diff --git a/vall_e/engines/__init__.py b/vall_e/engines/__init__.py
index f2029db..6d4be71 100755
--- a/vall_e/engines/__init__.py
+++ b/vall_e/engines/__init__.py
@@ -48,10 +48,10 @@ def load_engines(training=True):
 		if cfg.lora is not None:			
 			checkpoint_path = cfg.ckpt_dir / cfg.lora.full_name / "latest"
 
-			# to handle the issue of training with deepspeed, but inferencing with local
-			if checkpoint_path.exists() and backend == "local":
-				tag = open(checkpoint_path).read()
-				checkpoint_path = cfg.ckpt_dir / cfg.lora.full_name / tag / "state.pth"
+		# to handle the issue of training with deepspeed, but inferencing with local
+		if checkpoint_path.exists() and backend == "local":
+			tag = open(checkpoint_path).read()
+			checkpoint_path = checkpoint_path.parent / tag / "state.pth"
 
 		if not loads_state_dict and not checkpoint_path.exists() and load_path.exists():
 			print("Checkpoint missing, but weights found.")
diff --git a/vall_e/export.py b/vall_e/export.py
index f0cb66c..d5d958b 100755
--- a/vall_e/export.py
+++ b/vall_e/export.py
@@ -52,6 +52,7 @@ def convert_to_hf( state_dict, config = None, save_path = None ):
 
 	state_dict['module']['model.embed_tokens.weight'] = embedding.state_dict()
 	state_dict['module']['lm_head.weight'] = out_proj
+	
 	del state_dict['module']['classifier.bias']
 
 	return state_dict
@@ -130,7 +131,7 @@ def main():
 	# necessary to ensure we are actually exporting the weights right
 	cfg.inference.backend = cfg.trainer.backend
 
-	engines = load_engines(training=False)
+	engines = load_engines(training=False) # to ignore loading optimizer state
 	engines.export(userdata={"symmap": get_phone_symmap()}, callback=callback)
 
 if __name__ == "__main__":
diff --git a/vall_e/inference.py b/vall_e/inference.py
index 669cc5d..fa169b5 100755
--- a/vall_e/inference.py
+++ b/vall_e/inference.py
@@ -190,9 +190,7 @@ class TTS():
 			phns = to_device(phns, device=self.device, dtype=torch.uint8 if len(self.symmap) < 256 else torch.int16)
 			lang = to_device(lang, device=self.device, dtype=torch.uint8)
 
-			text_list = [ phns ]
-			proms_list = [ prom ]
-
+			# to-do: add in case for experimental.hf model
 			with torch.autocast("cuda", dtype=self.dtype, enabled=self.amp):
 				if model_ar is not None:
 					resps_list = model_ar(