diff --git a/src/axolotl/utils/data/pretraining.py b/src/axolotl/utils/data/pretraining.py
index 16f38218c..f493db70e 100644
--- a/src/axolotl/utils/data/pretraining.py
+++ b/src/axolotl/utils/data/pretraining.py
@@ -28,8 +28,10 @@ def encode_pretraining(
     )
     # Convert to PyTorch tensors
     input_ids = [torch.tensor(seq) for seq in res["input_ids"]]
+    targets = [torch.tensor(seq) for seq in res["input_ids"]]
     attention_mask = [torch.tensor(seq) for seq in res["attention_mask"]]
     new_input_ids = []
+    new_labels = []
     new_attention_mask = []
     # Append EOS and PAD tokens to input_ids, and correct attention_mask
     for i, _ in enumerate(input_ids):
@@ -40,22 +42,34 @@ def encode_pretraining(
             ),
             dim=0,
         )
+        targets[i] = torch.cat(
+            (
+                targets[i],
+                torch.tensor([tokenizer.eos_token_id, -100]),
+            ),
+            dim=0,
+        )
         attention_mask[i] = torch.cat((attention_mask[i], torch.tensor([1, 0])), dim=0)
 
     # Concatenate tokens so that their lengths are less than max_tokens
     buffer_input_ids = torch.tensor([], dtype=torch.long)
+    buffer_labels = torch.tensor([], dtype=torch.long)
     buffer_attention_mask = torch.tensor([], dtype=torch.long)
 
-    for ids, mask in zip(input_ids, attention_mask):
+    for ids, labels, mask in zip(input_ids, targets, attention_mask):
         if buffer_input_ids.numel() == max_tokens:
             new_input_ids.append(buffer_input_ids)
+            new_labels.append(buffer_labels)
             new_attention_mask.append(buffer_attention_mask)
             buffer_input_ids = torch.tensor([], dtype=torch.long)
+            buffer_labels = torch.tensor([], dtype=torch.long)
             buffer_attention_mask = torch.tensor([], dtype=torch.long)
             buffer_input_ids = torch.cat((buffer_input_ids, ids), dim=0)
+            buffer_labels = torch.cat((buffer_labels, labels), dim=0)
             buffer_attention_mask = torch.cat((buffer_attention_mask, mask), dim=0)
         elif buffer_input_ids.numel() + ids.numel() <= max_tokens:
             buffer_input_ids = torch.cat((buffer_input_ids, ids), dim=0)
+            buffer_labels = torch.cat((buffer_labels, labels), dim=0)
             buffer_attention_mask = torch.cat((buffer_attention_mask, mask), dim=0)
         else:
             buffer_input_ids = torch.cat(
@@ -69,6 +83,17 @@ def encode_pretraining(
                 ),
                 dim=0,
             )
+            buffer_labels = torch.cat(
+                (
+                    buffer_labels,
+                    torch.full(
+                        (max_tokens - buffer_labels.numel(),),
+                        -100,
+                        dtype=torch.long,
+                    ),
+                ),
+                dim=0,
+            )
             buffer_attention_mask = torch.cat(
                 (
                     buffer_attention_mask,
@@ -81,11 +106,14 @@ def encode_pretraining(
                 dim=0,
             )
             new_input_ids.append(buffer_input_ids)
+            new_labels.append(buffer_labels)
             new_attention_mask.append(buffer_attention_mask)
             buffer_input_ids = torch.tensor([], dtype=torch.long)
+            buffer_labels = torch.tensor([], dtype=torch.long)
             buffer_attention_mask = torch.tensor([], dtype=torch.long)
 
             buffer_input_ids = torch.cat((buffer_input_ids, ids), dim=0)
+            buffer_labels = torch.cat((buffer_labels, labels), dim=0)
             buffer_attention_mask = torch.cat((buffer_attention_mask, mask), dim=0)
 
     if buffer_input_ids.numel() > 0:  # for any leftover tokens
@@ -101,6 +129,17 @@ def encode_pretraining(
                 ),
                 dim=0,
             )
+            buffer_labels = torch.cat(
+                (
+                    buffer_labels,
+                    torch.full(
+                        (max_tokens - buffer_labels.numel(),),
+                        -100,
+                        dtype=torch.long,
+                    ),
+                ),
+                dim=0,
+            )
             buffer_attention_mask = torch.cat(
                 (
                     buffer_attention_mask,
@@ -113,11 +152,12 @@ def encode_pretraining(
                 dim=0,
             )
         new_input_ids.append(buffer_input_ids)
+        new_labels.append(buffer_labels)
         new_attention_mask.append(buffer_attention_mask)
 
     ret = {
         "input_ids": [seq.tolist() for seq in new_input_ids],
-        "labels": [seq.tolist() for seq in new_input_ids],
+        "labels": [seq.tolist() for seq in new_labels],
         "attention_mask": [seq.tolist() for seq in new_attention_mask],
     }