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