Hyper parameters play a crucial role in model training, so it's important to research and determine the right hyper parameters for each model. Instead of adhering to set training times, it's advisable to continue training as long as overfitting is not occurring, if computational resources allow. Additionally, using a versatile trainer like Axolotl is recommended for various model architectures, despite potential bugs that may need to be addressed.

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