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"""
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)
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