diff --git a/plugins/accelerated-moe/src/fms_acceleration_moe/utils/__init__.py b/plugins/accelerated-moe/src/fms_acceleration_moe/utils/__init__.py
index 660d2252..a3f2117d 100644
--- a/plugins/accelerated-moe/src/fms_acceleration_moe/utils/__init__.py
+++ b/plugins/accelerated-moe/src/fms_acceleration_moe/utils/__init__.py
@@ -13,7 +13,10 @@
 # limitations under the License.
 
 # Local
-from .checkpoint_utils import patch_huggingface_save_and_load_for_dtensors
+from .checkpoint_utils import (
+    patch_huggingface_save_and_load_for_dtensors,
+    recover_safetensors_from_dcp,
+)
 from .scattermoe_prepare import prepare_scattermoe
 
 # this is a special patch function to disable foreach for
diff --git a/plugins/accelerated-moe/src/fms_acceleration_moe/utils/checkpoint_utils.py b/plugins/accelerated-moe/src/fms_acceleration_moe/utils/checkpoint_utils.py
index 7c5c54b1..6292b002 100644
--- a/plugins/accelerated-moe/src/fms_acceleration_moe/utils/checkpoint_utils.py
+++ b/plugins/accelerated-moe/src/fms_acceleration_moe/utils/checkpoint_utils.py
@@ -457,75 +457,38 @@ def save_sharded_safetensors(
 # --------------------------- SCRIPT -------------------------
 
 
-# have it serve as a conversion script
-if __name__ == "__main__":
-    # Standard
-    import argparse
-
-    parser = argparse.ArgumentParser(
-        description=(
-            "Utility for converting ScatterMoE checkpoint back to the "
-            "orginal state dict format. "
-            "The ScatterMoE checkpoint was saved after the pretrained model "
-            "had been converted by a module swap, hence the state dict will "
-            "no longer resemble the original. This utility creaes"
-        )
-    )
-
-    parser.add_argument(
-        "checkpoint_dir",
-        help="Path to the checkpoint.",
-    )
-
-    parser.add_argument(
-        "output_dir", help="Path to the location to write the converted checkpoint."
-    )
-
-    parser.add_argument(
-        "pretrained_model_name_or_path",
-        help=(
-            "In order to reconstruct the state dict, we requre hints from "
-            "the original pretrained model checkpoint (from which this "
-            "checkpoint is obtained)."
-        ),
-        default=None,
-    )
-
-    args = parser.parse_args()
-
-    # search for an FSDP checkpoint. If it is an FSDP checkpoint, it must
-    # start with FSDP_MODEL_NAME
-    if args.checkpoint_dir.startswith(FSDP_MODEL_NAME):
-        checkpoint_dir = args.checkpoint_dir
+def recover_safetensors_from_dcp(
+    checkpoint_dir, pretrained_model_name_or_path, output_dir
+):
+    if checkpoint_dir.startswith(FSDP_MODEL_NAME):
         loader = get_state_dict_from_dcp_checkpoint
     else:
-        checkpoint_dir = [
+        fsdp_checkpoint_dirs = [
             x
-            for x in os.listdir(args.checkpoint_dir)
-            if os.path.isdir(os.path.join(args.checkpoint_dir, x))
+            for x in os.listdir(checkpoint_dir)
+            if os.path.isdir(os.path.join(checkpoint_dir, x))
             and x.startswith(FSDP_MODEL_NAME)
         ]
-        if len(checkpoint_dir) == 1:
-            checkpoint_dir = os.path.join(args.checkpoint_dir, checkpoint_dir[0])
+        if len(fsdp_checkpoint_dirs) == 1:
+            checkpoint_dir = os.path.join(checkpoint_dir, fsdp_checkpoint_dirs[0])
             loader = get_state_dict_from_dcp_checkpoint
-        elif len(checkpoint_dir) > 1:
+        elif len(fsdp_checkpoint_dirs) > 1:
             raise ValueError(
-                f"Found > 1 dirs in dcp checkpoint dir {args.checkpoint_dir} "
+                f"Found > 1 dirs in dcp checkpoint dir {checkpoint_dir} "
                 f"that starts with {FSDP_MODEL_NAME}. Please spectify the exact dir."
             )
         else:
             # then take it as a safetensors checkpoint
             # - do not support .bin checkpoints
-            checkpoint_dir = args.checkpoint_dir
             loader = get_state_dict_from_safe_checkpoint
 
     # - pretrained model name
-    _name_or_path = args.pretrained_model_name_or_path
+    _name_or_path = pretrained_model_name_or_path
 
     # assume output directory exists, we do not create it
     # - copy the config file if exists
     config_file = os.path.join(checkpoint_dir, CONFIG_NAME)
-    target_config_file = os.path.join(args.output_dir, CONFIG_NAME)
+    target_config_file = os.path.join(output_dir, CONFIG_NAME)
     if os.path.exists(config_file):
         shutil.copyfile(config_file, target_config_file)
 
@@ -544,6 +507,46 @@ def save_sharded_safetensors(
     # save it as a safetensors file
     save_sharded_safetensors(
         {k: v.contiguous() for k, v in state_dict.items()},
-        args.output_dir,
+        output_dir,
         metadata={"format": "pt"},
     )
+
+
+# have it serve as a conversion script
+if __name__ == "__main__":
+    # Standard
+    import argparse
+
+    parser = argparse.ArgumentParser(
+        description=(
+            "Utility for converting ScatterMoE checkpoint back to the "
+            "orginal state dict format. "
+            "The ScatterMoE checkpoint was saved after the pretrained model "
+            "had been converted by a module swap, hence the state dict will "
+            "no longer resemble the original. This utility creaes"
+        )
+    )
+
+    parser.add_argument(
+        "checkpoint_dir",
+        help="Path to the checkpoint.",
+    )
+
+    parser.add_argument(
+        "output_dir", help="Path to the location to write the converted checkpoint."
+    )
+
+    parser.add_argument(
+        "pretrained_model_name_or_path",
+        help=(
+            "In order to reconstruct the state dict, we requre hints from "
+            "the original pretrained model checkpoint (from which this "
+            "checkpoint is obtained)."
+        ),
+        default=None,
+    )
+
+    args = parser.parse_args()
+    recover_safetensors_from_dcp(
+        args.checkpoint_dir, args.pretrained_model_name_or_path, args.output_dir
+    )