src/axolotl/cli/train.py

"""
CLI to run training on a model
"""
import logging
from pathlib import Path

import fire
import transformers
from datasets import disable_caching

from axolotl.cli import (
    check_accelerate_default_config,
    check_user_token,
    load_cfg,
    load_datasets,
    load_rl_datasets,
    print_axolotl_text_art,
)
from axolotl.common.cli import TrainerCliArgs
from axolotl.train import train

LOG = logging.getLogger("axolotl.cli.train")


def do_cli(config: Path = Path("examples/"), **kwargs):
    # pylint: disable=duplicate-code
    parsed_cfg = load_cfg(config, **kwargs)
    print_axolotl_text_art()
    check_accelerate_default_config()
    check_user_token()
    parser = transformers.HfArgumentParser((TrainerCliArgs))
    parsed_cli_args, remaining_args = parser.parse_args_into_dataclasses(
        return_remaining_strings=True
    )

    if (
        remaining_args.get("disable_caching") is not None
        and remaining_args["disable_caching"]
    ):
        disable_caching()
    if parsed_cfg.rl:
        dataset_meta = load_rl_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
    else:
        dataset_meta = load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
    train(cfg=parsed_cfg, cli_args=parsed_cli_args, dataset_meta=dataset_meta)


if __name__ == "__main__":
    fire.Fire(do_cli)