diff --git a/src/utils/common.py b/src/utils/common.py
index 648f226f..ea15de5e 100644
--- a/src/utils/common.py
+++ b/src/utils/common.py
@@ -97,7 +97,7 @@ def _init_adapter(
 
     if finetuning_args.finetuning_type == "lora":
         logger.info("Fine-tuning method: LoRA")
-        lastest_checkpoint = None
+        latest_checkpoint = None
 
         if model_args.checkpoint_dir is not None:
             assert os.path.exists(os.path.join(model_args.checkpoint_dir[0], WEIGHTS_NAME)), \
@@ -106,7 +106,7 @@ def _init_adapter(
                 "The given checkpoint may be not a LoRA checkpoint, please specify `--finetuning_type full/freeze` instead."
 
             if (is_trainable and model_args.resume_lora_training) or (not is_mergeable): # continually train on the lora weights
-                checkpoints_to_merge, lastest_checkpoint = model_args.checkpoint_dir[:-1], model_args.checkpoint_dir[-1]
+                checkpoints_to_merge, latest_checkpoint = model_args.checkpoint_dir[:-1], model_args.checkpoint_dir[-1]
             else:
                 checkpoints_to_merge = model_args.checkpoint_dir
 
@@ -117,10 +117,10 @@ def _init_adapter(
             if len(checkpoints_to_merge) > 0:
                 logger.info("Merged {} model checkpoint(s).".format(len(checkpoints_to_merge)))
 
-            if lastest_checkpoint is not None: # resume lora training or quantized inference
-                model = PeftModel.from_pretrained(model, lastest_checkpoint, is_trainable=is_trainable)
+            if latest_checkpoint is not None: # resume lora training or quantized inference
+                model = PeftModel.from_pretrained(model, latest_checkpoint, is_trainable=is_trainable)
 
-        if is_trainable and lastest_checkpoint is None: # create new lora weights while training
+        if is_trainable and latest_checkpoint is None: # create new lora weights while training
             lora_config = LoraConfig(
                 task_type=TaskType.CAUSAL_LM,
                 inference_mode=False,