--- a
+++ b/src/model/modeling_dummy.py
@@ -0,0 +1,49 @@
+from dataclasses import dataclass
+from typing import Tuple
+
+import torch
+from transformers import AutoModel, AutoConfig
+from transformers import PreTrainedModel, PretrainedConfig
+from transformers.utils import ModelOutput
+
+
+@dataclass
+class DummyModelOutput(ModelOutput):
+    hidden_states: Tuple[torch.Tensor]
+
+
+class DummyModelConfig(PretrainedConfig):
+    model_type = "dummy_model"
+
+    def __init__(self, hidden_size: int = 768, **kwargs) -> None:
+        self.hidden_size = hidden_size
+        super().__init__(**kwargs)
+        return
+
+
+class DummyModel(PreTrainedModel):
+    config_class = DummyModelConfig
+
+    def __init__(self, config: DummyModelConfig) -> None:
+        super().__init__(config)
+        self.param = torch.nn.Parameter(torch.zeros(1), requires_grad=False)
+        return
+
+    def forward(self, x: torch.Tensor, **kwargs) -> DummyModelOutput:
+        assert x.shape[-1] == self.config.hidden_size
+        dtype = self.param.dtype
+        x = x.to(dtype)
+        return DummyModelOutput(hidden_states=(x,))
+
+
+AutoConfig.register("dummy_model", DummyModelConfig)
+AutoModel.register(DummyModelConfig, DummyModel)
+
+if __name__ == "__main__":
+    config = DummyModelConfig(hidden_size=3)
+    print(config)
+    model = AutoModel.from_config(config)
+    print(model)
+    x = torch.randn(1, 2, 3)
+    output = model(x)
+    print(output)