diff --git a/python/tvm/relax/frontend/torch/position_id_fix.py b/python/tvm/relax/frontend/torch/position_id_fix.py
new file mode 100644
index 000000000000..d9ee93ab72cc
--- /dev/null
+++ b/python/tvm/relax/frontend/torch/position_id_fix.py
@@ -0,0 +1,101 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import torch
+import torch.nn as nn
+from torch.export import export as torch_export
+from transformers import AutoModel
+
+from tvm.relax.frontend.torch import from_exported_program
+
+
+class StateDictWrapper(dict):
+    """Wrap exported state_dict and inject extra keys (non-persistent buffers)."""
+
+    def __init__(self, base_dict, extra):
+        super().__init__(base_dict)
+        self.extra = extra
+
+    def __getitem__(self, key):
+        if key in self.extra:
+            return self.extra[key]
+        return super().__getitem__(key)
+
+    def get(self, key, default=None):
+        if key in self.extra:
+            return self.extra[key]
+        return super().get(key, default)
+
+
+class M(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.bert = AutoModel.from_pretrained("bert-base-multilingual-uncased")
+        self.cls = nn.Linear(self.bert.config.hidden_size, 2)
+
+    def forward(self, x, mask=None):
+        out = self.bert(x, attention_mask=mask).last_hidden_state[:, 0, :]
+        return self.cls(out)
+
+
+def main():
+    torch.manual_seed(0)
+    m = M().eval()
+
+    x = torch.randint(0, 30522, (2, 16))
+    mask = torch.ones_like(x)
+
+    ep = torch_export(m, (x, mask))
+    print("\n torch.export completed successfully\n")
+
+    # --- Build extra buffers dict ---
+    extra = {}
+    for buf_name in m.bert.embeddings._non_persistent_buffers_set:
+        tensor = m.bert.embeddings._buffers.get(buf_name)
+        if tensor is not None:
+            extra[f"bert.embeddings.{buf_name}"] = tensor
+            print(f"Injecting buffer: bert.embeddings.{buf_name} -> shape {tensor.shape}")
+
+    # Wrap exported state_dict
+    sd_wrapped = StateDictWrapper(ep.state_dict, extra)
+
+    # EP wrapper to override state_dict access
+    class EPWrapper:
+        def __init__(self, ep, sd_wrapped):
+            self.__dict__["_ep"] = ep
+            self.__dict__["_sd"] = sd_wrapped
+
+        def __getattr__(self, name):
+            if name == "state_dict":
+                return self._sd
+            return getattr(self._ep, name)
+
+    ep_wrapped = EPWrapper(ep, sd_wrapped)
+
+    # Import to TVM
+    try:
+        mod = from_exported_program(ep_wrapped)
+        print("\n TVM import succeeded — all non-persistent buffers injected!\n")
+    except Exception as e:
+        print("\n TVM import failed with exception:")
+        import traceback
+
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/python/tvm/relax/frontend/torch/position_id_patch.py b/python/tvm/relax/frontend/torch/position_id_patch.py
new file mode 100644
index 000000000000..d9ee93ab72cc
--- /dev/null
+++ b/python/tvm/relax/frontend/torch/position_id_patch.py
@@ -0,0 +1,101 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+import torch
+import torch.nn as nn
+from torch.export import export as torch_export
+from transformers import AutoModel
+
+from tvm.relax.frontend.torch import from_exported_program
+
+
+class StateDictWrapper(dict):
+    """Wrap exported state_dict and inject extra keys (non-persistent buffers)."""
+
+    def __init__(self, base_dict, extra):
+        super().__init__(base_dict)
+        self.extra = extra
+
+    def __getitem__(self, key):
+        if key in self.extra:
+            return self.extra[key]
+        return super().__getitem__(key)
+
+    def get(self, key, default=None):
+        if key in self.extra:
+            return self.extra[key]
+        return super().get(key, default)
+
+
+class M(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.bert = AutoModel.from_pretrained("bert-base-multilingual-uncased")
+        self.cls = nn.Linear(self.bert.config.hidden_size, 2)
+
+    def forward(self, x, mask=None):
+        out = self.bert(x, attention_mask=mask).last_hidden_state[:, 0, :]
+        return self.cls(out)
+
+
+def main():
+    torch.manual_seed(0)
+    m = M().eval()
+
+    x = torch.randint(0, 30522, (2, 16))
+    mask = torch.ones_like(x)
+
+    ep = torch_export(m, (x, mask))
+    print("\n torch.export completed successfully\n")
+
+    # --- Build extra buffers dict ---
+    extra = {}
+    for buf_name in m.bert.embeddings._non_persistent_buffers_set:
+        tensor = m.bert.embeddings._buffers.get(buf_name)
+        if tensor is not None:
+            extra[f"bert.embeddings.{buf_name}"] = tensor
+            print(f"Injecting buffer: bert.embeddings.{buf_name} -> shape {tensor.shape}")
+
+    # Wrap exported state_dict
+    sd_wrapped = StateDictWrapper(ep.state_dict, extra)
+
+    # EP wrapper to override state_dict access
+    class EPWrapper:
+        def __init__(self, ep, sd_wrapped):
+            self.__dict__["_ep"] = ep
+            self.__dict__["_sd"] = sd_wrapped
+
+        def __getattr__(self, name):
+            if name == "state_dict":
+                return self._sd
+            return getattr(self._ep, name)
+
+    ep_wrapped = EPWrapper(ep, sd_wrapped)
+
+    # Import to TVM
+    try:
+        mod = from_exported_program(ep_wrapped)
+        print("\n TVM import succeeded — all non-persistent buffers injected!\n")
+    except Exception as e:
+        print("\n TVM import failed with exception:")
+        import traceback
+
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    main()