diff --git a/swift/megatron/convert.py b/swift/megatron/convert.py
index 2dc2263552..36f46eea75 100644
--- a/swift/megatron/convert.py
+++ b/swift/megatron/convert.py
@@ -1,6 +1,8 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 
 import math
+import os
+import shutil
 from contextlib import contextmanager
 from dataclasses import fields
 from typing import Any, Dict
@@ -15,7 +17,7 @@
 from megatron.training.initialize import initialize_megatron
 
 from swift.llm import ExportArguments, HfConfigFactory, prepare_model_template, to_device, to_float_dtype
-from swift.utils import get_logger, get_n_params_grads
+from swift.utils import get_logger, get_n_params_grads, is_master
 from .argument import MegatronArguments
 from .model import get_megatron_model_meta
 from .utils import (convert_hf_config, forward_step_helper, get_padding_to, patch_load_base_checkpoint,
@@ -332,6 +334,12 @@ def convert_mcore2hf(args: ExportArguments) -> None:
         bridge = megatron_model_meta.bridge_cls()
         logger.info('Converting weights and saving the model...')
         bridge.save_weights([mg_model], args.output_dir)
+        if is_master():
+            args_path = os.path.join(megatron_args.adapter_load or megatron_args.load or args.model, 'args.json')
+            if os.path.exists(args_path):
+                shutil.copy(args_path, os.path.join(args.output_dir, 'args.json'))
+            else:
+                args.save_args(args.output_dir)
         logger.info(f'Successfully saved HF model weights in `{args.output_dir}`.')
         if args.test_convert_precision:
             hf_model, template = prepare_model_template(args, model=args.output_dir)
diff --git a/swift/megatron/export/export.py b/swift/megatron/export/export.py
index a1f51ff47f..691e5f1c84 100644
--- a/swift/megatron/export/export.py
+++ b/swift/megatron/export/export.py
@@ -67,6 +67,7 @@ def convert_mcore2hf(self) -> None:
                 shutil.copy(args_path, os.path.join(args.save, 'args.json'))
         else:
             args.save_args(args.save)
+        logger.info(f'Successfully saved HF model weights in `{args.save}`.')
         if args.test_convert_precision:
             with disable_safe_ddp_context_use_barrier():
                 if save_peft_format:
@@ -114,13 +115,18 @@ def convert_hf2mcore(self) -> None:
                 logger.info('Merge LoRA...')
                 mg_model = peft_model.merge_and_unload()
         logger.info('Successfully transferred HF model weights to MG model.')
+        # hf_model does not support loading args.adapter_load, so test_convert_precision cannot be performed
+        support_convert_precision = args.adapter_load is None
         if args.test_convert_precision:
-            with disable_safe_ddp_context_use_barrier():
-                device_map = args.device_map or 'auto'
-                hf_model, template = prepare_model_template(
-                    args, device_map=device_map) if is_last_rank() else (None, template)
-            test_convert_precision(hf_model, mg_model, template, args.test_convert_dtype)
-            dist.barrier()
+            if support_convert_precision:
+                with disable_safe_ddp_context_use_barrier():
+                    device_map = args.device_map or 'auto'
+                    hf_model, template = prepare_model_template(
+                        args, device_map=device_map) if is_last_rank() else (None, template)
+                test_convert_precision(hf_model, mg_model, template, args.test_convert_dtype)
+                dist.barrier()
+            else:
+                logger.warning('Skip test_convert_precision because `--adapter_load` is specified.')
         args.save_args(args.save)
         logger.info('Saving the model...')
         save_peft_format = args.train_type == 'lora' and not args.merge_lora
diff --git a/swift/megatron/model/gpt_bridge.py b/swift/megatron/model/gpt_bridge.py
index 853193d378..07ae6dc870 100644
--- a/swift/megatron/model/gpt_bridge.py
+++ b/swift/megatron/model/gpt_bridge.py
@@ -1432,6 +1432,8 @@ def save_weights(self, mg_models, output_dir: str, is_peft_format: bool = False)
             if is_peft_format:
                 from swift.llm import get_multimodal_target_regex
                 peft_config = copy(mg_models[0].peft_config[self._adapter_name])
+                if args.task_type == 'seq_cls':
+                    peft_config.task_type = 'SEQ_CLS'
                 if args.is_multimodal and 'all-linear' in args.target_modules:
                     peft_config.target_modules = get_multimodal_target_regex(
                         self.hf_model,