diff --git a/eval_llm.py b/eval_llm.py
index 6339711..34b922a 100755
--- a/eval_llm.py
+++ b/eval_llm.py
@@ -22,7 +22,7 @@ def init_model(args):
         ckp = f'./{args.save_dir}/{args.weight}_{args.hidden_size}{moe_suffix}.pth'
         model.load_state_dict(torch.load(ckp, map_location=args.device), strict=True)
         if args.lora_weight != 'None':
-            apply_lora(model)
+            apply_lora(model, rank=args.lora_rank)
             load_lora(model, f'./{args.save_dir}/{args.lora_weight}_{args.hidden_size}{moe_suffix}.pth')
     else:
         model = AutoModelForCausalLM.from_pretrained(args.load_from, trust_remote_code=True)
@@ -35,6 +35,7 @@ def main():
     parser.add_argument('--save_dir', default='out', type=str, help="模型权重目录")
     parser.add_argument('--weight', default='full_sft', type=str, help="权重名称前缀（pretrain, full_sft, rlhf, reason, ppo_actor, grpo, spo）")
     parser.add_argument('--lora_weight', default='None', type=str, help="LoRA权重名称（None表示不使用，可选：lora_identity, lora_medical）")
+    parser.add_argument('--lora_rank', default=16, type=int, help="LoRA rank，需与训练时一致（默认16）")
     parser.add_argument('--hidden_size', default=768, type=int, help="隐藏层维度")
     parser.add_argument('--num_hidden_layers', default=8, type=int, help="隐藏层数量")
     parser.add_argument('--use_moe', default=0, type=int, choices=[0, 1], help="是否使用MoE架构（0=否，1=是）")
diff --git a/trainer/train_lora.py b/trainer/train_lora.py
index 4717292..35a87fc 100644
--- a/trainer/train_lora.py
+++ b/trainer/train_lora.py
@@ -69,6 +69,7 @@ def train_epoch(epoch, loader, iters, lora_params, start_step=0, wandb=None):
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="MiniMind LoRA Fine-tuning")
     parser.add_argument("--save_dir", type=str, default="../out", help="模型保存目录")
+    parser.add_argument("--lora_rank", default=16, type=int, help="LoRA rank")
     parser.add_argument("--lora_name", type=str, default="lora_medical", help="LoRA权重名称(如lora_identity/lora_medical等)")
     parser.add_argument("--epochs", type=int, default=10, help="训练轮数")
     parser.add_argument("--batch_size", type=int, default=32, help="batch size")
@@ -118,7 +119,7 @@ if __name__ == "__main__":
     
     # ========== 5. 定义模型、应用LoRA、冻结非LoRA参数 ==========
     model, tokenizer = init_model(lm_config, args.from_weight, device=args.device)
-    apply_lora(model)
+    apply_lora(model, rank=args.lora_rank)
     
     # 统计参数
     total_params = sum(p.numel() for p in model.parameters())