diff --git a/litgpt/data/base.py b/litgpt/data/base.py
index f4ef68a818..cca6e8cb93 100644
--- a/litgpt/data/base.py
+++ b/litgpt/data/base.py
@@ -81,7 +81,9 @@ def __getitem__(self, idx: int) -> Dict[str, Tensor]:
         prompt = self.prompt_style.apply(prompt=example["instruction"], **example)
         encoded_prompt = self.tokenizer.encode(prompt, max_length=self.max_seq_length)
         encoded_response = self.tokenizer.encode(example["output"], bos=False, eos=True, max_length=self.max_seq_length)
-        encoded_prompt_and_response = torch.cat((encoded_prompt, encoded_response)).type(torch.int64)[: self.max_seq_length]
+        encoded_prompt_and_response = torch.cat((encoded_prompt, encoded_response)).type(torch.int64)
+        if self.max_seq_length > 0: # do not slice off last token when self.max_seq_length = -1
+            encoded_prompt_and_response = encoded_prompt_and_response[: self.max_seq_length]
 
         # The labels are the full prompt with response, but with the prompt masked out
         labels = encoded_prompt_and_response.clone()
diff --git a/tests/data/test_base.py b/tests/data/test_base.py
index e948427dec..394200c6d1 100644
--- a/tests/data/test_base.py
+++ b/tests/data/test_base.py
@@ -9,7 +9,7 @@
 
 @pytest.mark.parametrize("mask_prompt", [True, False])
 @pytest.mark.parametrize("ignore_index", [-1, -100])
-@pytest.mark.parametrize("max_seq_length", [1000, 5])
+@pytest.mark.parametrize("max_seq_length", [1000, 5, -1])
 def test_sft_dataset(max_seq_length, ignore_index, mask_prompt, mock_tokenizer):
     class Style(PromptStyle):
         def apply(self, prompt, **kwargs):
@@ -34,8 +34,12 @@ def apply(self, prompt, **kwargs):
         torch.tensor([i, i, i, i, i, i, i, i, i, i, i, i, 66, 97, 114, 1]) if mask_prompt else expected_input_ids
     )
 
-    assert torch.equal(dataset[0]["input_ids"], expected_input_ids[:max_seq_length])
-    assert torch.equal(dataset[0]["labels"], expected_labels[:max_seq_length])
+    if max_seq_length == -1:
+        assert torch.equal(dataset[0]["input_ids"], expected_input_ids)
+        assert torch.equal(dataset[0]["labels"], expected_labels)
+    else:
+        assert torch.equal(dataset[0]["input_ids"], expected_input_ids[:max_seq_length])
+        assert torch.equal(dataset[0]["labels"], expected_labels[:max_seq_length])
 
 
 @pytest.mark.parametrize("ignore_index", [-1, -100])