diff --git a/frontend/train_page.py b/frontend/train_page.py
index d312dc8..6aa3d78 100644
--- a/frontend/train_page.py
+++ b/frontend/train_page.py
@@ -36,18 +36,20 @@ def train_page():
         per_device_train_batch_size_input = gr.Number(value=1, label="batch size", precision=0)
         epoch_input = gr.Number(value=1, label="epoch", precision=0)
         save_steps_input = gr.Number(value=20, label="保存步数", precision=0)  # 新增保存步数输入框
+        lora_rank_input = gr.Number(value=16, label="LoRA秩", precision=0)  # 新增LoRA秩输入框
         
         train_button = gr.Button("开始微调")
         
         # 训练状态输出
         output = gr.Textbox(label="训练日志", interactive=False)
         
-        def train_model(dataset_name, learning_rate, per_device_train_batch_size, epoch, save_steps):
+        def train_model(dataset_name, learning_rate, per_device_train_batch_size, epoch, save_steps, lora_rank):
             # 使用动态传入的超参数
             learning_rate = float(learning_rate)
             per_device_train_batch_size = int(per_device_train_batch_size)
             epoch = int(epoch)
             save_steps = int(save_steps)  # 新增保存步数参数
+            lora_rank = int(lora_rank)  # 新增LoRA秩参数
             
             # 模型配置参数
             dtype = None  # 数据类型，None表示自动选择
@@ -62,7 +64,7 @@ def train_page():
                 model,
                 # LoRA秩,用于控制低秩矩阵的维度,值越大表示可训练参数越多,模型性能可能更好但训练开销更大
                 # 建议: 8-32之间
-                r=16,
+                r=lora_rank,  # 使用动态传入的LoRA秩
                 # 需要应用LoRA的目标模块列表
                 target_modules=[
                     "q_proj", "k_proj", "v_proj", "o_proj",  # attention相关层
@@ -161,6 +163,5 @@ if __name__ == "__main__":
     from model_manage_page import model_manage_page
     init_global_var("workdir")
     demo = gr.TabbedInterface([model_manage_page(), train_page()], ["模型管理", "聊天"])
-    # demo = gr.TabbedInterface([ train_page()], ["模型管理", "聊天"])    
     demo.queue()
     demo.launch()
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