fix issue with completed model being empty

see https://github.com/huggingface/peft/issues/286#issuecomment-1501617281

scripts/finetune.py

@@ -369,22 +369,18 @@ def train(
     )
     model.config.use_cache = False
 
-    old_state_dict = model.state_dict
-    model.state_dict = (
-        lambda self, *_, **__: get_peft_model_state_dict(self, old_state_dict())
-    ).__get__(model, type(model))
-
     if torch.__version__ >= "2" and sys.platform != "win32":
         model = torch.compile(model)
 
+    # go ahead and presave, so we have the adapter config available to inspect
+    lora_config.save_pretrained(cfg.output_dir)
+
     # In case we want to stop early with ctrl+c, this is a nice to have to save the pretrained model
     signal.signal(
         signal.SIGINT,
         lambda signal, frame: (model.save_pretrained(cfg.output_dir), exit(0)),
     )
 
-    # go ahead and presave the adapter config
-    lora_config.save_pretrained(cfg.output_dir)
     trainer.train(resume_from_checkpoint=cfg.resume_from_checkpoint)
 
     model.save_pretrained(cfg.output_dir)