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"""Prepare and train a model on a dataset. Can also infer from a model or merge lora"""
import logging
from pathlib import Path
import fire
import transformers
from axolotl.cli import (
check_accelerate_default_config,
check_user_token,
do_inference,
do_merge_lora,
load_cfg,
load_datasets,
print_axolotl_text_art,
)
from axolotl.cli.shard import shard
from axolotl.common.cli import TrainerCliArgs
from axolotl.train import train
LOG = logging.getLogger("axolotl.scripts.finetune")
def do_cli(config: Path = Path("examples/"), **kwargs):
print_axolotl_text_art()
LOG.warning(
str(
PendingDeprecationWarning(
"scripts/finetune.py will be replaced with calling axolotl.cli.train"
)
)
)
parsed_cfg = load_cfg(config, **kwargs)
check_accelerate_default_config()
check_user_token()
parser = transformers.HfArgumentParser((TrainerCliArgs))
parsed_cli_args, _ = parser.parse_args_into_dataclasses(
return_remaining_strings=True
)
if parsed_cli_args.inference:
do_inference(cfg=parsed_cfg, cli_args=parsed_cli_args)
elif parsed_cli_args.merge_lora:
do_merge_lora(cfg=parsed_cfg, cli_args=parsed_cli_args)
elif parsed_cli_args.shard:
shard(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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