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"""
CLI to shard a trained model into 10GiB chunks
"""
import logging
from pathlib import Path
import fire
import transformers
from axolotl.cli import load_cfg, print_axolotl_text_art
from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
from axolotl.utils.dict import DictDefault
LOG = logging.getLogger("axolotl.scripts")
def shard(
*,
cfg: DictDefault,
cli_args: TrainerCliArgs,
):
model, _ = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
safe_serialization = cfg.save_safetensors is True
LOG.debug("Re-saving model w/ sharding")
model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
def do_cli(config: Path = Path("examples/"), **kwargs):
# pylint: disable=duplicate-code
print_axolotl_text_art()
parsed_cfg = load_cfg(config, **kwargs)
parser = transformers.HfArgumentParser((TrainerCliArgs))
parsed_cli_args, _ = parser.parse_args_into_dataclasses(
return_remaining_strings=True
)
parsed_cli_args.shard = True
shard(cfg=parsed_cfg, cli_args=parsed_cli_args)
if __name__ == "__main__":
fire.Fire(do_cli)
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