diff --git a/unsloth/models/llama.py b/unsloth/models/llama.py
index 62e06cd7a3..8cfca18755 100644
--- a/unsloth/models/llama.py
+++ b/unsloth/models/llama.py
@@ -259,17 +259,20 @@ def LlamaAttention_fast_forward_inference(
     Kn *= cos; Kn.addcmul_(RH_K, sin);
     
     # New KV cache
-    # Kn = torch.cat([K1, Kn], dim = 2)
-    # Vn = torch.cat([V1, Vn], dim = 2)
-    if not hasattr(self, "paged_attention"):
-        self.paged_attention = torch.empty((2, bsz, n_kv_heads, 2048, head_dim), dtype = dtype, device = "cuda")
-        self.paged_attention_K = self.paged_attention[0]
-        self.paged_attention_V = self.paged_attention[1]
-        self.paged_attention_K[:,:,:seq_len,:] = K1
-        self.paged_attention_V[:,:,:seq_len,:] = V1
-    pass
-    Kn = self.paged_attention_K[:,:,seq_len,:] = Kn
-    Vn = self.paged_attention_V[:,:,seq_len,:] = Vn
+    print(K1.shape, V1.shape)
+    print(Kn.shape, Vn.shape)
+    Kn = torch.cat([K1, Kn], dim = 2)
+    Vn = torch.cat([V1, Vn], dim = 2)
+    print(Kn.shape, Vn.shape)
+    # if not hasattr(self, "paged_attention"):
+    #     self.paged_attention = torch.empty((2048, 2, bsz, n_kv_heads, head_dim), dtype = dtype, device = "cuda")
+    #     self.paged_attention_K = self.paged_attention[0]
+    #     self.paged_attention_V = self.paged_attention[1]
+    #     self.paged_attention_K[:,:,:seq_len,:] = K1
+    #     self.paged_attention_V[:,:,:seq_len,:] = V1
+    # pass
+    # Kn = self.paged_attention_K[:,:,seq_len,:] = Kn
+    # Vn = self.paged_attention_V[:,:,seq_len,:] = Vn
 
     # Grouped query attention
     if n_groups != 1: