diff --git a/python/llm/src/ipex_llm/transformers/models/llama.py b/python/llm/src/ipex_llm/transformers/models/llama.py
index f7531561b64..57763d8f98c 100644
--- a/python/llm/src/ipex_llm/transformers/models/llama.py
+++ b/python/llm/src/ipex_llm/transformers/models/llama.py
@@ -1011,8 +1011,10 @@ def llama_attention_forward_4_36_quantized(
     kv_seq_len = key_states.shape[-2]
 
     if len(past_key_value.key_cache) <= self.layer_idx:
+        repeated_key_states = repeat_kv(key_states, self.num_key_value_groups)
+        repeated_value_states = repeat_kv(value_states, self.num_key_value_groups)
         attn_weights = torch.matmul(query_states,
-                                    key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+                                    repeated_key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
 
         if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
             invalidInputError(
@@ -1038,7 +1040,7 @@ def llama_attention_forward_4_36_quantized(
             # upcast attention to fp32
             attn_weights = nn.functional.softmax(attn_weights, dim=-1,
                                                  dtype=torch.float32).to(query_states.dtype)
-        attn_output = torch.matmul(attn_weights, value_states)
+        attn_output = torch.matmul(attn_weights, repeated_value_states)
         if use_cache:
             cache_kwargs = None
             key_states, value_states = past_key_value.update(key_states, value_states,
diff --git a/python/llm/src/ipex_llm/transformers/models/utils.py b/python/llm/src/ipex_llm/transformers/models/utils.py
index 241686935e5..8f3b98a8da1 100644
--- a/python/llm/src/ipex_llm/transformers/models/utils.py
+++ b/python/llm/src/ipex_llm/transformers/models/utils.py
@@ -395,6 +395,8 @@ def use_fused_layer_norm(x: torch.Tensor, training: bool):
 
 def fp16_fusion_check(proj, x, training):
     # only use fp16 fusion on PVC inference
+    if not hasattr(proj, "qtype"):
+        return False
     if proj.qtype != ggml_tensor_qtype["fp16"]:
         return False
     if proj.weight_type != 2: