diff --git a/README.md b/README.md
index c1db3a59ee68cbbcb46ebb0ca8babf024a5e311f..6ed1d0dbbf1b36f02eccc373dc9711bb17efc2f0 100644
--- a/README.md
+++ b/README.md
@@ -43,10 +43,28 @@ python train.py  --load_model "rwkv-80.pth" --wandb "" --proj_dir "out" \
 --accelerator gpu --devices 1 --precision bf16 --strategy deepspeed_stage_2_offload --grad_cp 1
 ```
 
+
+## 接入Instruction Tuning
+
+使用指令数据集进行监督训练，精调语言模型，指令数据集格式为句子对。这部分数据需要由开发人员来进行编写，有的语料需要涉及到推理过程。
+
+```
+python train_sft.py  --load_model "rwkv-100.pth" --wandb "" --proj_dir "out_sft" \
+--data_file "data/prompts.csv" --data_type "utf-8" --vocab_size 50277 \
+--ctx_len 1024 --epoch_steps 200 --epoch_count 1000 --epoch_begin 0 --epoch_save 1 \
+--micro_bsz 8 --n_layer 24 --n_embd 2048 --pre_ffn 0 --head_qk 0 \
+--lr_init 1e-5 --lr_final 1e-5 --warmup_steps 0 --beta1 0.9 --beta2 0.999 --adam_eps 1e-8 \
+--accelerator gpu --devices 1 --precision bf16 --strategy deepspeed_stage_2_offload --grad_cp 1 \
+--my_qa_mask 1
+```
+
+
 ## TODO
 
-### 接入Instruction Tuning
+### Reward Model
+
 
 ### 接入RLHF（Reinforcement Learning with Human Feedback）
 
 
+
diff --git a/src/dataset.py b/src/dataset.py
index aa8a65d36e1a99b6c170d60bdd991177fddba2de..8057d6ef9c2957a93f07e3dcc9e6b4c916fa99fe 100644
--- a/src/dataset.py
+++ b/src/dataset.py
@@ -243,14 +243,17 @@ class S2SDataset(Dataset):
 
     def __getitem__(self, index):
         ctx_len = self.args.ctx_len
-        req_len = ctx_len + 1
         question, answer = self.data[index]
         text = question + answer
-        text = text[:req_len]
+        text = text[:ctx_len]
+
+        text = text + [0] * (ctx_len - len(text))
         x = torch.tensor(text, dtype=torch.long)
+
+        answer = answer + [0] * (ctx_len - len(answer))
         y = torch.tensor(answer, dtype=torch.long)
 
-        z = [1] * len(question) + [0] * (req_len - len(question))
+        z = [1] * len(question) + [0] * (ctx_len - len(question))
         z = torch.tensor(z, dtype=torch.long)
 
         return x, y, z
diff --git a/src/trainer.py b/src/trainer.py
index 89407f0eec4319c040021d443b1e2c1548ba090a..e514d6504beaf58c3dba686f23ffa4a0b95b6318 100644
--- a/src/trainer.py
+++ b/src/trainer.py
@@ -117,7 +117,7 @@ class train_callback(pl.Callback):
     def on_train_epoch_start(self, trainer, pl_module):
         args = self.args
         dataset = trainer.train_dataloader.dataset.datasets
-        assert "MyDataset" in str(dataset)
+        assert "MyDataset" in str(dataset) or "S2SDataset" in str(dataset)
         dataset.global_rank = trainer.global_rank
         dataset.real_epoch = int(args.epoch_begin + trainer.current_epoch)
         dataset.world_size = trainer.world_size
diff --git a/train_sft.py b/train_sft.py
index fa06b1dd46f56040ec775abf3683983f94dd6105..51275db6167951a384691bf1774c757a6f98bf21 100644
--- a/train_sft.py
+++ b/train_sft.py
@@ -135,6 +135,7 @@ if __name__ == "__main__":
     warnings.filterwarnings("ignore", ".*Consider increasing the value of the `num_workers` argument*")
     warnings.filterwarnings("ignore", ".*The progress bar already tracks a metric with the*")
     # os.environ["WDS_SHOW_SEED"] = "1"
+    os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
     args.my_timestamp = datetime.datetime.today().strftime("%Y-%m-%d-%H-%M-%S")
     args.enable_checkpointing = False
@@ -264,7 +265,7 @@ if __name__ == "__main__":
         trainer.strategy.config["zero_optimization"]["allgather_bucket_size"] = args.ds_bucket_mb * 1000 * 1000
         trainer.strategy.config["zero_optimization"]["reduce_bucket_size"] = args.ds_bucket_mb * 1000 * 1000
 
-    # must set shuffle=False, persistent_workers=False (because worker is in another thread)
-    data_loader = DataLoader(train_data, shuffle=False, pin_memory=True, batch_size=args.micro_bsz, num_workers=1, persistent_workers=False, drop_last=True)
+    # must set shuffle=True, persistent_workers=False (because worker is in another thread)
+    data_loader = DataLoader(train_data, shuffle=True, pin_memory=True, batch_size=args.micro_bsz, num_workers=1, persistent_workers=False, drop_last=True)
 
     trainer.fit(model, data_loader)