Support PEFT models when saving the model using trainer (#24073)

* support PEFT models when saving the model using trainer

* fixup

src/transformers/trainer.py

@@ -147,6 +147,7 @@ from .utils import (
     is_datasets_available,
     is_in_notebook,
     is_ipex_available,
+    is_peft_available,
     is_safetensors_available,
     is_sagemaker_dp_enabled,
     is_sagemaker_mp_enabled,
@@ -205,6 +206,10 @@ if is_safetensors_available():
     import safetensors.torch
 
 
+if is_peft_available():
+    from peft import PeftModel
+
+
 skip_first_batches = None
 if is_accelerate_available():
     from accelerate import __version__ as accelerate_version
@@ -2897,13 +2902,15 @@ class Trainer:
         output_dir = output_dir if output_dir is not None else self.args.output_dir
         os.makedirs(output_dir, exist_ok=True)
         logger.info(f"Saving model checkpoint to {output_dir}")
+
+        supported_classes = (PreTrainedModel,) if not is_peft_available() else (PreTrainedModel, PeftModel)
         # Save a trained model and configuration using `save_pretrained()`.
         # They can then be reloaded using `from_pretrained()`
-        if not isinstance(self.model, PreTrainedModel):
+        if not isinstance(self.model, supported_classes):
             if state_dict is None:
                 state_dict = self.model.state_dict()
 
-            if isinstance(unwrap_model(self.model), PreTrainedModel):
+            if isinstance(unwrap_model(self.model), supported_classes):
                 unwrap_model(self.model).save_pretrained(
                     output_dir, state_dict=state_dict, safe_serialization=self.args.save_safetensors
                 )