diff --git a/ivy/functional/frontends/torch/tensor.py b/ivy/functional/frontends/torch/tensor.py
index d9a2b6dc3459..3a28b5727c6b 100644
--- a/ivy/functional/frontends/torch/tensor.py
+++ b/ivy/functional/frontends/torch/tensor.py
@@ -805,35 +805,24 @@ def new_empty(
         return torch_frontend.tensor(_data)
 
     def unfold(self, dimension, size, step):
-        # Ensure the dimension size is large enough for unfolding
-        if self.shape[dimension] < size:
-            raise ValueError(
-                f"Dimension size ({self.shape[dimension]}) is "
-                "smaller than the unfolding size ({size})."
-            )
-
         slices = []
         self_shape = tuple(self.shape)
-
-        # Create sliding window slices
         for i in range(0, self_shape[dimension] - size + 1, step):
             slicing = [slice(None)] * len(self.shape)
             slicing[dimension] = slice(i, i + size)
             slices.append(self.ivy_array[tuple(slicing)])
-
-        # Stack the slices along a new dimension at 'dimension + 1'
-        stacked = torch_frontend.stack(slices, dim=dimension + 1)
-
-        # Reshape the tensor to insert a new window dimension
+        stacked = torch_frontend.stack(slices, dim=dimension)
         new_shape = list(self.shape)
         num_slices = (self.shape[dimension] - size) // step + 1
-
-        # Replace size of the unfolded dimension with the number of slices
         new_shape[dimension] = num_slices
-
-        # Append the window size at the end (correct behavior)
-        new_shape.append(size)
-        return stacked.reshape(new_shape)
+        if dimension == -1:
+            new_shape.insert(dimension, size)
+        else:
+            new_shape.insert(dimension + 1, size)
+        reshaped = stacked.reshape(new_shape)
+        dims = list(range(len(stacked.shape)))
+        dims[-2], dims[-1] = dims[-1], dims[-2]
+        return reshaped.permute(*dims)
 
     def long(self, memory_format=None):
         self.ivy_array = ivy.astype(self.ivy_array, ivy.int64, copy=False)