AI safety tests found to rely on 'obvious' trigger words; with easy rephrasing, models labeled 'reasonably safe' suddenly fail, with attacks succeeding up to 98% of the time. New corporate research ...
Use the vitals package with ellmer to evaluate and compare the accuracy of LLMs, including writing evals to test local models ...
Tests on GPT and Claude found they ignored invented spells Fumbus and Driplo; training data can override new input, trust ...
In practice, the choice between small modular models and guardrail LLMs quickly becomes an operating model decision.
Exposed endpoints quietly expand attack surfaces across LLM infrastructure. Learn why endpoint privilege management is important to AI security.
Tech Xplore on MSN
Personalization features can make LLMs more agreeable, potentially creating a virtual echo chamber
Many of the latest large language models (LLMs) are designed to remember details from past conversations or store user profiles, enabling these models to personalize responses. But researchers from ...
Researchers from the University of Maryland, Lawrence Livermore, Columbia and TogetherAI have developed a training technique that triples LLM inference speed without auxiliary models or infrastructure ...
Users running a quantized 7B model on a laptop expect 40+ tokens per second. A 30B MoE model on a high-end mobile device ...
XDA Developers on MSN
I run local LLMs in one of the world's priciest energy markets, and I can barely tell
They really don't cost as much as you think to run.
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