diff --git a/model/model_lora.py b/model/model_lora.py
index ea53a27..b7c1d4c 100644
--- a/model/model_lora.py
+++ b/model/model_lora.py
@@ -18,7 +18,7 @@ class LoRA(nn.Module):
         return self.B(self.A(x))
 
 
-def apply_lora(model, rank=16):
+def apply_lora(model, rank=8):
     for name, module in model.named_modules():
         if isinstance(module, nn.Linear) and module.weight.shape[0] == module.weight.shape[1]:
             lora = LoRA(module.weight.shape[0], module.weight.shape[1], rank=rank).to(model.device)
diff --git a/trainer/train_lora.py b/trainer/train_lora.py
index 557c056..4711062 100644
--- a/trainer/train_lora.py
+++ b/trainer/train_lora.py
@@ -1,5 +1,6 @@
 import os
 import sys
+
 __package__ = "trainer"
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
 
@@ -7,13 +8,15 @@ import argparse
 import time
 import math
 import warnings
+import torch
+from torch import optim, nn
 import torch.distributed as dist
 from contextlib import nullcontext
 from torch.utils.data import DataLoader, DistributedSampler
 from transformers import AutoTokenizer, AutoModelForCausalLM
 from model.model_minimind import MiniMindConfig, MiniMindForCausalLM
 from dataset.lm_dataset import SFTDataset
-from model.model_lora import *
+from model.model_lora import load_lora, save_lora, apply_lora
 
 warnings.filterwarnings('ignore')
 
@@ -80,8 +83,10 @@ def train_epoch(epoch, wandb):
 
         if (step + 1) % args.save_interval == 0 and (not ddp or dist.get_rank() == 0):
             model.eval()
+            lora_save_path = f'{args.save_dir}/lora/{args.lora_name}_{lm_config.hidden_size}.pth'
+            os.makedirs(os.path.dirname(lora_save_path), exist_ok=True)
             # 【区别1】只保存lora权重即可
-            save_lora(model, f'{args.save_dir}/lora/{args.lora_name}_{lm_config.hidden_size}.pth')
+            save_lora(model, lora_save_path)
             model.train()
 
 
@@ -89,7 +94,7 @@ def init_model(lm_config):
     tokenizer = AutoTokenizer.from_pretrained('../model/')
     model = MiniMindForCausalLM(lm_config)
     moe_path = '_moe' if lm_config.use_moe else ''
-    ckp = f'{args.save_dir}/rlhf_{lm_config.hidden_size}{moe_path}.pth'
+    ckp = f'{args.save_dir}/full_sft_{lm_config.hidden_size}{moe_path}.pth'
     state_dict = torch.load(ckp, map_location=args.device)
     model.load_state_dict(state_dict, strict=False)
     return model.to(args.device), tokenizer
@@ -110,9 +115,9 @@ def init_distributed_mode():
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="MiniMind SFT with LoRA")
     parser.add_argument("--out_dir", type=str, default="../out")
-    parser.add_argument("--epochs", type=int, default=50)
-    parser.add_argument("--batch_size", type=int, default=16)
-    parser.add_argument("--learning_rate", type=float, default=5e-5)
+    parser.add_argument("--epochs", type=int, default=10)
+    parser.add_argument("--batch_size", type=int, default=32)
+    parser.add_argument("--learning_rate", type=float, default=1e-4)
     parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu")
     parser.add_argument("--dtype", type=str, default="bfloat16")
     parser.add_argument("--use_wandb", action="store_true")
@@ -123,17 +128,18 @@ if __name__ == "__main__":
     parser.add_argument("--grad_clip", type=float, default=1.0)
     parser.add_argument("--warmup_iters", type=int, default=0)
     parser.add_argument("--log_interval", type=int, default=100)
-    parser.add_argument("--save_interval", type=int, default=1)
+    parser.add_argument("--save_interval", type=int, default=100)
     parser.add_argument('--local_rank', type=int, default=-1)
     parser.add_argument('--hidden_size', default=512, type=int)
     parser.add_argument('--num_hidden_layers', default=8, type=int)
     parser.add_argument('--max_seq_len', default=512, type=int)
     parser.add_argument('--use_moe', default=False, type=bool)
-    parser.add_argument("--data_path", type=str, default="../dataset/lora_identity.jsonl")
-    parser.add_argument("--lora_name", type=str, default="lora_identity", help="根据任务保存成lora_(英文/医学/心理...)")
+    parser.add_argument("--data_path", type=str, default="../dataset/lora_medical.jsonl")
+    parser.add_argument("--lora_name", type=str, default="lora_medical", help="根据任务保存成lora_(英文/医学/心理...)")
     args = parser.parse_args()
 
-    lm_config = MiniMindConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, use_moe=args.use_moe)
+    lm_config = MiniMindConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers,
+                               use_moe=args.use_moe)
     args.save_dir = os.path.join(args.out_dir)
     os.makedirs(args.save_dir, exist_ok=True)
     os.makedirs(args.out_dir, exist_ok=True)