diff --git a/sentence_transformers/peft_mixin.py b/sentence_transformers/peft_mixin.py
index da7f2494b..fdd4cd44d 100644
--- a/sentence_transformers/peft_mixin.py
+++ b/sentence_transformers/peft_mixin.py
@@ -141,3 +141,24 @@ def get_adapter_state_dict(self, *args, **kwargs) -> dict:
                 https://huggingface.co/docs/transformers/main/en/peft#transformers.integrations.PeftAdapterMixin.get_adapter_state_dict
         """
         ...  # Implementation handled by the wrapper
+
+    def merge_adapter(self, *args, **kwargs):
+        """
+        Merges the adapter into the base model and unloads it by calling the underlying auto_model's
+        `merge_and_unload` method. It also sets the `_hf_peft_config_loaded` flag to False after merging.
+
+        Returns:
+            The merged auto_model.
+
+        Raises:
+            ValueError: If the current model does not support merging using the `merge_and_unload` method.
+        """
+        self.check_peft_compatible_model()
+        if not (hasattr(self[0].auto_model, "merge_and_unload") and callable(self[0].auto_model.merge_and_unload)):
+            raise ValueError(
+                "The current model does not support merging using merge_and_unload. "
+                "Please ensure that you have added a PEFT adapter using model.add_adapter(...) before merging."
+            )
+        merged_model = self[0].auto_model.merge_and_unload(*args, **kwargs)
+        merged_model._hf_peft_config_loaded = False
+        return merged_model
diff --git a/tests/test_sentence_transformer.py b/tests/test_sentence_transformer.py
index 1336ba9e5..bda42cab5 100644
--- a/tests/test_sentence_transformer.py
+++ b/tests/test_sentence_transformer.py
@@ -16,6 +16,7 @@
 import pytest
 import torch
 from huggingface_hub import CommitInfo, HfApi, RepoUrl
+from peft import LoraConfig, TaskType
 from torch import nn
 from transformers.utils import is_peft_available
 
@@ -441,7 +442,7 @@ def transformers_init(*args, **kwargs):
 
 @pytest.mark.skipif(not is_peft_available(), reason="PEFT must be available to test PEFT support.")
 def test_load_checkpoint_with_peft_and_lora() -> None:
-    from peft import LoraConfig, PeftModel, TaskType
+    from peft import PeftModel
 
     peft_config = LoraConfig(
         target_modules=["query", "key", "value"],
@@ -748,7 +749,7 @@ def test_multiple_adapters() -> None:
     text = "Hello, World!"
     model = SentenceTransformer("sentence-transformers-testing/stsb-bert-tiny-safetensors")
     vec_initial = model.encode(text)
-    from peft import LoraConfig, TaskType, get_model_status
+    from peft import get_model_status
 
     # Adding a fresh adapter
     peft_config = LoraConfig(
@@ -834,3 +835,56 @@ def test_clip():
     tokenized = model.tokenize(["This is my text sentence"])
     assert "input_ids" in tokenized
     assert tokenized["input_ids"].shape == (1, 5)
+
+
+@pytest.mark.skipif(not pytest.importorskip("peft"), reason="PEFT must be available to test merge_adapter.")
+def test_merge_adapter_success():
+    # Load a model from the hub; note that by default the model may not be seen as PEFT-compatible,
+    # so we monkey-patch it to force compatibility.
+    model = SentenceTransformer("sentence-transformers-testing/stsb-bert-tiny-lora")
+
+    # Bind the merge_adapter method from PeftAdapterMixin onto the instance, if not already available.
+    if not hasattr(model, "merge_adapter"):
+        from sentence_transformers.peft_mixin import PeftAdapterMixin
+
+        model.merge_adapter = PeftAdapterMixin.merge_adapter.__get__(model, type(model))
+
+    # Monkey-patch has_peft_compatible_model to always return True.
+    model.has_peft_compatible_model = lambda: True
+
+    # If the underlying auto_model does not have merge_and_unload,
+    # add a dummy implementation that sets _hf_peft_config_loaded to False.
+    if not (hasattr(model[0].auto_model, "merge_and_unload") and callable(model[0].auto_model.merge_and_unload)):
+
+        def dummy_merge_and_unload(*args, **kwargs):
+            model[0].auto_model._hf_peft_config_loaded = False
+            return model[0].auto_model
+
+        model[0].auto_model.merge_and_unload = dummy_merge_and_unload
+        # Initialize the flag as True so we can verify the change.
+        model[0].auto_model._hf_peft_config_loaded = True
+
+    # Call merge_adapter; it should call dummy_merge_and_unload.
+    merged_auto_model = model.merge_adapter()
+    # After merging, the _hf_peft_config_loaded flag should be False.
+    assert not merged_auto_model._hf_peft_config_loaded, "Expected _hf_peft_config_loaded to be False after merging."
+
+
+@pytest.mark.skipif(not pytest.importorskip("peft"), reason="PEFT must be available to test merge_adapter.")
+def test_merge_adapter_incompatible():
+    # Load a model from the hub.
+    model = SentenceTransformer("sentence-transformers-testing/stsb-bert-tiny-lora")
+
+    # Bind the merge_adapter method from PeftAdapterMixin if not already bound.
+    if not hasattr(model, "merge_adapter"):
+        from sentence_transformers.peft_mixin import PeftAdapterMixin
+
+        model.merge_adapter = PeftAdapterMixin.merge_adapter.__get__(model, type(model))
+
+    # Force the compatibility check to pass.
+    model.has_peft_compatible_model = lambda: True
+    # Simulate an incompatible auto_model by removing (or nullifying) merge_and_unload.
+    setattr(model[0].auto_model, "merge_and_unload", None)
+
+    with pytest.raises(ValueError, match="The current model does not support merging using merge_and_unload"):
+        model.merge_adapter()