diff --git a/examples/unittest/test_parallel_adapter.py b/examples/unittest/test_parallel_adapter.py
new file mode 100644
index 0000000..7773946
--- /dev/null
+++ b/examples/unittest/test_parallel_adapter.py
@@ -0,0 +1,45 @@
+# use tranformers as usual.
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+t5 = AutoModelForSeq2SeqLM.from_pretrained("t5-large")
+t5_tokenizer = AutoTokenizer.from_pretrained("t5-large")
+# A running example
+inputs_ids = t5_tokenizer.encode("Is Harry Poter wrtten by JKrowling", return_tensors="pt")
+t5_tokenizer.decode(t5.generate(inputs_ids)[0]) 
+# >>> '<pad><extra_id_0>? Is it Harry Potter?</s>'
+
+
+# use existing delta models
+from opendelta import AutoDeltaModel, AutoDeltaConfig
+
+# use default configuration for cunstomized wrapped models which have PLMs inside. This is a common need for users. 
+import torch.nn as nn
+class WrappedModel(nn.Module):
+  def __init__(self, inner_model):
+    super().__init__()
+    self.inner = inner_model
+  def forward(self, *args, **kwargs):
+    return self.inner(*args, **kwargs)
+
+wrapped_model = WrappedModel(WrappedModel(t5))
+
+# say we use LoRA
+delta_config = AutoDeltaConfig.from_dict({"delta_type":"parallel_adapter"})
+delta2 = AutoDeltaModel.from_config(delta_config, backbone_model=wrapped_model)
+delta2.log()
+# >>> root
+#       -- inner
+#          -- inner
+#             -- encoder
+#                -- block
+#                   -- 0
+#                      -- layer
+#                         ...
+#                         -- parallel_adapter
+#                         ...
+#                  -- 1
+#                     -- DenseRuleDense
+#                        -- wi
+#                           -- parallel_adapter
+#                  ...
+delta2.detach()
+delta2.log()