diff --git a/examples/aac_audiocaps/aac_config.py b/examples/aac_audiocaps/aac_config.py
index 397abc13..0798c87d 100644
--- a/examples/aac_audiocaps/aac_config.py
+++ b/examples/aac_audiocaps/aac_config.py
@@ -1,5 +1,9 @@
 from dataclasses import dataclass, field
 from typing import Optional, List
+
+from torch.distributed.fsdp import ShardingStrategy
+
+
 @dataclass
 class ModelConfig:
     file: str = "examples/aac_audiocaps/model/slam_model_aac.py:model_factory"
@@ -114,7 +118,7 @@ class FSDPConfig:
     mixed_precision: bool = True
     use_fp16: bool = False
     # sharding_strategy = "FULL_SHARD" #ShardingStrategy = ShardingStrategy.FULL_SHARD
-    sharding_strategy: str = "NO_SHARD" #ShardingStrategy.NO_SHARD #MZY: set NO_SHARD when use DDP
+    sharding_strategy: ShardingStrategy = "NO_SHARD" #ShardingStrategy.NO_SHARD #MZY: set NO_SHARD when use DDP
     checkpoint_type: str = "SHARDED_STATE_DICT"  # alternatively can use SHARDED_STATE_DICT save one file per rank, and can resize the world-size.
     fsdp_activation_checkpointing: bool = True
     fsdp_cpu_offload: bool = False
diff --git a/examples/asr_librispeech/asr_config.py b/examples/asr_librispeech/asr_config.py
index 280280dc..d6683157 100644
--- a/examples/asr_librispeech/asr_config.py
+++ b/examples/asr_librispeech/asr_config.py
@@ -1,5 +1,9 @@
 from dataclasses import dataclass, field
 from typing import Optional, List
+
+from torch.distributed.fsdp import ShardingStrategy
+
+
 @dataclass
 class ModelConfig:
     file: str = "examples/asr_librispeech/model/slam_model_asr.py:model_factory"
@@ -108,7 +112,7 @@ class FSDPConfig:
     mixed_precision: bool = True
     use_fp16: bool = False
     # sharding_strategy = "FULL_SHARD" #ShardingStrategy = ShardingStrategy.FULL_SHARD
-    sharding_strategy: str = "NO_SHARD" #ShardingStrategy.NO_SHARD #MZY: set NO_SHARD when use DDP
+    sharding_strategy: ShardingStrategy = "NO_SHARD" #ShardingStrategy.NO_SHARD #MZY: set NO_SHARD when use DDP
     checkpoint_type: str = "SHARDED_STATE_DICT"  # alternatively can use SHARDED_STATE_DICT save one file per rank, and can resize the world-size.
     fsdp_activation_checkpointing: bool = True
     fsdp_cpu_offload: bool = False
diff --git a/examples/drcap_zeroshot_aac/drcap_config.py b/examples/drcap_zeroshot_aac/drcap_config.py
index b4f4956f..328f0c23 100644
--- a/examples/drcap_zeroshot_aac/drcap_config.py
+++ b/examples/drcap_zeroshot_aac/drcap_config.py
@@ -1,5 +1,9 @@
 from dataclasses import dataclass, field
 from typing import Optional, List
+
+from torch.distributed.fsdp import ShardingStrategy
+
+
 @dataclass
 class ModelConfig:
     file: str = "examples/drcap_zeroshot_aac/model/slam_model_drcap.py:model_factory"
@@ -113,7 +117,7 @@ class FSDPConfig:
     mixed_precision: bool = True
     use_fp16: bool = False
     # sharding_strategy = "FULL_SHARD" #ShardingStrategy = ShardingStrategy.FULL_SHARD
-    sharding_strategy: str = "NO_SHARD" #ShardingStrategy.NO_SHARD #MZY: set NO_SHARD when use DDP
+    sharding_strategy: ShardingStrategy = "NO_SHARD" #ShardingStrategy.NO_SHARD #MZY: set NO_SHARD when use DDP
     checkpoint_type: str = "SHARDED_STATE_DICT"  # alternatively can use SHARDED_STATE_DICT save one file per rank, and can resize the world-size.
     fsdp_activation_checkpointing: bool = True
     fsdp_cpu_offload: bool = False
diff --git a/examples/mala_asr_slidespeech/mala_asr_config.py b/examples/mala_asr_slidespeech/mala_asr_config.py
index 7c8e2794..ba98ec78 100644
--- a/examples/mala_asr_slidespeech/mala_asr_config.py
+++ b/examples/mala_asr_slidespeech/mala_asr_config.py
@@ -1,5 +1,8 @@
 from dataclasses import dataclass, field
 from typing import Optional, List
+from torch.distributed.fsdp import ShardingStrategy
+
+
 @dataclass
 class ModelConfig:
     file: str = "examples/mala_asr_slidespeech/model/slam_model_mala_asr.py:model_factory"
@@ -109,7 +112,7 @@ class FSDPConfig:
     mixed_precision: bool = True
     use_fp16: bool = False
     # sharding_strategy = "FULL_SHARD" #ShardingStrategy = ShardingStrategy.FULL_SHARD
-    sharding_strategy: str = "NO_SHARD" #ShardingStrategy.NO_SHARD #MZY: set NO_SHARD when use DDP
+    sharding_strategy: ShardingStrategy = "NO_SHARD" #ShardingStrategy.NO_SHARD #MZY: set NO_SHARD when use DDP
     checkpoint_type: str = "SHARDED_STATE_DICT"  # alternatively can use SHARDED_STATE_DICT save one file per rank, and can resize the world-size.
     fsdp_activation_checkpointing: bool = True
     fsdp_cpu_offload: bool = False
diff --git a/examples/mc_musiccaps/mir_config.py b/examples/mc_musiccaps/mir_config.py
index a1279b12..e73fd279 100644
--- a/examples/mc_musiccaps/mir_config.py
+++ b/examples/mc_musiccaps/mir_config.py
@@ -1,5 +1,8 @@
 from dataclasses import dataclass, field
 from typing import Optional, List
+
+from torch.distributed.fsdp import ShardingStrategy
+
 @dataclass
 class ModelConfig:
     file: str = "examples/mc_musiccaps/model/slam_model_mir.py:model_factory"
@@ -112,7 +115,7 @@ class FSDPConfig:
     mixed_precision: bool = True
     use_fp16: bool = False
     # sharding_strategy = "FULL_SHARD" #ShardingStrategy = ShardingStrategy.FULL_SHARD
-    sharding_strategy: str = "NO_SHARD" #ShardingStrategy.NO_SHARD #MZY: set NO_SHARD when use DDP
+    sharding_strategy: ShardingStrategy = "NO_SHARD" #ShardingStrategy.NO_SHARD #MZY: set NO_SHARD when use DDP
     checkpoint_type: str = "SHARDED_STATE_DICT"  # alternatively can use SHARDED_STATE_DICT save one file per rank, and can resize the world-size.
     fsdp_activation_checkpointing: bool = True
     fsdp_cpu_offload: bool = False
diff --git a/examples/seld_spatialsoundqa/seld_config.py b/examples/seld_spatialsoundqa/seld_config.py
index 439e5819..ce69f27f 100644
--- a/examples/seld_spatialsoundqa/seld_config.py
+++ b/examples/seld_spatialsoundqa/seld_config.py
@@ -1,6 +1,9 @@
 from dataclasses import dataclass, field
 from typing import Optional, List
 
+from torch.distributed.fsdp import ShardingStrategy
+
+
 @dataclass
 class ModelConfig:
     file: str = "examples/seld_spatialsoundqa/model/slam_model_seld.py:model_factory"
@@ -97,7 +100,7 @@ class FSDPConfig:
     mixed_precision: bool = True
     use_fp16: bool = False
     # sharding_strategy = "FULL_SHARD" #ShardingStrategy = ShardingStrategy.FULL_SHARD
-    sharding_strategy: str = "NO_SHARD" #ShardingStrategy.NO_SHARD #MZY: set NO_SHARD when use DDP
+    sharding_strategy: ShardingStrategy = "NO_SHARD" #ShardingStrategy.NO_SHARD #MZY: set NO_SHARD when use DDP
     checkpoint_type: str = "SHARDED_STATE_DICT"  # alternatively can use SHARDED_STATE_DICT save one file per rank, and can resize the world-size.
     fsdp_activation_checkpointing: bool = True
     fsdp_cpu_offload: bool = False
diff --git a/examples/vallex/vallex_config.py b/examples/vallex/vallex_config.py
index a4cd350a..e03201ec 100644
--- a/examples/vallex/vallex_config.py
+++ b/examples/vallex/vallex_config.py
@@ -1,5 +1,9 @@
 from dataclasses import dataclass, field
 from typing import Optional, List
+
+from torch.distributed.fsdp import ShardingStrategy
+
+
 @dataclass
 class ModelConfig:
     llm_name: str = "vallex"
@@ -68,7 +72,7 @@ class FSDPConfig:
     mixed_precision: bool = True
     use_fp16: bool = False
     # sharding_strategy = "FULL_SHARD" #ShardingStrategy = ShardingStrategy.FULL_SHARD
-    sharding_strategy: str = "NO_SHARD" #ShardingStrategy.NO_SHARD #MZY: set NO_SHARD when use DDP
+    sharding_strategy: ShardingStrategy = "NO_SHARD" #ShardingStrategy.NO_SHARD #MZY: set NO_SHARD when use DDP
     checkpoint_type: str = "SHARDED_STATE_DICT"  # alternatively can use SHARDED_STATE_DICT save one file per rank, and can resize the world-size.
     fsdp_activation_checkpointing: bool = True
     fsdp_cpu_offload: bool = False
diff --git a/examples/vsr_LRS3/vsr_config.py b/examples/vsr_LRS3/vsr_config.py
index 3c051a01..0a271eef 100644
--- a/examples/vsr_LRS3/vsr_config.py
+++ b/examples/vsr_LRS3/vsr_config.py
@@ -1,5 +1,9 @@
 from dataclasses import dataclass, field
 from typing import Optional, List
+
+from torch.distributed.fsdp import ShardingStrategy
+
+
 @dataclass
 class ModelConfig:
     file: str = "examples/vsr_LRS3/model/slam_model_vsr.py:model_factory"
@@ -115,7 +119,7 @@ class FSDPConfig:
     mixed_precision: bool = True
     use_fp16: bool = False
     # sharding_strategy = "FULL_SHARD" #ShardingStrategy = ShardingStrategy.FULL_SHARD
-    sharding_strategy: str = "NO_SHARD" #ShardingStrategy.NO_SHARD #MZY: set NO_SHARD when use DDP
+    sharding_strategy: ShardingStrategy = "NO_SHARD" #ShardingStrategy.NO_SHARD #MZY: set NO_SHARD when use DDP
     checkpoint_type: str = "SHARDED_STATE_DICT"  # alternatively can use SHARDED_STATE_DICT save one file per rank, and can resize the world-size.
     fsdp_activation_checkpointing: bool = True
     fsdp_cpu_offload: bool = False
diff --git a/src/slam_llm/pipeline/finetune.py b/src/slam_llm/pipeline/finetune.py
index f9132263..4ced3c51 100644
--- a/src/slam_llm/pipeline/finetune.py
+++ b/src/slam_llm/pipeline/finetune.py
@@ -159,8 +159,8 @@ def main(kwargs: DictConfig):
         if not train_config.use_peft and train_config.freeze_layers:
 
             freeze_transformer_layers(train_config.num_freeze_layers)
-        from torch.distributed.fsdp import ShardingStrategy
-        fsdp_config.sharding_strategy = getattr(ShardingStrategy, fsdp_config.sharding_strategy)
+        # from torch.distributed.fsdp import ShardingStrategy
+        # fsdp_config.sharding_strategy = getattr(ShardingStrategy, fsdp_config.sharding_strategy)
         mixed_precision_policy, wrapping_policy = get_policies(fsdp_config, rank)
         my_auto_wrapping_policy = fsdp_auto_wrap_policy(model, LlamaDecoderLayer)