How to run performance and scale validation for OpenShift AI

This blog post discusses techniques for running performance and scalability validation of fine-tuning large language models (LLMs) using Red Hat OpenShift AI. It presents three distinct fine-tuning methods: full parameter fine-tuning, LoRA, and QLoRA, comparing their resource requirements, efficiency, and suitability for various scenarios. The article emphasizes the value of using OpenShift AI to streamline the AI development process while addressing traditional infrastructure challenges, and outlines the findings from experiments to improve model fine-tuning.