Chore: Update doc for PyTorch Energy Fitting

doc/backend.md

@@ -51,3 +51,7 @@ For example, when the model filename ends with `.pb` (the ProtoBuf file), DeePMD
 ## Convert model files between backends
 
 If a model is supported by two backends, one can use [`dp convert-backend`](./cli.rst) to convert the model file between these two backends.
+
+:::{warning}
+Currently, only the `se_e2_a` model fully supports the backend conversion between TensorFlow {{ tensorflow_icon }} and PyTorch {{ pytorch_icon }}.
+:::

doc/freeze/freeze.md

@@ -2,9 +2,13 @@
 
 The trained neural network is extracted from a checkpoint and dumped into a protobuf(.pb) file. This process is called "freezing" a model. The idea and part of our code are from [Morgan](https://blog.metaflow.fr/tensorflow-how-to-freeze-a-model-and-serve-it-with-a-python-api-d4f3596b3adc). To freeze a model, typically one does
 ```bash
-$ dp freeze -o graph.pb
+$ dp freeze -o model.pb
 ```
-in the folder where the model is trained. The output model is called `graph.pb`.
+or
+```bash
+$ dp --pt freeze -o model.pth
+```
+in the folder where the model is trained. The output model is called `model.pb` or `model.pth`.
 
 In [multi-task mode](../train/multi-task-training.md):
 - This process will in default output several models, each of which contains the common descriptor and

doc/model/pairtab.md

@@ -53,6 +53,10 @@ in the order of Type_0-Type_0, Type_0-Type_1, ..., Type_0-Type_N, Type_1-Type_1,
 
 The interaction should be smooth at the cut-off distance.
 
+:::{note}
+In instances where the interaction at the cut-off distance is not delineated within the table file, extrapolation will be conducted utilizing the available interaction data. This extrapolative procedure guarantees a smooth transition from the table-provided value to `0` whenever feasible.
+:::
+
 ## Interpolation with a short-range pairwise potential
 
 ```json