Training large-scale Generative AI models can be challenging due to the immense computational resources and time they require. This complexity gives rise to frequent instabilities, manifested as disruptive loss spikes during prolonged training periods. These instabilities can result in costly interruptions, requiring the training process to be paused and restarted. For example, the LLaMA2's 70-billion…
