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from clearml import PipelineDecorator
from llm_engineering.model.finetuning.sagemaker import run_finetuning_on_sagemaker
@PipelineDecorator.component(name="train")
def train(
finetuning_type: str,
num_train_epochs: int,
per_device_train_batch_size: int,
learning_rate: float,
dataset_huggingface_workspace: str = "mlabonne",
is_dummy: bool = False,
) -> None:
run_finetuning_on_sagemaker(
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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