diff --git a/common/args.py b/common/args.py
index ffecaaa..c4fbd46 100644
--- a/common/args.py
+++ b/common/args.py
@@ -5,6 +5,7 @@ from typing import get_origin, get_args, Optional, Union, List
 from pydantic import BaseModel
 from common.tabby_config import config
 
+
 def str_to_bool(value):
     """Converts a string into a boolean value"""
 
@@ -40,34 +41,43 @@ def init_argparser():
     for field_name, field_type in config.__annotations__.items():
         # Get the sub-model type (e.g., ModelA, ModelB)
         sub_model = field_type.__base__
-        
+
         # Create argument group for the sub-model
-        group = parser.add_argument_group(field_name, description=f"Arguments for {field_name}")
-        
+        group = parser.add_argument_group(
+            field_name, description=f"Arguments for {field_name}"
+        )
+
         # Loop through each field in the sub-model (e.g., ModelA, ModelB)
         for sub_field_name, sub_field_type in field_type.__annotations__.items():
             field = field_type.__fields__[sub_field_name]
-            help_text = field.description if field.description else "No description available"
+            help_text = (
+                field.description if field.description else "No description available"
+            )
 
             # Handle Optional types or other generic types
             origin = get_origin(sub_field_type)
             if origin is Union:  # Check if the type is Union (which includes Optional)
-                sub_field_type = next(t for t in get_args(sub_field_type) if t is not type(None))
-            elif origin is List : sub_field_type = get_args(sub_field_type)[0]
-
+                sub_field_type = next(
+                    t for t in get_args(sub_field_type) if t is not type(None)
+                )
+            elif origin is List:
+                sub_field_type = get_args(sub_field_type)[0]
 
             # Map Pydantic types to argparse types
             print(sub_field_type, type(sub_field_type))
-            if isinstance(sub_field_type, type) and issubclass(sub_field_type, (int, float, str, bool)):
+            if isinstance(sub_field_type, type) and issubclass(
+                sub_field_type, (int, float, str, bool)
+            ):
                 arg_type = sub_field_type
             else:
                 arg_type = str  # Default to string for unknown types
-            
+
             # Add the argument for each field in the sub-model
             group.add_argument(f"--{sub_field_name}", type=arg_type, help=help_text)
 
     return parser
 
+
 def convert_args_to_dict(args: argparse.Namespace, parser: argparse.ArgumentParser):
     """Broad conversion of surface level arg groups to dictionaries"""
 
@@ -81,4 +91,4 @@ def convert_args_to_dict(args: argparse.Namespace, parser: argparse.ArgumentPars
 
             arg_groups[group.title] = group_dict
 
-    return arg_groups
\ No newline at end of file
+    return arg_groups
diff --git a/main.py b/main.py
index 89cc6bf..7385a1d 100644
--- a/main.py
+++ b/main.py
@@ -69,12 +69,14 @@ async def entrypoint_async():
         model_path = pathlib.Path(config.model.model_dir)
         model_path = model_path / model_name
 
-        await model.load_model(model_path.resolve(), **config.model)
+        # TODO: remove model_dump()
+        await model.load_model(model_path.resolve(), **config.model.model_dump())
 
         # Load loras after loading the model
         if config.lora.loras:
             lora_dir = pathlib.Path(config.lora.lora_dir)
-            await model.container.load_loras(lora_dir.resolve(), **config.lora)
+            # TODO: remove model_dump()
+            await model.container.load_loras(lora_dir.resolve(), **config.lora.model_dump())
 
     # If an initial embedding model name is specified, create a separate container
     # and load the model
@@ -84,7 +86,8 @@ async def entrypoint_async():
         embedding_model_path = embedding_model_path / embedding_model_name
 
         try:
-            await model.load_embedding_model(embedding_model_path, **config.embeddings)
+            # TODO: remove model_dump()
+            await model.load_embedding_model(embedding_model_path, **config.embeddings.model_dump())
         except ImportError as ex:
             logger.error(ex.msg)