diff --git a/aurora/model/aurora.py b/aurora/model/aurora.py
index b701f3e..9fae270 100644
--- a/aurora/model/aurora.py
+++ b/aurora/model/aurora.py
@@ -204,7 +204,6 @@ def forward(self, batch: Batch) -> Batch:
         )
 
         # Remove batch and history dimension from static variables.
-        B, T = next(iter(batch.surf_vars.values()))[0]
         pred = dataclasses.replace(
             pred,
             static_vars={k: v[0, 0] for k, v in batch.static_vars.items()},
@@ -231,10 +230,20 @@ def load_checkpoint(self, repo: str, name: str, strict: bool = True) -> None:
             strict (bool, optional): Error if the model parameters are not exactly equal to the
                 parameters in the checkpoint. Defaults to `True`.
         """
+        path = hf_hub_download(repo_id=repo, filename=name)
+        self.load_checkpoint_local(path, strict=strict)
+
+    def load_checkpoint_local(self, path: str, strict: bool = True) -> None:
+        """Load a checkpoint directly from a file.
+
+        Args:
+            path (str): Path to the checkpoint.
+            strict (bool, optional): Error if the model parameters are not exactly equal to the
+                parameters in the checkpoint. Defaults to `True`.
+        """
         # Assume that all parameters are either on the CPU or on the GPU.
         device = next(self.parameters()).device
 
-        path = hf_hub_download(repo_id=repo, filename=name)
         d = torch.load(path, map_location=device, weights_only=True)
 
         # You can safely ignore all cumbersome processing below. We modified the model after we
diff --git a/docs/finetuning.md b/docs/finetuning.md
index 02ac314..8bdda7b 100644
--- a/docs/finetuning.md
+++ b/docs/finetuning.md
@@ -69,7 +69,7 @@ scales["new_atmos_var"] = 1.0
 ## Other Model Extensions
 
 It is possible to extend to model in any way you like.
-If you do this, you will likely you add or remove parameters.
+If you do this, you will likely add or remove parameters.
 Then `Aurora.load_checkpoint` will error,
 because the existing checkpoint now mismatches with the model's parameters.
 Simply set `Aurora.load_checkpoint(..., strict=False)` to ignore the mismatches: