diff --git a/onnxscript/function_libs/torch_lib/ops/core.py b/onnxscript/function_libs/torch_lib/ops/core.py
index 4d83675589..b287cec057 100644
--- a/onnxscript/function_libs/torch_lib/ops/core.py
+++ b/onnxscript/function_libs/torch_lib/ops/core.py
@@ -9200,16 +9200,21 @@ def aten_type_as(self: TTensor, other: TTensor2) -> TTensor2:
 def aten_unbind(self: TTensor, dim: int = 0) -> Sequence[TTensor]:
     """unbind.int(Tensor(a -> *) self, int dim=0) -> Tensor(a)[]"""
 
-    if isinstance(self.shape[dim], int) and not version_utils.torch_older_than("2.7"):
-        # We can create a definitive split op if the input shape is static
-        # Only torch>=2.7 supports correctly generating the correct number of outputs for Split
+    if isinstance(self.shape[dim], int):
         num_outputs = self.shape[dim]
-        if num_outputs != 1:
-            outputs = op.Split(self, axis=dim, num_outputs=num_outputs)
-        else:
-            outputs = [self]
-
-        return [op.Squeeze(out, [dim]) for out in outputs]
+        results = []
+        for i in range(num_outputs):
+            # Slice to get a single element at position i along dim
+            sliced = op.Slice(
+                self,
+                starts=op.Constant(value_ints=[i]),
+                ends=op.Constant(value_ints=[i + 1]),
+                axes=op.Constant(value_ints=[dim]),
+            )
+            # Squeeze to remove the dimension of size 1
+            squeezed = op.Squeeze(sliced, axes=[dim])
+            results.append(squeezed)
+        return results
 
     return op.SplitToSequence(self, axis=dim, keepdims=False)
 
diff --git a/tests/function_libs/torch_lib/e2e_ops_tests.py b/tests/function_libs/torch_lib/e2e_ops_tests.py
index 1546de59bd..a2ced58c44 100644
--- a/tests/function_libs/torch_lib/e2e_ops_tests.py
+++ b/tests/function_libs/torch_lib/e2e_ops_tests.py
@@ -520,6 +520,112 @@ def forward(self, x):
         )
         _testing.assert_onnx_program(onnx_program)
 
+    def test_unbind_dim0(self):
+        """Test unbind along dimension 0"""
+
+        class UnbindModel(torch.nn.Module):
+            def forward(self, x):
+                tensors = torch.unbind(x, dim=0)
+                return sum(tensors)
+
+        model = UnbindModel()
+        x = torch.randn(3, 4, 5)
+        onnx_program = torch.onnx.export(model, (x,), dynamo=True, verbose=False)
+        _testing.assert_onnx_program(onnx_program)
+
+    def test_unbind_dim1(self):
+        """Test unbind along dimension 1"""
+
+        class UnbindModel(torch.nn.Module):
+            def forward(self, x):
+                tensors = torch.unbind(x, dim=1)
+                return sum(tensors)
+
+        model = UnbindModel()
+        x = torch.randn(2, 3, 4)
+        onnx_program = torch.onnx.export(model, (x,), dynamo=True, verbose=False)
+        _testing.assert_onnx_program(onnx_program)
+
+    def test_unbind_negative_dim(self):
+        """Test unbind with negative dimension"""
+
+        class UnbindModel(torch.nn.Module):
+            def forward(self, x):
+                tensors = torch.unbind(x, dim=-1)
+                return sum(tensors)
+
+        model = UnbindModel()
+        x = torch.randn(2, 3, 4)
+        onnx_program = torch.onnx.export(model, (x,), dynamo=True, verbose=False)
+        _testing.assert_onnx_program(onnx_program)
+
+    def test_unbind_size_one(self):
+        """Test unbind with dimension of size 1"""
+
+        class UnbindModel(torch.nn.Module):
+            def forward(self, x):
+                tensors = torch.unbind(x, dim=0)
+                return tensors[0]
+
+        model = UnbindModel()
+        x = torch.randn(1, 4, 5)
+        onnx_program = torch.onnx.export(model, (x,), dynamo=True, verbose=False)
+        _testing.assert_onnx_program(onnx_program)
+
+    def test_unbind_with_lstm(self):
+        """Test unbind in LSTM context"""
+
+        class LSTMDecoder(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.embedding = torch.nn.Embedding(100, 64)
+                self.lstm = torch.nn.LSTM(64, 64, 2, batch_first=True)  # 2 layers
+                self.fc = torch.nn.Linear(64, 100)
+
+            def forward(self, tokens, h, c):
+                embedded = self.embedding(tokens).unsqueeze(0)
+                output, (h_out, c_out) = self.lstm(embedded, (h, c))
+                logits = self.fc(output.squeeze(0).squeeze(0))
+                return logits, h_out, c_out
+
+        model = LSTMDecoder()
+        model.eval()
+        tokens = torch.tensor([1])
+        h = torch.randn(2, 1, 64)  # 2 layers
+        c = torch.randn(2, 1, 64)  # 2 layers
+        onnx_program = torch.onnx.export(model, (tokens, h, c), dynamo=True, verbose=False)
+        _testing.assert_onnx_program(onnx_program)
+
+    def test_unbind_dynamic_dim0(self):
+        """Test unbind with dynamic dimension 0 - triggers SplitToSequence"""
+
+        class UnbindModel(torch.nn.Module):
+            def forward(self, x):
+                tensors = torch.unbind(x, dim=0)
+                return sum(tensors)
+
+        model = UnbindModel()
+        x = torch.randn(3, 4, 5)
+        onnx_program = torch.onnx.export(
+            model, (x,), dynamo=True, verbose=False, dynamic_shapes=({0: "batch_size"},)
+        )
+        _testing.assert_onnx_program(onnx_program)
+
+    def test_unbind_dynamic_dim1(self):
+        """Test unbind with dynamic dimension 1 - triggers SplitToSequence"""
+
+        class UnbindModel(torch.nn.Module):
+            def forward(self, x):
+                tensors = torch.unbind(x, dim=1)
+                return sum(tensors)
+
+        model = UnbindModel()
+        x = torch.randn(2, 3, 4)
+        onnx_program = torch.onnx.export(
+            model, (x,), dynamo=True, verbose=False, dynamic_shapes=({1: "seq_len"},)
+        )
+        _testing.assert_onnx_program(onnx_program)
+
 
 if __name__ == "__main__":
     unittest.main()