diff --git a/modelscope/models/nlp/plug/distributed_plug.py b/modelscope/models/nlp/plug/distributed_plug.py
index edfe5a4d..43501650 100644
--- a/modelscope/models/nlp/plug/distributed_plug.py
+++ b/modelscope/models/nlp/plug/distributed_plug.py
@@ -42,7 +42,7 @@ class DistributedPlug(TorchModel):
                 mpu.get_model_parallel_rank(),
                 sum([p.nelement() for p in model.parameters()])))
 
-        if self.config.deepspeed and self.args.fp16:
+        if self.config.deepspeed and self.config.fp16:
             model.half()
 
         # GPU allocation.
diff --git a/modelscope/models/nlp/plug/plug_for_text_generation.py b/modelscope/models/nlp/plug/plug_for_text_generation.py
index 5dc3bbe0..35565cc9 100644
--- a/modelscope/models/nlp/plug/plug_for_text_generation.py
+++ b/modelscope/models/nlp/plug/plug_for_text_generation.py
@@ -22,18 +22,18 @@ class PlugForTextGeneration(DistributedTorchModel):
         self.cls_token_id = cls_token_id
 
     def _forward_one(self, input: Dict[str, Any]) -> Dict[str, Tensor]:
-        return self.model(**input)
+        return self.__class__.model(input)
 
     def generate(self, input: Dict[str, Tensor]) -> Dict[str, Tensor]:
         batch_size = input['input_ids'].shape[0]
         dec_input_ids = torch.full([batch_size, 1], self.cls_token_id, dtype=torch.long)
         input["dec_input_ids"] = dec_input_ids
-        res = self.model_pool.map(DistributedPlug.forward, [input]*self.world_size)
-        return res[0]
+        res = self.forward(input)
+        return res
 
     def _instantiate_one(self, rank, model_dir):
         cfg = read_config(model_dir)
-        self.model = DistributedPlug(model_dir, rank, **cfg.model)
+        self.__class__.model = DistributedPlug(model_dir, rank, **cfg.model)
 
         
 
diff --git a/modelscope/models/nlp/utils/distributed.py b/modelscope/models/nlp/utils/distributed.py
index d4302941..c934dddf 100644
--- a/modelscope/models/nlp/utils/distributed.py
+++ b/modelscope/models/nlp/utils/distributed.py
@@ -52,8 +52,8 @@ def initialize_distributed(rank, mpu, world_size, model_parallel_size):
     init_method = 'tcp://'
     master_ip = os.getenv('MASTER_ADDR', '127.0.0.1')
     master_port = os.getenv('MASTER_PORT', '29500')
-    if not _is_free_port(int(master_port)):
-        master_port = str(_find_free_port())
+    # if not _is_free_port(int(master_port)):
+    #    master_port = str(_find_free_port())
     init_method += master_ip + ':' + master_port
     init_dist('pytorch', world_size=world_size, rank=rank, init_method=init_method)
     # Set the model-parallel communicators.