diff --git a/src/transformers/integrations/peft.py b/src/transformers/integrations/peft.py
index 4c1c12a4058e..32c8709c5fe2 100644
--- a/src/transformers/integrations/peft.py
+++ b/src/transformers/integrations/peft.py
@@ -279,7 +279,7 @@ def load_adapter(
             )
             peft_config.inference_mode = not is_trainable
 
-        if peft_config.peft_type != PeftType.LORA:
+        if hotswap and (peft_config.peft_type != PeftType.LORA):
             raise ValueError("Hotswapping is currently only supported for LoRA, please set `hotswap=False`.")
 
         if not hotswap:
diff --git a/tests/peft_integration/test_peft_integration.py b/tests/peft_integration/test_peft_integration.py
index 1e0e2335067d..0f4e16964265 100644
--- a/tests/peft_integration/test_peft_integration.py
+++ b/tests/peft_integration/test_peft_integration.py
@@ -889,6 +889,60 @@ def test_peft_pipeline_no_warning(self):
             # Generate text to verify pipeline works
             _ = lora_generator(text, max_new_tokens=20)
 
+    def test_non_lora_load_adapter(self):
+        """
+        Check that loading a non-LoRA adapter works. Using LoKr as an example, not testing all possible PEFT methods.
+        """
+        from peft import LoKrConfig, get_peft_model
+
+        inputs = torch.randint(0, 100, (1, 10)).to(torch_device)
+        atol, rtol = 1e-4, 1e-4
+
+        for model_id in self.transformers_test_model_ids:
+            for transformers_class in self.transformers_test_model_classes:
+                model = transformers_class.from_pretrained(model_id).to(torch_device)
+                with torch.inference_mode():
+                    output_base = model(inputs).logits
+
+                peft_config = LoKrConfig(init_weights=False)
+                peft_model = get_peft_model(model, peft_config)
+                with torch.inference_mode():
+                    output_peft = peft_model(inputs).logits
+
+                # sanity check: should be different
+                assert not torch.allclose(output_base, output_peft, atol=atol, rtol=rtol)
+
+                with tempfile.TemporaryDirectory() as tmpdirname:
+                    peft_model.save_pretrained(tmpdirname)
+                    del model, peft_model
+
+                    model = transformers_class.from_pretrained(tmpdirname).to(torch_device)
+                    with torch.inference_mode():
+                        output_transformers = model(inputs).logits
+                    self.assertTrue(torch.allclose(output_peft, output_transformers, atol=atol, rtol=rtol))
+
+    def test_non_lora_add_adapter(self):
+        """
+        Check that adding a non-LoRA adapter works. Using LoKr as an example, not testing all possible PEFT methods.
+        """
+        from peft import LoKrConfig
+
+        inputs = torch.randint(0, 100, (1, 10)).to(torch_device)
+        atol, rtol = 1e-4, 1e-4
+
+        for model_id in self.transformers_test_model_ids:
+            for transformers_class in self.transformers_test_model_classes:
+                model = transformers_class.from_pretrained(model_id).to(torch_device)
+                with torch.inference_mode():
+                    output_base = model(inputs).logits
+
+                peft_config = LoKrConfig(init_weights=False)
+                model.add_adapter(peft_config)
+                with torch.inference_mode():
+                    output_peft = model(inputs).logits
+                # should be different
+                assert not torch.allclose(output_base, output_peft, atol=atol, rtol=rtol)
+
 
 @require_peft
 @require_torch