Use the vitals package with ellmer to evaluate and compare the accuracy of LLMs, including writing evals to test local models ...

The company open-sourced an 8 billion parameter LLM, Steerling-8B, trained with a new architecture designed to make its ...

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 ...

Exposed endpoints quietly expand attack surfaces across LLM infrastructure. Learn why endpoint privilege management is important to AI security.

A team of researchers has found a way to steer the output of large language models by manipulating specific concepts inside these models. The new method could lead to more reliable, more efficient, ...

Local models work best when you meet them halfway ...

AI doesn’t just simulate human thinking and language—it mimics our cognitive biases too. Overconfidence is one of the most powerful and overlooked issues.

Enter large language model (LLM) evaluation. The purpose of LLM evaluation is to analyze and refine GenAI outputs to improve their accuracy and reliability while avoiding bias. The evaluation process ...

For enterprises, this means careful model selection, rigorous testing and ongoing evaluation are essential to ensure consistent, reliable AI behavior in production VANCOUVER, BC, /CNW/ - A new study ...

Vibe coding isn’t just prompting. Learn how to manage context windows, troubleshoot smarter, and build an AI Overview ...

In practice, the choice between small modular models and guardrail LLMs quickly becomes an operating model decision.

A system of five models helps peer reviewers to write more constructive comments, but it is not yet known whether this ...