diff --git a/inference.py b/inference.py
index 25e3b508..8a4efb51 100644
--- a/inference.py
+++ b/inference.py
@@ -115,7 +115,6 @@ def inference(config):
         train_outputs["embeddings"] = np.concatenate(train_outputs["embeddings"])
 
         df_train = Dataset.from_dict(train_outputs)
-        df_train = df_train.sort("index")
         curr_idx = df_train["index"]
 
         # compute mask in pyarrow since it's super fast
@@ -136,11 +135,11 @@ def inference(config):
         for batch in tqdm(val_dataloader, disable=local_rank != 0):
             batch["input_ids"] = batch["input_ids"].to(f"cuda:{local_rank}")
             batch["labels"] = batch["labels"].to(f"cuda:{local_rank}")
-            outputs = model(input_ids=batch["input_ids"], labels=batch["labels"])
+            outputs = model(input_ids=batch["input_ids"], labels=batch["labels"], output_hidden_states=True)
             loss = calc_cross_entropy_no_reduction(outputs.logits, batch["labels"])
             val_outputs["loss"].extend(loss)
 
-            logits = outputs.logits
+            embeddings = outputs.hidden_states[-1]
             batch_size = batch["input_ids"].shape[0]
             sequence_lengths = []
             # since we use mutiturn with multiple <|endoftext|>, we need to find the place where 
@@ -149,17 +148,17 @@ def inference(config):
                 indices = torch.where(item == tokenizer.pad_token_id)[0]
                 found = False
                 for index in indices:
+                    # case where sequence is less than max length
                     if torch.all(item[index:] == tokenizer.pad_token_id):
                         sequence_lengths.append(index)
                         found = True
                         break
-
-                # no match found
+                # case where sequence is >= max length
                 if not found:
                     sequence_lengths.append(len(item) - 1)
 
             sequence_lengths = torch.tensor(sequence_lengths)
-            pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+            pooled_logits = embeddings[torch.arange(batch_size, device=embeddings.device), sequence_lengths]
 
             val_outputs["embeddings"].append(pooled_logits)
             val_outputs["index"].extend(batch["index"].to(model.device))
@@ -172,7 +171,6 @@ def inference(config):
         val_outputs["embeddings"] = np.concatenate(val_outputs["embeddings"])
 
         df_val = Dataset.from_dict(val_outputs)
-        df_val = df_val.sort("index")
         curr_idx = df_val["index"]
 
         # compute mask in pyarrow since it's super fast
@@ -182,7 +180,6 @@ def inference(config):
         filtered_table = table.filter(mask)
         # convert from pyarrow to Dataset
         filtered_val = Dataset.from_dict(filtered_table.to_pydict())
-
         filtered_val = filtered_val.add_column("embeddings", df_val["embeddings"])
         filtered_val = filtered_val.add_column("loss", df_val["loss"])
         filtered_val = filtered_val.add_column("is_train", [False] * len(filtered_val))