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deepmodeling · Mar 2, 2024 · be183f1 · be183f1
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diff --git a/doc/development/create-a-model-pt.md b/doc/development/create-a-model-pt.md
@@ -156,6 +156,6 @@ The arguments here should be consistent with the class arguments of your new com
 
 ## Unit tests
 
-When transferring features from another backend to the PyTorch backend, it is essential to include a regression test in `/source/tests/consistent` to validate the consistency of the PyTorch backend with other backends. Presently, the regression tests cover self-consistency and cross-backend consistency between TensorFlow, PyTorch, and dpmodel (Numpy) through the serialization/deserialization technique.
+When transferring features from another backend to the PyTorch backend, it is essential to include a regression test in `/source/tests/consistent` to validate the consistency of the PyTorch backend with other backends. Presently, the regression tests cover self-consistency and cross-backend consistency between TensorFlow, PyTorch, and DP (Numpy) through the serialization/deserialization technique.
 
-During the development of new components within the PyTorch backend, it is necessary to provide a dpmodel (Numpy) implementation and incorporate corresponding regression tests. For PyTorch components, developers are also required to include a unit test using `torch.jit`.
+During the development of new components within the PyTorch backend, it is necessary to provide a DP (Numpy) implementation and incorporate corresponding regression tests. For PyTorch components, developers are also required to include a unit test using `torch.jit`.
diff --git a/doc/model/dprc.md b/doc/model/dprc.md
@@ -1,7 +1,7 @@
 # Deep Potential - Range Correction (DPRc) {{ tensorflow_icon }} {{ pytorch_icon }} {{ dpmodel_icon }}
 
 :::{note}
-**Supported backends**: TensorFlow {{ tensorflow_icon }}, PyTorch {{ pytorch_icon }}, DPModel {{ dpmodel_icon }}
+**Supported backends**: TensorFlow {{ tensorflow_icon }}, PyTorch {{ pytorch_icon }}, DP {{ dpmodel_icon }}
 :::
 
 Deep Potential - Range Correction (DPRc) is designed to combine with QM/MM method, and corrects energies from a low-level QM/MM method to a high-level QM/MM method:

diff --git a/doc/model/train-energy.md b/doc/model/train-energy.md
@@ -1,7 +1,7 @@
-# Fit energy {{ tensorflow_icon }} {{ pytorch_icon }}
+# Fit energy {{ tensorflow_icon }} {{ pytorch_icon }} {{ dpmodel_icon }}
 
 :::{note}
-**Supported backends**: TensorFlow {{ tensorflow_icon }}, PyTorch {{ pytorch_icon }}
+**Supported backends**: TensorFlow {{ tensorflow_icon }}, PyTorch {{ pytorch_icon }}, DP {{ dpmodel_icon }}
 :::
 
 In this section, we will take `$deepmd_source_dir/examples/water/se_e2_a/input.json` as an example of the input file.