diff --git a/timm/models/metaformers.py b/timm/models/metaformers.py
index e0189720..8098958c 100644
--- a/timm/models/metaformers.py
+++ b/timm/models/metaformers.py
@@ -521,14 +521,11 @@ class MetaFormerBlock(nn.Module):
                  norm_layer=nn.LayerNorm,
                  drop=0., drop_path=0.,
                  layer_scale_init_value=None,
-                 res_scale_init_value=None,
-                 downsample = nn.Identity()
+                 res_scale_init_value=None
                  ):
 
         super().__init__()
-        
-        self.downsample = downsample
-        
+                
         self.norm1 = norm_layer(dim)
         self.token_mixer = token_mixer(dim=dim, drop=drop)
         self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
@@ -546,7 +543,6 @@ class MetaFormerBlock(nn.Module):
             if res_scale_init_value else nn.Identity()
         
     def forward(self, x):
-        x = self.downsample(x)
         x = self.res_scale1(x) + \
             self.layer_scale1(
                 self.drop_path1(
@@ -653,18 +649,19 @@ class MetaFormer(nn.Module):
         stages = nn.ModuleList() # each stage consists of multiple metaformer blocks
         cur = 0
         for i in range(num_stage):
-            stage = nn.Sequential(*[MetaFormerBlock(
-                dim=dims[i],
-                token_mixer=token_mixers[i],
-                mlp=mlps[i],
-                norm_layer=norm_layers[i],
-                drop_path=dp_rates[cur + j],
-                layer_scale_init_value=layer_scale_init_values[i],
-                res_scale_init_value=res_scale_init_values[i],
-                downsample = downsample_layers[i]
-                ) for j in range(depths[i])]
+            stage = nn.Sequential(OrderedDict[
+                ('downsample', downsample_layers[i]),
+                ('blocks', nn.Sequential(*[MetaFormerBlock(
+                    dim=dims[i],
+                    token_mixer=token_mixers[i],
+                    mlp=mlps[i],
+                    norm_layer=norm_layers[i],
+                    drop_path=dp_rates[cur + j],
+                    layer_scale_init_value=layer_scale_init_values[i],
+                    res_scale_init_value=res_scale_init_values[i]
+                    ) for j in range(depths[i])])
+                )]
             )
-            
             stages.append(stage)
             cur += depths[i]