Fine-tune LLMs with Kubeflow Trainer on OpenShift AI

This article provides a comprehensive guide on fine-tuning large language models (LLMs) using the Kubeflow Training Operator on Red Hat OpenShift AI. It details the necessary prerequisites, steps to create a workbench, and execute fine-tuning jobs with various configurations. The guide explores GPU utilization strategies, optimization techniques, and emphasizes the importance of handling GPU traffic in distributed model training. A follow-up post is promised to discuss high-performance GPU interconnect technologies further.