diff --git a/docs/config_guide_cn.md b/docs/config_guide_cn.md
index c2fe2356..fe88d599 100644
--- a/docs/config_guide_cn.md
+++ b/docs/config_guide_cn.md
@@ -37,16 +37,16 @@ source = [HuggingFace, OpenAI, DashScope]
 
 目前, pai_rag 支持以上三种 embedding 源.
 
-如果 source = "HuggingFace", 您需要进一步指定 model_name 和 embed_batch_size。默认的模型名称和批处理大小分别为 bge-large-zh-v1.5 和 10。
+如果 source = "HuggingFace", 您需要进一步指定 model 和 embed_batch_size。默认的模型名称和批处理大小分别为 bge-large-zh-v1.5 和 10。
 
     source = "HuggingFace"
-    model_name = "bge-large-zh-v1.5"
+    model = "bge-large-zh-v1.5"
     embed_batch_size = 10
 
 或者, 如果你想使用其它 huggingface 模型, 请指定如下参数：
 
     source = "HuggingFace"
-    model_name = "xxx"
+    model = "xxx"
     model_dir = "xxx/xxx"
     embed_batch_size = 20 (for example)
 
diff --git a/docs/config_guide_en.md b/docs/config_guide_en.md
index e528903d..e3adabb2 100644
--- a/docs/config_guide_en.md
+++ b/docs/config_guide_en.md
@@ -37,16 +37,16 @@ source = [HuggingFace, OpenAI, DashScope]
 
 Currently, pai_rag supports three embedding sources.
 
-If source = "HuggingFace", you need to further specify model_name and embed_batch_size. The default model name and batch size are bge-large-zh-v1.5 and 10, respectively.
+If source = "HuggingFace", you need to further specify model_modelname and embed_batch_size. The default model name and batch size are bge-large-zh-v1.5 and 10, respectively.
 
     source = "HuggingFace"
-    model_name = "bge-large-zh-v1.5"
+    model = "bge-large-zh-v1.5"
     embed_batch_size = 10
 
 Alternatively, if you want to use other huggingface models, please specify parameters as below:
 
     source = "HuggingFace"
-    model_name = "xxx"
+    model = "xxx"
     model_dir = "xxx/xxx"
     embed_batch_size = 20 (for example)
 
diff --git a/example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/settings.toml b/example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/settings.toml
index aafa4b8b..4095d476 100644
--- a/example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/settings.toml
+++ b/example_data/function_tools/api-tool-with-intent-detection-for-travel-assistant/settings.toml
@@ -26,10 +26,10 @@ persist_path = "localdata/storage"
 type = "SimpleDirectoryReader"
 
 # embedding configurations, source support API: OpenAI,DashScope; and local model:HuggingFace
-# if use API, need set OPENAI_API_KEY or DASHSCOPE_API_KEY in ENV, If HuggingFace, need set model_name
+# if use API, need set OPENAI_API_KEY or DASHSCOPE_API_KEY in ENV, If HuggingFace, need set model
 # eg.
 # source = "HuggingFace"
-# model_name = "bge-large-zh-v1.5"
+# model = "bge-large-zh-v1.5"
 # embed_batch_size = 10
 [rag.embedding]
 source = "DashScope"
@@ -49,11 +49,11 @@ vector_store.type = "FAISS"
 # token = ""
 [rag.llm]
 source = "DashScope"
-name = "qwen-turbo"
+model = "qwen-turbo"
 
 [rag.llm.function_calling_llm]
 source = "DashScope"
-name = "qwen2-7b-instruct"
+model = "qwen2-7b-instruct"
 
 [rag.llm.multi_modal]
 source = ""
diff --git a/example_data/function_tools/python-tool-for-booking-demo/settings.toml b/example_data/function_tools/python-tool-for-booking-demo/settings.toml
index 1da5d480..2cb65635 100644
--- a/example_data/function_tools/python-tool-for-booking-demo/settings.toml
+++ b/example_data/function_tools/python-tool-for-booking-demo/settings.toml
@@ -26,10 +26,10 @@ persist_path = "localdata/storage"
 type = "SimpleDirectoryReader"
 
 # embedding configurations, source support API: OpenAI,DashScope; and local model:HuggingFace
-# if use API, need set OPENAI_API_KEY or DASHSCOPE_API_KEY in ENV, If HuggingFace, need set model_name
+# if use API, need set OPENAI_API_KEY or DASHSCOPE_API_KEY in ENV, If HuggingFace, need set model
 # eg.
 # source = "HuggingFace"
-# model_name = "bge-large-zh-v1.5"
+# model = "bge-large-zh-v1.5"
 # embed_batch_size = 10
 [rag.embedding]
 source = "DashScope"
@@ -49,11 +49,11 @@ vector_store.type = "FAISS"
 # token = ""
 [rag.llm]
 source = "DashScope"
-name = "qwen-turbo"
+model = "qwen-turbo"
 
 [rag.llm.function_calling_llm]
 source = "DashScope"
-name = "qwen2-7b-instruct"
+model = "qwen2-7b-instruct"
 
 [rag.llm.multi_modal]
 source = ""
diff --git a/src/pai_rag/app/web/rag_client.py b/src/pai_rag/app/web/rag_client.py
index 47802aec..5976e2f4 100644
--- a/src/pai_rag/app/web/rag_client.py
+++ b/src/pai_rag/app/web/rag_client.py
@@ -415,7 +415,6 @@ def patch_config(self, update_dict: Any):
         view_model: ViewModel = ViewModel.from_app_config(config)
         view_model.update(update_dict)
         new_config = view_model.to_app_config()
-
         r = requests.patch(
             self.config_url, json=new_config, timeout=DEFAULT_CLIENT_TIME_OUT
         )
diff --git a/src/pai_rag/app/web/view_model.py b/src/pai_rag/app/web/view_model.py
index 312caeb2..26e274f0 100644
--- a/src/pai_rag/app/web/view_model.py
+++ b/src/pai_rag/app/web/view_model.py
@@ -47,7 +47,7 @@ class ViewModel(BaseModel):
     llm: str = "PaiEas"
     llm_eas_url: str = None
     llm_eas_token: str = None
-    llm_eas_model_name: str = "model_name"
+    llm_eas_model_name: str = "model"
     llm_api_key: str = None
     llm_api_model_name: str = None
     llm_temperature: float = 0.1
@@ -57,7 +57,7 @@ class ViewModel(BaseModel):
     mllm: str = None
     mllm_eas_url: str = None
     mllm_eas_token: str = None
-    mllm_eas_model_name: str = "model_name"
+    mllm_eas_model_name: str = "model"
     mllm_api_key: str = None
     mllm_api_model_name: str = None
 
@@ -189,7 +189,7 @@ def from_app_config(config):
             "source", view_model.embed_source
         )
         view_model.embed_model = config["embedding"].get(
-            "model_name", view_model.embed_model
+            "model", view_model.embed_model
         )
         view_model.embed_api_key = config["embedding"].get(
             "api_key", view_model.embed_api_key
@@ -207,11 +207,11 @@ def from_app_config(config):
         )
         if view_model.llm.lower() == "paieas":
             view_model.llm_eas_model_name = config["llm"].get(
-                "model_name", view_model.llm_eas_model_name
+                "model", view_model.llm_eas_model_name
             )
         else:
             view_model.llm_api_model_name = config["llm"].get(
-                "model_name", view_model.llm_api_model_name
+                "model", view_model.llm_api_model_name
             )
 
         view_model.use_mllm = config["llm"]["multi_modal"].get(
@@ -233,14 +233,14 @@ def from_app_config(config):
                 "view_model.mllm_eas_model_name",
                 view_model.mllm_eas_model_name,
                 "2",
-                config["llm"]["multi_modal"]["model_name"],
+                config["llm"]["multi_modal"]["model"],
             )
             view_model.mllm_eas_model_name = config["llm"]["multi_modal"].get(
-                "model_name", view_model.mllm_eas_model_name
+                "model", view_model.mllm_eas_model_name
             )
         else:
             view_model.mllm_api_model_name = config["llm"]["multi_modal"].get(
-                "model_name", view_model.mllm_api_model_name
+                "model", view_model.mllm_api_model_name
             )
 
         view_model.use_oss = config["oss_store"].get("enable", view_model.use_oss)
@@ -436,7 +436,7 @@ def to_app_config(self):
         config = recursive_dict()
 
         config["embedding"]["source"] = self.embed_source
-        config["embedding"]["model_name"] = self.embed_model
+        config["embedding"]["model"] = self.embed_model
         config["embedding"]["api_key"] = self.embed_api_key
         config["embedding"]["embed_batch_size"] = int(self.embed_batch_size)
 
@@ -446,9 +446,9 @@ def to_app_config(self):
         config["llm"]["api_key"] = self.llm_api_key
         config["llm"]["temperature"] = self.llm_temperature
         if self.llm.lower() == "paieas":
-            config["llm"]["model_name"] = self.llm_eas_model_name
+            config["llm"]["model"] = self.llm_eas_model_name
         else:
-            config["llm"]["model_name"] = self.llm_api_model_name
+            config["llm"]["model"] = self.llm_api_model_name
 
         config["llm"]["multi_modal"]["enable"] = self.use_mllm
         config["llm"]["multi_modal"]["source"] = self.mllm
@@ -456,9 +456,9 @@ def to_app_config(self):
         config["llm"]["multi_modal"]["token"] = self.mllm_eas_token
         config["llm"]["multi_modal"]["api_key"] = self.mllm_api_key
         if self.mllm.lower() == "paieas":
-            config["llm"]["multi_modal"]["model_name"] = self.mllm_eas_model_name
+            config["llm"]["multi_modal"]["model"] = self.mllm_eas_model_name
         else:
-            config["llm"]["multi_modal"]["model_name"] = self.mllm_api_model_name
+            config["llm"]["multi_modal"]["model"] = self.mllm_api_model_name
 
         config["oss_store"]["enable"] = self.use_oss
         if os.getenv("OSS_ACCESS_KEY_ID") is None and self.oss_ak:
@@ -728,7 +728,7 @@ def to_component_settings(self) -> Dict[str, Dict[str, Any]]:
             "visible": self.llm.lower() == "paieas",
         }
         if self.llm.lower() == "paieas" and not self.llm_eas_model_name:
-            self.llm_eas_model_name = "model_name"
+            self.llm_eas_model_name = "model"
 
         settings["llm_eas_model_name"] = {
             "value": self.llm_eas_model_name,
diff --git a/src/pai_rag/config/settings.toml b/src/pai_rag/config/settings.toml
index 75e87d15..07a7054f 100644
--- a/src/pai_rag/config/settings.toml
+++ b/src/pai_rag/config/settings.toml
@@ -33,10 +33,10 @@ type = "local"
 type = "SimpleDirectoryReader"
 
 # embedding configurations, source support API: OpenAI,DashScope; and local model:HuggingFace
-# if use API, need set OPENAI_API_KEY or DASHSCOPE_API_KEY in ENV, If HuggingFace, need set model_name
+# if use API, need set OPENAI_API_KEY or DASHSCOPE_API_KEY in ENV, If HuggingFace, need set model
 # eg.
 # source = "HuggingFace"
-# model_name = "bge-large-zh-v1.5"
+# model = "bge-large-zh-v1.5"
 # embed_batch_size = 10
 [rag.embedding]
 source = "DashScope"
@@ -53,12 +53,12 @@ vector_store.type = "FAISS"
 # llm configurations, source support API: OpenAI,DashScope or PAI-EAS's deployment
 # eg.
 # source = "PaiEas"
-# name = ""
+# model = ""
 # endpoint = ""
 # token = ""
 [rag.llm]
 source = "DashScope"
-name = "qwen-turbo"
+model = "qwen-turbo"
 
 [rag.llm.function_calling_llm]
 source = ""
diff --git a/src/pai_rag/evaluation/settings_eval.toml b/src/pai_rag/evaluation/settings_eval.toml
index 763c6d09..e5b2114f 100644
--- a/src/pai_rag/evaluation/settings_eval.toml
+++ b/src/pai_rag/evaluation/settings_eval.toml
@@ -33,10 +33,10 @@ type = "local"
 type = "SimpleDirectoryReader"
 
 # embedding configurations, source support API: OpenAI,DashScope; and local model:HuggingFace
-# if use API, need set OPENAI_API_KEY or DASHSCOPE_API_KEY in ENV, If HuggingFace, need set model_name
+# if use API, need set OPENAI_API_KEY or DASHSCOPE_API_KEY in ENV, If HuggingFace, need set model
 # eg.
 # source = "HuggingFace"
-# model_name = "bge-large-zh-v1.5"
+# model = "bge-large-zh-v1.5"
 # embed_batch_size = 10
 [rag.embedding]
 source = "DashScope"
@@ -53,12 +53,12 @@ vector_store.type = "FAISS"
 # llm configurations, source support API: OpenAI,DashScope or PAI-EAS's deployment
 # eg.
 # source = "PaiEas"
-# name = ""
+# model = ""
 # endpoint = ""
 # token = ""
 [rag.llm]
 source = "DashScope"
-name = "qwen-turbo"
+model = "qwen-turbo"
 
 [rag.llm.function_calling_llm]
 source = ""
@@ -66,7 +66,7 @@ source = ""
 [rag.llm.multi_modal]
 enable = true
 source = "DashScope"
-name = "qwen-vl-plus"
+model = "qwen-vl-plus"
 
 [rag.node_enhancement]
 tree_depth = 3
diff --git a/src/pai_rag/integrations/embeddings/pai/embedding_utils.py b/src/pai_rag/integrations/embeddings/pai/embedding_utils.py
index 0162382e..4e5b3300 100644
--- a/src/pai_rag/integrations/embeddings/pai/embedding_utils.py
+++ b/src/pai_rag/integrations/embeddings/pai/embedding_utils.py
@@ -36,22 +36,22 @@ def create_embedding(embed_config: PaiBaseEmbeddingConfig):
     elif isinstance(embed_config, HuggingFaceEmbeddingConfig):
         pai_model_dir = os.getenv("PAI_RAG_MODEL_DIR", "./model_repository")
         embed_model = HuggingFaceEmbedding(
-            model_name=os.path.join(pai_model_dir, embed_config.model_name),
+            model_name=os.path.join(pai_model_dir, embed_config.model),
             embed_batch_size=embed_config.embed_batch_size,
             trust_remote_code=True,
         )
 
         logger.info(
-            f"Initialized HuggingFace embedding model {embed_config.model_name} from model_dir_path {pai_model_dir} with {embed_config.embed_batch_size} batch size."
+            f"Initialized HuggingFace embedding model {embed_config.model} from model_dir_path {pai_model_dir} with {embed_config.embed_batch_size} batch size."
         )
 
     elif isinstance(embed_config, CnClipEmbeddingConfig):
         embed_model = CnClipEmbedding(
-            model_name=embed_config.model_name,
+            model_name=embed_config.model,
             embed_batch_size=embed_config.embed_batch_size,
         )
         logger.info(
-            f"Initialized CnClip embedding model {embed_config.model_name} with {embed_config.embed_batch_size} batch size."
+            f"Initialized CnClip embedding model {embed_config.model} with {embed_config.embed_batch_size} batch size."
         )
 
     else:
diff --git a/src/pai_rag/integrations/embeddings/pai/pai_embedding_config.py b/src/pai_rag/integrations/embeddings/pai/pai_embedding_config.py
index 077b8a67..ea7a03c7 100644
--- a/src/pai_rag/integrations/embeddings/pai/pai_embedding_config.py
+++ b/src/pai_rag/integrations/embeddings/pai/pai_embedding_config.py
@@ -13,7 +13,7 @@ class SupportedEmbedType(str, Enum):
 
 class PaiBaseEmbeddingConfig(BaseModel):
     source: SupportedEmbedType
-    model_name: str
+    model: str
     embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE
 
     class Config:
@@ -36,24 +36,24 @@ def validate_case_insensitive(cls, value):
 
 class DashScopeEmbeddingConfig(PaiBaseEmbeddingConfig):
     source: Literal[SupportedEmbedType.dashscope] = SupportedEmbedType.dashscope
-    model_name: str | None = None  # use default
+    model: str | None = None  # use default
     api_key: str | None = None  # use default
 
 
 class OpenAIEmbeddingConfig(PaiBaseEmbeddingConfig):
     source: Literal[SupportedEmbedType.openai] = SupportedEmbedType.openai
-    model_name: str | None = None  # use default
+    model: str | None = None  # use default
     api_key: str | None = None  # use default
 
 
 class HuggingFaceEmbeddingConfig(PaiBaseEmbeddingConfig):
     source: Literal[SupportedEmbedType.huggingface] = SupportedEmbedType.huggingface
-    model_name: str | None = "bge-large-zh-v1.5"
+    model: str | None = "bge-large-zh-v1.5"
 
 
 class CnClipEmbeddingConfig(PaiBaseEmbeddingConfig):
     source: Literal[SupportedEmbedType.cnclip] = SupportedEmbedType.cnclip
-    model_name: str | None = "ViT-L-14"
+    model: str | None = "ViT-L-14"
 
 
 SupporttedEmbeddingClsMap = {
@@ -73,6 +73,6 @@ def parse_embed_config(config_data):
 
 
 if __name__ == "__main__":
-    embedding_config_data = {"source": "Openai", "model_name": "gpt-1", "api_key": None}
+    embedding_config_data = {"source": "Openai", "model": "gpt-1", "api_key": None}
 
     print(parse_embed_config(embedding_config_data))
diff --git a/src/pai_rag/integrations/llms/pai/llm_config.py b/src/pai_rag/integrations/llms/pai/llm_config.py
index 4ca108e8..127a1252 100644
--- a/src/pai_rag/integrations/llms/pai/llm_config.py
+++ b/src/pai_rag/integrations/llms/pai/llm_config.py
@@ -126,7 +126,7 @@ class PaiBaseLlmConfig(BaseModel):
     temperature: float = DEFAULT_TEMPERATURE
     system_prompt: str = None
     max_tokens: int = DEFAULT_MAX_TOKENS
-    model_name: str = None
+    model: str = None
 
     @classmethod
     def get_subclasses(cls):
@@ -150,20 +150,20 @@ class DashScopeLlmConfig(PaiBaseLlmConfig):
     source: Literal[SupportedLlmType.dashscope] = SupportedLlmType.dashscope
     api_key: str | None = None
     base_url: str = "https://dashscope.aliyuncs.com/compatible-mode/v1"
-    model_name: str = "qwen-turbo"
+    model: str = "qwen-turbo"
 
 
 class OpenAILlmConfig(PaiBaseLlmConfig):
     source: Literal[SupportedLlmType.openai] = SupportedLlmType.openai
     api_key: str | None = None
-    model_name: str = "gpt-3.5-turbo"
+    model: str = "gpt-3.5-turbo"
 
 
 class PaiEasLlmConfig(PaiBaseLlmConfig):
     source: Literal[SupportedLlmType.paieas] = SupportedLlmType.paieas
     endpoint: str
     token: str
-    model_name: str = "default"
+    model: str = "default"
 
 
 SupporttedLlmClsMap = {cls.get_type(): cls for cls in PaiBaseLlmConfig.get_subclasses()}
@@ -183,7 +183,7 @@ def parse_llm_config(config_data):
 if __name__ == "__main__":
     llm_config_data = {
         "source": "dashscope",
-        "model_name": "qwen-turbo",
+        "model": "qwen-turbo",
         "api_key": None,
         "max_tokens": 1024,
     }
diff --git a/src/pai_rag/integrations/llms/pai/llm_utils.py b/src/pai_rag/integrations/llms/pai/llm_utils.py
index a82629a9..11a390a5 100644
--- a/src/pai_rag/integrations/llms/pai/llm_utils.py
+++ b/src/pai_rag/integrations/llms/pai/llm_utils.py
@@ -21,13 +21,13 @@ def create_llm(llm_config: PaiBaseLlmConfig):
         logger.info(
             f"""
             [Parameters][LLM:OpenAI]
-                model = {llm_config.model_name},
+                model = {llm_config.model},
                 temperature = {llm_config.temperature},
                 system_prompt = {llm_config.system_prompt}
             """
         )
         llm = OpenAI(
-            model=llm_config.model_name,
+            model=llm_config.model,
             temperature=llm_config.temperature,
             system_prompt=llm_config.system_prompt,
             api_key=llm_config.api_key,
@@ -38,13 +38,13 @@ def create_llm(llm_config: PaiBaseLlmConfig):
         logger.info(
             f"""
             [Parameters][LLM:DashScope]
-                model = {llm_config.model_name},
+                model = {llm_config.model},
                 temperature = {llm_config.temperature},
                 system_prompt = {llm_config.system_prompt}
             """
         )
         llm = OpenAILike(
-            model=llm_config.model_name,
+            model=llm_config.model,
             api_base=llm_config.base_url,
             temperature=llm_config.temperature,
             system_prompt=llm_config.system_prompt,
@@ -57,13 +57,13 @@ def create_llm(llm_config: PaiBaseLlmConfig):
         logger.info(
             f"""
             [Parameters][LLM:PAI-EAS]
-                model = {llm_config.model_name},
+                model = {llm_config.model},
                 endpoint = {llm_config.endpoint},
                 token = {llm_config.token}
             """
         )
         llm = OpenAILike(
-            model=llm_config.model_name,
+            model=llm_config.model,
             api_base=llm_config.endpoint,
             temperature=llm_config.temperature,
             system_prompt=llm_config.system_prompt,
@@ -82,13 +82,13 @@ def create_multi_modal_llm(llm_config: PaiBaseLlmConfig):
         logger.info(
             f"""
             [Parameters][LLM:OpenAI]
-                model = {llm_config.model_name},
+                model = {llm_config.model},
                 temperature = {llm_config.temperature},
                 system_prompt = {llm_config.system_prompt}
             """
         )
         llm = OpenAIMultiModal(
-            model=llm_config.model_name,
+            model=llm_config.model,
             temperature=llm_config.temperature,
             system_prompt=llm_config.system_prompt,
             api_key=llm_config.api_key,
@@ -98,13 +98,13 @@ def create_multi_modal_llm(llm_config: PaiBaseLlmConfig):
         logger.info(
             f"""
             [Parameters][LLM:DashScope]
-                model = {llm_config.model_name},
+                model = {llm_config.model},
                 temperature = {llm_config.temperature},
                 system_prompt = {llm_config.system_prompt}
             """
         )
         llm = OpenAIAlikeMultiModal(
-            model=llm_config.model_name,
+            model=llm_config.model,
             api_base=llm_config.base_url,
             temperature=llm_config.temperature,
             system_prompt=llm_config.system_prompt,
@@ -116,13 +116,13 @@ def create_multi_modal_llm(llm_config: PaiBaseLlmConfig):
         logger.info(
             f"""
             [Parameters][LLM:PAI-EAS]
-                model = {llm_config.model_name},
+                model = {llm_config.model},
                 endpoint = {llm_config.endpoint},
                 token = {llm_config.token}
             """
         )
         llm = OpenAIAlikeMultiModal(
-            model=llm_config.model_name,
+            model=llm_config.model,
             api_base=llm_config.endpoint,
             temperature=llm_config.temperature,
             system_prompt=llm_config.system_prompt,
diff --git a/src/pai_rag/integrations/llms/pai/pai_llm.py b/src/pai_rag/integrations/llms/pai/pai_llm.py
index 73a0c211..1d5cc4a9 100644
--- a/src/pai_rag/integrations/llms/pai/pai_llm.py
+++ b/src/pai_rag/integrations/llms/pai/pai_llm.py
@@ -34,7 +34,7 @@ class PaiLlm(OpenAILike):
     def __init__(self, llm_config: PaiBaseLlmConfig):
         super().__init__()
         self._llm = create_llm(llm_config)
-        self.model = llm_config.model_name
+        self.model = llm_config.model
         self._llm.callback_manager = Settings.callback_manager
         self.callback_manager = Settings.callback_manager
 
diff --git a/src/pai_rag/utils/download_models.py b/src/pai_rag/utils/download_models.py
index 066b89cd..56bb7ec4 100644
--- a/src/pai_rag/utils/download_models.py
+++ b/src/pai_rag/utils/download_models.py
@@ -26,25 +26,25 @@ def __init__(self):
             self.model_info = response.json()
             logger.info(f"Model info loaded {self.model_info}.")
 
-    def load_model(self, model_name):
-        model_path = os.path.join(self.download_directory_path, model_name)
+    def load_model(self, model):
+        model_path = os.path.join(self.download_directory_path, model)
         with TemporaryDirectory() as temp_dir:
             if not os.path.exists(model_path):
-                logger.info(f"start downloading model {model_name}.")
+                logger.info(f"start downloading model {model}.")
                 start_time = time.time()
-                if model_name in self.model_info["basic_models"]:
-                    model_id = self.model_info["basic_models"][model_name]
-                elif model_name in self.model_info["extra_models"]:
-                    model_id = self.model_info["extra_models"][model_name]
+                if model in self.model_info["basic_models"]:
+                    model_id = self.model_info["basic_models"][model]
+                elif model in self.model_info["extra_models"]:
+                    model_id = self.model_info["extra_models"][model]
                 else:
-                    raise ValueError(f"{model_name} is not a valid model name.")
+                    raise ValueError(f"{model} is not a valid model name.")
                 temp_model_dir = snapshot_download(model_id, cache_dir=temp_dir)
 
                 shutil.move(temp_model_dir, model_path)
                 end_time = time.time()
                 duration = end_time - start_time
                 logger.info(
-                    f"Finished downloading model {model_name} to {model_path}, took {duration:.2f} seconds."
+                    f"Finished downloading model {model} to {model_path}, took {duration:.2f} seconds."
                 )
 
     def load_basic_models(self):
@@ -52,8 +52,8 @@ def load_basic_models(self):
             response = requests.get(OSS_URL)
             response.raise_for_status()
             self.model_info = response.json()
-        for model_name in self.model_info["basic_models"].keys():
-            self.load_model(model_name)
+        for model in self.model_info["basic_models"].keys():
+            self.load_model(model)
 
     def load_mineru_config(self):
         source_path = "magic-pdf.template.json"
@@ -80,15 +80,15 @@ def load_mineru_config(self):
             "Copy magic-pdf.template.json to ~/magic-pdf.json and modify models-dir to model path."
         )
 
-    def load_models(self, model_name):
-        if model_name is None:
-            model_names = [
-                model_name for model_name in self.model_info["basic_models"].keys()
-            ] + [model_name for model_name in self.model_info["extra_models"].keys()]
-            for model_name in model_names:
-                self.load_model(model_name)
+    def load_models(self, model):
+        if model is None:
+            models = [model for model in self.model_info["basic_models"].keys()] + [
+                model for model in self.model_info["extra_models"].keys()
+            ]
+            for model in models:
+                self.load_model(model)
         else:
-            self.load_model(model_name)
+            self.load_model(model)
 
 
 @click.command()
@@ -100,7 +100,7 @@ def load_models(self, model_name):
     help="model name. Default: download all models provided",
     default=None,
 )
-def load_models(model_name):
+def load_models(model):
     download_models = ModelScopeDownloader()
-    download_models.load_models(model_name)
+    download_models.load_models(model)
     download_models.load_mineru_config()
diff --git a/tests/modules/agent/test_fc_agent.py b/tests/modules/agent/test_fc_agent.py
index 0022e8c0..410b1e07 100644
--- a/tests/modules/agent/test_fc_agent.py
+++ b/tests/modules/agent/test_fc_agent.py
@@ -5,7 +5,7 @@
 from pai_rag.integrations.llms.pai.pai_llm import PaiLlm
 from pai_rag.integrations.llms.pai.llm_config import DashScopeLlmConfig
 
-fc_llm_config = DashScopeLlmConfig(model_name="qwen-max")
+fc_llm_config = DashScopeLlmConfig(model="qwen-max")
 fc_llm = PaiLlm(fc_llm_config)
 
 
diff --git a/tests/modules/intentdetection/test_intent_detection.py b/tests/modules/intentdetection/test_intent_detection.py
index 26e09e85..25a0d86a 100644
--- a/tests/modules/intentdetection/test_intent_detection.py
+++ b/tests/modules/intentdetection/test_intent_detection.py
@@ -3,7 +3,7 @@
 from pai_rag.integrations.llms.pai.pai_llm import PaiLlm
 from pai_rag.integrations.llms.pai.llm_config import DashScopeLlmConfig
 
-fc_llm_config = DashScopeLlmConfig(model_name="qwen-max")
+fc_llm_config = DashScopeLlmConfig(model="qwen-max")
 fc_llm = PaiLlm(fc_llm_config)
 
 intents = {
diff --git a/tests/modules/llm/test_function_calling_llm.py b/tests/modules/llm/test_function_calling_llm.py
index faffb3d3..75c0724b 100644
--- a/tests/modules/llm/test_function_calling_llm.py
+++ b/tests/modules/llm/test_function_calling_llm.py
@@ -2,7 +2,7 @@
 from pai_rag.integrations.llms.pai.pai_llm import PaiLlm
 from pai_rag.integrations.llms.pai.llm_config import DashScopeLlmConfig
 
-fc_llm_config = DashScopeLlmConfig(model_name="qwen-max")
+fc_llm_config = DashScopeLlmConfig(model="qwen-max")
 fc_llm = PaiLlm(fc_llm_config)
 
 
diff --git a/tests/modules/llm/test_llm.py b/tests/modules/llm/test_llm.py
index fd22dcb3..0d50128c 100644
--- a/tests/modules/llm/test_llm.py
+++ b/tests/modules/llm/test_llm.py
@@ -6,7 +6,7 @@
 
 @pytest.fixture(scope="module", autouse=True)
 def llm():
-    llm_config = DashScopeLlmConfig(model_name="qwen-turbo")
+    llm_config = DashScopeLlmConfig(model="qwen-turbo")
     return PaiLlm(llm_config)