mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-11-06 10:52:14 +08:00
66 lines
2.7 KiB
Python
66 lines
2.7 KiB
Python
# Inspired by:
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# https://github.com/lvwerra/trl/blob/main/examples/summarization/scripts/reward_summarization.py
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# https://github.com/CarperAI/trlx/blob/main/examples/summarize_rlhf/reward_model/train_reward_model_gptj.py
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from typing import Optional, List
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from transformers import Seq2SeqTrainingArguments, TrainerCallback
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from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset
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from llmtuner.extras.callbacks import LogCallback
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from llmtuner.extras.ploting import plot_loss
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from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments
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from llmtuner.tuner.core import load_model_and_tokenizer
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from llmtuner.tuner.rm.metric import compute_accuracy
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from llmtuner.tuner.rm.collator import PairwiseDataCollatorWithPadding
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from llmtuner.tuner.rm.trainer import PairwisePeftTrainer
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def run_rm(
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model_args: ModelArguments,
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data_args: DataArguments,
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training_args: Seq2SeqTrainingArguments,
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finetuning_args: FinetuningArguments,
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callbacks: Optional[List[TrainerCallback]] = [LogCallback()]
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):
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dataset = get_dataset(model_args, data_args)
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model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, training_args.do_train, stage="rm")
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dataset = preprocess_dataset(dataset, tokenizer, data_args, training_args, stage="rm")
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data_collator = PairwiseDataCollatorWithPadding(tokenizer)
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training_args.remove_unused_columns = False # important for pairwise dataset
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# Initialize our Trainer
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trainer = PairwisePeftTrainer(
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finetuning_args=finetuning_args,
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model=model,
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args=training_args,
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tokenizer=tokenizer,
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data_collator=data_collator,
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callbacks=callbacks,
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compute_metrics=compute_accuracy,
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**split_dataset(dataset, data_args.dev_ratio, training_args.do_train)
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)
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# Training
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if training_args.do_train:
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train_result = trainer.train()
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trainer.log_metrics("train", train_result.metrics)
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trainer.save_metrics("train", train_result.metrics)
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trainer.save_state()
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trainer.save_model()
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if trainer.is_world_process_zero() and model_args.plot_loss:
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plot_loss(training_args.output_dir, keys=["loss", "eval_loss"])
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# Evaluation
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if training_args.do_eval:
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metrics = trainer.evaluate(metric_key_prefix="eval")
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trainer.log_metrics("eval", metrics)
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trainer.save_metrics("eval", metrics)
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# Predict
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if training_args.do_predict:
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predict_results = trainer.predict(dataset, metric_key_prefix="predict")
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trainer.log_metrics("predict", predict_results.metrics)
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trainer.save_metrics("predict", predict_results.metrics)
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trainer.save_predictions(predict_results)
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