diff --git a/intel_extension_for_transformers/llm/runtime/graph/__init__.py b/intel_extension_for_transformers/llm/runtime/graph/__init__.py
index 489cfbb35e8..716b32c275b 100644
--- a/intel_extension_for_transformers/llm/runtime/graph/__init__.py
+++ b/intel_extension_for_transformers/llm/runtime/graph/__init__.py
@@ -78,13 +78,13 @@ def get_model_type(model_config):
     def init(self, model_name, use_quant=True, use_cache=False, use_gptq=False, **quant_kwargs):
         self.config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
         self.tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
-        model_type = Model.get_model_type(self.config)
-        self.__import_package(model_type)
+        self.model_type = Model.get_model_type(self.config)
+        self.__import_package(self.model_type)
 
         # check cache and quantization
         output_path = "runtime_outs"
         os.makedirs(output_path, exist_ok=True)
-        fp32_bin = "{}/ne_{}_f32.bin".format(output_path, model_type)
+        fp32_bin = "{}/ne_{}_f32.bin".format(output_path, self.model_type)
         quant_desc = quant_kwargs['weight_dtype']
         if quant_kwargs['use_ggml']:
             quant_desc += "_ggml"
@@ -96,7 +96,7 @@ def init(self, model_name, use_quant=True, use_cache=False, use_gptq=False, **qu
                 quant_desc += "_g{}".format(quant_kwargs['group_size'])
         if use_gptq:
             quant_desc = "gptq"
-        quant_bin = "{}/ne_{}_q_{}.bin".format(output_path, model_type, quant_desc)
+        quant_bin = "{}/ne_{}_q_{}.bin".format(output_path, self.model_type, quant_desc)
 
         if not use_quant:
             self.bin_file = fp32_bin
@@ -199,7 +199,7 @@ def is_token_end(self):
         return self.model.is_token_end()
     
     def eos_token_id(self):
-        if self.tokenizer.eos_token_id == None:
+        if self.model_type == 'qwen':
             return self.tokenizer.special_tokens['<|endoftext|>']
         return self.tokenizer.eos_token_id
 
diff --git a/intel_extension_for_transformers/llm/runtime/graph/scripts/convert_llama.py b/intel_extension_for_transformers/llm/runtime/graph/scripts/convert_llama.py
index dceeeab9125..b3e6a6b7d5a 100644
--- a/intel_extension_for_transformers/llm/runtime/graph/scripts/convert_llama.py
+++ b/intel_extension_for_transformers/llm/runtime/graph/scripts/convert_llama.py
@@ -247,7 +247,8 @@ def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]) ->
         expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))
         actual_ids = sorted(added_tokens.values())
         if expected_ids != actual_ids:
-            raise Exception(f"Expected added token IDs to be sequential and start at {len(added_tokens)}; got {actual_ids}")
+            print(f"Expected added token IDs to be sequential and start at {len(added_tokens)}; got {actual_ids}")
+            added_tokens = {}
         items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])
         self.added_tokens_list = [text for (text, idx) in items]
         self.vocab_size_base: int = vocab_size
diff --git a/intel_extension_for_transformers/llm/runtime/graph/scripts/convert_mistral.py b/intel_extension_for_transformers/llm/runtime/graph/scripts/convert_mistral.py
index 1db1fdd7f89..d35bf0b05ff 100644
--- a/intel_extension_for_transformers/llm/runtime/graph/scripts/convert_mistral.py
+++ b/intel_extension_for_transformers/llm/runtime/graph/scripts/convert_mistral.py
@@ -247,7 +247,8 @@ def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]) ->
         expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))
         actual_ids = sorted(added_tokens.values())
         if expected_ids != actual_ids:
-            raise Exception(f"Expected added token IDs to be sequential and start at {len(added_tokens)}; got {actual_ids}")
+            print(f"Expected added token IDs to be sequential and start at {len(added_tokens)}; got {actual_ids}")
+            added_tokens = {}
         items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])
         self.added_tokens_list = [text for (text, idx) in items]
         self.vocab_size_base: int = vocab_size