diff --git a/python/llm/src/ipex_llm/transformers/npu_pipeline_model/convert_pipeline.py b/python/llm/src/ipex_llm/transformers/npu_pipeline_model/convert_pipeline.py
index 5efeb3b3ee3..05f70dbc863 100644
--- a/python/llm/src/ipex_llm/transformers/npu_pipeline_model/convert_pipeline.py
+++ b/python/llm/src/ipex_llm/transformers/npu_pipeline_model/convert_pipeline.py
@@ -105,18 +105,12 @@ def generate(
             key.to(torch.float16).numpy().tofile(os.path.join(temp_dir, f"key_cache_{layer}.bin"))
             val.to(torch.float16).numpy().tofile(os.path.join(temp_dir, f"value_cache_{layer}.bin"))
 
-        token = input_id.to(torch.int32).item()
-        output_tokens.append(torch.tensor([token]))
-        if streamer is not None:
-            streamer.put(torch.tensor([token]))
-
         if "eos_token_id" not in new_generate_kwargs:
             eos = 0xffffffff
         else:
             eos = new_generate_kwargs["eos_token_id"]
 
         time_t1 = time.perf_counter()
-        idx += 1
 
         # start generate_serve by Thread
         thread = threading.Thread(target=generate_serve,
@@ -124,7 +118,7 @@ def generate(
                                         self.head_dim, self.num_layers,
                                         self.vocab_size,
                                         self.transpose_value_cache,
-                                        new_tokens - 2))
+                                        new_tokens - 1))
         thread.start()
 
         in_pipe_path = "\\\\.\\pipe\\llminputpipe"
@@ -146,7 +140,7 @@ def generate(
             else:
                 break
 
-        time_start = time.perf_counter()
+        time_t2 = time.perf_counter()
 
         bdata = str.encode(str(temp_dir))
         invalidInputError(len(bdata) <= 2000,
@@ -162,6 +156,8 @@ def generate(
                 break
             token = int.from_bytes(data, sys.byteorder)
             idx += 1
+            if idx == 1:
+                time_t3 = time.perf_counter()
             if token == eos:
                 break
             output_tokens.append(torch.tensor([token]))
@@ -177,13 +173,14 @@ def generate(
     time_end = time.perf_counter()
 
     if do_print:
-        print(f" Start the thread and connect the pipe time: {(time_start - time_t1):.2f} s")
+        print(f" Start the thread and connect the pipe time: {(time_t2 - time_t1):.2f} s")
         print(f" Number of input tokens: {input_length}")
         print(f" Generated tokens: {idx}")
-        print(f" First token generation time: {(time_t1 - time_start_all):.2f} s")
-        print(f" Generation average latency: {(time_end - time_start) * 1000 /(idx - 1):.2f} ms, "
-              f"({(idx - 1)/(time_end - time_start):.2f} token/s)")
-        print(f" Generation time: {(time_end - time_start_all - (time_start - time_t1)):.2f} s\n")
+        print(" First token generation time: "
+              f"{(time_t3 - time_start_all - (time_t2 - time_t1)):.2f} s")
+        print(f" Generation average latency: {(time_end - time_t3) * 1000 /(idx - 1):.2f} ms, "
+              f"({(idx - 1)/(time_end - time_t3):.2f} token/s)")
+        print(f" Generation time: {(time_end - time_start_all - (time_t2 - time_t1)):.2f} s\n")
     return output