diff --git a/src/compressed_tensors/quantization/lifecycle/apply.py b/src/compressed_tensors/quantization/lifecycle/apply.py
index 79aac4e9b..1c3faf07b 100644
--- a/src/compressed_tensors/quantization/lifecycle/apply.py
+++ b/src/compressed_tensors/quantization/lifecycle/apply.py
@@ -14,7 +14,7 @@
 
 import logging
 import re
-from collections import OrderedDict
+from collections import OrderedDict, defaultdict
 from copy import deepcopy
 from typing import Dict, Iterable, List, Optional
 from typing import OrderedDict as OrderedDictType
@@ -125,13 +125,14 @@ def apply_quantization_config(model: Module, config: QuantizationConfig) -> Dict
             target_to_scheme[target] = scheme
 
     # list of submodules to ignore
-    ignored_submodules = []
+    ignored_submodules = defaultdict(list)
     # mark appropriate layers for quantization by setting their quantization schemes
     for name, submodule in iter_named_leaf_modules(model):
         # potentially fix module name to remove FSDP wrapper prefix
         name = fix_fsdp_module_name(name)
-        if find_name_or_class_matches(name, submodule, config.ignore):
-            ignored_submodules.append(name)
+        if matches := find_name_or_class_matches(name, submodule, config.ignore):
+            for match in matches:
+                ignored_submodules[match].append(name)
             continue  # layer matches ignore list, continue
         targets = find_name_or_class_matches(name, submodule, target_to_scheme)
         if targets:
diff --git a/src/compressed_tensors/quantization/lifecycle/calibration.py b/src/compressed_tensors/quantization/lifecycle/calibration.py
index b1fe2126a..cef81adbe 100644
--- a/src/compressed_tensors/quantization/lifecycle/calibration.py
+++ b/src/compressed_tensors/quantization/lifecycle/calibration.py
@@ -36,8 +36,8 @@ def set_module_for_calibration(module: Module, quantize_weights_upfront: bool =
     apply to full model with `model.apply(set_module_for_calibration)`
 
     :param module: module to set for calibration
-    :param quantize_weights_upfront: whether to automatically run weight quantization at the
-    start of calibration
+    :param quantize_weights_upfront: whether to automatically
+       run weight quantization at the start of calibration
     """
     if not getattr(module, "quantization_scheme", None):
         # no quantization scheme nothing to do
diff --git a/tests/test_quantization/lifecycle/test_apply.py b/tests/test_quantization/lifecycle/test_apply.py
index 558b9ee1f..42efe4420 100644
--- a/tests/test_quantization/lifecycle/test_apply.py
+++ b/tests/test_quantization/lifecycle/test_apply.py
@@ -15,6 +15,7 @@
 import re
 from typing import Optional
 
+import pytest
 import torch
 from compressed_tensors.config import CompressionFormat
 from compressed_tensors.quantization import (
@@ -223,3 +224,51 @@ def get_sample_tinyllama_quant_config(status: str = "frozen"):
         "ignore": ["LlamaRotaryEmbedding", "model.layers.1.mlp.down_proj"],
     }
     return QuantizationConfig.parse_obj(config_dict)
+
+
+@pytest.mark.parametrize(
+    "ignore,should_raise_warning",
+    [
+        [("lm_head", "re:.*gate"), False],
+        [("lm_head", "re:.*foobarbaz"), True],
+    ],
+)
+def test_apply_quantization_status(caplog, ignore, should_raise_warning):
+    import logging
+
+    from transformers import AutoModelForCausalLM
+
+    # load a dense, unquantized tiny llama model
+    device = "cuda:0"
+    model_name = "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T"
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name, device_map=device, torch_dtype="auto"
+    )
+
+    quantization_config_dict = {
+        "quant_method": "sparseml",
+        "format": "pack-quantized",
+        "global_compression_ratio": None,
+        "config_groups": {
+            "group_1": {
+                "weights": {
+                    "num_bits": 4,
+                    "type": "int",
+                    "symmetric": False,
+                    "strategy": "tensor",
+                },
+                "targets": ["Linear"],
+            }
+        },
+    }
+    quantization_config_dict["ignore"] = ignore
+
+    config = QuantizationConfig(**quantization_config_dict)
+    config.quantization_status = QuantizationStatus.CALIBRATION
+
+    if should_raise_warning:
+        # mismatch in the ignore key of quantization_config_dict
+        with caplog.at_level(logging.WARNING):
+            apply_quantization_config(model, config)
+    else:
+        apply_quantization_config(model, config)