diff --git a/src/compressed_tensors/utils/match.py b/src/compressed_tensors/utils/match.py
index f26400b0..ff3d2344 100644
--- a/src/compressed_tensors/utils/match.py
+++ b/src/compressed_tensors/utils/match.py
@@ -31,17 +31,18 @@
     "match_modules_set",
     "is_match",
     "is_narrow_match",
+    "FusedMapping",
 ]
 
 
-FusedMappping = Mapping[str, Iterable[str]]
+FusedMapping = Mapping[str, Iterable[str]]
 
 
 def match_named_modules(
     model: torch.nn.Module,
     targets: Optional[Iterable[str]],
     ignore: Optional[Iterable[str]] = None,
-    fused: Optional[FusedMappping] = None,
+    fused: Optional[FusedMapping] = None,
     warn_on_fail: bool = False,
 ) -> Generator[Tuple[str, torch.nn.Module]]:
     """
@@ -80,7 +81,7 @@ def match_named_parameters(
     model: torch.nn.Module,
     targets: Optional[Iterable[str]],
     ignore: Optional[Iterable[str]] = None,
-    fused: Optional[FusedMappping] = None,
+    fused: Optional[FusedMapping] = None,
     warn_on_fail: bool = False,
 ) -> Generator[Tuple[str, torch.nn.Module, torch.nn.Parameter]]:
     """
@@ -159,9 +160,10 @@ def match_targets(
 
 def match_modules_set(
     model: torch.nn.Module,
-    targets: Optional[Iterable[str]],
+    targets: Iterable[str] | None,
     ignore: Optional[Iterable[str]] = None,
-) -> Generator[Iterable[torch.nn.Module]]:
+    return_unmatched: bool = False,
+) -> Generator[Iterable[torch.nn.Module], None, None | dict[str, torch.nn.Module]]:
     """
     Yields modules grouped with the same order and size as `targets`.
     Values are returned in order of `model.named_modules()`
@@ -192,11 +194,21 @@ def match_modules_set(
     For example, matching layer norms to their subsequent linear layers
     ```python3
     for norm, q, k, v in match_modules_set(model, (norm_tgt, q_tgt, k_tgt, v_tgt)):
-        fuse_norm_linears(norm, [q, k, v])
+        apply_smoothing(norm, [q, k, v])
+    ```
+
+    Or for matching fused modules in a model
+    ```python3
+    for q, k, v in match_modules_set(model, (q_tgt, k_tgt, v_tgt)):
+        fuse_global_scales(q, k, v)
+    ```
 
     :param model: model containing modules to match against
     :param targets: target strings, potentially containing "re:" prefixes
     :param ignore: targets to ignore, potentially containing "re:" prefixes
+    :param return_unmatched: if True, return any modules which are partial matches,
+        otherwise raise a ValueError. Default is False.
+    :return: if return_unmatched is True, any modules which are partial matches
     """
     targets = targets or []
     ignore = ignore or []
@@ -207,7 +219,10 @@ def match_modules_set(
         for target in targets:
             if is_match(name, module, target, ignore):
                 if matches[target] is not None:
-                    raise ValueError(f"Matched a {target} twice before completing set")
+                    raise ValueError(
+                        f"Matched a {target} twice before "
+                        f"completing set ({matches[target]}, {name})"
+                    )
                 matches[target] = module
 
         # once we have a full set, yield and reset
@@ -215,10 +230,12 @@ def match_modules_set(
             yield [matches[target] for target in targets]  # ensure correct ordering
             matches = dict.fromkeys(targets, None)
 
-    # check that none are left over
-    unmatched_keys = [match for match, value in matches.items() if value is not None]
-    if len(unmatched_keys):
-        raise ValueError(f"Unable to match targets into set: {unmatched_keys}")
+    # handle unmatched remainder, if any
+    if any(value is not None for value in matches.values()):
+        if not return_unmatched:
+            raise ValueError(f"Unable to match targets into set: {matches}")
+        else:
+            return matches
 
 
 def is_match(
@@ -226,7 +243,7 @@ def is_match(
     module: torch.nn.Module,
     targets: Union[str, Iterable[str]],
     ignore: Union[str, Iterable[str]] = tuple(),
-    fused: Optional[FusedMappping] = None,
+    fused: Optional[FusedMapping] = None,
 ) -> bool:
     """
     Returns true if either module name or module parent classes match against target
@@ -289,7 +306,7 @@ def is_narrow_match(
     )
 
 
-def _match_name(name: str, target: str, fused: Optional[FusedMappping] = None) -> bool:
+def _match_name(name: str, target: str, fused: Optional[FusedMapping] = None) -> bool:
     """
     Returns true if target string begins with "re:" and regex matches or if target
     string exactly matches name. If the name refers to a fused module defined by vLLM,
diff --git a/tests/test_utils/test_match.py b/tests/test_utils/test_match.py
index 1129120c..a15d52d8 100644
--- a/tests/test_utils/test_match.py
+++ b/tests/test_utils/test_match.py
@@ -457,7 +457,21 @@ def test_incomplete_set_error(self):
         targets = ["layer1", "nonexistent_module"]
 
         with pytest.raises(ValueError, match="Unable to match targets into set"):
-            list(match_modules_set(model, targets))
+            list(match_modules_set(model, targets, return_unmatched=False))
+
+    def test_incomplete_set_return(self):
+        """Test error when unable to complete a set"""
+        model = DummyModel()
+        targets = ["layer1", "nonexistent_module"]
+
+        try:
+            while True:
+                next(match_modules_set(model, targets, return_unmatched=True))
+        except StopIteration as exception:
+            assert exception.value == {
+                "layer1": model.layer1,
+                "nonexistent_module": None,
+            }
 
     def test_duplicate_match_error(self):
         """Test error when same target matches multiple times before set completion"""