diff --git a/examples/kt_optimize_rules/Qwen3Moe-sft-amx.yaml b/examples/kt_optimize_rules/Qwen3Moe-sft-amx.yaml
index b8eceb27d..3fd78d13f 100644
--- a/examples/kt_optimize_rules/Qwen3Moe-sft-amx.yaml
+++ b/examples/kt_optimize_rules/Qwen3Moe-sft-amx.yaml
@@ -7,7 +7,7 @@
       prefill_device: "cuda"
 
 - match:
-    name: "^lm_head$"  # regular expression 
+    name: "^lm_head$"  # regular expression
     class: torch.nn.Linear  # only match modules matching name and class simultaneously
   replace:
     class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
@@ -18,7 +18,7 @@
       prefill_op: "KLinearTorch"
 
 # - match:
-#     name: "^model\\.layers\\..*$"  # regular expression 
+#     name: "^model\\.layers\\..*$"  # regular expression
 #     class: torch.nn.Linear  # only match modules matching name and class simultaneously
 #   replace:
 #     class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
@@ -28,7 +28,7 @@
 #       generate_op: "KLinearTorch"
 #       prefill_op: "KLinearTorch"
 - match:
-    name: "^model\\.layers\\.(?!.*mlp\\.shared_expert_gate).*$"  # regular expression 
+    name: "^model\\.layers\\.(?!.*mlp\\.shared_expert_gate).*$"  # regular expression
     class: torch.nn.Linear  # only match modules matching name and class simultaneously
   replace:
     class: ktransformers.operators.linear.KTransformersLinear  # optimized Kernel on quantized data types
@@ -77,4 +77,4 @@
   replace:
     class: "ktransformers.operators.models.KQwen3MoeModel"
     kwargs:
-      per_layer_prefill_intput_threshold: 0
\ No newline at end of file
+      per_layer_prefill_intput_threshold: 0
diff --git a/src/llamafactory/hparams/model_args.py b/src/llamafactory/hparams/model_args.py
index 9ee13a002..02c100ec8 100644
--- a/src/llamafactory/hparams/model_args.py
+++ b/src/llamafactory/hparams/model_args.py
@@ -174,6 +174,10 @@ class BaseModelArguments:
         default=True,
         metadata={"help": "Whether or not to use KV cache in generation."},
     )
+    use_v1_kernels: bool = field(
+        default=False,
+        metadata={"help": "Whether or not to use high-performance kernels in training."},
+    )
     infer_dtype: Literal["auto", "float16", "bfloat16", "float32"] = field(
         default="auto",
         metadata={"help": "Data type for model weights and activations at inference."},
diff --git a/src/llamafactory/model/loader.py b/src/llamafactory/model/loader.py
index 10c257a5a..25710c31d 100644
--- a/src/llamafactory/model/loader.py
+++ b/src/llamafactory/model/loader.py
@@ -213,6 +213,17 @@ def load_model(
     else:
         model.train()
 
+    # Borrowing the kernel plugins ability of v1 to temporarily apply the NPU fusion operator to v0,
+    # it is turned off by default, and can be discarded after the transition period ends.
+    if model_args.use_v1_kernels and is_trainable:
+        logger.warning_rank0(
+            "You are try to using future feature about kernels, please note that this feature "
+            "is not supported for all models. If get any error, please disable this feature, or report the issue."
+        )
+        from ..v1.plugins.model_plugins.kernels.registry import apply_available_kernels
+
+        model = apply_available_kernels(model)
+
     trainable_params, all_param = count_parameters(model)
     if is_trainable:
         param_stats = (