diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py
index 32bac2c9e..0365d57d3 100755
--- a/convert-hf-to-gguf.py
+++ b/convert-hf-to-gguf.py
@@ -177,7 +177,7 @@ class Model:
                 return False
         return name == (key_name + suffix)
 
-    def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", ".bias", ".beta", ".gamma")) -> str:
+    def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", ".bias")) -> str:
         new_name = self.tensor_map.get_name(key=name, try_suffixes=try_suffixes)
         if new_name is None:
             raise ValueError(f"Can not map tensor {name!r}")
@@ -249,9 +249,6 @@ class Model:
             if name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")):
                 continue
 
-            if name.startswith("bert."):
-                name = name.removeprefix("bert.")
-                
             old_dtype = data_torch.dtype
 
             # convert any unsupported data types to float32
@@ -2194,12 +2191,15 @@ class BertModel(Model):
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
-
+        
+        name = name.removeprefix("bert.")
+        
         # we are only using BERT for embeddings so we don't need the pooling layer
         if name in ("embeddings.position_ids", "pooler.dense.weight", "pooler.dense.bias") or "cls." in name:
             return [] # we don't need these
-
-        return [(self.map_tensor_name(name), data_torch)]
+        
+        try_suffixes = (".weight", ".bias", ".beta", ".gamma")
+        return [(self.map_tensor_name(name, try_suffixes), data_torch)]
 
 
 @Model.register("NomicBertModel")