diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 2bf97475f78dd..92e2480431b97 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -5022,18 +5022,73 @@ def set_vocab(self):
 
         from transformers import AutoTokenizer
         tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
-        vocab_size = hparams.get("padded_vocab_size",hparams["vocab_size"])
+        vocab_size = hparams.get("padded_vocab_size", hparams.get("vocab_size"))
         assert max(tokenizer.get_vocab().values()) < vocab_size
 
-        tokens, toktypes, tokpre = self.get_vocab_base()
-        self.gguf_writer.add_tokenizer_model("gpt2")
+        tokpre = self.get_vocab_base_pre(tokenizer)
+
+        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.get_vocab().items()}
+        added_vocab = tokenizer.get_added_vocab()
+
+        added_tokens_decoder = tokenizer.added_tokens_decoder
+
+        for i in range(vocab_size):
+            if i not in reverse_vocab:
+                tokens.append(f"[PAD{i}]")
+                toktypes.append(gguf.TokenType.UNUSED)
+            else:
+                token: str = reverse_vocab[i]
+                if token in added_vocab:
+                    # The tokenizer in llama.cpp assumes the CONTROL and USER_DEFINED tokens are pre-normalized.
+                    # To avoid unexpected issues - we make sure to normalize non-normalized tokens
+                    if not added_tokens_decoder[i].normalized:
+                        previous_token = token
+                        token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False))
+                        if previous_token != token:
+                            logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer")
+
+                    if added_tokens_decoder[i].special or self.does_token_look_special(token):
+                        toktypes.append(gguf.TokenType.CONTROL)
+                    
+                else:
+                    toktypes.append(gguf.TokenType.NORMAL)
+                tokens.append(token)
+ 
         self.gguf_writer.add_tokenizer_pre(tokpre)
         self.gguf_writer.add_token_list(tokens)
         self.gguf_writer.add_token_types(toktypes)
-        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        try:
+            tokenizer_file = self.dir_model / 'tokenizer.json'
+            if not tokenizer_file.is_file():  
+                raise ValueError("tokenizer.json not found")
+                  
+            # for https://huggingface.co/THUDM/glm-4-9b
+            special_vocab=gguf.SpecialVocab(
+                self.dir_model, 
+                load_merges=True,
+                n_vocab=vocab_size
+            ) 
+        
+            self.gguf_writer.add_tokenizer_model("gpt2")
+                
+        except Exception as e:
+            logger.warning(f'Failed to load special tokens: {e}')
+            # for https://huggingface.co/THUDM/glm-4-9b-hf
+            special_vocab=gguf.SpecialVocab(
+                self.dir_model, 
+                load_merges=False,
+                n_vocab=vocab_size
+            )
+            self.gguf_writer.add_tokenizer_model("llama")
+
         # only add special tokens when they were not already loaded from config.json
-        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])
-        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])
+   
+        #TODO In llama.cpp, special tokens are mapped one-to-one between a token and a coordinate. However, in reality, a transformer might associate a special token like eos_token_id with multiple tokens.
+        #     Currently, llama.cpp only supports a one-to-one mapping.
+        #     This can lead to an issue where the model fails to terminate properly.
+        #     You can see a temporary workaround here. https://github.com/ggml-org/llama.cpp/issues/9606
+        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"]) 
+        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"]) 
         # this one is usually not in config.json anyway
         special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])
         special_vocab.add_to_gguf(self.gguf_writer)
@@ -5045,7 +5100,7 @@ def set_gguf_parameters(self):
         self.gguf_writer.add_context_length(self.hparams.get("seq_length", n_embed))
         self.gguf_writer.add_embedding_length(n_embed)
         self.gguf_writer.add_feed_forward_length(self.hparams.get("ffn_hidden_size", self.hparams.get("intermediate_size", 4 * n_embed)))
-        self.gguf_writer.add_block_count(self.hparams.get("num_layers", self.hparams["num_hidden_layers"]))
+        self.gguf_writer.add_block_count(self.hparams.get("num_layers", self.hparams.get("num_hidden_layers")))
         self.gguf_writer.add_head_count(n_head)
         self.gguf_writer.add_head_count_kv(n_head_kv)
         self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("layernorm_epsilon",1e-5))