diff --git a/examples/language/gpt/titans/configs/gpt2_small_zero3_pp1d.py b/examples/language/gpt/titans/configs/gpt2_small_zero3_pp1d.py
index 8ef81cb0a..7bf533039 100644
--- a/examples/language/gpt/titans/configs/gpt2_small_zero3_pp1d.py
+++ b/examples/language/gpt/titans/configs/gpt2_small_zero3_pp1d.py
@@ -12,11 +12,11 @@ TENSOR_SHAPE = (BATCH_SIZE // NUM_MICRO_BATCHES, SEQ_LEN, HIDDEN_SIZE)
 
 # if you do no want zero, just comment out this dictionary
 zero = dict(model_config=dict(tensor_placement_policy='cuda', shard_strategy=TensorShardStrategy()),
-            optimizer_config=dict(initial_scale=2**16))
+            optimizer_config=dict(initial_scale=2**5))
 
 optimizer = dict(
     type=HybridAdam,
-    lr=0.00015,
+    lr=0.000015,
     weight_decay=1e-2,
 )
 
diff --git a/examples/language/gpt/titans/dataset/webtext.py b/examples/language/gpt/titans/dataset/webtext.py
new file mode 100644
index 000000000..09d8870b5
--- /dev/null
+++ b/examples/language/gpt/titans/dataset/webtext.py
@@ -0,0 +1,39 @@
+import json
+import os
+
+import torch
+from torch.utils.data import Dataset
+from transformers import GPT2Tokenizer
+
+from colossalai.registry import DATASETS
+
+
+@DATASETS.register_module
+class WebtextDataset(Dataset):
+
+    def __init__(self, path, seq_len=1024) -> None:
+        super().__init__()
+        root = os.path.dirname(path)
+        encoded_data_cache_path = os.path.join(root, f'gpt_webtext_{seq_len}.pt')
+        if os.path.isfile(encoded_data_cache_path):
+            seq_len_, data, attention_mask = torch.load(encoded_data_cache_path)
+            if seq_len_ == seq_len:
+                self.data = data
+                self.attention_mask = attention_mask
+                return
+        raw_data = []
+        with open(path) as f:
+            for line in f.readlines():
+                raw_data.append(json.loads(line)['text'])
+        tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+        tokenizer.pad_token = tokenizer.unk_token
+        encoded_data = tokenizer(raw_data, padding=True, truncation=True, max_length=seq_len, return_tensors='pt')
+        self.data = encoded_data['input_ids']
+        self.attention_mask = encoded_data['attention_mask']
+        torch.save((seq_len, self.data, self.attention_mask), encoded_data_cache_path)
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, index):
+        return {'input_ids': self.data[index], 'attention_mask': self.attention_mask[index]}, self.data[index]
diff --git a/examples/language/gpt/titans/train_gpt.py b/examples/language/gpt/titans/train_gpt.py
index 1380b4b3a..4db7a081f 100644
--- a/examples/language/gpt/titans/train_gpt.py
+++ b/examples/language/gpt/titans/train_gpt.py
@@ -30,7 +30,7 @@ VOCAB_SIZE = 50257
 def main():
     parser = colossalai.get_default_parser()
     parser.add_argument('--from_torch', default=False, action='store_true')
-    parser.add_argument('--use_dummy_dataset', default=True, action='store_true')
+    parser.add_argument('--use_dummy_dataset', default=False, action='store_true')
     args = parser.parse_args()
     disable_existing_loggers()
     if args.from_torch: