def parse_arge()

in sagemaker/28_train_llms_with_qlora/scripts/run_clm.py [0:0]

• 38 lines of code
• 5 McCabe index (conditional complexity)

def parse_arge():
    """Parse the arguments."""
    parser = argparse.ArgumentParser()
    # add model id and dataset path argument
    parser.add_argument(
        "--model_id",
        type=str,
        default="google/flan-t5-xl",
        help="Model id to use for training.",
    )
    parser.add_argument("--dataset_path", type=str, default="lm_dataset", help="Path to dataset.")
    # add training hyperparameters for epochs, batch size, learning rate, and seed
    parser.add_argument("--epochs", type=int, default=3, help="Number of epochs to train for.")
    parser.add_argument(
        "--per_device_train_batch_size",
        type=int,
        default=1,
        help="Batch size to use for training.",
    )
    parser.add_argument("--lr", type=float, default=5e-5, help="Learning rate to use for training.")
    parser.add_argument("--seed", type=int, default=42, help="Seed to use for training.")
    parser.add_argument(
        "--gradient_checkpointing",
        type=bool,
        default=True,
        help="Path to deepspeed config file.",
    )
    parser.add_argument(
        "--bf16",
        type=bool,
        default=True if torch.cuda.get_device_capability()[0] == 8 else False,
        help="Whether to use bf16.",
    )
    parser.add_argument(
        "--merge_weights",
        type=bool,
        default=True,
        help="Whether to merge LoRA weights with base model.",
    )
    args = parser.parse_known_args()
    return args