diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py b/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
index 72dfcec4d..9c84237c4 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
@@ -439,7 +439,6 @@ class ZipformerEncoderLayer(nn.Module):
             # to work correctly.
             layer_skip_rate: FloatLike = ScheduledFloat((0.0, 0.5), (4000.0, 0.05), default=0),
             attention_skip_rate: FloatLike = ScheduledFloat((0.0, 0.2), (4000.0, 0.05), (16000, 0.0), default=0),
-            small_conv_skip_rate: FloatLike = ScheduledFloat((0.0, 0.5), (4000.0, 0.2), (16000, 0.1), default=0),
             conv_skip_rate: FloatLike = ScheduledFloat((0.0, 0.2), (4000.0, 0.05), (16000, 0.0), default=0),
             const_attention_rate: FloatLike = ScheduledFloat((0.0, 0.25), (4000.0, 0.025), default=0),
             ff2_skip_rate: FloatLike = 0.01,
@@ -457,10 +456,6 @@ class ZipformerEncoderLayer(nn.Module):
         # contributing too much early on.
         self.conv_skip_rate = copy.deepcopy(conv_skip_rate)
 
-        # skip rate for small_conv_module; it is fairly high and remains nonzero
-        # because we don't want this submodule to contribute too much.
-        self.small_conv_skip_rate = copy.deepcopy(small_conv_skip_rate)
-
         # ff2_skip_rate is to prevent the ff2 module from having output that's too big
         # compared to its residual.
         self.ff2_skip_rate = copy.deepcopy(ff2_skip_rate)
@@ -489,9 +484,7 @@ class ZipformerEncoderLayer(nn.Module):
                                                dropout)
 
         self.nonlin_attention = NonlinAttention(embed_dim,
-                                                hidden_channels=embed_dim // 2)
-
-        self.small_conv_module = SmallConvolutionModule(embed_dim)
+                                                hidden_channels=3 * embed_dim // 4)
 
         self.conv_module = ConvolutionModule(embed_dim,
                                              cnn_module_kernel)
@@ -637,9 +630,6 @@ class ZipformerEncoderLayer(nn.Module):
                                                                selected_attn_weights[0:1]))
 
 
-        if torch.jit.is_scripting() or random.random() >=  float(self.small_conv_skip_rate):
-            src = src + self.small_conv_module(src, src_key_padding_mask=src_key_padding_mask)
-
         src = src + self.feed_forward1(src)
 
         ## pooling module