diff --git a/trackers/core/reid/model.py b/trackers/core/reid/model.py
index bbd7aacf..de84df76 100644
--- a/trackers/core/reid/model.py
+++ b/trackers/core/reid/model.py
@@ -67,18 +67,25 @@ def _initialize_reid_model_from_timm(
     return cls(model, device, transforms, model_metadata)
 
 
-def _initialize_reid_model_from_checkpoint(cls, checkpoint_path: str):
-    state_dict, config = load_safetensors_checkpoint(checkpoint_path)
+def _initialize_reid_model_from_checkpoint(cls, checkpoint_path: str, config_path: str):
+    state_dict, config = load_safetensors_checkpoint(checkpoint_path, config_path)
+    model_name = config.get("architecture")
+    if model_name is None:
+        raise ValueError(
+            f"The config at {config_path} is missing the 'architecture' key."
+        )
+    init_kwargs = {}
+    init_kwargs["pretrained"] = False
     reid_model_instance = _initialize_reid_model_from_timm(
-        cls, **config["model_metadata"]
+        cls, model_name_or_checkpoint_path=model_name, device="auto", **init_kwargs
     )
-    if config["projection_dimension"]:
+    if config.get("projection_dimension"):
         reid_model_instance._add_projection_layer(
-            projection_dimension=config["projection_dimension"]
+            projection_dimension=config.get("projection_dimension")
         )
     for k, v in state_dict.items():
-        state_dict[k].to(reid_model_instance.device)
-    reid_model_instance.backbone_model.load_state_dict(state_dict)
+        state_dict[k] = v.to(reid_model_instance.device)
+    reid_model_instance.backbone_model.load_state_dict(state_dict, strict=False)
     return reid_model_instance
 
 
@@ -122,6 +129,7 @@ def __init__(
     def from_timm(
         cls,
         model_name_or_checkpoint_path: str,
+        config_path: Optional[str] = None,
         device: Optional[str] = "auto",
         get_pooled_features: bool = True,
         **kwargs,
@@ -134,6 +142,8 @@ def from_timm(
             model_name_or_checkpoint_path (str): Name of the timm model to use or
                 path to a safetensors checkpoint. If the exact model name is not
                 found, the closest match from `timm.list_models` will be used.
+            config_path (str): Path to the config file for the local
+                safetensors checkpoint.
             device (str): Device to run the model on.
             get_pooled_features (bool): Whether to get the pooled features from the
                 model or not.
@@ -143,9 +153,13 @@ def from_timm(
         Returns:
             ReIDModel: A new instance of `ReIDModel`.
         """
-        if os.path.exists(model_name_or_checkpoint_path):
+        if (
+            config_path is not None
+            and os.path.exists(model_name_or_checkpoint_path)
+            and os.path.exists(config_path)
+        ):
             return _initialize_reid_model_from_checkpoint(
-                cls, model_name_or_checkpoint_path
+                cls, model_name_or_checkpoint_path, config_path
             )
         else:
             return _initialize_reid_model_from_timm(
diff --git a/trackers/utils/torch_utils.py b/trackers/utils/torch_utils.py
index 2d0c4b71..45b36e05 100644
--- a/trackers/utils/torch_utils.py
+++ b/trackers/utils/torch_utils.py
@@ -59,28 +59,31 @@ def parse_device_spec(device_spec: Union[str, torch.device]) -> torch.device:
 
 
 def load_safetensors_checkpoint(
-    checkpoint_path: str, device: str = "cpu"
+    checkpoint_path: str,
+    config_path: str,
+    device: str = "cpu",
 ) -> Tuple[dict[str, torch.Tensor], dict[str, Any]]:
     """
-    Load a safetensors checkpoint into a dictionary of tensors and a dictionary
-    of metadata.
+    Load a safetensors checkpoint into a dictionary of tensors and a
+    separate JSON config file.
 
     Args:
         checkpoint_path (str): The path to the safetensors checkpoint.
+        config_path (str): The path to the JSON config file.
         device (str): The device to load the checkpoint on.
 
     Returns:
-        Tuple[dict[str, torch.Tensor], dict[str, Any]]: A tuple containing the
-            state_dict and the config.
+        state_dict (dict): model weights
+        config (dict): model config
     """
     state_dict = {}
     with safe_open(checkpoint_path, framework="pt", device=device) as f:
         for key in f.keys():
             state_dict[key] = f.get_tensor(key)
-        metadata = f.metadata()
-        config = json.loads(metadata["config"]) if "config" in metadata else {}
-    model_metadata = config.pop("model_metadata") if "model_metadata" in config else {}
-    if "kwargs" in model_metadata:
+    with open(config_path, "r") as f:
+        config = json.load(f)
+    model_metadata = config.pop("model_metadata", {})
+    if isinstance(model_metadata, dict) and "kwargs" in model_metadata:
         kwargs = model_metadata.pop("kwargs")
         model_metadata = {**kwargs, **model_metadata}
     config["model_metadata"] = model_metadata