diff --git a/finetune/train.py b/finetune/train.py
index 1fc535c..34408dd 100755
--- a/finetune/train.py
+++ b/finetune/train.py
@@ -184,16 +184,21 @@ def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
         )
 
 """
-Manually calculate the accuracy, f1, matthews_correlation, precision, recall with sklearn.
-"""
-def calculate_metric_with_sklearn(logits: np.ndarray, labels: np.ndarray):
+Compute metrics used for huggingface trainer.
+""" 
+def compute_metrics(eval_pred):
+    logits = eval_pred.predictions[0] # predictions[1] is label pass through preprocess_logits_for_metrics()
+    labels = eval_pred.label_ids
+
     if logits.ndim == 3:
         # Reshape logits to 2D if needed
         logits = logits.reshape(-1, logits.shape[-1])
+
     predictions = np.argmax(logits, axis=-1)
     valid_mask = labels != -100  # Exclude padding tokens (assuming -100 is the padding token ID)
     valid_predictions = predictions[valid_mask]
     valid_labels = labels[valid_mask]
+
     return {
         "accuracy": sklearn.metrics.accuracy_score(valid_labels, valid_predictions),
         "f1": sklearn.metrics.f1_score(
@@ -207,19 +212,22 @@ def calculate_metric_with_sklearn(logits: np.ndarray, labels: np.ndarray):
         ),
         "recall": sklearn.metrics.recall_score(
             valid_labels, valid_predictions, average="macro", zero_division=0
-        ),
+        )
     }
 
-
 """
-Compute metrics used for huggingface trainer.
+Fix memory problem in eval_step when using compute_metrics
 """ 
-def compute_metrics(eval_pred):
-    logits, labels = eval_pred
-    if isinstance(logits, tuple):  # Unpack logits if it's a tuple
-        logits = logits[0]
-    return calculate_metric_with_sklearn(logits, labels)
+def preprocess_logits_for_metrics(logits, labels):
+    """
+    Original Trainer may have a memory leak. 
+    This is a workaround to avoid storing too many tensors that are not needed.
+    """
+    # logits = (model output logits, dnabert output)
+    # model output logits : (bs, 2) 
+    # dnabert output : (bs, 452, 768)
 
+    return logits[0], labels
 
 
 def train():
@@ -281,7 +289,8 @@ def train():
                                    compute_metrics=compute_metrics,
                                    train_dataset=train_dataset,
                                    eval_dataset=val_dataset,
-                                   data_collator=data_collator)
+                                   data_collator=data_collator,
+                                   preprocess_logits_for_metrics=preprocess_logits_for_metrics)
     trainer.train()
 
     if training_args.save_model: