[data4elm] support resume from lora checkpoint

src/lmflow/models/hf_model_mixin.py

@@ -365,16 +365,7 @@ def __prepare_model_for_training(
 
         if model_args.use_lora or model_args.use_dora:
             model.enable_input_require_grads()
-            if model_args.lora_model_path is not None:
-                # Load model from LoRA weights
-                model = PeftModel.from_pretrained(
-                    model,
-                    model_args.lora_model_path,
-                    is_trainable=True,
-                )
-            else:
-                # New LoRA Finetuning
-                model = get_peft_model(model, self.peft_config)
+            model = get_peft_model(model, self.peft_config)
             model.print_trainable_parameters()
 
         # We resize the embeddings only when necessary to avoid index errors.

src/lmflow/pipeline/finetuner.py

@@ -36,7 +36,6 @@
 from lmflow.args import OptimizerNames, DatasetArguments, ModelArguments, FinetunerArguments
 from lmflow.datasets.dataset import Dataset
 from lmflow.pipeline.base_tuner import BaseTuner
-from lmflow.pipeline.utils.peft_trainer import PeftTrainer, PeftSavingCallback
 
 
 logger = logging.getLogger(__name__)
@@ -499,12 +498,8 @@ def compute_metrics(eval_preds):
         # Initialize our Trainer
         training_args = finetuner_args
 
-        if model_args.use_lora:
-            FinetuningTrainer = PeftTrainer
-            trainer_callbacks = [PeftSavingCallback]
-        else:
-            FinetuningTrainer = Trainer
-            trainer_callbacks = []
+        FinetuningTrainer = Trainer
+        trainer_callbacks = []
         if data_collator is None:
             data_collator = default_data_collator