-You can customize the training process with a config file (`--config custom_finetune.yaml`), choose different fine-tuning strategies (`--strategy full`), or fine-tune from a previously trained model (`--model-path ./previous_finetune_workdir`). Possible strategies include full, final_mlp, last_layer, last_layer_and_final_mlp, first_layer, and first_layer_and_last_layer. The default configuration in `so3lr/config/finetune.yaml` includes settings for the optimizer, learning rate schedule, batch size, loss weights, and data filtering.
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