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from clearml import PipelineDecorator
from steps import training as training_steps
@PipelineDecorator.pipeline(name="TODO", project="CS370")
def training(
finetuning_type: str = "sft",
num_train_epochs: int = 3,
per_device_train_batch_size: int = 2,
learning_rate: float = 3e-4,
dataset_huggingface_workspace: str = "mlabonne",
is_dummy: bool = False,
) -> None:
training_steps.train(
finetuning_type=finetuning_type,
num_train_epochs=num_train_epochs,
per_device_train_batch_size=per_device_train_batch_size,
learning_rate=learning_rate,
dataset_huggingface_workspace=dataset_huggingface_workspace,
is_dummy=is_dummy,
)
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