()
| 38 | |
| 39 | |
| 40 | def train(): |
| 41 | parser = transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments)) |
| 42 | model_args, data_args, training_args = parser.parse_args_into_dataclasses() |
| 43 | |
| 44 | model, tokenizer = build_model(model_args, training_args) |
| 45 | |
| 46 | data_module = make_supervised_data_module(tokenizer=tokenizer, data_args=data_args) |
| 47 | |
| 48 | trainer = Trainer(model=model, tokenizer=tokenizer, args=training_args, **data_module) |
| 49 | |
| 50 | if model_args.lora: |
| 51 | old_state_dict = model.state_dict |
| 52 | model.state_dict = ( |
| 53 | lambda self, *_, **__: get_peft_model_state_dict(self, old_state_dict()) |
| 54 | ).__get__(model, type(model)) |
| 55 | if torch.__version__ >= "2": |
| 56 | model = torch.compile(model) |
| 57 | |
| 58 | if list(pathlib.Path(training_args.output_dir).glob("checkpoint-*")): |
| 59 | trainer.train(resume_from_checkpoint=True) |
| 60 | else: |
| 61 | trainer.train() |
| 62 | |
| 63 | trainer.save_state() |
| 64 | if model_args.lora: |
| 65 | model.save_pretrained(os.path.join(training_args.output_dir, "lora")) |
| 66 | tokenizer.save_pretrained(os.path.join(training_args.output_dir, "lora")) |
| 67 | safe_save_model_for_hf_trainer(trainer=trainer, output_dir=training_args.output_dir) |
| 68 | |
| 69 | |
| 70 | if __name__ == "__main__": |
no test coverage detected