diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py
index 0749ed276..918a90e58 100755
--- a/convert-hf-to-gguf.py
+++ b/convert-hf-to-gguf.py
@@ -23,7 +23,7 @@ if 'NO_LOCAL_GGUF' not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
 import gguf
 
-from convert import HfVocab
+from convert import LlamaHfVocab
 
 
 ###### MODEL DEFINITIONS ######
@@ -370,12 +370,8 @@ class Model(ABC):
         special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
         special_vocab.add_to_gguf(self.gguf_writer)
 
-    def _set_vocab_hf(self):
-        path = self.dir_model
-        added_tokens_path = self.dir_model
-        vocab = HfVocab(
-            path, added_tokens_path if added_tokens_path.exists() else None
-        )
+    def _set_vocab_llama_hf(self):
+        vocab = LlamaHfVocab(self.dir_model)
         tokens = []
         scores = []
         toktypes = []
@@ -1097,7 +1093,7 @@ class MiniCPMModel(Model):
         self.gguf_writer.add_file_type(self.ftype)
 
     def set_vocab(self):
-        self._set_vocab_hf()
+        self._set_vocab_llama_hf()
 
     def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
         if n_kv_head is not None and n_head != n_kv_head:
@@ -1698,11 +1694,8 @@ class BertModel(Model):
             self.gguf_writer.add_pooling_type(pooling_type)
 
     def set_vocab(self):
-        path = self.dir_model
-        added_tokens_path = self.dir_model if self.dir_model.exists() else None
-
         # use huggingface vocab to get all tokens
-        vocab = HfVocab(path, added_tokens_path)
+        vocab = LlamaHfVocab(self.dir_model, ignore_nonllama=True)
         tokens, scores, toktypes = zip(*vocab.all_tokens())
         assert len(tokens) == vocab.vocab_size
         self.vocab_size = vocab.vocab_size
diff --git a/convert.py b/convert.py
index 065646c3d..1669fb006 100755
--- a/convert.py
+++ b/convert.py
@@ -516,7 +516,7 @@ class LlamaHfVocab(Vocab):
     tokenizer_model = "llama"
     name = "hfft"
 
-    def __init__(self, base_path: Path):
+    def __init__(self, base_path: Path, ignore_nonllama: bool = False):
         fname_tokenizer = base_path / FAST_TOKENIZER_FILE
         # if this fails, FileNotFoundError propagates to caller
         with open(fname_tokenizer, encoding='utf-8') as f:
@@ -524,7 +524,9 @@ class LlamaHfVocab(Vocab):
 
         # pre-check so we know if we need transformers
         tokenizer_model: dict[str, Any] = tokenizer_json['model']
-        if (
+        if ignore_nonllama:
+            pass  # workaround incorrect use of this class for WordPiece
+        elif (
             tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False)
             or tokenizer_json['decoder']['type'] != 'Sequence'
         ):