diff --git a/lightllm/server/api_server.py b/lightllm/server/api_server.py
index a863ab55..c2fe98e4 100644
--- a/lightllm/server/api_server.py
+++ b/lightllm/server/api_server.py
@@ -187,11 +187,6 @@ async def chat_completions(
             HTTPStatus.BAD_REQUEST, "The function call feature is not supported"
         )
 
-    if request.stop is not None:
-        return create_error_response(
-            HTTPStatus.BAD_REQUEST, "The stop parameter is not currently supported"
-        )
-
     created_time = int(time.time())
     prompt = await build_prompt(request)
     sampling_params = SamplingParams(
@@ -203,6 +198,7 @@ async def chat_completions(
         top_k=request.top_k,
         ignore_eos=request.ignore_eos,
         max_new_tokens=request.max_tokens,
+        stop_sequences=request.stop
     )
     sampling_params.verify()
 
diff --git a/lightllm/server/httpserver/manager.py b/lightllm/server/httpserver/manager.py
index 8fd62897..d7f5f4d0 100644
--- a/lightllm/server/httpserver/manager.py
+++ b/lightllm/server/httpserver/manager.py
@@ -53,6 +53,8 @@ async def generate(self, prompt, sampling_params, request_id):
             raise ValueError(
                 f"the req token total len + 1 (input len + output len + 1) is too long > max_total_token_num:{self.total_token_num}"
             )
+        
+        sampling_params.stop_sentences_to_token_ids(self.tokenizer)
 
         self.send_to_router.send_pyobj((prompt_ids, sampling_params, request_id))
         event = asyncio.Event()
diff --git a/lightllm/server/io_struct.py b/lightllm/server/io_struct.py
index a6cdde7f..5324ee26 100644
--- a/lightllm/server/io_struct.py
+++ b/lightllm/server/io_struct.py
@@ -26,6 +26,15 @@ def to_req_detokenization_state(self):
         if self.output_metadata_list:
             out.gen_metadata.update(self.output_metadata_list[-1])
         return out
+    
+    def stop_sequences_matched(self):
+        for stop_token_ids in self.sample_params.stop_sequences:
+            stop_len = len(stop_token_ids)
+            if stop_len > 0:
+                if len(self.output_ids) >= stop_len:
+                    if all(self.output_ids[-(stop_len - i)] == stop_token_ids[i] for i in range(stop_len)):
+                        return True
+        return False
 
     def __repr__(self):
         return (f"request_id(n={self.request_id}, "
@@ -78,6 +87,9 @@ def calcu_used_tokens(self):
     def mark_finished_req(self, eos_id):
         has_new_finish = False
         for req in self.reqs:
+            if req.stop_sequences_matched():
+                req.has_generate_finished = True
+                has_new_finish = True
             if req.output_ids[-1] == eos_id and req.sample_params.ignore_eos == False:
                 req.has_generate_finished = True
                 has_new_finish = True
diff --git a/lightllm/server/sampling_params.py b/lightllm/server/sampling_params.py
index dbe4dd80..8af532df 100644
--- a/lightllm/server/sampling_params.py
+++ b/lightllm/server/sampling_params.py
@@ -15,7 +15,8 @@ def __init__(
         top_p: float = 1.0,
         top_k: int = -1,  # -1 is for all 
         ignore_eos: bool = False,
-        max_new_tokens: int = 16
+        max_new_tokens: int = 16,
+        stop_sequences: Optional[Union[str, List[str]]] = None  # 停止句子条件
     ) -> None:
         self.do_sample = do_sample
         self.presence_penalty = presence_penalty
@@ -25,6 +26,7 @@ def __init__(
         self.top_k = top_k
         self.ignore_eos = ignore_eos
         self.max_new_tokens = max_new_tokens
+        self.stop_sequences = stop_sequences
         if self.do_sample == False:
             self.temperature = 1.0
             self.top_p = 1.0
@@ -47,7 +49,23 @@ def verify(self):
             raise ValueError(f"top_k must be -1 (disable), or at least 1, got {self.top_k}.")
         if self.max_new_tokens < 1:
             raise ValueError(f"max_new_tokens must be at least 1 , got {self.max_new_tokens}.")
-        return 
+        return
+
+    def stop_sentences_to_token_ids(self, tokenizer):
+        if self.stop_sequences is None:
+            self.stop_sequences = []
+        else:
+            if isinstance(self.stop_sequences, str):
+                self.stop_sequences = [self.stop_sequences]
+            new_stop_sequences = []
+            for stop_str in self.stop_sequences:
+                stop_str_ids = tokenizer.encode(stop_str)
+                if stop_str_ids is not None and len(stop_str_ids) >= 1: # remove bos_token_id
+                    stop_str_ids = stop_str_ids[1:]
+                if len(stop_str_ids) > 0:
+                    new_stop_sequences.append(stop_str_ids)
+            self.stop_sequences = new_stop_sequences
+        return
     
     def to_dict(self):
         ret = {}