diff --git a/src/nanotron/parallel/parameters.py b/src/nanotron/parallel/parameters.py
index 702a1e80..28995e98 100644
--- a/src/nanotron/parallel/parameters.py
+++ b/src/nanotron/parallel/parameters.py
@@ -1,6 +1,6 @@
 import dataclasses
 from copy import deepcopy
-from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
 
 import torch
 from functorch.dim import tree_map
@@ -266,10 +266,14 @@ def wrap(e):
         unwrapped_args = tree_map(unwrap, args)
         unwrapped_kwargs = tree_map(unwrap, kwargs)
 
-        OPS_THAT_RETURN_ORIGINAL_TENSOR = [
+        OPS_THAT_RETURN_ORIGINAL_TENSOR: List[Union[Callable, str]] = [
             # NOTE: transpose operation
             torch.ops.aten.t.default,
             torch.ops.aten.view.default,
+            # NOTE: torch.ops.attn.slice.default doesn't exist
+            # so we use str(op) instead
+            "aten.slice.Tensor",
+            # torch.ops.attn.slice.default, # NOTE: param[local_slices]
             torch.ops.aten.detach.default,
             # NOTE: F.embedding()
             torch.ops.aten.embedding.default,
@@ -292,7 +296,9 @@ def wrap(e):
         else:
             outputs = func(*unwrapped_args, **unwrapped_kwargs)
 
-            if func in OPS_THAT_RETURN_ORIGINAL_TENSOR:
+            if any(func == x for x in OPS_THAT_RETURN_ORIGINAL_TENSOR if not isinstance(x, str)) or any(
+                str(func) == x for x in OPS_THAT_RETURN_ORIGINAL_TENSOR if isinstance(x, str)
+            ):
                 return outputs
             else:
                 return tree_map(wrap, outputs)
diff --git a/tests/test_parameter.py b/tests/test_parameter.py
index 1630a6e2..580f73d1 100644
--- a/tests/test_parameter.py
+++ b/tests/test_parameter.py
@@ -108,3 +108,22 @@ def _test_create_param_that_share_metadata(parallel_context: ParallelContext):
         assert p1_v == p2_v
 
     parallel_context.destroy()
+
+
+@rerun_if_address_is_in_use()
+def test_slice_param():
+    init_distributed(tp=2, dp=1, pp=1)(_test_slice_param)()
+
+
+def _test_slice_param(parallel_context: ParallelContext):
+    param = torch.nn.Parameter(torch.randn(16, 64))
+    split_config = SplitConfig(
+        split_dim=0,
+        contiguous_chunks=(8, 8),
+    )
+    param = create_sharded_parameter_from_config(parameter=param, pg=parallel_context.tp_pg, split_config=split_config)
+
+    sliced = param[2:4, 4:6]
+
+    assert isinstance(sliced, torch.Tensor)
+    assert sliced.shape == (2, 2)
diff --git a/tests/test_zero.py b/tests/test_zero.py
index 830ea739..98e1b0df 100644
--- a/tests/test_zero.py
+++ b/tests/test_zero.py
@@ -276,7 +276,10 @@ def _test_zero_optimizer_with_tp(
                 for local_global_slices_pair in sharded_info.local_global_slices_pairs:
                     local_slices = local_global_slices_pair.local_slices
                     global_slices = local_global_slices_pair.global_slices
-                    param[local_slices].copy_(ref_param[global_slices])
+
+                    with torch.no_grad():
+                        # param.data[local_slices].copy_(ref_param.data[global_slices])
+                        param[local_slices].copy_(ref_param[global_slices])
             else:
                 param.copy_(ref_param)