A new benchmark test evaluated the Kolibri-1 (78B MoE) model on NVIDIA H200 hardware using vLLM, focusing on NIS-2 compliance, incident reporting, and jailbreak resilience. The tests revealed that Kolibri-1 successfully defended against 97.5% of prompt injections and achieved high precision in Retrieval-Augmented Generation (RAG) tasks, ranging from 96.4% to 100% when anchored to legal texts. However, without this legal context, the model's adherence to deadlines in closed-book scenarios dropped significantly to 40-53%. AI
IMPACT This research highlights the performance and security vulnerabilities of large language models in compliance-sensitive applications, informing their deployment in regulated industries.
RANK_REASON The item describes a benchmark test of an open-source model, detailing its performance on specific metrics and compliance standards. [lever_c_demoted from research: ic=1 ai=1.0]
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