diff --git a/torchchat/generate.py b/torchchat/generate.py
index 4a67195fb..66f26ff9f 100644
--- a/torchchat/generate.py
+++ b/torchchat/generate.py
@@ -1189,12 +1189,27 @@ def callback(x, *, done_generating=False):
                 f"Mean Accepted: {sum([idx * i for idx, i in enumerate(counts_aggregated)])/sum(counts_aggregated)}"
             )
 
-        print(
-            f"\n      Average tokens/sec (total): {torch.mean(torch.tensor(aggregate_metrics['tokens_per_sec'])).item():.2f} \
-                \nAverage tokens/sec (first token): {torch.mean(torch.tensor(aggregate_metrics['first_token_per_sec'])).item():.2f} \
-                \nAverage tokens/sec (next tokens): {torch.mean(torch.tensor(aggregate_metrics['next_tokens_per_sec'])).item():.2f} \n\
+        avg_tokens_sec = torch.mean(
+            torch.tensor(aggregate_metrics["tokens_per_sec"])
+        ).item()
+        avg_first_token_sec = torch.mean(
+            torch.tensor(aggregate_metrics["first_token_per_sec"])
+        ).item()
+        avg_next_tokens_sec = torch.mean(
+            torch.tensor(aggregate_metrics["next_tokens_per_sec"])
+        ).item()
+
+        if not (
+            torch.isnan(torch.tensor(avg_tokens_sec))
+            or torch.isnan(torch.tensor(avg_first_token_sec))
+            or torch.isnan(torch.tensor(avg_next_tokens_sec))
+        ):
+            print(
+                f"\n      Average tokens/sec (total): {avg_tokens_sec:.2f} \
+                \nAverage tokens/sec (first token): {avg_first_token_sec:.2f} \
+                \nAverage tokens/sec (next tokens): {avg_next_tokens_sec:.2f} \n\
                 "
-        )
+            )
         if torch.cuda.is_available():
             print(f"Memory used: {torch.cuda.max_memory_reserved() / 1e9:.02f} GB")