diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 672ad87f05..90ce425b14 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -29,7 +29,7 @@ repos:
         exclude: ^source/3rdparty
   - repo: https://github.com/astral-sh/ruff-pre-commit
     # Ruff version.
-    rev: v0.4.1
+    rev: v0.4.2
     hooks:
       - id: ruff
         args: ["--fix"]
diff --git a/backend/read_env.py b/backend/read_env.py
index c97c854a13..14935dcc0f 100644
--- a/backend/read_env.py
+++ b/backend/read_env.py
@@ -63,7 +63,7 @@ def get_argument_from_env() -> Tuple[str, list, list, dict, str]:
         if hipcc_flags is not None:
             os.environ["HIPFLAGS"] = os.environ.get("HIPFLAGS", "") + " " + hipcc_flags
     else:
-        raise RuntimeError("Unsupported DP_VARIANT option: %s" % dp_variant)
+        raise RuntimeError(f"Unsupported DP_VARIANT option: {dp_variant}")
 
     if os.environ.get("DP_BUILD_TESTING", "0") == "1":
         cmake_args.append("-DBUILD_TESTING:BOOL=TRUE")
diff --git a/data/raw/copy_raw.py b/data/raw/copy_raw.py
index 69ccdf5c63..2a4078df4d 100755
--- a/data/raw/copy_raw.py
+++ b/data/raw/copy_raw.py
@@ -85,7 +85,7 @@ def _main():
     )
     args = parser.parse_args()
 
-    print("# copy the system by %s copies" % args.ncopies)  # noqa: T201
+    print(f"# copy the system by {args.ncopies} copies")  # noqa: T201
     assert np.all(
         np.array(args.ncopies, dtype=int) >= np.array([1, 1, 1], dtype=int)
     ), "number of copies should be larger than or equal to 1"
diff --git a/deepmd/dpmodel/descriptor/make_base_descriptor.py b/deepmd/dpmodel/descriptor/make_base_descriptor.py
index 940bd0cd27..a984c96f33 100644
--- a/deepmd/dpmodel/descriptor/make_base_descriptor.py
+++ b/deepmd/dpmodel/descriptor/make_base_descriptor.py
@@ -135,7 +135,7 @@ def deserialize(cls, data: dict) -> "BD":
             """
             if cls is BD:
                 return BD.get_class_by_type(data["type"]).deserialize(data)
-            raise NotImplementedError("Not implemented in class %s" % cls.__name__)
+            raise NotImplementedError(f"Not implemented in class {cls.__name__}")
 
         @classmethod
         @abstractmethod
diff --git a/deepmd/dpmodel/fitting/make_base_fitting.py b/deepmd/dpmodel/fitting/make_base_fitting.py
index c7341798c3..72dc83c29e 100644
--- a/deepmd/dpmodel/fitting/make_base_fitting.py
+++ b/deepmd/dpmodel/fitting/make_base_fitting.py
@@ -88,7 +88,7 @@ def deserialize(cls, data: dict) -> "BF":
             """
             if cls is BF:
                 return BF.get_class_by_type(data["type"]).deserialize(data)
-            raise NotImplementedError("Not implemented in class %s" % cls.__name__)
+            raise NotImplementedError(f"Not implemented in class {cls.__name__}")
 
     setattr(BF, fwd_method_name, BF.fwd)
     delattr(BF, "fwd")
diff --git a/deepmd/dpmodel/model/base_model.py b/deepmd/dpmodel/model/base_model.py
index de69afcf6c..b416e57fb8 100644
--- a/deepmd/dpmodel/model/base_model.py
+++ b/deepmd/dpmodel/model/base_model.py
@@ -126,7 +126,7 @@ def deserialize(cls, data: dict) -> "BaseBaseModel":
                 if model_type == "standard":
                     model_type = data.get("fitting", {}).get("type", "ener")
                 return cls.get_class_by_type(model_type).deserialize(data)
-            raise NotImplementedError("Not implemented in class %s" % cls.__name__)
+            raise NotImplementedError(f"Not implemented in class {cls.__name__}")
 
         model_def_script: str
 
diff --git a/deepmd/entrypoints/doc.py b/deepmd/entrypoints/doc.py
index e55e84f9d3..2f0d57e645 100644
--- a/deepmd/entrypoints/doc.py
+++ b/deepmd/entrypoints/doc.py
@@ -16,5 +16,5 @@ def doc_train_input(*, out_type: str = "rst", **kwargs):
     elif out_type == "json":
         doc_str = gen_json()
     else:
-        raise RuntimeError("Unsupported out type %s" % out_type)
+        raise RuntimeError(f"Unsupported out type {out_type}")
     print(doc_str)  # noqa: T201
diff --git a/deepmd/entrypoints/neighbor_stat.py b/deepmd/entrypoints/neighbor_stat.py
index a68a3fd3bb..ef02561ba3 100644
--- a/deepmd/entrypoints/neighbor_stat.py
+++ b/deepmd/entrypoints/neighbor_stat.py
@@ -91,6 +91,6 @@ def neighbor_stat(
     data.get_batch()
     nei = NeighborStat(data.get_ntypes(), rcut, mixed_type=mixed_type)
     min_nbor_dist, max_nbor_size = nei.get_stat(data)
-    log.info("min_nbor_dist: %f" % min_nbor_dist)
-    log.info("max_nbor_size: %s" % str(max_nbor_size))
+    log.info(f"min_nbor_dist: {min_nbor_dist:f}")
+    log.info(f"max_nbor_size: {max_nbor_size!s}")
     return min_nbor_dist, max_nbor_size
diff --git a/deepmd/entrypoints/test.py b/deepmd/entrypoints/test.py
index 1540a5a1b9..5f0225c0cd 100644
--- a/deepmd/entrypoints/test.py
+++ b/deepmd/entrypoints/test.py
@@ -460,14 +460,14 @@ def test_ener(
         save_txt_file(
             detail_path.with_suffix(".e.out"),
             pe,
-            header="%s: data_e pred_e" % system,
+            header=f"{system}: data_e pred_e",
             append=append_detail,
         )
         pe_atom = pe / natoms
         save_txt_file(
             detail_path.with_suffix(".e_peratom.out"),
             pe_atom,
-            header="%s: data_e pred_e" % system,
+            header=f"{system}: data_e pred_e",
             append=append_detail,
         )
         if not out_put_spin:
@@ -481,7 +481,7 @@ def test_ener(
             save_txt_file(
                 detail_path.with_suffix(".f.out"),
                 pf,
-                header="%s: data_fx data_fy data_fz pred_fx pred_fy pred_fz" % system,
+                header=f"{system}: data_fx data_fy data_fz pred_fx pred_fy pred_fz",
                 append=append_detail,
             )
         else:
@@ -496,14 +496,13 @@ def test_ener(
             save_txt_file(
                 detail_path.with_suffix(".fr.out"),
                 pf_real,
-                header="%s: data_fx data_fy data_fz pred_fx pred_fy pred_fz" % system,
+                header=f"{system}: data_fx data_fy data_fz pred_fx pred_fy pred_fz",
                 append=append_detail,
             )
             save_txt_file(
                 detail_path.with_suffix(".fm.out"),
                 pf_mag,
-                header="%s: data_fmx data_fmy data_fmz pred_fmx pred_fmy pred_fmz"
-                % system,
+                header=f"{system}: data_fmx data_fmy data_fmz pred_fmx pred_fmy pred_fmz",
                 append=append_detail,
             )
         pv = np.concatenate(
diff --git a/deepmd/env.py b/deepmd/env.py
index 8215de39ac..139e95b824 100644
--- a/deepmd/env.py
+++ b/deepmd/env.py
@@ -46,9 +46,9 @@
     global_float_prec = "float"
 else:
     raise RuntimeError(
-        "Unsupported float precision option: %s. Supported: high,"
+        f"Unsupported float precision option: {dp_float_prec}. Supported: high,"
         "low. Please set precision with environmental variable "
-        "DP_INTERFACE_PREC." % dp_float_prec
+        "DP_INTERFACE_PREC."
     )
 
 
diff --git a/deepmd/main.py b/deepmd/main.py
index b503107c73..9d023221a6 100644
--- a/deepmd/main.py
+++ b/deepmd/main.py
@@ -650,7 +650,7 @@ def main_parser() -> argparse.ArgumentParser:
 
     # --version
     parser.add_argument(
-        "--version", action="version", version="DeePMD-kit v%s" % __version__
+        "--version", action="version", version=f"DeePMD-kit v{__version__}"
     )
 
     # * train nvnmd script ******************************************************************
diff --git a/deepmd/pt/optimizer/LKF.py b/deepmd/pt/optimizer/LKF.py
index 6196414243..6683d24fe7 100644
--- a/deepmd/pt/optimizer/LKF.py
+++ b/deepmd/pt/optimizer/LKF.py
@@ -75,7 +75,7 @@ def __init_P(self):
 
         P = []
         params_packed_index = []
-        logging.info("LKF parameter nums: %s" % param_nums)
+        logging.info(f"LKF parameter nums: {param_nums}")
         if self.dist_init:
             block_num = 0
             for param_num in param_nums:
diff --git a/deepmd/pt/train/training.py b/deepmd/pt/train/training.py
index 5ba4b6f336..6934a9d8fe 100644
--- a/deepmd/pt/train/training.py
+++ b/deepmd/pt/train/training.py
@@ -651,7 +651,7 @@ def warm_up_linear(step, warmup_steps):
                 self.wrapper.parameters(), 0.98, 0.99870, self.opt_param["kf_blocksize"]
             )
         else:
-            raise ValueError("Not supported optimizer type '%s'" % self.opt_type)
+            raise ValueError(f"Not supported optimizer type '{self.opt_type}'")
 
         # Get model prob for multi-task
         if self.multi_task:
@@ -808,7 +808,7 @@ def fake_model():
                         learning_rate=pref_lr,
                     )
             else:
-                raise ValueError("Not supported optimizer type '%s'" % self.opt_type)
+                raise ValueError(f"Not supported optimizer type '{self.opt_type}'")
 
             # Log and persist
             if _step_id % self.disp_freq == 0:
@@ -1170,7 +1170,7 @@ def print_on_training(self, fout, step_id, cur_lr, train_results, valid_results)
                     prop_fmt = "   %11.2e"
                     for k in sorted(train_results[model_key].keys()):
                         print_str += prop_fmt % (train_results[model_key][k])
-        print_str += "   %8.1e\n" % cur_lr
+        print_str += f"   {cur_lr:8.1e}\n"
         fout.write(print_str)
         fout.flush()
 
diff --git a/deepmd/tf/cluster/local.py b/deepmd/tf/cluster/local.py
index 60961a0d65..009a182e55 100644
--- a/deepmd/tf/cluster/local.py
+++ b/deepmd/tf/cluster/local.py
@@ -45,7 +45,7 @@ def get_gpus():
         stdout, stderr = p.communicate()
         if p.returncode != 0:
             decoded = stderr.decode("UTF-8")
-            raise RuntimeError("Failed to detect availbe GPUs due to:\n%s" % decoded)
+            raise RuntimeError(f"Failed to detect availbe GPUs due to:\n{decoded}")
         decoded = stdout.decode("UTF-8").strip()
         num_gpus = int(decoded)
         return list(range(num_gpus)) if num_gpus > 0 else None
diff --git a/deepmd/tf/descriptor/descriptor.py b/deepmd/tf/descriptor/descriptor.py
index 6a4ed5f354..fd6f6729e8 100644
--- a/deepmd/tf/descriptor/descriptor.py
+++ b/deepmd/tf/descriptor/descriptor.py
@@ -247,7 +247,7 @@ def enable_compression(
         This method is called by others when the descriptor supported compression.
         """
         raise NotImplementedError(
-            "Descriptor %s doesn't support compression!" % type(self).__name__
+            f"Descriptor {type(self).__name__} doesn't support compression!"
         )
 
     def enable_mixed_precision(self, mixed_prec: Optional[dict] = None) -> None:
@@ -263,8 +263,7 @@ def enable_mixed_precision(self, mixed_prec: Optional[dict] = None) -> None:
         This method is called by others when the descriptor supported compression.
         """
         raise NotImplementedError(
-            "Descriptor %s doesn't support mixed precision training!"
-            % type(self).__name__
+            f"Descriptor {type(self).__name__} doesn't support mixed precision training!"
         )
 
     @abstractmethod
@@ -315,8 +314,7 @@ def init_variables(
         This method is called by others when the descriptor supported initialization from the given variables.
         """
         raise NotImplementedError(
-            "Descriptor %s doesn't support initialization from the given variables!"
-            % type(self).__name__
+            f"Descriptor {type(self).__name__} doesn't support initialization from the given variables!"
         )
 
     def get_tensor_names(self, suffix: str = "") -> Tuple[str]:
@@ -333,7 +331,7 @@ def get_tensor_names(self, suffix: str = "") -> Tuple[str]:
             Names of tensors
         """
         raise NotImplementedError(
-            "Descriptor %s doesn't support this property!" % type(self).__name__
+            f"Descriptor {type(self).__name__} doesn't support this property!"
         )
 
     def pass_tensors_from_frz_model(
@@ -353,7 +351,7 @@ def pass_tensors_from_frz_model(
         :meth:`get_tensor_names`.
         """
         raise NotImplementedError(
-            "Descriptor %s doesn't support this method!" % type(self).__name__
+            f"Descriptor {type(self).__name__} doesn't support this method!"
         )
 
     def build_type_exclude_mask(
@@ -498,7 +496,7 @@ def deserialize(cls, data: dict, suffix: str = "") -> "Descriptor":
             return Descriptor.get_class_by_type(
                 j_get_type(data, cls.__name__)
             ).deserialize(data, suffix=suffix)
-        raise NotImplementedError("Not implemented in class %s" % cls.__name__)
+        raise NotImplementedError(f"Not implemented in class {cls.__name__}")
 
     def serialize(self, suffix: str = "") -> dict:
         """Serialize the model.
@@ -513,4 +511,4 @@ def serialize(self, suffix: str = "") -> dict:
         suffix : str, optional
             Name suffix to identify this descriptor
         """
-        raise NotImplementedError("Not implemented in class %s" % self.__name__)
+        raise NotImplementedError(f"Not implemented in class {self.__name__}")
diff --git a/deepmd/tf/descriptor/loc_frame.py b/deepmd/tf/descriptor/loc_frame.py
index ee414fc0bb..963e9bf607 100644
--- a/deepmd/tf/descriptor/loc_frame.py
+++ b/deepmd/tf/descriptor/loc_frame.py
@@ -427,12 +427,8 @@ def init_variables(
         suffix : str, optional
             The suffix of the scope
         """
-        self.davg = get_tensor_by_name_from_graph(
-            graph, "descrpt_attr%s/t_avg" % suffix
-        )
-        self.dstd = get_tensor_by_name_from_graph(
-            graph, "descrpt_attr%s/t_std" % suffix
-        )
+        self.davg = get_tensor_by_name_from_graph(graph, f"descrpt_attr{suffix}/t_avg")
+        self.dstd = get_tensor_by_name_from_graph(graph, f"descrpt_attr{suffix}/t_std")
 
     @classmethod
     def update_sel(cls, global_jdata: dict, local_jdata: dict):
diff --git a/deepmd/tf/descriptor/se.py b/deepmd/tf/descriptor/se.py
index 5a3c5dd447..a14dc0a5ce 100644
--- a/deepmd/tf/descriptor/se.py
+++ b/deepmd/tf/descriptor/se.py
@@ -141,12 +141,8 @@ def init_variables(
         self.embedding_net_variables = get_embedding_net_variables_from_graph_def(
             graph_def, suffix=suffix
         )
-        self.davg = get_tensor_by_name_from_graph(
-            graph, "descrpt_attr%s/t_avg" % suffix
-        )
-        self.dstd = get_tensor_by_name_from_graph(
-            graph, "descrpt_attr%s/t_std" % suffix
-        )
+        self.davg = get_tensor_by_name_from_graph(graph, f"descrpt_attr{suffix}/t_avg")
+        self.dstd = get_tensor_by_name_from_graph(graph, f"descrpt_attr{suffix}/t_std")
 
     @property
     def precision(self) -> tf.DType:
diff --git a/deepmd/tf/descriptor/se_a.py b/deepmd/tf/descriptor/se_a.py
index 4f7897e76c..a3c96d3b9f 100644
--- a/deepmd/tf/descriptor/se_a.py
+++ b/deepmd/tf/descriptor/se_a.py
@@ -554,12 +554,8 @@ def enable_compression(
             min_nbor_dist, table_extrapolate, table_stride_1, table_stride_2
         )
 
-        self.davg = get_tensor_by_name_from_graph(
-            graph, "descrpt_attr%s/t_avg" % suffix
-        )
-        self.dstd = get_tensor_by_name_from_graph(
-            graph, "descrpt_attr%s/t_std" % suffix
-        )
+        self.davg = get_tensor_by_name_from_graph(graph, f"descrpt_attr{suffix}/t_avg")
+        self.dstd = get_tensor_by_name_from_graph(graph, f"descrpt_attr{suffix}/t_std")
 
     def enable_mixed_precision(self, mixed_prec: Optional[dict] = None) -> None:
         """Reveive the mixed precision setting.
@@ -1305,14 +1301,14 @@ def init_variables(
         super().init_variables(graph=graph, graph_def=graph_def, suffix=suffix)
         try:
             self.original_sel = get_tensor_by_name_from_graph(
-                graph, "descrpt_attr%s/original_sel" % suffix
+                graph, f"descrpt_attr{suffix}/original_sel"
             )
         except GraphWithoutTensorError:
             # original_sel is not restored in old graphs, assume sel never changed before
             pass
         # check sel == original sel?
         try:
-            sel = get_tensor_by_name_from_graph(graph, "descrpt_attr%s/sel" % suffix)
+            sel = get_tensor_by_name_from_graph(graph, f"descrpt_attr{suffix}/sel")
         except GraphWithoutTensorError:
             # sel is not restored in old graphs
             pass
@@ -1387,7 +1383,7 @@ def deserialize(cls, data: dict, suffix: str = ""):
             The deserialized model
         """
         if cls is not DescrptSeA:
-            raise NotImplementedError("Not implemented in class %s" % cls.__name__)
+            raise NotImplementedError(f"Not implemented in class {cls.__name__}")
         data = data.copy()
         check_version_compatibility(data.pop("@version", 1), 1, 1)
         data.pop("@class", None)
@@ -1422,7 +1418,7 @@ def serialize(self, suffix: str = "") -> dict:
         """
         if type(self) is not DescrptSeA:
             raise NotImplementedError(
-                "Not implemented in class %s" % self.__class__.__name__
+                f"Not implemented in class {self.__class__.__name__}"
             )
         if self.stripped_type_embedding:
             raise NotImplementedError(
diff --git a/deepmd/tf/descriptor/se_atten.py b/deepmd/tf/descriptor/se_atten.py
index 0ba426ee4b..9b13c3944b 100644
--- a/deepmd/tf/descriptor/se_atten.py
+++ b/deepmd/tf/descriptor/se_atten.py
@@ -492,12 +492,8 @@ def enable_compression(
         )
         self.two_embd = make_data(self, self.final_type_embedding)
 
-        self.davg = get_tensor_by_name_from_graph(
-            graph, "descrpt_attr%s/t_avg" % suffix
-        )
-        self.dstd = get_tensor_by_name_from_graph(
-            graph, "descrpt_attr%s/t_std" % suffix
-        )
+        self.davg = get_tensor_by_name_from_graph(graph, f"descrpt_attr{suffix}/t_avg")
+        self.dstd = get_tensor_by_name_from_graph(graph, f"descrpt_attr{suffix}/t_std")
 
     def build(
         self,
@@ -1672,7 +1668,7 @@ def deserialize(cls, data: dict, suffix: str = ""):
             The deserialized model
         """
         if cls is not DescrptSeAtten:
-            raise NotImplementedError("Not implemented in class %s" % cls.__name__)
+            raise NotImplementedError(f"Not implemented in class {cls.__name__}")
         data = data.copy()
         check_version_compatibility(data.pop("@version"), 1, 1)
         data.pop("@class")
@@ -1711,7 +1707,7 @@ def serialize(self, suffix: str = "") -> dict:
         """
         if type(self) not in [DescrptSeAtten, DescrptDPA1Compat]:
             raise NotImplementedError(
-                "Not implemented in class %s" % self.__class__.__name__
+                f"Not implemented in class {self.__class__.__name__}"
             )
         if self.stripped_type_embedding:
             raise NotImplementedError(
@@ -2075,7 +2071,7 @@ def deserialize(cls, data: dict, suffix: str = ""):
             The deserialized model
         """
         if cls is not DescrptDPA1Compat:
-            raise NotImplementedError("Not implemented in class %s" % cls.__name__)
+            raise NotImplementedError(f"Not implemented in class {cls.__name__}")
         data = data.copy()
         check_version_compatibility(data.pop("@version"), 1, 1)
         data.pop("@class")
diff --git a/deepmd/tf/descriptor/se_r.py b/deepmd/tf/descriptor/se_r.py
index aef40b74bf..64a599716f 100644
--- a/deepmd/tf/descriptor/se_r.py
+++ b/deepmd/tf/descriptor/se_r.py
@@ -376,12 +376,8 @@ def enable_compression(
             min_nbor_dist, table_extrapolate, table_stride_1, table_stride_2
         )
 
-        self.davg = get_tensor_by_name_from_graph(
-            graph, "descrpt_attr%s/t_avg" % suffix
-        )
-        self.dstd = get_tensor_by_name_from_graph(
-            graph, "descrpt_attr%s/t_std" % suffix
-        )
+        self.davg = get_tensor_by_name_from_graph(graph, f"descrpt_attr{suffix}/t_avg")
+        self.dstd = get_tensor_by_name_from_graph(graph, f"descrpt_attr{suffix}/t_std")
 
     def build(
         self,
@@ -737,7 +733,7 @@ def deserialize(cls, data: dict, suffix: str = ""):
             The deserialized model
         """
         if cls is not DescrptSeR:
-            raise NotImplementedError("Not implemented in class %s" % cls.__name__)
+            raise NotImplementedError(f"Not implemented in class {cls.__name__}")
         data = data.copy()
         check_version_compatibility(data.pop("@version", 1), 1, 1)
         embedding_net_variables = cls.deserialize_network(
@@ -770,7 +766,7 @@ def serialize(self, suffix: str = "") -> dict:
         """
         if type(self) is not DescrptSeR:
             raise NotImplementedError(
-                "Not implemented in class %s" % self.__class__.__name__
+                f"Not implemented in class {self.__class__.__name__}"
             )
         if self.embedding_net_variables is None:
             raise RuntimeError("init_variables must be called before serialize")
diff --git a/deepmd/tf/descriptor/se_t.py b/deepmd/tf/descriptor/se_t.py
index 4f6cda6c9c..77234cb92e 100644
--- a/deepmd/tf/descriptor/se_t.py
+++ b/deepmd/tf/descriptor/se_t.py
@@ -368,12 +368,8 @@ def enable_compression(
             min_nbor_dist, table_extrapolate, table_stride_1 * 10, table_stride_2 * 10
         )
 
-        self.davg = get_tensor_by_name_from_graph(
-            graph, "descrpt_attr%s/t_avg" % suffix
-        )
-        self.dstd = get_tensor_by_name_from_graph(
-            graph, "descrpt_attr%s/t_std" % suffix
-        )
+        self.davg = get_tensor_by_name_from_graph(graph, f"descrpt_attr{suffix}/t_avg")
+        self.dstd = get_tensor_by_name_from_graph(graph, f"descrpt_attr{suffix}/t_std")
 
     def build(
         self,
diff --git a/deepmd/tf/entrypoints/compress.py b/deepmd/tf/entrypoints/compress.py
index 1f2bbc93a0..1e50d88b63 100644
--- a/deepmd/tf/entrypoints/compress.py
+++ b/deepmd/tf/entrypoints/compress.py
@@ -102,11 +102,10 @@ def compress(
     except GraphWithoutTensorError as e:
         if training_script is None:
             raise RuntimeError(
-                "The input frozen model: %s has no training script or min_nbor_dist information, "
+                f"The input frozen model: {input} has no training script or min_nbor_dist information, "
                 "which is not supported by the model compression interface. "
                 "Please consider using the --training-script command within the model compression interface to provide the training script of the input frozen model. "
                 "Note that the input training script must contain the correct path to the training data."
-                % input
             ) from e
         elif not os.path.exists(training_script):
             raise RuntimeError(
@@ -164,10 +163,10 @@ def compress(
         )
     except GraphTooLargeError as e:
         raise RuntimeError(
-            "The uniform step size of the tabulation's first table is %f, "
+            f"The uniform step size of the tabulation's first table is {step:f}, "
             "which is too small. This leads to a very large graph size, "
             "exceeding protobuf's limitation (2 GB). You should try to "
-            "increase the step size." % step
+            "increase the step size."
         ) from e
 
     # reset the graph, otherwise the size limitation will be only 2 GB / 2 = 1 GB
@@ -180,10 +179,10 @@ def compress(
         freeze(checkpoint_folder=checkpoint_folder, output=output, node_names=None)
     except GraphTooLargeError as e:
         raise RuntimeError(
-            "The uniform step size of the tabulation's first table is %f, "
+            f"The uniform step size of the tabulation's first table is {step:f}, "
             "which is too small. This leads to a very large graph size, "
             "exceeding protobuf's limitation (2 GB). You should try to "
-            "increase the step size." % step
+            "increase the step size."
         ) from e
 
 
diff --git a/deepmd/tf/entrypoints/freeze.py b/deepmd/tf/entrypoints/freeze.py
index 3d2a609797..a2b07f2c5e 100755
--- a/deepmd/tf/entrypoints/freeze.py
+++ b/deepmd/tf/entrypoints/freeze.py
@@ -368,9 +368,9 @@ def freeze_graph(
         different_set -= set(optional_node)
         if different_set:
             log.warning(
-                "The following nodes are not in the graph: %s. "
+                f"The following nodes are not in the graph: {different_set}. "
                 "Skip freezeing these nodes. You may be freezing "
-                "a checkpoint generated by an old version." % different_set
+                "a checkpoint generated by an old version."
             )
         # use intersection as output list
         output_node = list(set(output_node) & set(input_node))
diff --git a/deepmd/tf/fit/dos.py b/deepmd/tf/fit/dos.py
index 7989752e5a..d967ce03d0 100644
--- a/deepmd/tf/fit/dos.py
+++ b/deepmd/tf/fit/dos.py
@@ -611,21 +611,21 @@ def init_variables(
             self.fitting_net_variables.update(shared_variables)
         if self.numb_fparam > 0:
             self.fparam_avg = get_tensor_by_name_from_graph(
-                graph, "fitting_attr%s/t_fparam_avg" % suffix
+                graph, f"fitting_attr{suffix}/t_fparam_avg"
             )
             self.fparam_inv_std = get_tensor_by_name_from_graph(
-                graph, "fitting_attr%s/t_fparam_istd" % suffix
+                graph, f"fitting_attr{suffix}/t_fparam_istd"
             )
         if self.numb_aparam > 0:
             self.aparam_avg = get_tensor_by_name_from_graph(
-                graph, "fitting_attr%s/t_aparam_avg" % suffix
+                graph, f"fitting_attr{suffix}/t_aparam_avg"
             )
             self.aparam_inv_std = get_tensor_by_name_from_graph(
-                graph, "fitting_attr%s/t_aparam_istd" % suffix
+                graph, f"fitting_attr{suffix}/t_aparam_istd"
             )
         try:
             self.bias_dos = get_tensor_by_name_from_graph(
-                graph, "fitting_attr%s/t_bias_dos" % suffix
+                graph, f"fitting_attr{suffix}/t_bias_dos"
             )
         except GraphWithoutTensorError:
             # for compatibility, old models has no t_bias_dos
diff --git a/deepmd/tf/fit/ener.py b/deepmd/tf/fit/ener.py
index d38d0416af..873f7258db 100644
--- a/deepmd/tf/fit/ener.py
+++ b/deepmd/tf/fit/ener.py
@@ -779,21 +779,21 @@ def init_variables(
             self.fitting_net_variables.update(shared_variables)
         if self.numb_fparam > 0:
             self.fparam_avg = get_tensor_by_name_from_graph(
-                graph, "fitting_attr%s/t_fparam_avg" % suffix
+                graph, f"fitting_attr{suffix}/t_fparam_avg"
             )
             self.fparam_inv_std = get_tensor_by_name_from_graph(
-                graph, "fitting_attr%s/t_fparam_istd" % suffix
+                graph, f"fitting_attr{suffix}/t_fparam_istd"
             )
         if self.numb_aparam > 0:
             self.aparam_avg = get_tensor_by_name_from_graph(
-                graph, "fitting_attr%s/t_aparam_avg" % suffix
+                graph, f"fitting_attr{suffix}/t_aparam_avg"
             )
             self.aparam_inv_std = get_tensor_by_name_from_graph(
-                graph, "fitting_attr%s/t_aparam_istd" % suffix
+                graph, f"fitting_attr{suffix}/t_aparam_istd"
             )
         try:
             self.bias_atom_e = get_tensor_by_name_from_graph(
-                graph, "fitting_attr%s/t_bias_atom_e" % suffix
+                graph, f"fitting_attr{suffix}/t_bias_atom_e"
             )
         except GraphWithoutTensorError:
             # for compatibility, old models has no t_bias_atom_e
diff --git a/deepmd/tf/fit/fitting.py b/deepmd/tf/fit/fitting.py
index 0f73230bc8..d2aebd8f97 100644
--- a/deepmd/tf/fit/fitting.py
+++ b/deepmd/tf/fit/fitting.py
@@ -63,8 +63,7 @@ def init_variables(
         This method is called by others when the fitting supported initialization from the given variables.
         """
         raise NotImplementedError(
-            "Fitting %s doesn't support initialization from the given variables!"
-            % type(self).__name__
+            f"Fitting {type(self).__name__} doesn't support initialization from the given variables!"
         )
 
     @abstractmethod
@@ -107,7 +106,7 @@ def deserialize(cls, data: dict, suffix: str = "") -> "Fitting":
             return Fitting.get_class_by_type(
                 j_get_type(data, cls.__name__)
             ).deserialize(data, suffix=suffix)
-        raise NotImplementedError("Not implemented in class %s" % cls.__name__)
+        raise NotImplementedError(f"Not implemented in class {cls.__name__}")
 
     def serialize(self, suffix: str = "") -> dict:
         """Serialize the fitting.
@@ -122,7 +121,7 @@ def serialize(self, suffix: str = "") -> dict:
         suffix : str, optional
             Name suffix to identify this fitting
         """
-        raise NotImplementedError("Not implemented in class %s" % self.__name__)
+        raise NotImplementedError(f"Not implemented in class {self.__name__}")
 
     def serialize_network(
         self,
diff --git a/deepmd/tf/model/dos.py b/deepmd/tf/model/dos.py
index 2d244f5733..61809eff30 100644
--- a/deepmd/tf/model/dos.py
+++ b/deepmd/tf/model/dos.py
@@ -229,6 +229,6 @@ def init_variables(
             self.fitting.init_variables(graph, graph_def, suffix=suffix)
             tf.constant("compressed_model", name="model_type", dtype=tf.string)
         else:
-            raise RuntimeError("Unknown model type %s" % model_type)
+            raise RuntimeError(f"Unknown model type {model_type}")
         if self.typeebd is not None:
             self.typeebd.init_variables(graph, graph_def, suffix=suffix)
diff --git a/deepmd/tf/model/ener.py b/deepmd/tf/model/ener.py
index 4a0195b334..c8b9b2d6f5 100644
--- a/deepmd/tf/model/ener.py
+++ b/deepmd/tf/model/ener.py
@@ -376,7 +376,7 @@ def init_variables(
             self.fitting.init_variables(graph, graph_def, suffix=suffix)
             tf.constant("compressed_model", name="model_type", dtype=tf.string)
         else:
-            raise RuntimeError("Unknown model type %s" % model_type)
+            raise RuntimeError(f"Unknown model type {model_type}")
         if (
             self.typeebd is not None
             and self.typeebd.type_embedding_net_variables is None
diff --git a/deepmd/tf/model/model.py b/deepmd/tf/model/model.py
index fc8f862e3b..2718d53cbe 100644
--- a/deepmd/tf/model/model.py
+++ b/deepmd/tf/model/model.py
@@ -283,11 +283,11 @@ def build_descrpt(
         else:
             tf.constant(
                 self.rcut,
-                name="descrpt_attr%s/rcut" % suffix,
+                name=f"descrpt_attr{suffix}/rcut",
                 dtype=GLOBAL_TF_FLOAT_PRECISION,
             )
             tf.constant(
-                self.ntypes, name="descrpt_attr%s/ntypes" % suffix, dtype=tf.int32
+                self.ntypes, name=f"descrpt_attr{suffix}/ntypes", dtype=tf.int32
             )
             if "global_feed_dict" in input_dict:
                 feed_dict = input_dict["global_feed_dict"]
@@ -302,7 +302,7 @@ def build_descrpt(
                 )
             return_elements = [
                 *self.descrpt.get_tensor_names(suffix=suffix),
-                "o_descriptor%s:0" % suffix,
+                f"o_descriptor{suffix}:0",
             ]
             if frz_model is not None:
                 imported_tensors = self._import_graph_def_from_frz_model(
@@ -571,7 +571,7 @@ def deserialize(cls, data: dict, suffix: str = "") -> "Model":
                 data,
                 suffix=suffix,
             )
-        raise NotImplementedError("Not implemented in class %s" % cls.__name__)
+        raise NotImplementedError(f"Not implemented in class {cls.__name__}")
 
     def serialize(self, suffix: str = "") -> dict:
         """Serialize the model.
@@ -586,7 +586,7 @@ def serialize(self, suffix: str = "") -> dict:
         suffix : str, optional
             Name suffix to identify this descriptor
         """
-        raise NotImplementedError("Not implemented in class %s" % self.__name__)
+        raise NotImplementedError(f"Not implemented in class {self.__name__}")
 
 
 @Model.register("standard")
diff --git a/deepmd/tf/model/tensor.py b/deepmd/tf/model/tensor.py
index ff20b771a7..b2afe0d71f 100644
--- a/deepmd/tf/model/tensor.py
+++ b/deepmd/tf/model/tensor.py
@@ -234,7 +234,7 @@ def init_variables(
             self.fitting.init_variables(graph, graph_def, suffix=suffix)
             tf.constant("compressed_model", name="model_type", dtype=tf.string)
         else:
-            raise RuntimeError("Unknown model type %s" % model_type)
+            raise RuntimeError(f"Unknown model type {model_type}")
 
 
 class WFCModel(TensorModel):
diff --git a/deepmd/tf/nvnmd/entrypoints/train.py b/deepmd/tf/nvnmd/entrypoints/train.py
index 18c644a7f6..e6ca374977 100644
--- a/deepmd/tf/nvnmd/entrypoints/train.py
+++ b/deepmd/tf/nvnmd/entrypoints/train.py
@@ -126,7 +126,7 @@ def train_nvnmd(
 ):
     # test input
     if not os.path.exists(INPUT):
-        log.warning("The input script %s does not exist" % (INPUT))
+        log.warning(f"The input script {INPUT} does not exist")
     # STEP1
     PATH_CNN = "nvnmd_cnn"
     CONFIG_CNN = os.path.join(PATH_CNN, "config.npy")
diff --git a/deepmd/tf/nvnmd/entrypoints/wrap.py b/deepmd/tf/nvnmd/entrypoints/wrap.py
index f2be8352e2..adaec39f3e 100644
--- a/deepmd/tf/nvnmd/entrypoints/wrap.py
+++ b/deepmd/tf/nvnmd/entrypoints/wrap.py
@@ -137,7 +137,7 @@ def wrap(self):
             # DEVELOP_DEBUG
             if jdata_sys["debug"]:
                 log.info("%s: %d x % d bit" % (k, h, w * 4))
-                FioTxt().save("nvnmd/wrap/h%s.txt" % (k), d)
+                FioTxt().save(f"nvnmd/wrap/h{k}.txt", d)
             datas[ii] = d
         # update h & w of nvnmd_cfg
         nvnmd_cfg.size["NH_DATA"] = nhs
diff --git a/deepmd/tf/nvnmd/utils/fio.py b/deepmd/tf/nvnmd/utils/fio.py
index 82567e3917..3efd7520dd 100644
--- a/deepmd/tf/nvnmd/utils/fio.py
+++ b/deepmd/tf/nvnmd/utils/fio.py
@@ -180,7 +180,7 @@ def save(self, file_name: str, data: List[str]):
             buff.extend(list(bytearray.fromhex(si))[::-1])
         #
         with open(file_name, "wb") as fp:
-            fp.write(struct.pack("%sB" % len(buff), *buff))
+            fp.write(struct.pack(f"{len(buff)}B", *buff))
 
 
 class FioTxt:
diff --git a/deepmd/tf/train/trainer.py b/deepmd/tf/train/trainer.py
index 931cf87246..dc9f81957a 100644
--- a/deepmd/tf/train/trainer.py
+++ b/deepmd/tf/train/trainer.py
@@ -244,7 +244,7 @@ def build(self, data=None, stop_batch=0, origin_type_map=None, suffix=""):
                     )
                 else:
                     log.info(
-                        "fitting net %s training without frame parameter" % fitting_key
+                        f"fitting net {fitting_key} training without frame parameter"
                     )
 
         if not self.is_compress:
@@ -440,8 +440,7 @@ def _build_optimizer(self, fitting_key=None):
             _TF_VERSION = Version(TF_VERSION)
             if _TF_VERSION < Version("1.14.0"):
                 raise RuntimeError(
-                    "TensorFlow version %s is not compatible with the mixed precision setting. Please consider upgrading your TF version!"
-                    % TF_VERSION
+                    f"TensorFlow version {TF_VERSION} is not compatible with the mixed precision setting. Please consider upgrading your TF version!"
                 )
             elif _TF_VERSION < Version("2.4.0"):
                 optimizer = tf.train.experimental.enable_mixed_precision_graph_rewrite(
@@ -505,14 +504,14 @@ def _init_session(self):
                     fp = open(self.disp_file, "w")
                     fp.close()
             elif self.run_opt.init_mode == "init_from_model":
-                log.info("initialize from model %s" % self.run_opt.init_model)
+                log.info(f"initialize from model {self.run_opt.init_model}")
                 run_sess(self.sess, init_op)
                 self.saver.restore(self.sess, self.run_opt.init_model)
                 run_sess(self.sess, self.global_step.assign(0))
                 fp = open(self.disp_file, "w")
                 fp.close()
             elif self.run_opt.init_mode == "restart":
-                log.info("restart from model %s" % self.run_opt.restart)
+                log.info(f"restart from model {self.run_opt.restart}")
                 run_sess(self.sess, init_op)
                 self.saver.restore(self.sess, self.run_opt.restart)
             elif self.run_opt.init_mode == "init_from_frz_model":
@@ -837,7 +836,7 @@ def save_checkpoint(self, cur_batch: int):
         # make symlinks from prefix with step to that without step to break nothing
         # get all checkpoint files
         symlink_prefix_files(ckpt_prefix, self.save_ckpt)
-        log.info("saved checkpoint %s" % self.save_ckpt)
+        log.info(f"saved checkpoint {self.save_ckpt}")
 
     def get_feed_dict(self, batch, is_training):
         feed_dict = {}
@@ -963,7 +962,7 @@ def print_on_training(
                 prop_fmt = "   %11.2e"
                 for k in train_results.keys():
                     print_str += prop_fmt % (train_results[k])
-            print_str += "   %8.1e\n" % cur_lr
+            print_str += f"   {cur_lr:8.1e}\n"
             log.info(
                 format_training_message_per_task(
                     batch=cur_batch,
@@ -995,7 +994,7 @@ def print_on_training(
                     prop_fmt = "   %11.2e"
                     for k in train_results[fitting_key].keys():
                         print_str += prop_fmt % (train_results[fitting_key][k])
-                print_str += "   %8.1e\n" % cur_lr_dict[fitting_key]
+                print_str += f"   {cur_lr_dict[fitting_key]:8.1e}\n"
                 log.info(
                     format_training_message_per_task(
                         batch=cur_batch,
@@ -1090,9 +1089,8 @@ def _init_from_frz_model(self):
         except GraphWithoutTensorError as e:
             # throw runtime error if the frozen_model has no model type information...
             raise RuntimeError(
-                "The input frozen model: %s has no 'model_type' information, "
+                f"The input frozen model: {self.run_opt.init_frz_model} has no 'model_type' information, "
                 "which is not supported by the 'dp train init-frz-model' interface. "
-                % self.run_opt.init_frz_model
             ) from e
         else:
             self.model_type = bytes.decode(t_model_type)
@@ -1144,9 +1142,8 @@ def _init_from_pretrained_model(
         except GraphWithoutTensorError as e:
             # throw runtime error if the frozen_model has no model type information...
             raise RuntimeError(
-                "The input frozen pretrained model: %s has no 'model_type' information, "
+                f"The input frozen pretrained model: {self.run_opt.finetune} has no 'model_type' information, "
                 "which is not supported by the 'dp train finetune' interface. "
-                % self.run_opt.finetune
             ) from e
         else:
             self.model_type = bytes.decode(t_model_type)
diff --git a/deepmd/tf/utils/convert.py b/deepmd/tf/utils/convert.py
index 625f54a9a0..9005fb7d8e 100644
--- a/deepmd/tf/utils/convert.py
+++ b/deepmd/tf/utils/convert.py
@@ -77,8 +77,7 @@ def convert_to_21(input_model: str, output_model: str, version: Optional[str] =
         convert_pb_to_pbtxt(input_model, "frozen_model.pbtxt")
     if version is None:
         raise ValueError(
-            "The version of the DP graph %s cannot be detected. Please do the conversion manually."
-            % (input_model)
+            f"The version of the DP graph {input_model} cannot be detected. Please do the conversion manually."
         )
     if version in SpecifierSet("<1.0"):
         convert_dp012_to_dp10("frozen_model.pbtxt")
diff --git a/deepmd/tf/utils/finetune.py b/deepmd/tf/utils/finetune.py
index 3d11130ba7..4e55b9f5bb 100644
--- a/deepmd/tf/utils/finetune.py
+++ b/deepmd/tf/utils/finetune.py
@@ -33,10 +33,9 @@ def replace_model_params_with_pretrained_model(
         t_jdata = get_tensor_by_name(pretrained_model, "train_attr/training_script")
     except GraphWithoutTensorError as e:
         raise RuntimeError(
-            "The input frozen pretrained model: %s has no training script, "
+            f"The input frozen pretrained model: {input} has no training script, "
             "which is not supported to perform finetuning. "
             "Please use the model pretrained with v2.1.5 or higher version of DeePMD-kit."
-            % input
         ) from e
     pretrained_jdata = json.loads(t_jdata)
 
diff --git a/deepmd/tf/utils/multi_init.py b/deepmd/tf/utils/multi_init.py
index aafa9461b0..aef4bf4af9 100644
--- a/deepmd/tf/utils/multi_init.py
+++ b/deepmd/tf/utils/multi_init.py
@@ -33,10 +33,9 @@ def replace_model_params_with_frz_multi_model(
         t_jdata = get_tensor_by_name(pretrained_model, "train_attr/training_script")
     except GraphWithoutTensorError as e:
         raise RuntimeError(
-            "The input frozen pretrained model: %s has no training script, "
+            f"The input frozen pretrained model: {input} has no training script, "
             "which is not supported to perform multi-task training. "
             "Please use the model pretrained with v2.1.5 or higher version of DeePMD-kit."
-            % input
         ) from e
     pretrained_jdata = json.loads(t_jdata)
 
diff --git a/deepmd/utils/data.py b/deepmd/utils/data.py
index d01509d6f5..cd0e414b5f 100644
--- a/deepmd/utils/data.py
+++ b/deepmd/utils/data.py
@@ -384,7 +384,7 @@ def get_natoms_vec(self, ntypes: int):
     def avg(self, key):
         """Return the average value of an item."""
         if key not in self.data_dict.keys():
-            raise RuntimeError("key %s has not been added" % key)
+            raise RuntimeError(f"key {key} has not been added")
         info = self.data_dict[key]
         ndof = info["ndof"]
         eners = []
@@ -670,7 +670,7 @@ def _load_data(
                 data = np.repeat(data, repeat).reshape([nframes, -1])
             return np.float32(1.0), data
         elif must:
-            raise RuntimeError("%s not found!" % path)
+            raise RuntimeError(f"{path} not found!")
         else:
             if atomic and type_sel is not None and not output_natoms_for_type_sel:
                 ndof = ndof_ * natoms_sel
diff --git a/deepmd/utils/data_system.py b/deepmd/utils/data_system.py
index 640083bc33..693845f8d0 100644
--- a/deepmd/utils/data_system.py
+++ b/deepmd/utils/data_system.py
@@ -661,7 +661,7 @@ def print_summary(
     )
     log.info("found %d system(s):" % nsystems)
     log.info(
-        ("%s  " % _format_name_length("system", sys_width))
+        ("{}  ".format(_format_name_length("system", sys_width)))
         + ("%6s  %6s  %6s  %9s  %3s" % ("natoms", "bch_sz", "n_bch", "prob", "pbc"))
     )
     for ii in range(nsystems):
diff --git a/deepmd/utils/neighbor_stat.py b/deepmd/utils/neighbor_stat.py
index 34200df007..54a4c16b24 100644
--- a/deepmd/utils/neighbor_stat.py
+++ b/deepmd/utils/neighbor_stat.py
@@ -67,16 +67,15 @@ def get_stat(self, data: DeepmdDataSystem) -> Tuple[float, np.ndarray]:
         for mn, dt, jj in self.iterator(data):
             if np.isinf(dt):
                 log.warning(
-                    "Atoms with no neighbors found in %s. Please make sure it's what you expected."
-                    % jj
+                    f"Atoms with no neighbors found in {jj}. Please make sure it's what you expected."
                 )
             if dt < min_nbor_dist:
                 if math.isclose(dt, 0.0, rel_tol=1e-6):
                     # it's unexpected that the distance between two atoms is zero
                     # zero distance will cause nan (#874)
                     raise RuntimeError(
-                        "Some atoms are overlapping in %s. Please check your"
-                        " training data to remove duplicated atoms." % jj
+                        f"Some atoms are overlapping in {jj}. Please check your"
+                        " training data to remove duplicated atoms."
                     )
                 min_nbor_dist = dt
             max_nbor_size = np.maximum(mn, max_nbor_size)
diff --git a/deepmd/utils/path.py b/deepmd/utils/path.py
index 858e31a39d..377953cc35 100644
--- a/deepmd/utils/path.py
+++ b/deepmd/utils/path.py
@@ -42,7 +42,7 @@ def __new__(cls, path: str, mode: str = "r"):
             elif os.path.isfile(path.split("#")[0]):
                 # assume h5 if it is not dir
                 return super().__new__(DPH5Path)
-            raise FileNotFoundError("%s not found" % path)
+            raise FileNotFoundError(f"{path} not found")
         return super().__new__(cls)
 
     @abstractmethod
diff --git a/doc/conf.py b/doc/conf.py
index 704ad45479..7eafdb2c37 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -150,7 +150,7 @@
 import typing
 
 for typing_type in typing.__all__:
-    numpydoc_xref_aliases[typing_type] = "typing.%s" % typing_type
+    numpydoc_xref_aliases[typing_type] = f"typing.{typing_type}"
 
 rst_epilog = f"""
 .. |ACTIVATION_FN| replace:: {list_to_doc(ACTIVATION_FN_DICT.keys())}
diff --git a/source/install/build_tf.py b/source/install/build_tf.py
index d639e2cf51..5e988dd721 100755
--- a/source/install/build_tf.py
+++ b/source/install/build_tf.py
@@ -192,7 +192,7 @@ def path(self) -> Path:
     @property
     def gzip_path(self) -> Path:
         if self.gzip is None:
-            raise RuntimeError("gzip is None for %s" % self.path)
+            raise RuntimeError(f"gzip is None for {self.path}")
         return PACKAGE_DIR / self.gzip
 
     @property
@@ -257,10 +257,9 @@ def __call__(self):
                     dd()
                 else:
                     dlog.info(
-                        "Skip installing %s, which has been already installed"
-                        % dd.__class__.__name__
+                        f"Skip installing {dd.__class__.__name__}, which has been already installed"
                     )
-            dlog.info("Start installing %s..." % self.__class__.__name__)
+            dlog.info(f"Start installing {self.__class__.__name__}...")
             with tempfile.TemporaryDirectory() as tmpdirname:
                 self._prefix = Path(tmpdirname)
                 self.build()
@@ -621,7 +620,7 @@ def dependencies(self) -> Dict[str, Build]:
 
     def build(self):
         tf_res = self.resources["tensorflow"]
-        src = tf_res.gzip_path / ("tensorflow-%s" % self.version)
+        src = tf_res.gzip_path / (f"tensorflow-{self.version}")
         with set_directory(src):
             # configure -- need bazelisk in PATH
             call(
diff --git a/source/tests/pt/model/test_model.py b/source/tests/pt/model/test_model.py
index 71ad64d99d..6eb460e808 100644
--- a/source/tests/pt/model/test_model.py
+++ b/source/tests/pt/model/test_model.py
@@ -74,7 +74,7 @@ def torch2tf(torch_name, last_layer_id=None):
         else:
             ret = "final_layer_type_%d/%s:0" % (element_id, weight_type)
     else:
-        raise RuntimeError("Unexpected parameter name: %s" % torch_name)
+        raise RuntimeError(f"Unexpected parameter name: {torch_name}")
     return ret
 
 
diff --git a/source/tests/pt/test_loss.py b/source/tests/pt/test_loss.py
index 66460dfef1..dcd59cd56e 100644
--- a/source/tests/pt/test_loss.py
+++ b/source/tests/pt/test_loss.py
@@ -204,10 +204,10 @@ def fake_model():
         for key in ["ener", "force", "virial"]:
             self.assertTrue(
                 np.allclose(
-                    base_more_loss["l2_%s_loss" % key], my_more_loss["l2_%s_loss" % key]
+                    base_more_loss[f"l2_{key}_loss"], my_more_loss[f"l2_{key}_loss"]
                 )
             )
-            self.assertTrue(np.isnan(my_more_loss_absent["l2_%s_loss" % key]))
+            self.assertTrue(np.isnan(my_more_loss_absent[f"l2_{key}_loss"]))
 
 
 class TestEnerSpinLoss(unittest.TestCase):
@@ -401,10 +401,10 @@ def fake_model():
         for key in ["ener", "force_r", "force_m"]:
             self.assertTrue(
                 np.allclose(
-                    base_more_loss["l2_%s_loss" % key], my_more_loss["l2_%s_loss" % key]
+                    base_more_loss[f"l2_{key}_loss"], my_more_loss[f"l2_{key}_loss"]
                 )
             )
-            self.assertTrue(np.isnan(my_more_loss_absent["l2_%s_loss" % key]))
+            self.assertTrue(np.isnan(my_more_loss_absent[f"l2_{key}_loss"]))
 
 
 if __name__ == "__main__":
diff --git a/source/tests/tf/common.py b/source/tests/tf/common.py
index d4f3cc8392..705e9f7faa 100644
--- a/source/tests/tf/common.py
+++ b/source/tests/tf/common.py
@@ -667,8 +667,7 @@ def __init__(self, sys_path, set_prefix, seed=None, shuffle_test=True):
         ]
         if any(has_fparam) and (not all(has_fparam)):
             raise RuntimeError(
-                "system %s: if any set has frame parameter, then all sets should have frame parameter"
-                % sys_path
+                f"system {sys_path}: if any set has frame parameter, then all sets should have frame parameter"
             )
         if all(has_fparam):
             self.has_fparam = 0
@@ -680,8 +679,7 @@ def __init__(self, sys_path, set_prefix, seed=None, shuffle_test=True):
         ]
         if any(has_aparam) and (not all(has_aparam)):
             raise RuntimeError(
-                "system %s: if any set has frame parameter, then all sets should have frame parameter"
-                % sys_path
+                f"system {sys_path}: if any set has frame parameter, then all sets should have frame parameter"
             )
         if all(has_aparam):
             self.has_aparam = 0
@@ -771,7 +769,7 @@ def load_data(self, set_name, data_name, shape, is_necessary=True):
                 return data
             return 1, data
         elif is_necessary:
-            raise OSError("%s not found!" % path)
+            raise OSError(f"{path} not found!")
         else:
             data = np.zeros(shape)
         return 0, data
@@ -1025,7 +1023,7 @@ def print_summary(self):
         sys_width = 42
         tmp_msg += "---Summary of DataSystem-----------------------------------------\n"
         tmp_msg += "find %d system(s):\n" % self.nsystems
-        tmp_msg += "%s  " % self.format_name_length("system", sys_width)
+        tmp_msg += "{}  ".format(self.format_name_length("system", sys_width))
         tmp_msg += "{}  {}  {}\n".format("natoms", "bch_sz", "n_bch")
         for ii in range(self.nsystems):
             tmp_msg += "%s  %6d  %6d  %5d\n" % (