer for production AI systems—can be dramatically more efficient through architectural innovation (encoder vs. decoder). This challenges the assumption that safety requires massive models and directly impacts deployment costs for enterprises at scale.
The key facts
9 to know300M parameter model
Matches or exceeds accuracy of models 23-90x its size
16x higher throughput than SOTA
16.6x lower latency than SOTA
Encoder architecture (vs. decoder-only standard)
Four safety tasks in single forward pass: prompt safety, jailbreak detection, harm classification, refusal detection
Evaluated across nine safety benchmarks
Apache 2.0 license on Hugging Face
Released by Fastino Labs
Go to the source
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Publisher excerpt: Fastino Labs has released GLiGuard, a 300M parameter open-source safety moderation model that evaluates four safety tasks — prompt safety, jailbreak strategy detection, harm category classification, and refusal detection — in a single forward pass. Built on an encoder architecture rather than the…