diff --git a/examples/language/grok-1/inference.py b/examples/language/grok-1/inference.py
index a73820af9..faef7ae9d 100644
--- a/examples/language/grok-1/inference.py
+++ b/examples/language/grok-1/inference.py
@@ -1,7 +1,7 @@
 import time
 
 import torch
-from transformers import AutoModelForCausalLM, LlamaTokenizerFast
+from transformers import AutoModelForCausalLM, AutoTokenizer
 from utils import get_defualt_parser, inference, print_output
 
 if __name__ == "__main__":
@@ -9,6 +9,9 @@ if __name__ == "__main__":
     args = parser.parse_args()
     start = time.time()
     torch.set_default_dtype(torch.bfloat16)
+
+    tokenizer = AutoTokenizer.from_pretrained(args.pretrained, trust_remote_code=True)
+
     model = AutoModelForCausalLM.from_pretrained(
         args.pretrained,
         trust_remote_code=True,
@@ -18,10 +21,6 @@ if __name__ == "__main__":
     model.eval()
     init_time = time.time() - start
 
-    # A transformers-compatible version of the grok-1 tokenizer by Xenova
-    # https://huggingface.co/Xenova/grok-1-tokenizer
-    tokenizer = LlamaTokenizerFast.from_pretrained("Xenova/grok-1-tokenizer")
-
     for text in args.text:
         output = inference(
             model,
diff --git a/examples/language/grok-1/inference_tp.py b/examples/language/grok-1/inference_tp.py
index 604de1487..cf05880dc 100644
--- a/examples/language/grok-1/inference_tp.py
+++ b/examples/language/grok-1/inference_tp.py
@@ -2,7 +2,7 @@ import time
 
 import torch
 from grok1_policy import Grok1ForCausalLMPolicy
-from transformers import AutoModelForCausalLM, LlamaTokenizerFast
+from transformers import AutoModelForCausalLM, AutoTokenizer
 from utils import get_defualt_parser, inference, print_output
 
 import colossalai
@@ -27,6 +27,9 @@ if __name__ == "__main__":
     )
     booster = Booster(plugin=plugin)
     torch.set_default_dtype(torch.bfloat16)
+
+    tokenizer = AutoTokenizer.from_pretrained(args.pretrained, trust_remote_code=True)
+
     with LazyInitContext(default_device=get_current_device()):
         model = AutoModelForCausalLM.from_pretrained(
             args.pretrained, trust_remote_code=True, torch_dtype=torch.bfloat16
@@ -35,10 +38,6 @@ if __name__ == "__main__":
     model.eval()
     init_time = time.time() - start
 
-    # A transformers-compatible version of the grok-1 tokenizer by Xenova
-    # https://huggingface.co/Xenova/grok-1-tokenizer
-    tokenizer = LlamaTokenizerFast.from_pretrained("Xenova/grok-1-tokenizer")
-
     for text in args.text:
         output = inference(
             model.unwrap(),