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import argparse
import json
from autotrain.trainers.clm.params import LLMTrainingParams
from autotrain.trainers.common import monitor
def parse_args():
# get training_config.json from the end user
parser = argparse.ArgumentParser()
parser.add_argument("--training_config", type=str, required=True)
return parser.parse_args()
@monitor
def train(config):
if isinstance(config, dict):
config = LLMTrainingParams(**config)
if config.trainer == "default":
from autotrain.trainers.clm.train_clm_default import train as train_default
train_default(config)
elif config.trainer == "sft":
from autotrain.trainers.clm.train_clm_sft import train as train_sft
train_sft(config)
elif config.trainer == "reward":
from autotrain.trainers.clm.train_clm_reward import train as train_reward
train_reward(config)
elif config.trainer == "dpo":
from autotrain.trainers.clm.train_clm_dpo import train as train_dpo
train_dpo(config)
elif config.trainer == "orpo":
from autotrain.trainers.clm.train_clm_orpo import train as train_orpo
train_orpo(config)
else:
raise ValueError(f"trainer `{config.trainer}` not supported")
if __name__ == "__main__":
_args = parse_args()
training_config = json.load(open(_args.training_config))
_config = LLMTrainingParams(**training_config)
train(_config)
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