diff --git a/aviary/predict.py b/aviary/predict.py
index a7b55efe..0fed9fd4 100644
--- a/aviary/predict.py
+++ b/aviary/predict.py
@@ -157,7 +157,10 @@ def make_ensemble_predictions(
 
 @print_walltime(end_desc="predict_from_wandb_checkpoints")
 def predict_from_wandb_checkpoints(
-    runs: list[wandb.apis.public.Run], cache_dir: str, **kwargs: Any
+    runs: list[wandb.apis.public.Run],
+    checkpoint_filename: str = "checkpoint.pth",
+    cache_dir: str = "./checkpoint_cache",
+    **kwargs: Any,
 ) -> pd.DataFrame | tuple[pd.DataFrame, pd.DataFrame]:
     """Download and cache checkpoints for an ensemble of models, then make
     predictions on some dataset. Finally print ensemble metrics and store
@@ -167,6 +170,7 @@ def predict_from_wandb_checkpoints(
         runs (list[wandb.apis.public.Run]): List of WandB runs to download model
             checkpoints from which are then loaded into memory to generate
             predictions for the input_col in df.
+        checkpoint_filename (str): Name of the checkpoint file to download.
         cache_dir (str): Directory to cache downloaded checkpoints in.
         **kwargs: Additional keyword arguments to pass to make_ensemble_predictions().
 
@@ -194,7 +198,7 @@ def predict_from_wandb_checkpoints(
         out_dir = f"{cache_dir}/{run_path}"
         os.makedirs(out_dir, exist_ok=True)
 
-        checkpoint_path = f"{out_dir}/checkpoint.pth"
+        checkpoint_path = f"{out_dir}/{checkpoint_filename}"
         checkpoint_paths.append(checkpoint_path)
         print(f"{idx:>3}/{len(runs)}: {run.url}\n\t{checkpoint_path}\n")
 
@@ -202,7 +206,7 @@ def predict_from_wandb_checkpoints(
             md_file.write(f"[{run.name}]({run.url})\n")
 
         if not os.path.isfile(checkpoint_path):
-            run.file("checkpoint.pth").download(root=out_dir)
+            run.file(f"{checkpoint_filename}").download(root=out_dir)
 
     if target_col in kwargs:
         df, ensemble_metrics = make_ensemble_predictions(checkpoint_paths, **kwargs)