diff --git a/src/llmtuner/chat/hf_engine.py b/src/llmtuner/chat/hf_engine.py
index 97160d57..5cb8bfe4 100644
--- a/src/llmtuner/chat/hf_engine.py
+++ b/src/llmtuner/chat/hf_engine.py
@@ -65,12 +65,13 @@ class HuggingfaceEngine(BaseEngine):
         prompt_length = len(prompt_ids)
         inputs = torch.tensor([prompt_ids], device=model.device)
 
-        do_sample = input_kwargs.pop("do_sample", None)
-        temperature = input_kwargs.pop("temperature", None)
-        top_p = input_kwargs.pop("top_p", None)
-        top_k = input_kwargs.pop("top_k", None)
-        num_return_sequences = input_kwargs.pop("num_return_sequences", None)
-        repetition_penalty = input_kwargs.pop("repetition_penalty", None)
+        do_sample = input_kwargs.pop("do_sample", generating_args["do_sample"])
+        temperature = input_kwargs.pop("temperature", generating_args["temperature"])
+        top_p = input_kwargs.pop("top_p", generating_args["top_p"])
+        top_k = input_kwargs.pop("top_k", generating_args["top_k"])
+        num_return_sequences = input_kwargs.pop("num_return_sequences", 1)
+        repetition_penalty = input_kwargs.pop("repetition_penalty", generating_args["repetition_penalty"])
+        length_penalty = input_kwargs.pop("length_penalty", generating_args["length_penalty"])
         max_length = input_kwargs.pop("max_length", None)
         max_new_tokens = input_kwargs.pop("max_new_tokens", None)
         stop = input_kwargs.pop("stop", None)
@@ -78,14 +79,16 @@ class HuggingfaceEngine(BaseEngine):
         if stop is not None:
             raise ValueError("Stop parameter is not supported in Huggingface engine yet.")
 
+        generating_args = generating_args.copy()
         generating_args.update(
             dict(
-                do_sample=do_sample if do_sample is not None else generating_args["do_sample"],
-                temperature=temperature or generating_args["temperature"],
-                top_p=top_p or generating_args["top_p"],
-                top_k=top_k or generating_args["top_k"],
-                num_return_sequences=num_return_sequences or 1,
-                repetition_penalty=repetition_penalty or generating_args["repetition_penalty"],
+                do_sample=do_sample,
+                temperature=temperature,
+                top_p=top_p,
+                top_k=top_k,
+                num_return_sequences=num_return_sequences,
+                repetition_penalty=repetition_penalty,
+                length_penalty=length_penalty,
                 eos_token_id=[tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids,
                 pad_token_id=tokenizer.pad_token_id,
             )
@@ -94,6 +97,10 @@ class HuggingfaceEngine(BaseEngine):
         if isinstance(num_return_sequences, int) and num_return_sequences > 1:
             generating_args["do_sample"] = True
 
+        if not generating_args["do_sample"]:
+            generating_args.pop("temperature", None)
+            generating_args.pop("top_p", None)
+
         if max_length:
             generating_args.pop("max_new_tokens", None)
             generating_args["max_length"] = max_length
diff --git a/src/llmtuner/chat/vllm_engine.py b/src/llmtuner/chat/vllm_engine.py
index d50e41aa..faf8c9fe 100644
--- a/src/llmtuner/chat/vllm_engine.py
+++ b/src/llmtuner/chat/vllm_engine.py
@@ -89,43 +89,34 @@ class VllmEngine(BaseEngine):
         )
         prompt_length = len(prompt_ids)
 
-        temperature = input_kwargs.pop("temperature", None)
-        top_p = input_kwargs.pop("top_p", None)
-        top_k = input_kwargs.pop("top_k", None)
-        num_return_sequences = input_kwargs.pop("num_return_sequences", None)
-        repetition_penalty = input_kwargs.pop("repetition_penalty", None)
+        use_beam_search = self.generating_args["num_beams"] > 1
+        temperature = input_kwargs.pop("temperature", self.generating_args["temperature"])
+        top_p = input_kwargs.pop("top_p", self.generating_args["top_p"])
+        top_k = input_kwargs.pop("top_k", self.generating_args["top_k"])
+        num_return_sequences = input_kwargs.pop("num_return_sequences", 1)
+        repetition_penalty = input_kwargs.pop("repetition_penalty", self.generating_args["repetition_penalty"])
+        length_penalty = input_kwargs.pop("length_penalty", self.generating_args["length_penalty"])
         max_length = input_kwargs.pop("max_length", None)
         max_new_tokens = input_kwargs.pop("max_new_tokens", None)
         stop = input_kwargs.pop("stop", None)
 
-        generating_args = self.generating_args.copy()
-        generating_args.update(
-            dict(
-                temperature=temperature or generating_args["temperature"],
-                top_p=top_p or generating_args["top_p"],
-                top_k=top_k or generating_args["top_k"],
-                num_return_sequences=num_return_sequences or 1,
-                repetition_penalty=repetition_penalty or generating_args["repetition_penalty"],
-            )
-        )
-
         if max_length:
-            generating_args["max_new_tokens"] = max_length - prompt_length
+            max_tokens = max_length - prompt_length
 
         if max_new_tokens:
-            generating_args["max_new_tokens"] = max_new_tokens
+            max_tokens = max_new_tokens
 
         sampling_params = SamplingParams(
-            n=generating_args["num_return_sequences"],
-            repetition_penalty=generating_args["repetition_penalty"],
-            temperature=generating_args["temperature"],
-            top_p=generating_args["top_p"],
-            top_k=generating_args["top_k"],
-            use_beam_search=generating_args["num_beams"] > 1,
-            length_penalty=generating_args["length_penalty"],
+            n=num_return_sequences,
+            repetition_penalty=repetition_penalty,
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+            use_beam_search=use_beam_search,
+            length_penalty=length_penalty,
             stop=stop,
             stop_token_ids=[self.tokenizer.eos_token_id] + self.tokenizer.additional_special_tokens_ids,
-            max_tokens=generating_args["max_new_tokens"],
+            max_tokens=max_tokens,
             skip_special_tokens=True,
         )