diff --git a/chgnet/model/model.py b/chgnet/model/model.py
index 7c6fd783..76fd0271 100644
--- a/chgnet/model/model.py
+++ b/chgnet/model/model.py
@@ -324,12 +324,11 @@ def forward(
         return_site_energies: bool = False,
         return_atom_feas: bool = False,
         return_crystal_feas: bool = False,
-    ) -> dict:
+    ) -> dict[str, Tensor]:
         """Get prediction associated with input graphs
         Args:
             graphs (List): a list of CrystalGraphs
-            task (str): the prediction task
-                        eg: 'e', 'em', 'ef', 'efs', 'efsm'
+            task (str): the prediction task. One of 'e', 'em', 'ef', 'efs', 'efsm'.
                 Default = 'e'
             return_site_energies (bool): whether to return per-site energies,
                 only available if self.mlp_first == True
@@ -662,26 +661,28 @@ def from_file(cls, path, **kwargs):
 
     @classmethod
     def load(cls, model_name="0.3.0"):
-        """Load pretrained CHGNet."""
-        current_dir = os.path.dirname(os.path.abspath(__file__))
-        if model_name == "0.3.0":
-            return cls.from_file(
-                os.path.join(
-                    current_dir,
-                    "../pretrained/0.3.0/chgnet_0.3.0_e29f68s314m37.pth.tar",
-                )
-            )
-        elif model_name == "0.2.0":  # noqa: RET505
-            return cls.from_file(
-                os.path.join(
-                    current_dir,
-                    "../pretrained/0.2.0/chgnet_0.2.0_e30f77s348m32.pth.tar",
-                ),
-                mlp_out_bias=True,
-            )
-        else:
+        """Load pretrained CHGNet model.
+
+        Args:
+            model_name (str, optional): Defaults to "0.3.0".
+
+        Raises:
+            ValueError: On unknown model_name.
+        """
+        checkpoint_path = {
+            "0.3.0": "../pretrained/0.3.0/chgnet_0.3.0_e29f68s314m37.pth.tar",
+            "0.2.0": "../pretrained/0.2.0/chgnet_0.2.0_e30f77s348m32.pth.tar",
+        }.get(model_name)
+
+        if checkpoint_path is None:
             raise ValueError(f"Unknown {model_name=}")
 
+        return cls.from_file(
+            os.path.join(module_dir, checkpoint_path),
+            mlp_out_bias=model_name == "0.2.0",
+            version=model_name,
+        )
+
 
 @dataclass
 class BatchedGraph: