diff --git a/timm/models/mobilevit.py b/timm/models/mobilevit.py
index 8e8f4428..6466f127 100644
--- a/timm/models/mobilevit.py
+++ b/timm/models/mobilevit.py
@@ -20,7 +20,7 @@ import torch
 import torch.nn.functional as F
 from torch import nn
 
-from timm.layers import to_2tuple, make_divisible, GroupNorm1, ConvMlp, DropPath
+from timm.layers import to_2tuple, make_divisible, GroupNorm1, ConvMlp, DropPath, is_exportable
 from ._builder import build_model_with_cfg
 from ._features_fx import register_notrace_module
 from ._registry import register_model
@@ -564,6 +564,7 @@ class MobileVitV2Block(nn.Module):
 
         self.patch_size = to_2tuple(patch_size)
         self.patch_area = self.patch_size[0] * self.patch_size[1]
+        self.coreml_exportable = is_exportable()
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         B, C, H, W = x.shape
@@ -580,7 +581,10 @@ class MobileVitV2Block(nn.Module):
 
         # Unfold (feature map -> patches), [B, C, H, W] -> [B, C, P, N]
         C = x.shape[1]
-        x = x.reshape(B, C, num_patch_h, patch_h, num_patch_w, patch_w).permute(0, 1, 3, 5, 2, 4)
+        if self.coreml_exportable:
+            x = F.unfold(x, kernel_size=(patch_h, patch_w), stride=(patch_h, patch_w))
+        else:
+            x = x.reshape(B, C, num_patch_h, patch_h, num_patch_w, patch_w).permute(0, 1, 3, 5, 2, 4)
         x = x.reshape(B, C, -1, num_patches)
 
         # Global representations
@@ -588,8 +592,14 @@ class MobileVitV2Block(nn.Module):
         x = self.norm(x)
 
         # Fold (patches -> feature map), [B, C, P, N] --> [B, C, H, W]
-        x = x.reshape(B, C, patch_h, patch_w, num_patch_h, num_patch_w).permute(0, 1, 4, 2, 5, 3)
-        x = x.reshape(B, C, num_patch_h * patch_h, num_patch_w * patch_w)
+        if self.coreml_exportable:
+            # adopted from https://github.com/apple/ml-cvnets/blob/main/cvnets/modules/mobilevit_block.py#L609-L624
+            x = x.reshape(B, C * patch_h * patch_w, num_patch_h, num_patch_w)
+            x = F.pixel_shuffle(x, upscale_factor=patch_h)
+        else:
+            x = x.reshape(B, C, patch_h, patch_w, num_patch_h, num_patch_w).permute(0, 1, 4, 2, 5, 3)
+            x = x.reshape(B, C, num_patch_h * patch_h, num_patch_w * patch_w)
+
 
         x = self.conv_proj(x)
         return x