diff --git a/src/llamafactory/model/model_utils/packing.py b/src/llamafactory/model/model_utils/packing.py
index eec5d957..07405db5 100644
--- a/src/llamafactory/model/model_utils/packing.py
+++ b/src/llamafactory/model/model_utils/packing.py
@@ -74,13 +74,13 @@ def get_seqlens_in_batch(attention_mask: "torch.Tensor") -> "torch.Tensor":
     """
     bsz = attention_mask.size(0)
     dtype, device = attention_mask.dtype, attention_mask.device
-    max_num = torch.max(attention_mask)
+    max_num = torch.max(attention_mask).item()
     counts: "torch.Tensor" = torch.zeros((bsz, max_num), dtype=dtype, device=device)
     for i in range(max_num):
         counts[:, i] = torch.sum(attention_mask == (i + 1), dim=-1)
 
     counts = counts.flatten()
-    seqlens = counts[counts.nonzero().squeeze()]
+    seqlens = counts[counts.nonzero().squeeze(dim=-1)]
     return seqlens
 
 
diff --git a/tests/model/model_utils/test_packing.py b/tests/model/model_utils/test_packing.py
index 6fd9ba3b..8056099f 100644
--- a/tests/model/model_utils/test_packing.py
+++ b/tests/model/model_utils/test_packing.py
@@ -28,6 +28,11 @@ def test_get_seqlens_in_batch():
     assert list(seqlens_in_batch.size()) == [5]
     assert torch.all(seqlens_in_batch == torch.tensor([2, 3, 1, 2, 3]))
 
+    attention_mask_with_indices = torch.tensor([[1, 1, 1]])
+    seqlens_in_batch = get_seqlens_in_batch(attention_mask_with_indices)
+    assert list(seqlens_in_batch.size()) == [1]
+    assert torch.all(seqlens_in_batch == torch.tensor([3]))
+
 
 def test_get_unpad_data():
     attention_mask_with_indices = torch.tensor(
@@ -40,3 +45,9 @@ def test_get_unpad_data():
     assert torch.all(indices == torch.tensor([0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11]))
     assert torch.all(cu_seqlens == torch.tensor([0, 2, 5, 6, 8, 11], dtype=torch.int32))
     assert max_seqlen_in_batch == 3
+
+    attention_mask_with_indices = torch.tensor([[1, 1, 1]])
+    indices, cu_seqlens, max_seqlen_in_batch = get_unpad_data(attention_mask_with_indices)
+    assert torch.all(indices == torch.tensor([0, 1, 2]))
+    assert torch.all(cu_seqlens == torch.tensor([0, 3], dtype=torch.int32))
+    assert max_seqlen_in_batch == 3