qlora-train/trainer.py
2025-04-28 08:50:31 +00:00

48 lines
1.8 KiB
Python

from transformers import Trainer, TrainingArguments, DataCollatorForSeq2Seq
class TrainerManager:
def __init__(self, config, model, tokenizer, train_dataset, eval_dataset):
self.config = config
self.model = model
self.tokenizer = tokenizer
self.train_dataset = train_dataset
self.eval_dataset = eval_dataset
def create_trainer(self):
args = TrainingArguments(
output_dir=self.config.output_dir,
per_device_train_batch_size=self.config.per_device_train_batch_size,
gradient_accumulation_steps=self.config.gradient_accumulation_steps,
num_train_epochs=self.config.num_train_epochs,
learning_rate=self.config.learning_rate,
lr_scheduler_type=self.config.lr_scheduler_type,
warmup_steps=self.config.warmup_steps,
logging_steps=self.config.logging_steps,
save_steps=self.config.save_steps,
evaluation_strategy=self.config.evaluation_strategy,
eval_steps=self.config.eval_steps,
save_strategy=self.config.save_strategy,
save_total_limit=self.config.save_total_limit,
bf16=True,
report_to="none",
remove_unused_columns=False,
deepspeed=self.config.deepspeed,
load_best_model_at_end=True,
metric_for_best_model="eval_loss",
greater_is_better=False,
)
collator = DataCollatorForSeq2Seq(self.tokenizer, model=self.model, padding=True)
from callbacks import build_callbacks
trainer = Trainer(
model=self.model,
args=args,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
data_collator=collator,
callbacks=build_callbacks(self.config),
)
return trainer