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In this contributed article, editorial consultant Jelani Harper points out that whether it’s some iteration of GPT, LLaMA, or any other foundation model, the enterprise merit obtained from successfully employing these constructs revolves around the ability to fine-tune them. LoRA and other Parameter-Efficient Fine-Tuning approaches produce this result in a manner that’s cost-effective and efficient—particularly when accessed through no-code cloud frameworks supplying GPUs and access to the models.

Nutanix (NASDAQ: NTNX), a leader in hybrid multicloud computing, announced the Nutanix GPT-in-a-Box™ solution for customers looking to jump-start their artificial intelligence (AI) and machine learning (ML) innovation, while maintaining control over their data. The new offering is a full-stack software-defined AI-ready platform, along with services to help organizations size and configure hardware and software infrastructure suitable to deploy a curated set of large language models (LLMs) using the leading

A team of computer scientists at the University of Massachusetts Amherst, led by Emery Berger, recently unveiled a prize-winning Python profiler called Scalene. Programs written with Python are notoriously slow—up to 60,000 times slower than code written in other programming languages—and Scalene works to efficiently identify exactly where Python is lagging, allowing programmers to troubleshoot and streamline their code for higher performance.

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