diff --git a/timm/layers/ml_decoder.py b/timm/layers/ml_decoder.py
index 71460889f..2c57a86d5 100644
--- a/timm/layers/ml_decoder.py
+++ b/timm/layers/ml_decoder.py
@@ -321,12 +321,15 @@ def __init__(
         dim,
         num_classes,
         num_groups,
+        shared: bool = False,
     ):
         super().__init__()
+        # 1 group for all queries (shared_fc, use with class_embed) or 1 group for each query (default, used in paper)
         self.num_classes = num_classes
         duplicate_factor = int(num_classes / num_groups + 0.999)
+        num_groups = 1 if shared else num_groups
         self.weight = nn.Parameter(torch.Tensor(num_groups, dim, duplicate_factor))
-        self.bias = nn.Parameter(torch.Tensor(num_classes))
+        self.bias = nn.Parameter(torch.Tensor(self.num_classes))
         nn.init.xavier_normal_(self.weight)
         nn.init.constant_(self.bias, 0)
     
@@ -424,11 +427,7 @@ def __init__(
             drop=proj_drop,
         )
         
-        # 1 group for all queries (shared_fc, use with class_embed) or 1 group for each query (default, used in paper)
-        duplicate_factor = int(num_classes / num_groups + 0.999)
-        num_fc_classes = duplicate_factor if self.shared_fc else num_classes
-        num_fc_groups = 1 if self.shared_fc else num_groups
-        self.fc = GroupLinear(dim, num_fc_classes, num_fc_groups)
+        self.fc = GroupLinear(dim, num_classes, num_groups, shared=self.shared_fc)
     
     def _resolve_query(self, q):
         if q is not None: