diff --git a/unsloth/models/llama.py b/unsloth/models/llama.py
index d0ff413925..c4b8581147 100644
--- a/unsloth/models/llama.py
+++ b/unsloth/models/llama.py
@@ -318,6 +318,13 @@ def LlamaAttention_fast_forward_inference(
     #     Knn, Vnn = Knn, Vnn
     # pass
 
+    # when qlen==vlen and attn_mask is None, we should use causal attention
+    Q_len = Qn.shape[-2]
+    K_len = Knn.shape[-2]
+    if attention_mask is None and Q_len == K_len:
+        is_causal = True
+    else:
+        is_causal = False
     # Attention
     if bsz == 1:
         Qn *= self.scalar # See https://github.com/ggerganov/llama.cpp/issues/7805#issuecomment-2153349963
@@ -518,11 +525,18 @@ def LlamaAttention_fast_forward(
         V = V.transpose(1, 2)
         A = flash_attn_func(Q, K, V, causal = True)
     else:
+        # when qlen==vlen and attn_mask is None, we should use causal attention
+        Q_len = Q.shape[-2]
+        K_len = K.shape[-2]
+        if attention_mask is None and Q_len == K_len:
+            is_causal = True
+        else:
+            is_causal = False
         # Grouped query attention
         if SDPA_HAS_GQA:
             # Needs (batch_size, n_heads, seq_len, head_dim)
             # is_casual and attention_mask must not be both set!
-            A = scaled_dot_product_attention(Q, K, V, attn_mask = attention_mask, is_causal = False, enable_gqa = n_groups != 1)
+            A = scaled_dot_product_attention(Q, K, V, attn_mask = attention_mask, is_causal = is_causal, enable_gqa = n_groups != 1)
             # Go back to (batch_size, seq_len, n_heads, head_dim)
             A = A.transpose(1, 2)#.contiguous()
         else:
@@ -537,7 +551,7 @@ def LlamaAttention_fast_forward(
             Q, K, V = Q.contiguous(), K.contiguous(), V.contiguous()
             # Needs (batch_size, n_heads, seq_len, head_dim)
             # is_casual and attention_mask must not be both set!
-            A = scaled_dot_product_attention(Q, K, V, attn_mask = attention_mask, is_causal = False)
+            A = scaled_dot_product_attention(Q, K, V, attn_mask = attention_mask, is_causal = is_causal)
             # Go back to (batch_size, seq_len, n_heads, head_dim)
             A = A.transpose(1, 2).contiguous()
         pass