diff --git a/compiler/bindings/python/iree/compiler/tools/import_onnx/__main__.py b/compiler/bindings/python/iree/compiler/tools/import_onnx/__main__.py
index a92ff49dbb63..2059c417b99b 100644
--- a/compiler/bindings/python/iree/compiler/tools/import_onnx/__main__.py
+++ b/compiler/bindings/python/iree/compiler/tools/import_onnx/__main__.py
@@ -70,7 +70,7 @@ def __init__(
         context_cache: "onnx_importer.ContextCache",
         module_op: Operation,
         module_cache: "onnx_importer.ModuleCache",
-        max_numel: int,
+        numel_threshold: int,
     ):
         super().__init__(
             graph_info,
@@ -83,7 +83,7 @@ def __init__(
         self.last_global_op = None
         self.symbol_table = SymbolTable(module_op)
         self.symbol_table.insert(parent_op)
-        self.max_numel = max_numel
+        self.numel_threshold = numel_threshold
         self.param_archive = rt.ParameterIndex()
 
     def sanitize_name(self, name: str) -> str:
@@ -116,7 +116,7 @@ def create_tensor_global(
         with InsertionPoint.at_block_begin(
             self._m.regions[0].blocks[0]
         ), Location.unknown():
-            # After lowering to linalg-on-tensors, the data type need to be signless.
+            # After lowering to linalg-on-tensors, the data type needs to be signless.
             # So, we construct the globals to have signless types, and use
             # torch_c.from_builtin_tensor to convert to the correct frontend type.
             vtensor_type = RankedTensorType.get(
@@ -151,7 +151,7 @@ def define_function(
         cls,
         graph_info: onnx_importer.GraphInfo,
         module_op: Operation,
-        max_numel: int,
+        numel_threshold: int,
         context_cache: Optional["onnx_importer.ContextCache"] = None,
         module_cache: Optional["onnx_importer.ModuleCache"] = None,
         private: bool = False,
@@ -193,7 +193,7 @@ def define_function(
             context_cache=cc,
             module_op=module_op,
             module_cache=mc,
-            max_numel=max_numel,
+            numel_threshold=numel_threshold,
         )
         for node_name, input_value in zip(graph_info.input_map.keys(), block.arguments):
             imp._nv_map[node_name] = input_value
@@ -207,10 +207,10 @@ def import_initializer(
         # up the name from the tensor proto itself
         iname = extern_name if extern_name else initializer.name
         dims = list(initializer.dims)
-        acc = 1
+        numel = 1
         for d in dims:
-            acc = acc * d
-        if acc < self.max_numel:
+            numel = numel * d
+        if numel < self.numel_threshold:
             imported_tensor = super().import_initializer(initializer)
             self._nv_map[iname] = imported_tensor
             return imported_tensor
@@ -242,7 +242,9 @@ def main(args: argparse.Namespace):
 
     imp: Any = None
     if args.externalize_params:
-        imp = IREENodeImporter.define_function(model_info.main_graph, m, args.max_numel)
+        imp = IREENodeImporter.define_function(
+            model_info.main_graph, m, args.numel_threshold
+        )
     else:
         imp = onnx_importer.NodeImporter.define_function(model_info.main_graph, m)
     imp.import_all()
@@ -251,7 +253,13 @@ def main(args: argparse.Namespace):
         m.verify()
 
     if args.externalize_params:
-        imp.param_archive.create_archive_file(args.save_params_to)
+        default_param_path = Path(args.output_file).parent / Path(args.output_file).stem
+        param_path = (
+            (str(default_param_path) + "_params.irpa")
+            if args.save_params_to is None
+            else args.save_params_to
+        )
+        imp.param_archive.create_archive_file(param_path)
 
     # TODO: This isn't very efficient output. If these files ever
     # get large, enable bytecode and direct binary emission to save
@@ -274,7 +282,8 @@ def load_onnx_model(args: argparse.Namespace) -> onnx.ModelProto:
         raw_model = onnx.load(args.input_file, load_external_data=False)
         onnx.load_external_data_for_model(raw_model, str(args.data_dir))
 
-    if args.opset_version:
+    # Only change the opset version if it is greater than the current one.
+    if args.opset_version and args.opset_version > raw_model.opset_import[0].version:
         raw_model = onnx.version_converter.convert_version(
             raw_model, args.opset_version
         )
@@ -381,8 +390,8 @@ def parse_arguments(argv=None) -> argparse.Namespace:
         type=int,
     )
     parser.add_argument(
-        "--max-numel",
-        help="Maximum number of elements allowed in an inlined parameter constant.",
+        "--numel-threshold",
+        help="Minimum number of elements for an initializer to be externalized. Only has an effect if 'externalize-params' is true.",
         type=int,
         default=100,
     )
@@ -394,8 +403,8 @@ def parse_arguments(argv=None) -> argparse.Namespace:
     )
     parser.add_argument(
         "--save-params-to",
-        help="Location to save the externalized parameters",
-        default="params.irpa",
+        help="Location to save the externalized parameters. When not set, the parameters will be written to '<output_file_name>_params.irpa'.",
+        default=None,
     )
     args = parser.parse_args(argv)
     return args