diff --git a/src/transformers/models/llama/convert_llama_weights_to_hf.py b/src/transformers/models/llama/convert_llama_weights_to_hf.py
index 3dc6c7d697004..9a0a2e672ff77 100644
--- a/src/transformers/models/llama/convert_llama_weights_to_hf.py
+++ b/src/transformers/models/llama/convert_llama_weights_to_hf.py
@@ -17,12 +17,22 @@
 import math
 import os
 import shutil
+import warnings
 
 import torch
 
 from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
 
 
+try:
+    from transformers import LlamaTokenizerFast
+except ImportError as e:
+    warnings.warn(e)
+    warnings.warn(
+        "The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"
+    )
+    LlamaTokenizerFast = None
+
 """
 Sample usage:
 
@@ -232,9 +242,10 @@ def permute(w):
 
 
 def write_tokenizer(tokenizer_path, input_tokenizer_path):
-    print(f"Fetching the tokenizer from {input_tokenizer_path}.")
     # Initialize the tokenizer based on the `spm` model
-    tokenizer = LlamaTokenizer(input_tokenizer_path)
+    tokenizer_class = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
+    print("Saving a {tokenizer_class} to {tokenizer_path}")
+    tokenizer = tokenizer_class(input_tokenizer_path)
     tokenizer.save_pretrained(tokenizer_path)
 
 
@@ -259,10 +270,8 @@ def main():
             input_base_path=os.path.join(args.input_dir, args.model_size),
             model_size=args.model_size,
         )
-    write_tokenizer(
-        tokenizer_path=args.output_dir,
-        input_tokenizer_path=os.path.join(args.input_dir, "tokenizer.model"),
-    )
+    spm_path = os.path.join(args.input_dir, "tokenizer.model")
+    write_tokenizer(args.output_dir, spm_path)
 
 
 if __name__ == "__main__":