[misc] upgrade format to py39 (#7256)

src/llamafactory/model/loader.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import os
-from typing import TYPE_CHECKING, Any, Dict, Optional, TypedDict
+from typing import TYPE_CHECKING, Any, Optional, TypedDict
 
 import torch
 from transformers import (
@@ -51,9 +51,8 @@ class TokenizerModule(TypedDict):
     processor: Optional["ProcessorMixin"]
 
 
-def _get_init_kwargs(model_args: "ModelArguments") -> Dict[str, Any]:
-    r"""
-    Gets arguments to load config/tokenizer/model.
+def _get_init_kwargs(model_args: "ModelArguments") -> dict[str, Any]:
+    r"""Get arguments to load config/tokenizer/model.
 
     Note: including inplace operation of model_args.
     """
@@ -68,8 +67,7 @@ def _get_init_kwargs(model_args: "ModelArguments") -> Dict[str, Any]:
 
 
 def load_tokenizer(model_args: "ModelArguments") -> "TokenizerModule":
-    r"""
-    Loads pretrained tokenizer and optionally loads processor.
+    r"""Load pretrained tokenizer and optionally loads processor.
 
     Note: including inplace operation of model_args.
     """
@@ -110,9 +108,7 @@ def load_tokenizer(model_args: "ModelArguments") -> "TokenizerModule":
 
 
 def load_config(model_args: "ModelArguments") -> "PretrainedConfig":
-    r"""
-    Loads model config.
-    """
+    r"""Load model config."""
     init_kwargs = _get_init_kwargs(model_args)
     return AutoConfig.from_pretrained(model_args.model_name_or_path, **init_kwargs)
 
@@ -124,9 +120,7 @@ def load_model(
     is_trainable: bool = False,
     add_valuehead: bool = False,
 ) -> "PreTrainedModel":
-    r"""
-    Loads pretrained model.
-    """
+    r"""Load pretrained model."""
     init_kwargs = _get_init_kwargs(model_args)
     config = load_config(model_args)
     patch_config(config, tokenizer, model_args, init_kwargs, is_trainable)
@@ -194,8 +188,9 @@ def load_model(
 
     trainable_params, all_param = count_parameters(model)
     if is_trainable:
-        param_stats = "trainable params: {:,} || all params: {:,} || trainable%: {:.4f}".format(
-            trainable_params, all_param, 100 * trainable_params / all_param
+        param_stats = (
+            f"trainable params: {trainable_params:,} || "
+            f"all params: {all_param:,} || trainable%: {100 * trainable_params / all_param:.4f}"
         )
     else:
         param_stats = f"all params: {all_param:,}"