diff --git a/python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GPTQ/generate.py b/python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GPTQ/generate.py
index c45963f5..50041d41 100644
--- a/python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GPTQ/generate.py
+++ b/python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GPTQ/generate.py
@@ -47,13 +47,10 @@ if __name__ == '__main__':
                                                  load_in_4bit=True,
                                                  torch_dtype=torch.float,
                                                  trust_remote_code=True,).to("xpu")
-    
+
     # Load tokenizer
-    if "qwen" in model_path.lower():
-        tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
-    else:
-        tokenizer = LlamaTokenizer.from_pretrained(model_path, trust_remote_code=True)
-    
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
     # Generate predicted tokens
     with torch.inference_mode():
         prompt = LLAMA2_PROMPT_FORMAT.format(prompt=args.prompt)
diff --git a/python/llm/src/ipex_llm/transformers/models/common.py b/python/llm/src/ipex_llm/transformers/models/common.py
index e1522c4e..86b0d46b 100644
--- a/python/llm/src/ipex_llm/transformers/models/common.py
+++ b/python/llm/src/ipex_llm/transformers/models/common.py
@@ -19,17 +19,21 @@ from typing import List
 
 
 def merge_linear(linears: List[torch.nn.Linear]) -> torch.nn.Linear:
-    new_weight = torch.cat(list(linear.weight.data for linear in linears), dim=0)
-    if linears[0].bias is not None:
-        new_linear = torch.nn.Linear(0, 0, bias=True)
-        new_bias = torch.cat(list(linear.bias.data for linear in linears), dim=0)
-        new_linear.bias = torch.nn.Parameter(new_bias, requires_grad=False)
+    if hasattr(linears[0], "weight"):
+        # For GPTQ model, it might be qweight
+        new_weight = torch.cat(list(linear.weight.data for linear in linears), dim=0)
+        if linears[0].bias is not None:
+            new_linear = torch.nn.Linear(0, 0, bias=True)
+            new_bias = torch.cat(list(linear.bias.data for linear in linears), dim=0)
+            new_linear.bias = torch.nn.Parameter(new_bias, requires_grad=False)
+        else:
+            new_linear = torch.nn.Linear(0, 0, bias=False)
+        new_linear.weight = torch.nn.Parameter(new_weight, requires_grad=False)
+        new_linear.in_features = new_weight.size(1)
+        new_linear.out_features = new_weight.size(0)
+        return new_linear
     else:
-        new_linear = torch.nn.Linear(0, 0, bias=False)
-    new_linear.weight = torch.nn.Parameter(new_weight, requires_grad=False)
-    new_linear.in_features = new_weight.size(1)
-    new_linear.out_features = new_weight.size(0)
-    return new_linear
+        return None
 
 
 def merge_qkv_base(module: torch.nn.Module, attention_class):
@@ -39,8 +43,9 @@ def merge_qkv_base(module: torch.nn.Module, attention_class):
             module.k_proj,
             module.v_proj,
         ])
-        module.qkv_proj = qkv_proj
-        del module.q_proj, module.k_proj, module.v_proj
+        if qkv_proj is not None:
+            module.qkv_proj = qkv_proj
+            del module.q_proj, module.k_proj, module.v_proj
 
 
 def fuse_mlp_base(module: torch.nn.Module, act: int, x: torch.Tensor):