src/open_r1/grpo.py [59:79]:
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    logger.info(f"Model parameters {model_args}")
    logger.info(f"Script parameters {script_args}")
    logger.info(f"Training parameters {training_args}")

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

    if "wandb" in training_args.report_to:
        init_wandb_training(training_args)

    # Load the dataset
    dataset = get_dataset(script_args)

    ################
    # Load tokenizer
    ################
    tokenizer = get_tokenizer(model_args, training_args)
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src/open_r1/sft.py [73:91]:
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    logger.info(f"Model parameters {model_args}")
    logger.info(f"Script parameters {script_args}")
    logger.info(f"Training parameters {training_args}")

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

    if "wandb" in training_args.report_to:
        init_wandb_training(training_args)

    ######################################
    # Load dataset, tokenizer, and model #
    ######################################
    dataset = get_dataset(script_args)
    tokenizer = get_tokenizer(model_args, training_args)
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