diff --git a/bindings/python/examples/train_bytelevel_bpe.py b/bindings/python/examples/train_bytelevel_bpe.py
new file mode 100644
index 00000000..347a0f1b
--- /dev/null
+++ b/bindings/python/examples/train_bytelevel_bpe.py
@@ -0,0 +1,49 @@
+import argparse
+import glob
+
+from tokenizers import Tokenizer, models, pre_tokenizers, decoders, trainers
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--files",
+                    default=None,
+                    metavar="path",
+                    type=str,
+                    required=True,
+                    help="The files to use as training; accept '**/*.txt' type of patterns \
+                          if enclosed in quotes")
+parser.add_argument("--out",
+                    default="./",
+                    type=str,
+                    help="Path to the output directory, where the files will be saved")
+parser.add_argument("--name",
+                    default="bpe-bytelevel",
+                    type=str,
+                    help="The name of the output vocab files")
+args = parser.parse_args()
+
+files = glob.glob(args.files)
+if not files:
+    print(f"File does not exist: {args.files}")
+    exit(1)
+
+
+# Initialize an empty tokenizer
+tokenizer = Tokenizer(models.BPE.empty())
+
+# Customize pre-tokenization and decoding
+tokenizer.with_pre_tokenizer(pre_tokenizers.ByteLevel.new(add_prefix_space=False))
+tokenizer.with_decoder(decoders.ByteLevel.new())
+
+# And then train
+trainer = trainers.BpeTrainer.new(
+    vocab_size=50000,
+    min_frequency=2,
+    show_progress=True,
+    special_tokens=[ "<s>", "<pad>", "</s" ],
+    initial_alphabet=pre_tokenizers.ByteLevel.alphabet()
+)
+tokenizer.train(trainer, files)
+
+# Save the files
+tokenizer.model.save(args.out, args.name)