diff --git a/README.MD b/README.MD
index 729baa3..4613874 100644
--- a/README.MD
+++ b/README.MD
@@ -10,6 +10,7 @@
 
 ## update information
 ```text
+    11-20 support seed for generator sample
     11-06 fix pydantic 2 and support api_keys in config
     11-04 support yi aigc-zoo>=0.2.7.post2 , 支持 pydantic >= 2 
     11-01 support bluelm aigc-zoo>=0.2.7.post1
diff --git a/serving/model_handler/baichuan2_13b/infer.py b/serving/model_handler/baichuan2_13b/infer.py
index 428879f..d093b90 100644
--- a/serving/model_handler/baichuan2_13b/infer.py
+++ b/serving/model_handler/baichuan2_13b/infer.py
@@ -13,7 +13,7 @@
 from transformers import HfArgumentParser, BitsAndBytesConfig, GenerationConfig
 from aigc_zoo.model_zoo.baichuan.baichuan2_13b.llm_model import MyTransformer,BaichuanConfig,BaichuanTokenizer,\
     MyBaichuanForCausalLM,PetlArguments,PetlModel
-from serving.model_handler.base import EngineAPI_Base,CompletionResult, CompletionResult,LoraModelState, load_lora_config, GenerateProcess,WorkMode
+from serving.model_handler.base import EngineAPI_Base,CompletionResult, CompletionResult,LoraModelState, load_lora_config, GenArgs,WorkMode
 from serving.prompt import *
 
 
@@ -123,12 +123,11 @@ def get_default_gen_args(self):
         return default_kwargs
 
     def chat_stream(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self,is_stream=True)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self, is_stream=True)
         chunk = args_process.chunk
         default_kwargs=self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         stopping_criteria = default_kwargs.pop('stopping_criteria', None)
         generation_config = GenerationConfig(**default_kwargs)
         for response in self.get_model().chat(tokenizer=self.tokenizer,
@@ -156,11 +155,10 @@ def chat_stream(self,messages: List[Dict], **kwargs):
 
 
     def chat(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         stopping_criteria = default_kwargs.pop('stopping_criteria', None)
         generation_config = GenerationConfig(**default_kwargs)
         response = self.get_model().chat(tokenizer=self.tokenizer,
@@ -177,6 +175,7 @@ def chat(self,messages: List[Dict], **kwargs):
 
 
     def embedding(self, query, **kwargs):
+        args_process = GenArgs(kwargs, self)
         model = self.get_model()
         inputs = self.tokenizer(query, return_tensors="pt")
         inputs = inputs.to(model.device)
diff --git a/serving/model_handler/baichuan2_7b/infer.py b/serving/model_handler/baichuan2_7b/infer.py
index 1fb735b..5a512e7 100644
--- a/serving/model_handler/baichuan2_7b/infer.py
+++ b/serving/model_handler/baichuan2_7b/infer.py
@@ -14,7 +14,7 @@
 from aigc_zoo.model_zoo.baichuan.baichuan2_7b.llm_model import MyTransformer,BaichuanConfig,BaichuanTokenizer,\
     MyBaichuanForCausalLM,PetlArguments,PetlModel
 from serving.model_handler.base import EngineAPI_Base,CompletionResult,LoraModelState, \
-    load_lora_config,GenerateProcess,WorkMode
+    load_lora_config,GenArgs,WorkMode
 from serving.prompt import *
 
 class NN_DataHelper(DataHelper):pass
@@ -125,12 +125,11 @@ def get_default_gen_args(self):
         return default_kwargs
 
     def chat_stream(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self,is_stream=True)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self, is_stream=True)
         chunk = args_process.chunk
         default_kwargs=self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         stopping_criteria = default_kwargs.pop('stopping_criteria', None)
         generation_config = GenerationConfig(**default_kwargs)
         for response in self.get_model().chat(tokenizer=self.tokenizer,
@@ -158,11 +157,10 @@ def chat_stream(self,messages: List[Dict], **kwargs):
 
 
     def chat(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         stopping_criteria = default_kwargs.pop('stopping_criteria', None)
         generation_config = GenerationConfig(**default_kwargs)
         response = self.get_model().chat(tokenizer=self.tokenizer,
@@ -177,6 +175,7 @@ def chat(self,messages: List[Dict], **kwargs):
 
 
     def embedding(self, query, **kwargs):
+        args_process = GenArgs(kwargs, self)
         model = self.get_model()
         inputs = self.tokenizer(query, return_tensors="pt")
         inputs = inputs.to(model.device)
diff --git a/serving/model_handler/baichuan_13b/infer.py b/serving/model_handler/baichuan_13b/infer.py
index f7783ba..3c4d611 100644
--- a/serving/model_handler/baichuan_13b/infer.py
+++ b/serving/model_handler/baichuan_13b/infer.py
@@ -13,7 +13,7 @@
 from transformers import HfArgumentParser, BitsAndBytesConfig, GenerationConfig
 from aigc_zoo.model_zoo.baichuan.baichuan_13b.llm_model import MyTransformer,BaichuanConfig,BaichuanTokenizer,\
     MyBaichuanForCausalLM,PetlArguments,PetlModel
-from serving.model_handler.base import EngineAPI_Base,CompletionResult,LoraModelState, load_lora_config, GenerateProcess,WorkMode
+from serving.model_handler.base import EngineAPI_Base,CompletionResult,LoraModelState, load_lora_config, GenArgs,WorkMode
 from serving.prompt import *
 
 
@@ -123,12 +123,11 @@ def get_default_gen_args(self):
         return default_kwargs
 
     def chat_stream(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self,is_stream=True)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self, is_stream=True)
         chunk = args_process.chunk
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         stopping_criteria = default_kwargs.pop('stopping_criteria', None)
         generation_config = GenerationConfig(**default_kwargs)
         for response in self.get_model().chat(tokenizer=self.tokenizer,
@@ -155,11 +154,10 @@ def chat_stream(self,messages: List[Dict], **kwargs):
 
 
     def chat(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         stopping_criteria = default_kwargs.pop('stopping_criteria', None)
         generation_config = GenerationConfig(**default_kwargs)
         response = self.get_model().chat(tokenizer=self.tokenizer,
@@ -174,10 +172,10 @@ def chat(self,messages: List[Dict], **kwargs):
         })
 
     def generate(self, messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query = args_process.get_chat_info(messages, chat_format="generate")
         response = self.get_model().generate(query=query, **kwargs)
         return CompletionResult(result={
@@ -187,6 +185,7 @@ def generate(self, messages: List[Dict], **kwargs):
 
 
     def embedding(self, query, **kwargs):
+        args_process = GenArgs(kwargs, self)
         model = self.get_model()
         inputs = self.tokenizer(query, return_tensors="pt")
         inputs = inputs.to(model.device)
diff --git a/serving/model_handler/baichuan_7b/infer.py b/serving/model_handler/baichuan_7b/infer.py
index a60a66e..34e91f5 100644
--- a/serving/model_handler/baichuan_7b/infer.py
+++ b/serving/model_handler/baichuan_7b/infer.py
@@ -14,7 +14,7 @@
 from transformers import HfArgumentParser, BitsAndBytesConfig, GenerationConfig
 from aigc_zoo.model_zoo.baichuan.baichuan_7b.llm_model import MyTransformer,BaiChuanConfig,BaiChuanTokenizer,PetlArguments,PetlModel
 from aigc_zoo.generator_utils.generator_llm import Generate
-from serving.model_handler.base import EngineAPI_Base,CompletionResult, LoraModelState, load_lora_config,GenerateProcess,WorkMode
+from serving.model_handler.base import EngineAPI_Base,CompletionResult, LoraModelState, load_lora_config,GenArgs,WorkMode
 from serving.prompt import *
 
 class NN_DataHelper(DataHelper):pass
@@ -134,12 +134,11 @@ def get_default_gen_args(self):
         return default_kwargs
 
     def chat_stream(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self,is_stream=True)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self, is_stream=True)
         chunk = args_process.chunk
         default_kwargs= self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        generation_config = GenerationConfig(**args_process.postprocess(default_kwargs))
+        generation_config = GenerationConfig(**args_process.build_args(default_kwargs))
         query, history = args_process.get_chat_info(messages)
         prompt = get_chat_default(self.tokenizer, query, history)
 
@@ -177,13 +176,12 @@ def stream_generator():
 
 
     def chat(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
         query, history = args_process.get_chat_info(messages)
         prompt = get_chat_default(self.tokenizer, query, history)
-        response = self.gen_core.generate(query=prompt, **args_process.postprocess(default_kwargs))
+        response = self.gen_core.generate(query=prompt, **args_process.build_args(default_kwargs))
         response = args_process.postprocess_response(response, **kwargs)
         # history = history + [(query, response)]
         return CompletionResult(result={
@@ -192,10 +190,10 @@ def chat(self,messages: List[Dict], **kwargs):
         })
 
     def generate(self,messages: List[Dict],**kwargs):
-        args_process = GenerateProcess(self)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query = args_process.get_chat_info(messages,chat_format="generate")
         response = self.gen_core.generate(query=query, **kwargs)
         return CompletionResult(result={
@@ -204,6 +202,7 @@ def generate(self,messages: List[Dict],**kwargs):
         })
 
     def embedding(self, query, **kwargs):
+        args_process = GenArgs(kwargs, self)
         model = self.get_model()
         inputs = self.tokenizer(query, return_tensors="pt")
         inputs = inputs.to(model.device)
diff --git a/serving/model_handler/base/__init__.py b/serving/model_handler/base/__init__.py
index 5d3b2a6..207d0aa 100644
--- a/serving/model_handler/base/__init__.py
+++ b/serving/model_handler/base/__init__.py
@@ -4,6 +4,6 @@
 
 from .infer import EngineAPI_Base,flat_input,CompletionResult
 from .data_define import ChunkData, LoraModelState, WorkMode
-from .data_process import GenerateProcess
+from .data_process import GenArgs
 from .loaders import load_lora_config
 from .utils import is_quantization_bnb
diff --git a/serving/model_handler/base/data_process.py b/serving/model_handler/base/data_process.py
index e51f7ef..7bdbb2d 100644
--- a/serving/model_handler/base/data_process.py
+++ b/serving/model_handler/base/data_process.py
@@ -156,22 +156,42 @@ def _calc_stopped_samples(self, input_ids:  torch.LongTensor) -> bool:
             del ids
         return False
 
-class GenerateProcess:
-    def __init__(self,this_obj,is_stream=False):
+class GenArgs:
+    def __init__(self,args_dict:Dict, this_obj,is_stream=False):
+        if args_dict is None:
+            args_dict = {}
+
         self.tokenizer: Optional[PreTrainedTokenizer] = this_obj.tokenizer
         self.config: Optional[PretrainedConfig] = this_obj.config
         self.is_stream = is_stream
         self.chunk: Optional[ChunkData] = None
         self.this_obj = this_obj
 
-    def preprocess(self, args_dict: dict):
+        # support seed
+        self.multinomial_fn = torch.multinomial
+        self.__preprocess(args_dict)
+    def __del__(self):
+        # restore
+        if torch.multinomial != self.multinomial_fn:
+            torch.multinomial = self.multinomial_fn
+
+    def __preprocess(self, args_dict):
         if self.is_stream:
             nchar = args_dict.pop('nchar',1)
             gtype = args_dict.pop('gtype',"total")
             self.chunk = ChunkData(nchar=nchar, stop=args_dict.get('stop', None), mode=gtype)
+
+        seed = args_dict.pop('seed',None)
+
+        #进程隔离，互不影响
+        if isinstance(seed,int):
+            device = self.this_obj.get_model().device
+            torch.multinomial = lambda *args, **kwargs: self.multinomial_fn(*args,
+                                                                     generator=torch.Generator(device=device).manual_seed(seed),
+                                                                     **kwargs)
         return args_dict
 
-    def postprocess(self, args_dict):
+    def build_args(self, args_dict):
         stop = args_dict.pop('stop',None)
         if stop is None:
             return args_dict
diff --git a/serving/model_handler/bluelm/infer.py b/serving/model_handler/bluelm/infer.py
index c790a67..54d7d3c 100644
--- a/serving/model_handler/bluelm/infer.py
+++ b/serving/model_handler/bluelm/infer.py
@@ -18,7 +18,7 @@
 from aigc_zoo.model_zoo.bluelm.llm_model import MyBlueLMForCausalLM,BlueLMTokenizer,BlueLMConfig
 from aigc_zoo.model_zoo.llm.llm_model import MyTransformer,PetlArguments,PetlModel,AutoConfig
 from aigc_zoo.generator_utils.generator_llm import Generate
-from serving.model_handler.base import EngineAPI_Base,CompletionResult, LoraModelState, load_lora_config, GenerateProcess,WorkMode,ChunkData
+from serving.model_handler.base import EngineAPI_Base,CompletionResult, LoraModelState, load_lora_config, GenArgs,WorkMode,ChunkData
 from serving.prompt import *
 
 class NN_DataHelper(DataHelper):pass
@@ -147,11 +147,10 @@ def get_default_gen_args(self):
         return default_kwargs
 
     def chat_stream(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self,is_stream=True)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self, is_stream=True)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         prefix,query, history = args_process.get_chat_info_with_system(messages)
         prompt = get_chat_bluelm(self.tokenizer, query, history=history, prefix=prefix)
         skip_word_list = default_kwargs.get('eos_token_id', None) or [self.tokenizer.eos_token_id]
@@ -164,11 +163,10 @@ def chat_stream(self,messages: List[Dict], **kwargs):
 
 
     def chat(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         prefix,query, history = args_process.get_chat_info_with_system(messages)
         prompt = get_chat_bluelm(self.tokenizer, query, history=history, prefix=prefix)
         response = self.gen_core.generate(query=prompt, **default_kwargs)
@@ -180,10 +178,10 @@ def chat(self,messages: List[Dict], **kwargs):
 
 
     def generate(self,messages: List[Dict],**kwargs):
-        args_process = GenerateProcess(self)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query = args_process.get_chat_info(messages,chat_format="generate")
         response = self.gen_core.generate(query=query, **default_kwargs)
         return CompletionResult(result={
@@ -192,6 +190,7 @@ def generate(self,messages: List[Dict],**kwargs):
         })
 
     def embedding(self, query, **kwargs):
+        args_process = GenArgs(kwargs, self)
         model = self.get_model()
         inputs = self.tokenizer(query, return_tensors="pt")
         inputs = inputs.to(model.device)
diff --git a/serving/model_handler/chatglm/infer.py b/serving/model_handler/chatglm/infer.py
index 4333591..b8ec92e 100644
--- a/serving/model_handler/chatglm/infer.py
+++ b/serving/model_handler/chatglm/infer.py
@@ -13,7 +13,7 @@
 from transformers import HfArgumentParser
 from aigc_zoo.model_zoo.chatglm.llm_model import MyTransformer, ChatGLMTokenizer, PetlArguments, setup_model_profile, \
     ChatGLMConfig,PetlModel
-from serving.model_handler.base import EngineAPI_Base,CompletionResult,LoraModelState, load_lora_config, GenerateProcess,WorkMode
+from serving.model_handler.base import EngineAPI_Base,CompletionResult,LoraModelState, load_lora_config, GenArgs,WorkMode
 from serving.prompt import *
 
 class NN_DataHelper(DataHelper):pass
@@ -148,12 +148,11 @@ def get_default_gen_args(self):
         return default_kwargs
 
     def chat_stream(self, messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self,is_stream=True)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self, is_stream=True)
         chunk = args_process.chunk
         default_kwargs=self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query,history = args_process.get_chat_info(messages)
         for response, history in self.model.stream_chat(self.tokenizer, query=query,history=history, **kwargs):
             chunk.step(response)
@@ -175,11 +174,10 @@ def chat_stream(self, messages: List[Dict], **kwargs):
             }, complete=False)
 
     def chat(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self)
         default_kwargs=self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query, history = args_process.get_chat_info(messages)
         response, history = self.model.chat(self.tokenizer, query=query,history=history, **default_kwargs)
         response = args_process.postprocess_response(response, **kwargs)
@@ -189,10 +187,10 @@ def chat(self,messages: List[Dict], **kwargs):
         })
 
     def generate(self,messages: List[Dict],**kwargs):
-        args_process = GenerateProcess(self)
+        args_process = GenArgs(kwargs, self)
         default_kwargs=self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query = args_process.get_chat_info(messages,chat_format="generate")
         output,_ = self.model.chat(self.tokenizer, query=query,**default_kwargs)
         output_scores = default_kwargs.get('output_scores', False)
@@ -205,6 +203,7 @@ def generate(self,messages: List[Dict],**kwargs):
         })
 
     def embedding(self, query, **kwargs):
+        args_process = GenArgs(kwargs, self)
         model = self.get_model()
         inputs = self.tokenizer(query, return_tensors="pt")
         inputs = inputs.to(model.device)
diff --git a/serving/model_handler/chatglm2/infer.py b/serving/model_handler/chatglm2/infer.py
index f4644f4..4867a81 100644
--- a/serving/model_handler/chatglm2/infer.py
+++ b/serving/model_handler/chatglm2/infer.py
@@ -13,7 +13,7 @@
 from transformers import HfArgumentParser
 from aigc_zoo.model_zoo.chatglm2.llm_model import MyTransformer, ChatGLMTokenizer, PetlArguments, \
     setup_model_profile, ChatGLMConfig
-from serving.model_handler.base import EngineAPI_Base,CompletionResult,LoraModelState, load_lora_config, GenerateProcess,WorkMode
+from serving.model_handler.base import EngineAPI_Base,CompletionResult,LoraModelState, load_lora_config, GenArgs,WorkMode
 from serving.prompt import *
 
 
@@ -152,12 +152,11 @@ def get_default_gen_args(self):
         return default_kwargs
 
     def chat_stream(self, messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self,is_stream=True)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self, is_stream=True)
         chunk = args_process.chunk
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query, history = args_process.get_chat_info(messages)
         for response, history in self.get_model().stream_chat(self.tokenizer, query=query,history=history, **default_kwargs):
             chunk.step(response)
@@ -178,11 +177,10 @@ def chat_stream(self, messages: List[Dict], **kwargs):
             }, complete=False)
 
     def chat(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query, history = args_process.get_chat_info(messages)
         response, history = self.model.chat(self.tokenizer, query=query,history=history, **default_kwargs)
         response = args_process.postprocess_response(response, **kwargs)
@@ -192,10 +190,10 @@ def chat(self,messages: List[Dict], **kwargs):
         })
 
     def generate(self,messages: List[Dict],**kwargs):
-        args_process = GenerateProcess(self)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query = args_process.get_chat_info(messages,chat_format="generate")
         output,_ = self.model.chat(self.tokenizer, query=query,**default_kwargs)
         output_scores = default_kwargs.get('output_scores', False)
@@ -208,6 +206,7 @@ def generate(self,messages: List[Dict],**kwargs):
         })
 
     def embedding(self, query, **kwargs):
+        args_process = GenArgs(kwargs, self)
         model = self.get_model()
         inputs = self.tokenizer(query, return_tensors="pt")
         inputs = inputs.to(model.device)
diff --git a/serving/model_handler/chatglm3/infer.py b/serving/model_handler/chatglm3/infer.py
index befe5c8..fed7fab 100644
--- a/serving/model_handler/chatglm3/infer.py
+++ b/serving/model_handler/chatglm3/infer.py
@@ -14,7 +14,7 @@
 from transformers import HfArgumentParser
 from aigc_zoo.model_zoo.chatglm3.llm_model import MyTransformer, ChatGLMTokenizer, PetlArguments, \
     setup_model_profile, ChatGLMConfig
-from serving.model_handler.base import EngineAPI_Base,CompletionResult,LoraModelState, load_lora_config, GenerateProcess,WorkMode
+from serving.model_handler.base import EngineAPI_Base,CompletionResult,LoraModelState, load_lora_config, GenArgs,WorkMode
 from serving.prompt import *
 
 
@@ -169,12 +169,11 @@ def get_default_gen_args(self):
         return default_kwargs
 
     def chat_stream(self, messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self,is_stream=True)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self, is_stream=True)
         chunk = args_process.chunk
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         role,query,history = _preprocess_messages_for_chatglm3(messages)
         for response, history in self.get_model().stream_chat(self.tokenizer, query=query,role=role,history=history,with_postprocess=False,**default_kwargs):
             chunk.step(response)
@@ -195,11 +194,10 @@ def chat_stream(self, messages: List[Dict], **kwargs):
             }, complete=False)
 
     def chat(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         role, query, history = _preprocess_messages_for_chatglm3(messages)
         response, history = self.model.chat(self.tokenizer, query=query,role=role,history=messages,with_postprocess=False,**default_kwargs)
         if isinstance(response,str):
@@ -213,10 +211,10 @@ def chat(self,messages: List[Dict], **kwargs):
         })
 
     def generate(self,messages: List[Dict],**kwargs):
-        args_process = GenerateProcess(self)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query = messages[0]["content"]
         output,_ = self.model.chat(self.tokenizer,query=query,with_postprocess=False, **default_kwargs)
         output_scores = default_kwargs.get('output_scores', False)
@@ -229,6 +227,7 @@ def generate(self,messages: List[Dict],**kwargs):
         })
 
     def embedding(self, query, **kwargs):
+        args_process = GenArgs(kwargs, self)
         model = self.get_model()
         inputs = self.tokenizer(query, return_tensors="pt")
         inputs = inputs.to(model.device)
diff --git a/serving/model_handler/internlm/infer.py b/serving/model_handler/internlm/infer.py
index 95caefa..35fd49a 100644
--- a/serving/model_handler/internlm/infer.py
+++ b/serving/model_handler/internlm/infer.py
@@ -15,7 +15,7 @@
 from aigc_zoo.model_zoo.internlm.llm_model import MyTransformer,InternLMConfig,InternLMTokenizer,\
     InternLMForCausalLM,PetlArguments,PetlModel
 from serving.model_handler.base import EngineAPI_Base, CompletionResult,LoraModelState, load_lora_config, \
-    GenerateProcess, WorkMode
+    GenArgs, WorkMode
 from serving.prompt import *
 
 class NN_DataHelper(DataHelper):pass
@@ -134,11 +134,10 @@ def get_default_gen_args(self):
         return default_kwargs
 
     def chat_stream(self, messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self,is_stream=True)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self, is_stream=True)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query, history = args_process.get_chat_info(messages)
         skip_word_list = [self.tokenizer.eos_token_id,2, 103028]
         streamer = args_process.get_streamer(skip_word_list)
@@ -148,11 +147,10 @@ def chat_stream(self, messages: List[Dict], **kwargs):
 
 
     def chat(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query, history = args_process.get_chat_info(messages)
         response, history = self.model.chat(self.tokenizer, query=query,history=history, **default_kwargs)
         response = args_process.postprocess_response(response, **kwargs)
@@ -162,10 +160,10 @@ def chat(self,messages: List[Dict], **kwargs):
         })
 
     def generate(self,messages: List[Dict],**kwargs):
-        args_process = GenerateProcess(self)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query = args_process.get_chat_info(messages,chat_format="generate")
         output,_ = self.model.chat(self.tokenizer, query=query **default_kwargs)
         output_scores = default_kwargs.get('output_scores', False)
@@ -178,6 +176,7 @@ def generate(self,messages: List[Dict],**kwargs):
         })
 
     def embedding(self, query, **kwargs):
+        args_process = GenArgs(kwargs, self)
         model = self.get_model()
         inputs = self.tokenizer(query, return_tensors="pt")
         inputs = inputs.to(model.device)
diff --git a/serving/model_handler/llama/infer.py b/serving/model_handler/llama/infer.py
index a34da1e..a847b06 100644
--- a/serving/model_handler/llama/infer.py
+++ b/serving/model_handler/llama/infer.py
@@ -17,7 +17,7 @@
 from deep_training.nlp.models.rellama.modeling_llama import LlamaForCausalLM
 from aigc_zoo.model_zoo.llm.llm_model import MyTransformer,PetlArguments,PetlModel,AutoConfig
 from aigc_zoo.generator_utils.generator_llm import Generate
-from serving.model_handler.base import EngineAPI_Base,CompletionResult, LoraModelState, load_lora_config, GenerateProcess,WorkMode,ChunkData
+from serving.model_handler.base import EngineAPI_Base,CompletionResult, LoraModelState, load_lora_config, GenArgs,WorkMode,ChunkData
 from serving.prompt import *
 
 class NN_DataHelper(DataHelper):pass
@@ -152,11 +152,10 @@ def get_default_gen_args(self):
         return default_kwargs
 
     def chat_stream(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self,is_stream=True)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self, is_stream=True)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         prefix,query, history = args_process.get_chat_info_with_system(messages)
         if self.is_openbuddy:
             prompt = get_chat_openbuddy(self.tokenizer, query, history=history, prefix=prefix)
@@ -176,11 +175,10 @@ def chat_stream(self,messages: List[Dict], **kwargs):
 
 
     def chat(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         prefix,query, history = args_process.get_chat_info_with_system(messages)
         if self.is_openbuddy:
             prompt = get_chat_openbuddy(self.tokenizer, query, history=history, prefix=prefix)
@@ -199,10 +197,10 @@ def chat(self,messages: List[Dict], **kwargs):
 
 
     def generate(self,messages: List[Dict],**kwargs):
-        args_process = GenerateProcess(self)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query = args_process.get_chat_info(messages,chat_format="generate")
         response = self.gen_core.generate(query=query, **default_kwargs)
         return CompletionResult(result={
@@ -211,6 +209,7 @@ def generate(self,messages: List[Dict],**kwargs):
         })
 
     def embedding(self, query, **kwargs):
+        args_process = GenArgs(kwargs, self)
         model = self.get_model()
         inputs = self.tokenizer(query, return_tensors="pt")
         inputs = inputs.to(model.device)
diff --git a/serving/model_handler/llm/infer.py b/serving/model_handler/llm/infer.py
index 9e8a86f..f0de39a 100644
--- a/serving/model_handler/llm/infer.py
+++ b/serving/model_handler/llm/infer.py
@@ -14,7 +14,7 @@
 from aigc_zoo.model_zoo.llm.llm_model import MyTransformer,PetlArguments,PetlModel,AutoConfig
 from aigc_zoo.generator_utils.generator_llm import Generate
 from serving.model_handler.base import EngineAPI_Base, CompletionResult,LoraModelState, load_lora_config, \
-    GenerateProcess, WorkMode
+    GenArgs, WorkMode
 from serving.prompt import *
 
 
@@ -116,11 +116,10 @@ def get_default_gen_args(self):
         return default_kwargs
 
     def chat_stream(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self,is_stream=True)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self, is_stream=True)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         prefix,query, history = args_process.get_chat_info_with_system(messages)
         prompt = get_chat_default(self.tokenizer, query, history=history, prefix=prefix)
         skip_word_list = default_kwargs.get('eos_token_id', None) or [self.tokenizer.eos_token_id]
@@ -131,11 +130,10 @@ def chat_stream(self,messages: List[Dict], **kwargs):
 
 
     def chat(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         prefix,query, history = args_process.get_chat_info_with_system(messages)
         prompt = get_chat_default(self.tokenizer, query, history=history, prefix=prefix)
         response = self.gen_core.generate(query=prompt, **default_kwargs)
@@ -149,6 +147,7 @@ def chat(self,messages: List[Dict], **kwargs):
 
 
     def embedding(self, query, **kwargs):
+        args_process = GenArgs(kwargs, self)
         model = self.get_model()
         inputs = self.tokenizer(query, return_tensors="pt")
         inputs = inputs.to(model.device)
diff --git a/serving/model_handler/moss/infer.py b/serving/model_handler/moss/infer.py
index 2d5d80f..6bf3842 100644
--- a/serving/model_handler/moss/infer.py
+++ b/serving/model_handler/moss/infer.py
@@ -14,7 +14,7 @@
 from aigc_zoo.model_zoo.moss.llm_model import MyTransformer,MossConfig,MossTokenizer,PetlArguments,PetlModel
 from aigc_zoo.generator_utils.generator_moss import Generate
 from serving.model_handler.base import EngineAPI_Base, CompletionResult, CompletionResult, LoraModelState, \
-    load_lora_config, GenerateProcess, WorkMode
+    load_lora_config, GenArgs, WorkMode
 from serving.prompt import *
 
 
@@ -126,11 +126,10 @@ def get_default_gen_args(self):
         return default_kwargs
 
     def chat_stream(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self,is_stream=True)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self, is_stream=True)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query, history = args_process.get_chat_info(messages)
         skip_word_list = default_kwargs.get('eos_token_id', None) or [self.tokenizer.eos_token_id]
         streamer = args_process.get_streamer(skip_word_list)
@@ -140,11 +139,10 @@ def chat_stream(self,messages: List[Dict], **kwargs):
 
 
     def chat(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query, history = args_process.get_chat_info(messages)
         response,history = self.gen_core.chat(query=query, history=history, **default_kwargs)
         response = args_process.postprocess_response(response, **kwargs)
@@ -155,10 +153,10 @@ def chat(self,messages: List[Dict], **kwargs):
 
 
     def generate(self,messages: List[Dict],**kwargs):
-        args_process = GenerateProcess(self)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query = args_process.get_chat_info(messages,chat_format="generate")
         response = self.model.generate(query=query, **kwargs)
         return CompletionResult(result={
@@ -167,6 +165,7 @@ def generate(self,messages: List[Dict],**kwargs):
         })
 
     def embedding(self, query, **kwargs):
+        args_process = GenArgs(kwargs, self)
         model = self.get_model()
         inputs = self.tokenizer(query, return_tensors="pt")
         inputs = inputs.to(model.device)
diff --git a/serving/model_handler/qwen/infer.py b/serving/model_handler/qwen/infer.py
index db84170..e805e84 100644
--- a/serving/model_handler/qwen/infer.py
+++ b/serving/model_handler/qwen/infer.py
@@ -12,7 +12,7 @@
 from transformers import HfArgumentParser, BitsAndBytesConfig
 from aigc_zoo.model_zoo.qwen.llm_model import MyTransformer, QWenTokenizer, PetlArguments, \
     setup_model_profile, QWenConfig
-from serving.model_handler.base import EngineAPI_Base,CompletionResult,LoraModelState, load_lora_config, GenerateProcess,WorkMode
+from serving.model_handler.base import EngineAPI_Base,CompletionResult,LoraModelState, load_lora_config, GenArgs,WorkMode
 from serving.prompt import *
 
 
@@ -139,11 +139,10 @@ def get_default_gen_args(self):
         return default_kwargs
 
     def chat_stream(self, messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self,is_stream=True)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self, is_stream=True)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query, history = args_process.get_chat_info(messages)
         skip_word_list = [self.tokenizer.im_end_id, self.tokenizer.im_start_id, self.tokenizer.eos_token_id or 151643]
         skip_word_list += default_kwargs.get('stop_words_ids', [])
@@ -155,11 +154,10 @@ def chat_stream(self, messages: List[Dict], **kwargs):
 
 
     def chat(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query, history = args_process.get_chat_info(messages)
         response, history = self.model.chat(self.tokenizer, query=query,history=history, **default_kwargs)
         response = args_process.postprocess_response(response, **kwargs)
@@ -169,10 +167,10 @@ def chat(self,messages: List[Dict], **kwargs):
         })
 
     def generate(self,messages: List[Dict],**kwargs):
-        args_process = GenerateProcess(self)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query = args_process.get_chat_info(messages,chat_format="generate")
         output = self.model.chat(self.tokenizer, query=query,**default_kwargs)
         output_scores = default_kwargs.get('output_scores', False)
@@ -185,6 +183,7 @@ def generate(self,messages: List[Dict],**kwargs):
         })
 
     def embedding(self, query, **kwargs):
+        args_process = GenArgs(kwargs, self)
         from deep_training.nlp.models.qwen.modeling_qwen import QWenLMHeadModel
         model: QWenLMHeadModel = self.get_model()
         inputs = self.tokenizer(query, return_tensors="pt")
diff --git a/serving/model_handler/rwkv/infer.py b/serving/model_handler/rwkv/infer.py
index 4b459ce..c7dd671 100644
--- a/serving/model_handler/rwkv/infer.py
+++ b/serving/model_handler/rwkv/infer.py
@@ -14,7 +14,7 @@
 from aigc_zoo.model_zoo.rwkv4.llm_model import MyTransformer, RwkvConfig, \
     set_model_profile,PetlArguments,PetlModel
 from aigc_zoo.utils.rwkv4_generate import Generate
-from serving.model_handler.base import EngineAPI_Base,CompletionResult,LoraModelState, load_lora_config, GenerateProcess,WorkMode
+from serving.model_handler.base import EngineAPI_Base,CompletionResult,LoraModelState, load_lora_config, GenArgs,WorkMode
 from serving.prompt import *
 
 
@@ -122,11 +122,10 @@ def get_default_gen_args(self):
 
 
     def chat_stream(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self,is_stream=True)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self, is_stream=True)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query, history = args_process.get_chat_info(messages)
         prompt = get_chat_default(self.tokenizer, query, history)
         skip_word_list = default_kwargs.get('eos_token_id', None) or [self.tokenizer.eos_token_id]
@@ -136,11 +135,10 @@ def chat_stream(self,messages: List[Dict], **kwargs):
         return None
 
     def chat(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query, history = args_process.get_chat_info(messages)
         prompt = get_chat_default(self.tokenizer, query, history)
         response = Generate.generate(self.get_model(),
@@ -154,10 +152,10 @@ def chat(self,messages: List[Dict], **kwargs):
 
 
     def generate(self,messages: List[Dict],**kwargs):
-        args_process = GenerateProcess(self)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query = args_process.get_chat_info(messages,chat_format="generate")
         response = Generate.generate(self.get_model(),
                                      tokenizer=self.tokenizer,
@@ -168,6 +166,7 @@ def generate(self,messages: List[Dict],**kwargs):
         })
 
     def embedding(self, query, **kwargs):
+        args_process = GenArgs(kwargs, self)
         model = self.get_model()
         inputs = self.tokenizer(query, return_tensors="pt")
         inputs = inputs.to(model.device)
diff --git a/serving/model_handler/skywork/infer.py b/serving/model_handler/skywork/infer.py
index b791254..cd62be7 100644
--- a/serving/model_handler/skywork/infer.py
+++ b/serving/model_handler/skywork/infer.py
@@ -17,7 +17,7 @@
 from aigc_zoo.model_zoo.skywork.llm_model import MySkyworkForCausalLM,SkyworkConfig,SkyworkTokenizer
 from aigc_zoo.model_zoo.llm.llm_model import MyTransformer,PetlArguments,PetlModel,AutoConfig
 from aigc_zoo.generator_utils.generator_llm import Generate
-from serving.model_handler.base import EngineAPI_Base,CompletionResult, LoraModelState, load_lora_config, GenerateProcess,WorkMode,ChunkData
+from serving.model_handler.base import EngineAPI_Base,CompletionResult, LoraModelState, load_lora_config, GenArgs,WorkMode,ChunkData
 from serving.prompt import *
 
 class NN_DataHelper(DataHelper):pass
@@ -153,11 +153,10 @@ def get_default_gen_args(self):
         return default_kwargs
 
     def chat_stream(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self,is_stream=True)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self, is_stream=True)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         prefix,query, history = args_process.get_chat_info_with_system(messages)
         prompt = get_chat_skywork(self.tokenizer, query, history=history, prefix=prefix)
         skip_word_list = default_kwargs.get('eos_token_id', None) or [self.tokenizer.eos_token_id]
@@ -170,11 +169,10 @@ def chat_stream(self,messages: List[Dict], **kwargs):
 
 
     def chat(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         prefix,query, history = args_process.get_chat_info_with_system(messages)
         prompt = get_chat_skywork(self.tokenizer, query, history=history, prefix=prefix)
         response = self.gen_core.generate(query=prompt, **default_kwargs)
@@ -186,10 +184,10 @@ def chat(self,messages: List[Dict], **kwargs):
 
 
     def generate(self,messages: List[Dict],**kwargs):
-        args_process = GenerateProcess(self)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query = args_process.get_chat_info(messages,chat_format="generate")
         response = self.gen_core.generate(query=query, **default_kwargs)
         return CompletionResult(result={
@@ -198,6 +196,7 @@ def generate(self,messages: List[Dict],**kwargs):
         })
 
     def embedding(self, query, **kwargs):
+        args_process = GenArgs(kwargs, self)
         model = self.get_model()
         inputs = self.tokenizer(query, return_tensors="pt")
         inputs = inputs.to(model.device)
diff --git a/serving/model_handler/t5/infer.py b/serving/model_handler/t5/infer.py
index 432531d..7924de9 100644
--- a/serving/model_handler/t5/infer.py
+++ b/serving/model_handler/t5/infer.py
@@ -14,7 +14,7 @@
 from aigc_zoo.model_zoo.t5.llm_model import MyTransformer,PetlArguments,PetlModel,AutoConfig
 from aigc_zoo.generator_utils.generator_llm import Generate
 from serving.model_handler.base import EngineAPI_Base, CompletionResult,LoraModelState, load_lora_config, \
-    GenerateProcess, WorkMode
+    GenArgs, WorkMode
 from serving.prompt import *
 
 
@@ -143,11 +143,10 @@ def get_default_gen_args(self):
         return default_kwargs
 
     def chat_stream(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self,is_stream=True)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self, is_stream=True)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query, history = args_process.get_chat_info(messages)
         prompt = get_chat_chatyaun(self.tokenizer, query, history)
         skip_word_list = default_kwargs.get('eos_token_id', None) or [self.tokenizer.eos_token_id]
@@ -158,11 +157,10 @@ def chat_stream(self,messages: List[Dict], **kwargs):
 
 
     def chat(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query, history = args_process.get_chat_info(messages)
         prompt = get_chat_chatyaun(self.tokenizer, query, history)
         response = self.gen_core.generate(prompt, **default_kwargs)
@@ -175,10 +173,10 @@ def chat(self,messages: List[Dict], **kwargs):
         })
 
     def generate(self, messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query = args_process.get_chat_info(messages, chat_format="generate")
         response = Generate.generate(self.get_model(),
                                      tokenizer=self.tokenizer,
@@ -191,6 +189,7 @@ def generate(self, messages: List[Dict], **kwargs):
 
 
     def embedding(self, query, **kwargs):
+        args_process = GenArgs(kwargs, self)
         model = self.get_model()
         inputs = self.tokenizer(query, return_tensors="pt")
         inputs = inputs.to(model.device)
diff --git a/serving/model_handler/xverse/infer.py b/serving/model_handler/xverse/infer.py
index ff1d349..9c2436a 100644
--- a/serving/model_handler/xverse/infer.py
+++ b/serving/model_handler/xverse/infer.py
@@ -13,7 +13,7 @@
 from deep_training.nlp.layers.rope_scale.patch import RotaryNtkScaledArguments
 from transformers import HfArgumentParser, GenerationConfig
 from aigc_zoo.utils.xverse_generate import Generate
-from serving.model_handler.base import EngineAPI_Base,CompletionResult,LoraModelState, load_lora_config, GenerateProcess,WorkMode
+from serving.model_handler.base import EngineAPI_Base,CompletionResult,LoraModelState, load_lora_config, GenArgs,WorkMode
 from transformers import AutoModelForCausalLM
 from deep_training.utils.hf import register_transformer_model, register_transformer_config  # noqa
 # from deep_training.nlp.models.xverse.modeling_xverse import XverseForCausalLM, XverseConfig
@@ -142,11 +142,10 @@ def get_default_gen_args(self):
         return default_kwargs
 
     def chat_stream(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self,is_stream=True)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self, is_stream=True)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
 
         stopping_criteria = default_kwargs.pop('stopping_criteria',None)
         generation_config = GenerationConfig(**default_kwargs)
@@ -164,11 +163,10 @@ def chat_stream(self,messages: List[Dict], **kwargs):
 
 
     def chat(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         stopping_criteria = default_kwargs.pop('stopping_criteria', None)
         generation_config = GenerationConfig(**default_kwargs)
         response = self.get_model().chat(tokenizer=self.tokenizer,
@@ -184,14 +182,14 @@ def chat(self,messages: List[Dict], **kwargs):
 
 
     def generate(self,messages: List[Dict],**kwargs):
-        args_process = GenerateProcess(self)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = dict(
             eos_token_id=self.config.eos_token_id,
             pad_token_id=self.config.eos_token_id,
             do_sample=True, top_p=0.7, temperature=0.95,
         )
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query = args_process.get_chat_info(messages, chat_format="generate")
         response = Generate.generate(self.get_model(),
                                      tokenizer=self.tokenizer,
@@ -202,6 +200,7 @@ def generate(self,messages: List[Dict],**kwargs):
         })
 
     def embedding(self, query, **kwargs):
+        args_process = GenArgs(kwargs, self)
         model = self.get_model()
         inputs = self.tokenizer(query, return_tensors="pt")
         inputs = inputs.to(model.device)
diff --git a/serving/model_handler/yi/infer.py b/serving/model_handler/yi/infer.py
index 4240c73..bbae135 100644
--- a/serving/model_handler/yi/infer.py
+++ b/serving/model_handler/yi/infer.py
@@ -17,7 +17,7 @@
 from aigc_zoo.model_zoo.yi.llm_model import MyYiForCausalLM,YiConfig,YiTokenizer
 from aigc_zoo.model_zoo.llm.llm_model import MyTransformer,PetlArguments,PetlModel,AutoConfig
 from aigc_zoo.generator_utils.generator_llm import Generate
-from serving.model_handler.base import EngineAPI_Base,CompletionResult, LoraModelState, load_lora_config, GenerateProcess,WorkMode,ChunkData
+from serving.model_handler.base import EngineAPI_Base,CompletionResult, LoraModelState, load_lora_config, GenArgs,WorkMode,ChunkData
 from serving.prompt import *
 
 class NN_DataHelper(DataHelper):pass
@@ -153,11 +153,10 @@ def get_default_gen_args(self):
         return default_kwargs
 
     def chat_stream(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self,is_stream=True)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self, is_stream=True)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         prefix,query, history = args_process.get_chat_info_with_system(messages)
         # 模板
         prompt = get_chat_yi(self.tokenizer, query, history=history, prefix=prefix)
@@ -171,11 +170,10 @@ def chat_stream(self,messages: List[Dict], **kwargs):
 
 
     def chat(self,messages: List[Dict], **kwargs):
-        args_process = GenerateProcess(self)
-        args_process.preprocess(kwargs)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         prefix,query, history = args_process.get_chat_info_with_system(messages)
         # 模板
         prompt = get_chat_yi(self.tokenizer, query, history=history, prefix=prefix)
@@ -188,10 +186,10 @@ def chat(self,messages: List[Dict], **kwargs):
 
 
     def generate(self,messages: List[Dict],**kwargs):
-        args_process = GenerateProcess(self)
+        args_process = GenArgs(kwargs, self)
         default_kwargs = self.get_default_gen_args()
         default_kwargs.update(kwargs)
-        args_process.postprocess(default_kwargs)
+        args_process.build_args(default_kwargs)
         query = args_process.get_chat_info(messages,chat_format="generate")
         response = self.gen_core.generate(query=query, **default_kwargs)
         return CompletionResult(result={
@@ -200,6 +198,7 @@ def generate(self,messages: List[Dict],**kwargs):
         })
 
     def embedding(self, query, **kwargs):
+        args_process = GenArgs(kwargs, self)
         model = self.get_model()
         inputs = self.tokenizer(query, return_tensors="pt")
         inputs = inputs.to(model.device)
diff --git a/serving/openai_api/custom.py b/serving/openai_api/custom.py
index a98099c..3bef583 100644
--- a/serving/openai_api/custom.py
+++ b/serving/openai_api/custom.py
@@ -10,6 +10,11 @@
 ]
 
 
+class Tools(BaseModel):
+    type: Optional[str] = "function"
+    function: Optional[Dict] = None
+
+
 class CustomChatParams(BaseModel):
     model: str
     temperature: Optional[float] = 0.7
@@ -43,8 +48,18 @@ class CustomChatParams(BaseModel):
     forced_eos_token_id: Optional[int] = None
     guidance_scale: Optional[float] = None
     low_memory: Optional[bool] = None
+
+    # Deprecated
     functions: Optional[List[Dict[str, Any]]] = None
-    function_call: Union[str, Dict[str, str]] = "auto"
+    # Deprecated
+    function_call: Optional[Union[str, Dict[str, str]]] = "auto"
+
+    seed: Optional[int] = None
+
+    # 取代 functions
+    tools: Optional[List[Tools]] = None
+    # 取代 function_call
+    tool_choice: Optional[Union[str, Dict[str, str]]] = "auto"
 
 #     class Config:
 #         underscore_attrs_are_private = True
@@ -77,6 +92,7 @@ def _update_params(self, r):
             "forced_eos_token_id": self.forced_eos_token_id,
             "guidance_scale": self.guidance_scale,
             "low_memory": self.low_memory,
+            "seed": self.seed,
         }
         if self.frequency_penalty is not None and self.frequency_penalty > 0:
             params["repetition_penalty"] = self.frequency_penalty
diff --git a/serving/openai_api/openai_api_protocol.py b/serving/openai_api/openai_api_protocol.py
index 74e542e..8134ee5 100644
--- a/serving/openai_api/openai_api_protocol.py
+++ b/serving/openai_api/openai_api_protocol.py
@@ -83,7 +83,7 @@ class ChatCompletionRequest(CustomChatParams):
     messages: List[ChatMessage]
 
     def build_messages(self):
-        messages = [message.dict() for message in self.messages]
+        messages = [message.model_dump() for message in self.messages]
         assert self.messages[-1].role in [Role.USER,Role.OBSERVATION]
         return [messages]
 
diff --git a/serving/serve/api_react.py b/serving/serve/api_react.py
index b361f13..7bbfc26 100644
--- a/serving/serve/api_react.py
+++ b/serving/serve/api_react.py
@@ -19,6 +19,12 @@ def build_react_functions(request: Union[CompletionRequest,ChatCompletionRequest
 
     function_call = request.function_call
     functions = request.functions
+    tools = request.tools
+    tool_choice = request.tool_choice
+    if tools is not None and tool_choice is not None:
+        functions = [_.function for _ in tools if _.function]
+        function_call = tool_choice
+
     messages = request.messages
     use_function = False
     if isinstance(messages, list) and isinstance(messages[0], dict):
@@ -32,7 +38,6 @@ def build_react_functions(request: Union[CompletionRequest,ChatCompletionRequest
             request.messages, functions = get_react_prompt_for_qwen(messages, functions, function_call)
         elif model_type in ["chatglm","chatglm3"]:
             request.messages, functions = get_react_prompt_for_chatglm3(messages, functions, function_call)
-
     return functions
 
 
diff --git a/tests/test_openai.py b/tests/test_openai.py
index 48dd4d7..50a85ca 100644
--- a/tests/test_openai.py
+++ b/tests/test_openai.py
@@ -25,6 +25,7 @@
     "n": 1, # 返回 n 个choices
     "max_tokens": 512,
     # "stop": ["Observation:","Observation:\n"]
+    "seed": None,
 }
 
 
diff --git a/tests/test_openai_chat.py b/tests/test_openai_chat.py
index 67a4038..be49626 100644
--- a/tests/test_openai_chat.py
+++ b/tests/test_openai_chat.py
@@ -24,7 +24,9 @@
     "nchar": 1,# stream 字符
     "n": 1, # 返回 n 个choices
     "max_tokens": 512,
-    "stop": ["Observation:"]
+    "stop": ["Observation:"],
+    "seed": None,
+    # "seed": "46",
 }
 
 
diff --git a/tests/web_demo.py b/tests/web_demo.py
index e2235f0..4b7c019 100644
--- a/tests/web_demo.py
+++ b/tests/web_demo.py
@@ -38,7 +38,7 @@ def postprocess(self, y):
     return y
 
 
-gr.Chatbot.postprocess = postprocess
+gr.Chatbot.build_args = postprocess
 
 def parse_text(text):
     """copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/"""