Accelerated expert-parallel distributed tuning in Red Hat OpenShift AI

This blog post discusses the use of fms-hf-tuning, an open-source tuning library for optimizing AI and agentic applications on Red Hat OpenShift AI. It focuses on efficient distributed fine-tuning of foundation models, including techniques for data preprocessing, memory efficiency, and expert parallel training. The post includes insights on deploying and serving models, along with practical code examples for practitioners looking to enhance their machine learning workflows.