diff --git a/3-full_sft.py b/3-full_sft.py
index 6bee3c1..fdf673a 100644
--- a/3-full_sft.py
+++ b/3-full_sft.py
@@ -14,7 +14,7 @@ from contextlib import nullcontext
 from torch import optim
 from torch.nn.parallel import DistributedDataParallel
 from torch.utils.data import DataLoader, DistributedSampler
-from transformers import AutoTokenizer, AutoModel
+from transformers import AutoTokenizer, AutoModelForCausalLM
 from model.model import Transformer
 from model.LMConfig import LMConfig
 from model.dataset import SFTDataset
@@ -118,7 +118,7 @@ def init_model():
                 state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
         model.load_state_dict(state_dict, strict=False)
     else:
-        model = AutoModel.from_pretrained('./minimind', trust_remote_code=True)
+        model = AutoModelForCausalLM.from_pretrained('./minimind-v1-small', trust_remote_code=True)
 
     Logger(f'LLM总参数量：{count_parameters(model) / 1e6:.3f} 百万')
     model = model.to(args.device)
@@ -143,7 +143,7 @@ if __name__ == "__main__":
     parser.add_argument("--out_dir", type=str, default="out", help="Output directory")
     parser.add_argument("--epochs", type=int, default=19, help="Number of epochs")
     parser.add_argument("--batch_size", type=int, default=32, help="Batch size")
-    parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate")
+    parser.add_argument("--learning_rate", type=float, default=1.5e-4, help="Learning rate")
     parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="Device to use")
     parser.add_argument("--dtype", type=str, default="bfloat16", help="Data type")
     parser.add_argument("--use_wandb", action="store_true", help="Use Weights & Biases")