diff --git a/llms/mlx_lm/utils.py b/llms/mlx_lm/utils.py
index b3667609..2a1608d9 100644
--- a/llms/mlx_lm/utils.py
+++ b/llms/mlx_lm/utils.py
@@ -299,12 +299,14 @@ def load_config(model_path: Path) -> dict:
     return config
 
 
-def load_model(model_path: Path, lazy: bool = False) -> nn.Module:
+def load_model(model_path: Path, model_config: dict = {}, lazy: bool = False) -> nn.Module:
     """
     Load and initialize the model from a given path.
 
     Args:
         model_path (Path): The path to load the model from.
+        model_config(dict, optional): Configuration parameters for the model.
+            Defaults to an empty dictionary.
         lazy (bool): If False eval the model parameters to make sure they are
             loaded in memory before returning, otherwise they will be loaded
             when needed. Default: ``False``
@@ -318,6 +320,7 @@ def load_model(model_path: Path, lazy: bool = False) -> nn.Module:
     """
 
     config = load_config(model_path)
+    config.update(model_config)
 
     weight_files = glob.glob(str(model_path / "model*.safetensors"))
 
@@ -365,6 +368,7 @@ def load_model(model_path: Path, lazy: bool = False) -> nn.Module:
 def load(
     path_or_hf_repo: str,
     tokenizer_config={},
+    model_config={},
     adapter_path: Optional[str] = None,
     lazy: bool = False,
 ) -> Tuple[nn.Module, TokenizerWrapper]:
@@ -375,6 +379,8 @@ def load(
         path_or_hf_repo (Path): The path or the huggingface repository to load the model from.
         tokenizer_config (dict, optional): Configuration parameters specifically for the tokenizer.
             Defaults to an empty dictionary.
+        model_config(dict, optional): Configuration parameters specifically for the model.
+            Defaults to an empty dictionary.
         adapter_path (str, optional): Path to the LoRA adapters. If provided, applies LoRA layers
             to the model. Default: ``None``.
         lazy (bool): If False eval the model parameters to make sure they are
@@ -389,7 +395,7 @@ def load(
     """
     model_path = get_model_path(path_or_hf_repo)
 
-    model = load_model(model_path, lazy)
+    model = load_model(model_path, model_config, lazy)
     if adapter_path is not None:
         model = apply_lora_layers(model, adapter_path)
         model.eval()