diff --git a/docs/source/en/peft.md b/docs/source/en/peft.md
index 302b614e5..d86a36e62 100644
--- a/docs/source/en/peft.md
+++ b/docs/source/en/peft.md
@@ -98,7 +98,7 @@ You can use [`~peft.PeftModel.add_adapter`] to add a new adapter to a model with
 
 ```py
 from transformers import AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer
-from peft import PeftConfig
+from peft import LoraConfig
 
 model_id = "facebook/opt-350m"
 model = AutoModelForCausalLM.from_pretrained(model_id)
@@ -208,6 +208,26 @@ model.save_pretrained(save_dir)
 model = AutoModelForCausalLM.from_pretrained(save_dir)
 ```
 
+## Add additional trainable layers to a PEFT adapter
+
+You can also fine-tune additional trainable adapters on top of a model that has adapters attached by passing `modules_to_save` in your PEFT config. For example, if you want to also fine-tune the lm_head on top of a model with a LoRA adapter:
+
+```py
+from transformers import AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer
+from peft import LoraConfig
+
+model_id = "facebook/opt-350m"
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+lora_config = LoraConfig(
+    target_modules=["q_proj", "k_proj"],
+    modules_to_save=["lm_head"],
+)
+
+model.add_adapter(lora_config)
+```
+
+
 <!--
 TODO: (@younesbelkada @stevhliu)
 -   Link to PEFT docs for further details
diff --git a/src/transformers/integrations/peft.py b/src/transformers/integrations/peft.py
index 8be09e863..e04d23995 100644
--- a/src/transformers/integrations/peft.py
+++ b/src/transformers/integrations/peft.py
@@ -292,11 +292,12 @@ class PeftAdapterMixin:
             )
 
         from peft.tuners.tuners_utils import BaseTunerLayer
+        from peft.utils import ModulesToSaveWrapper
 
         _adapters_has_been_set = False
 
         for _, module in self.named_modules():
-            if isinstance(module, BaseTunerLayer):
+            if isinstance(module, (BaseTunerLayer, ModulesToSaveWrapper)):
                 # For backward compatbility with previous PEFT versions
                 if hasattr(module, "set_adapter"):
                     module.set_adapter(adapter_name)
@@ -322,9 +323,10 @@ class PeftAdapterMixin:
             raise ValueError("No adapter loaded. Please load an adapter first.")
 
         from peft.tuners.tuners_utils import BaseTunerLayer
+        from peft.utils import ModulesToSaveWrapper
 
         for _, module in self.named_modules():
-            if isinstance(module, BaseTunerLayer):
+            if isinstance(module, (BaseTunerLayer, ModulesToSaveWrapper)):
                 # The recent version of PEFT need to call `enable_adapters` instead
                 if hasattr(module, "enable_adapters"):
                     module.enable_adapters(enabled=False)
diff --git a/tests/peft_integration/test_peft_integration.py b/tests/peft_integration/test_peft_integration.py
index 95f5f8447..50890b886 100644
--- a/tests/peft_integration/test_peft_integration.py
+++ b/tests/peft_integration/test_peft_integration.py
@@ -182,6 +182,44 @@ class PeftIntegrationTester(unittest.TestCase, PeftTesterMixin):
                     model_from_pretrained = transformers_class.from_pretrained(tmpdirname).to(torch_device)
                     self.assertTrue(self._check_lora_correctly_converted(model_from_pretrained))
 
+    def test_peft_add_adapter_modules_to_save(self):
+        """
+        Simple test that tests if `add_adapter` works as expected when training with
+        modules to save.
+        """
+        from peft import LoraConfig
+        from peft.utils import ModulesToSaveWrapper
+
+        for model_id in self.transformers_test_model_ids:
+            for transformers_class in self.transformers_test_model_classes:
+                dummy_input = torch.LongTensor([[0, 1, 2, 3, 4, 5, 6, 7]]).to(torch_device)
+
+                model = transformers_class.from_pretrained(model_id).to(torch_device)
+                peft_config = LoraConfig(init_lora_weights=False, modules_to_save=["lm_head"])
+                model.add_adapter(peft_config)
+                self._check_lora_correctly_converted(model)
+
+                _has_modules_to_save_wrapper = False
+                for name, module in model.named_modules():
+                    if isinstance(module, ModulesToSaveWrapper):
+                        _has_modules_to_save_wrapper = True
+                        self.assertTrue(module.modules_to_save.default.weight.requires_grad)
+                        self.assertTrue("lm_head" in name)
+                        break
+
+                self.assertTrue(_has_modules_to_save_wrapper)
+                state_dict = model.get_adapter_state_dict()
+
+                self.assertTrue("lm_head.weight" in state_dict.keys())
+
+                logits = model(dummy_input).logits
+                loss = logits.mean()
+                loss.backward()
+
+                for _, param in model.named_parameters():
+                    if param.requires_grad:
+                        self.assertTrue(param.grad is not None)
+
     def test_peft_add_adapter_training_gradient_checkpointing(self):
         """
         Simple test that tests if `add_adapter` works as expected when training with