Spaces:
Running
Running
| # Copyright 2025 HuggingFace Inc. and the LlamaFactory team. | |
| # | |
| # This code is inspired by the HuggingFace's transformers library. | |
| # https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/summarization/run_summarization.py | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| from typing import TYPE_CHECKING, Optional | |
| from ...data import PairwiseDataCollatorWithPadding, get_dataset, get_template_and_fix_tokenizer | |
| from ...extras.ploting import plot_loss | |
| from ...model import load_model, load_tokenizer | |
| from ..callbacks import fix_valuehead_checkpoint | |
| from ..trainer_utils import create_modelcard_and_push | |
| from .metric import ComputeAccuracy | |
| from .trainer import PairwiseTrainer | |
| if TYPE_CHECKING: | |
| from transformers import Seq2SeqTrainingArguments, TrainerCallback | |
| from ...hparams import DataArguments, FinetuningArguments, ModelArguments | |
| def run_rm( | |
| model_args: "ModelArguments", | |
| data_args: "DataArguments", | |
| training_args: "Seq2SeqTrainingArguments", | |
| finetuning_args: "FinetuningArguments", | |
| callbacks: Optional[list["TrainerCallback"]] = None, | |
| ): | |
| tokenizer_module = load_tokenizer(model_args) | |
| tokenizer = tokenizer_module["tokenizer"] | |
| template = get_template_and_fix_tokenizer(tokenizer, data_args) | |
| dataset_module = get_dataset(template, model_args, data_args, training_args, stage="rm", **tokenizer_module) | |
| model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train, add_valuehead=True) | |
| data_collator = PairwiseDataCollatorWithPadding( | |
| template=template, model=model, pad_to_multiple_of=8, **tokenizer_module | |
| ) | |
| # Initialize our Trainer | |
| trainer = PairwiseTrainer( | |
| model=model, | |
| args=training_args, | |
| finetuning_args=finetuning_args, | |
| data_collator=data_collator, | |
| callbacks=callbacks, | |
| compute_metrics=ComputeAccuracy(), | |
| **dataset_module, | |
| **tokenizer_module, | |
| ) | |
| # Training | |
| if training_args.do_train: | |
| train_result = trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint) | |
| trainer.save_model() | |
| if training_args.should_save: | |
| fix_valuehead_checkpoint(model, training_args.output_dir, training_args.save_safetensors) | |
| trainer.log_metrics("train", train_result.metrics) | |
| trainer.save_metrics("train", train_result.metrics) | |
| trainer.save_state() | |
| if trainer.is_world_process_zero() and finetuning_args.plot_loss: | |
| keys = ["loss"] | |
| if isinstance(dataset_module.get("eval_dataset"), dict): | |
| keys += sum( | |
| [[f"eval_{key}_loss", f"eval_{key}_accuracy"] for key in dataset_module["eval_dataset"].keys()], [] | |
| ) | |
| else: | |
| keys += ["eval_loss", "eval_accuracy"] | |
| plot_loss(training_args.output_dir, keys=keys) | |
| # Evaluation | |
| if training_args.do_eval: | |
| metrics = trainer.evaluate(metric_key_prefix="eval") | |
| trainer.log_metrics("eval", metrics) | |
| trainer.save_metrics("eval", metrics) | |
| # Predict | |
| if training_args.do_predict: | |
| predict_results = trainer.predict(dataset_module["eval_dataset"], metric_key_prefix="predict") | |
| trainer.log_metrics("predict", predict_results.metrics) | |
| trainer.save_metrics("predict", predict_results.metrics) | |
| trainer.save_predictions(predict_results) | |
| # Create model card | |
| create_modelcard_and_push(trainer, model_args, data_args, training_args, finetuning_args) | |