Benchmarking

Large language models (LLMs) have rapidly evolved, becoming integral to applications ranging from conversational AI to complex reasoning tasks. However, as models grow in size and capability, effectively evaluating their performance has become increasingly challenging. Traditional benchmarking metrics like perplexity and BLEU scores often fail to capture the nuances ofContinue Reading

Open foundation models (FMs) allow organizations to build customized AI applications by fine-tuning for their specific domains or tasks, while retaining control over costs and deployments. However, deployment can be a significant portion of the effort, often requiring 30% of project time because engineers must carefully optimize instance types andContinue Reading