diff --git a/src/transformers/quantizers/quantizer_torchao.py b/src/transformers/quantizers/quantizer_torchao.py
index 344c9e3534ed..bb8b6673ac01 100644
--- a/src/transformers/quantizers/quantizer_torchao.py
+++ b/src/transformers/quantizers/quantizer_torchao.py
@@ -297,6 +297,8 @@ def create_quantized_param(
 
             # handle ModuleFqnToConfig, introduced in torchao 0.12.0+
             if self.quantization_config._get_ao_version() >= version.Version("0.12.0"):
+                import re
+
                 from torchao.quantization import ModuleFqnToConfig
 
                 config = self.quantization_config.get_apply_tensor_subclass()
@@ -306,7 +308,16 @@ def create_quantized_param(
                     if module_fqn in config.module_fqn_to_config:
                         c = config.module_fqn_to_config[module_fqn]
                     else:
-                        c = config.module_fqn_to_config.get("_default", None)
+                        for maybe_module_fqn_pattern in config.module_fqn_to_config:
+                            if not maybe_module_fqn_pattern.startswith("re:"):
+                                continue
+                            elif re.fullmatch(maybe_module_fqn_pattern[3:], module_fqn):
+                                # we'll apply the config for first fully matched pattern
+                                c = config.module_fqn_to_config[maybe_module_fqn_pattern]
+                                break
+                        else:
+                            c = config.module_fqn_to_config.get("_default", None)
+
                     if c is not None:
                         # filter_fn: not filtering out any modules
                         quantize_(module, c, filter_fn=lambda x, fqn: True)
diff --git a/tests/quantization/torchao_integration/test_torchao.py b/tests/quantization/torchao_integration/test_torchao.py
index 1ddc2de0801f..0d1ace7ede2a 100644
--- a/tests/quantization/torchao_integration/test_torchao.py
+++ b/tests/quantization/torchao_integration/test_torchao.py
@@ -46,6 +46,8 @@
         TensorCoreTiledLayout,
     )
     from torchao.quantization import (
+        Float8Tensor,
+        Float8WeightOnlyConfig,
         Int8WeightOnlyConfig,
         IntxWeightOnlyConfig,
         MappingType,
@@ -278,6 +280,89 @@ def test_per_module_config_skip(self):
         self.assertTrue(tokenizer.decode(output[0], skip_special_tokens=True) in EXPECTED_OUTPUT)
 
 
+    @require_torchao_version_greater_or_equal("0.13.0")
+    def test_module_fqn_to_config_regex_basic(self):
+        linear_config = Int8WeightOnlyConfig()
+        config = ModuleFqnToConfig({"_default": linear_config, r"re:model\.layers\..+\.self_attn\.q_proj": None})
+        quant_config = TorchAoConfig(quant_type=config)
+        quantized_model = AutoModelForCausalLM.from_pretrained(
+            self.model_name,
+            device_map=self.device,
+            quantization_config=quant_config,
+        )
+        # making sure `model.layers.0.self_attn.q_proj` is skipped
+        self.assertTrue(not isinstance(quantized_model.model.layers[0].self_attn.q_proj.weight, AffineQuantizedTensor))
+        tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+
+        input_ids = tokenizer(self.input_text, return_tensors="pt").to(self.device)
+
+        output = quantized_model.generate(**input_ids, max_new_tokens=self.max_new_tokens)
+        EXPECTED_OUTPUT = [
+            "What are we having for dinner?\n\nJessica: (smiling)",
+            "What are we having for dinner?\n\nJess: (smiling) I",
+        ]
+        self.assertTrue(tokenizer.decode(output[0], skip_special_tokens=True) in EXPECTED_OUTPUT)
+
+    @require_torchao_version_greater_or_equal("0.13.0")
+    def test_module_fqn_to_config_regex_fullmatch(self):
+        """Testing that we will only match the fqns that fully
+        matches the regex
+        """
+        linear1_config = Int8WeightOnlyConfig()
+        linear2_config = Float8WeightOnlyConfig()
+        # intentially removing `j` after `q_proj` so it's not a full match
+        config = ModuleFqnToConfig({r"re:model\.layers\.+\.self_attn\.q_pro": linear1_config, "model.layers.3.self_attn.q_proj": linear2_config})
+        quant_config = TorchAoConfig(quant_type=config)
+        quantized_model = AutoModelForCausalLM.from_pretrained(
+            self.model_name,
+            device_map=self.device,
+            quantization_config=quant_config,
+        )
+        # highest precedence is fully specified module fqn
+        self.assertTrue(isinstance(quantized_model.model.layers[3].self_attn.q_proj.weight, Float8Tensor))
+        # because regex `model\.layers\.+*\.self_attn\.q_pro` didin't fully match `model.layers.1.self_attn.q_proj` (missing last `j`)
+        # this layer is not expected to be quantized to int8
+        self.assertTrue(not isinstance(quantized_model.model.layers[1].self_attn.q_proj.weight, AffineQuantizedTensor))
+        tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+
+        input_ids = tokenizer(self.input_text, return_tensors="pt").to(self.device)
+
+        output = quantized_model.generate(**input_ids, max_new_tokens=self.max_new_tokens)
+        EXPECTED_OUTPUT = [
+            "What are we having for dinner?\n\nJessica: (smiling)",
+            "What are we having for dinner?\n\nJess: (smiling) I",
+        ]
+        self.assertTrue(tokenizer.decode(output[0], skip_special_tokens=True) in EXPECTED_OUTPUT)
+
+    @require_torchao_version_greater_or_equal("0.13.0")
+    def test_module_fqn_to_config_regex_precedence(self):
+        linear1_config = Int8WeightOnlyConfig()
+        linear2_config = Float8WeightOnlyConfig()
+        config = ModuleFqnToConfig({r"re:model\.layers\..+\.self_attn\.q_proj": None, "model.layers.3.self_attn.q_proj": linear2_config, "_default": linear1_config})
+        quant_config = TorchAoConfig(quant_type=config)
+        quantized_model = AutoModelForCausalLM.from_pretrained(
+            self.model_name,
+            device_map=self.device,
+            quantization_config=quant_config,
+        )
+        # highest precedence is fully specified module fqn
+        self.assertTrue(isinstance(quantized_model.model.layers[3].self_attn.q_proj.weight, Float8Tensor))
+        # second precedence: regex
+        self.assertTrue(not isinstance(quantized_model.model.layers[1].self_attn.q_proj.weight, AffineQuantizedTensor))
+        # last precedence: _default
+        self.assertTrue(isinstance(quantized_model.model.layers[1].self_attn.k_proj.weight, AffineQuantizedTensor))
+        tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+
+        input_ids = tokenizer(self.input_text, return_tensors="pt").to(self.device)
+
+        output = quantized_model.generate(**input_ids, max_new_tokens=self.max_new_tokens)
+        EXPECTED_OUTPUT = [
+            "What are we having for dinner?\n\nJessica: (smiling)",
+            "What are we having for dinner?\n\nJess: (smiling) I",
+        ]
+        self.assertTrue(tokenizer.decode(output[0], skip_special_tokens=True) in EXPECTED_OUTPUT)
+
+
 @require_torch_accelerator
 class TorchAoAcceleratorTest(TorchAoTest):
     device = torch_device