diff --git a/paddlenlp/trainer/utils/ckpt_converter.py b/paddlenlp/trainer/utils/ckpt_converter.py
index 23f085e18f44..198cd8df35c9 100644
--- a/paddlenlp/trainer/utils/ckpt_converter.py
+++ b/paddlenlp/trainer/utils/ckpt_converter.py
@@ -19,17 +19,17 @@
 from typing import List, Union
 
 import paddle
-from paddle.distributed.checkpoint.load_state_dict import (
+from paddle.distributed.fleet.utils.log_util import logger
+from paddle.distributed.flex_checkpoint.dcp.load_state_dict import (
     _load_state_dict,
     get_rank_to_read_files,
 )
-from paddle.distributed.checkpoint.metadata import (
+from paddle.distributed.flex_checkpoint.dcp.metadata import (
     LocalTensorIndex,
     LocalTensorMetadata,
     Metadata,
 )
-from paddle.distributed.checkpoint.utils import flatten_state_dict
-from paddle.distributed.fleet.utils.log_util import logger
+from paddle.distributed.flex_checkpoint.dcp.utils import flatten_state_dict
 
 MODEL_WEIGHT_SUFFIX = ".pdparams"
 OPTIMIZER_WEIGHT_SUFFIX = ".pdopt"
@@ -206,7 +206,7 @@ def gen_metadata_and_prepare_source_state_dict(self):
                 global_offset = [0] * self.tp_degree
                 for item in shard_info:
                     tp_rank = item[0]["tp_rank"]
-                    state_name_with_tp_rank = state_name + "_tp" + "{:02d}".format(tp_rank)
+                    state_name_with_tp_rank = state_name + "_tp" + f"{tp_rank:02d}"
                     local_tensor_meta_data = LocalTensorMetadata((global_offset[tp_rank],), item[1], item[2])
                     local_tensor_index = LocalTensorIndex(state_name_with_tp_rank, (global_offset[tp_rank],))
                     global_offset[tp_rank] += item[1][0]
@@ -225,7 +225,7 @@ def gen_metadata_and_prepare_source_state_dict(self):
                 renamed_state_dict = {}
                 (tp_rank, pp_rank, sharding_rank) = self.get_distribution_rank_from_file_name(file_name)
                 for state_name, state_value in state_dict.items():
-                    state_name_with_tp_rank = state_name + "_tp" + "{:02d}".format(tp_rank)
+                    state_name_with_tp_rank = state_name + "_tp" + f"{tp_rank:02d}"
                     renamed_state_dict[state_name_with_tp_rank] = state_value
 
                 source_state_dict_for_merge_sharding[file_name] = renamed_state_dict
@@ -235,7 +235,7 @@ def gen_metadata_and_prepare_source_state_dict(self):
             sharding_metas_keys = []
             for i in range(self.tp_degree):
                 for j in range(self.pp_degree):
-                    sharding_metas_keys.append("tp{:02d}_pp{:02d}".format(i, j))
+                    sharding_metas_keys.append(f"tp{i:02d}_pp{j:02d}")
             for key in sharding_metas_keys:
                 param_meta = self.model_meta["sharding_metas"][key]["param_meta"]
                 for param_name, param_shape_and_dtype in param_meta.items():
@@ -253,7 +253,7 @@ def gen_metadata_and_prepare_source_state_dict(self):
             all_param_meta = {}
             for i in range(self.tp_degree):
                 for j in range(self.pp_degree):
-                    key = "tp{:02d}_pp{:02d}".format(i, j)
+                    key = f"tp{i:02d}_pp{j:02d}"
                     param_meta = self.model_meta["sharding_metas"][key]["param_meta"]
                     for param_name, param_shape_and_dtype in param_meta.items():
                         all_param_meta[param_name] = param_shape_and_dtype
@@ -269,7 +269,7 @@ def gen_metadata_and_prepare_source_state_dict(self):
             with paddle.base.dygraph.guard(place=paddle.CPUPlace()):
                 for key in cur_rank_need_load_model_state_keys:
                     for tp_rank in range(self.tp_degree):
-                        tp_rank_suffix = "_tp{:02d}".format(tp_rank)
+                        tp_rank_suffix = f"_tp{tp_rank:02d}"
                         optimizer_state_dict[key + ".moment1" + tp_rank_suffix] = paddle.zeros(
                             (param_flattened_shapes[key],), "float32"
                         )
@@ -353,7 +353,7 @@ def gen_metadata_and_prepare_source_state_dict(self):
                 else:
                     concat_optimier_state_dict[opt_state_name_removed_tp_rank] = tp_tensors[0]
 
-            fake_file_name = "{:02d}".format(self.cur_rank) + ".distcp"
+            fake_file_name = f"{self.cur_rank:02d}" + ".distcp"
             local_tensor_meta_data = {}
             local_tensor_index = {}
             for k, v in concat_optimier_state_dict.items():
@@ -472,7 +472,7 @@ def gen_metadata_and_prepare_source_state_dict(self):
                                 reshaped_v = v.reshape(shape)
                                 target_state_dict[k] = reshaped_v
 
-                fake_file_name = "{:02d}".format(self.cur_rank) + ".distcp"
+                fake_file_name = f"{self.cur_rank:02d}" + ".distcp"
                 local_tensor_meta_data = {}
                 local_tensor_index = {}
                 for k, v in target_state_dict.items():
@@ -911,7 +911,7 @@ def rename_using_model_meta(self, file_name):
                 self.model_meta = json.load(file)
 
         (tp_rank, pp_rank, sharding_rank) = self.get_distribution_rank_from_file_name(file_name)
-        dist_strategy_key = "tp" + "{:02d}".format(tp_rank) + "_" + "pp" + "{:02d}".format(pp_rank)
+        dist_strategy_key = "tp" + f"{tp_rank:02d}" + "_" + "pp" + f"{pp_rank:02d}"
         # Map model weight names to their corresponding names of master_weights in the optimizer state.
         if file_name.endswith(OPTIMIZER_WEIGHT_SUFFIX):
             structure_name_mapping = self.model_meta["sharding_metas"][dist_strategy_key]["structure_name_mapping"]