diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py b/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
index 9f7dfe961..9ccdaab99 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
@@ -341,11 +341,6 @@ class ZipformerEncoderLayer(nn.Module):
                                               feedforward_dim,
                                               dropout)
 
-        self.feed_forward3 = FeedforwardModule(d_model,
-                                              feedforward_dim,
-                                              dropout)
-
-
         self.conv_module1 = ConvolutionModule(d_model,
                                               cnn_module_kernel)
 
@@ -451,16 +446,13 @@ class ZipformerEncoderLayer(nn.Module):
         if torch.jit.is_scripting() or random.random() > dynamic_dropout:
             src = src + self.conv_module1(src, src_key_padding_mask=src_key_padding_mask)
 
-
-        src = src + self.feed_forward2(src)
-
         if torch.jit.is_scripting() or use_self_attn:
             src = src + self.self_attn.forward2(src, attn_weights)
 
         if torch.jit.is_scripting() or random.random() > dynamic_dropout:
             src = src + self.conv_module2(src, src_key_padding_mask=src_key_padding_mask)
 
-        src = src + self.feed_forward3(src)
+        src = src + self.feed_forward2(src)
 
         src = self.norm_final(self.balancer(src))
 
@@ -855,7 +847,7 @@ class SimpleCombiner(torch.nn.Module):
                  min_weight: Tuple[float] = (0., 0.)):
         super(SimpleCombiner, self).__init__()
         assert dim2 >= dim1
-        self.weight1 = nn.Parameter(torch.zeros(()))
+        self.weight1 = nn.Parameter(torch.ones(dim2) * min_weight[0])
         self.min_weight = min_weight
 
     def forward(self,
@@ -878,9 +870,6 @@ class SimpleCombiner(torch.nn.Module):
                                     max=1.0-self.min_weight[1])
 
 
-        src1 = src1 * weight1
-        src2 = src2 * (1.0 - weight1)
-
         src1_dim = src1.shape[-1]
         src2_dim = src2.shape[-1]
         if src1_dim != src2_dim:
@@ -893,6 +882,8 @@ class SimpleCombiner(torch.nn.Module):
             else:
                 src1 = src1[:src2_dim]
 
+        src1 = src1 * weight1
+        src2 = src2 * (1.0 - weight1)
 
         return src1 + src2