scripts/run_cpt.py [34:89]:
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    get_checkpoint,
    get_datasets,
    get_kbit_device_map,
    get_peft_config,
    get_quantization_config,
    get_tokenizer,
)
from trl import SFTTrainer


logger = logging.getLogger(__name__)


def main():
    parser = H4ArgumentParser((ModelArguments, DataArguments, SFTConfig))
    model_args, data_args, training_args = parser.parse()

    # Set seed for reproducibility
    set_seed(training_args.seed)

    ###############
    # Setup logging
    ###############
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%Y-%m-%d %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )
    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process a small summary
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
        + f" distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    logger.info(f"Model parameters {model_args}")
    logger.info(f"Data parameters {data_args}")
    logger.info(f"Training/evaluation parameters {training_args}")

    # Check for last checkpoint
    last_checkpoint = get_checkpoint(training_args)
    if last_checkpoint is not None and training_args.resume_from_checkpoint is None:
        logger.info(f"Checkpoint detected, resuming training at {last_checkpoint=}.")

    ###############
    # Load datasets
    ###############
    raw_datasets = get_datasets(
        data_args,
        splits=data_args.dataset_splits,
        configs=data_args.dataset_configs,
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scripts/run_sft.py [36:91]:
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    get_checkpoint,
    get_datasets,
    get_kbit_device_map,
    get_peft_config,
    get_quantization_config,
    get_tokenizer,
)
from trl import SFTTrainer, setup_chat_format


logger = logging.getLogger(__name__)


def main():
    parser = H4ArgumentParser((ModelArguments, DataArguments, SFTConfig))
    model_args, data_args, training_args = parser.parse()

    # Set seed for reproducibility
    set_seed(training_args.seed)

    ###############
    # Setup logging
    ###############
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%Y-%m-%d %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )
    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process a small summary
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
        + f" distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    logger.info(f"Model parameters {model_args}")
    logger.info(f"Data parameters {data_args}")
    logger.info(f"Training/evaluation parameters {training_args}")

    # Check for last checkpoint
    last_checkpoint = get_checkpoint(training_args)
    if last_checkpoint is not None and training_args.resume_from_checkpoint is None:
        logger.info(f"Checkpoint detected, resuming training at {last_checkpoint=}.")

    ###############
    # Load datasets
    ###############
    raw_datasets = get_datasets(
        data_args,
        splits=data_args.dataset_splits,
        configs=data_args.dataset_configs,
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