diff --git a/optimum/habana/transformers/models/llama/modeling_llama.py b/optimum/habana/transformers/models/llama/modeling_llama.py
index 55d4475a8..03fe62491 100755
--- a/optimum/habana/transformers/models/llama/modeling_llama.py
+++ b/optimum/habana/transformers/models/llama/modeling_llama.py
@@ -403,15 +403,18 @@ def allocate(self, inp_seq_len, dtype, device, shape):
 
     @staticmethod
     def update(prev, cur, dim, idx, inp_seq_len):
-        orig_cur = cur
-        if prev.shape == cur.shape:
-            prev.copy_(cur)
-            return orig_cur
-        if idx is not None and cur.shape[2] > 1 and cur.shape[2] <= prev.shape[2]:
-            # Initialize
-            prev[:, :, :inp_seq_len, :].copy_(cur)
-            return orig_cur
+        if inp_seq_len != -1:
+            # reuse cache logic
+            orig_cur = cur
+            if prev.shape == cur.shape:
+                prev.copy_(cur)
+                return orig_cur
+            if cur.shape[2] > 1 and cur.shape[2] <= prev.shape[2]:
+                # Initialize
+                prev[:, :, :inp_seq_len, :].copy_(cur)
+                return orig_cur
         if idx is not None:
+            # 2+ tokenizer logic if model is static shape optimized
             prev.index_copy_(dim, idx - 1, cur)
             return prev
         else:
@@ -654,9 +657,11 @@ def pre_attn_forward(
                     past_value = torch.zeros(
                         key_states.shape, dtype=self.get_k_proj_weight_dtype(), device=key_states.device
                     )
+                    past_key.copy_(key_states)
+                    past_value.copy_(value_states)
                     # Return list instead of tuple
                     past_key_value = [past_key, past_value]
-                if (
+                elif (
                     token_idx is not None
                     and num_virtual_tokens is not None
                     and num_virtual_tokens == past_key_value[0].shape[-2]