diff --git a/src/transformers/models/qwen2/tokenization_qwen2.py b/src/transformers/models/qwen2/tokenization_qwen2.py
index 100786ae580ee8..901423947a72ae 100644
--- a/src/transformers/models/qwen2/tokenization_qwen2.py
+++ b/src/transformers/models/qwen2/tokenization_qwen2.py
@@ -43,8 +43,8 @@
 PRETOKENIZE_REGEX = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
 
 
-# copied from transformers.models.gpt2.tokenization_gpt2.bytes_to_unicode
 @lru_cache()
+# Copied from transformers.models.gpt2.tokenization_gpt2.bytes_to_unicode
 def bytes_to_unicode():
     """
     Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
@@ -214,6 +214,7 @@ def __init__(
         )
 
     @property
+    # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
     def vocab_size(self) -> int:
         return len(self.encoder)
 
diff --git a/tests/models/qwen2/test_tokenization_qwen2.py b/tests/models/qwen2/test_tokenization_qwen2.py
index 8d2865ac44dc5a..d882baff1fe574 100644
--- a/tests/models/qwen2/test_tokenization_qwen2.py
+++ b/tests/models/qwen2/test_tokenization_qwen2.py
@@ -18,9 +18,9 @@
 import os
 import unittest
 
-from transformers import Qwen2Tokenizer, Qwen2TokenizerFast
+from transformers import AddedToken, Qwen2Tokenizer, Qwen2TokenizerFast
 from transformers.models.qwen2.tokenization_qwen2 import VOCAB_FILES_NAMES, bytes_to_unicode
-from transformers.testing_utils import require_tokenizers
+from transformers.testing_utils import require_tokenizers, slow
 
 from ...test_tokenization_common import TokenizerTesterMixin
 
@@ -38,7 +38,12 @@ class Qwen2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
     def setUp(self):
         super().setUp()
 
+        # this make sure the vocabuary is complete at the byte level.
         vocab = list(bytes_to_unicode().values())
+        # the vocabulary, note:
+        # - `"\u0120n"`, `"\u0120lowest"`, `"\u0120newer"`, and `"\u0120wider"` are ineffective, because there are
+        #   not in the merges.
+        # - `"01"` is ineffective, because the merge is ineffective due to pretokenization.
         vocab.extend(
             [
                 "\u0120l",
@@ -58,6 +63,7 @@ def setUp(self):
 
         vocab_tokens = dict(zip(vocab, range(len(vocab))))
 
+        # note: `"0 1"` is in the merges, but the pretokenization rules render it ineffective
         merges = [
             "#version: 0.2",
             "\u0120 l",
@@ -70,7 +76,6 @@ def setUp(self):
             "\u00cf \u0135",
         ]
 
-        # unk_token is needed, because this stub tokenizer is not complete at the byte level
         self.special_tokens_map = {"eos_token": "<|endoftext|>"}
 
         self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
@@ -92,7 +97,6 @@ def get_input_output_texts(self, tokenizer):
         # this case should cover
         # - NFC normalization (code point U+03D3 has different normalization forms under NFC, NFD, NFKC, and NFKD)
         # - the pretokenization rules (spliting digits and merging symbols with \n\r)
-        # - no space added by default to the left of the special tokens in decode
         input_text = "lower lower newer 010;}\n<|endoftext|>\u03d2\u0301"
         output_text = "lower lower newer 010;}\n<|endoftext|>\u03d3"
         return input_text, output_text
@@ -133,3 +137,81 @@ def test_pretokenized_inputs(self):
         # which eats the whitespaces, which, in turn, is not reversible.
         # the results, by nature, should be different.
         pass
+
+    def test_nfc_normalization(self):
+        # per https://unicode.org/faq/normalization.html, there are three characters whose normalization forms
+        # under NFC, NFD, NFKC, and NFKD are all different
+        # using these, we can make sure only NFC is applied
+        input_string = "\u038e\u03ab\u1e61"  # the NFKC form
+        output_string = "\u03d3\u03d4\u1e9b"  # the NFC form
+
+        if self.test_slow_tokenizer:
+            tokenizer = self.get_tokenizer()
+            tokenizer_output_string, _ = tokenizer.prepare_for_tokenization(input_string)
+            self.assertEqual(tokenizer_output_string, output_string)
+
+        if self.test_rust_tokenizer:
+            tokenizer = self.get_rust_tokenizer()
+            # we can check the class of the normalizer, but it would be okay if Sequence([NFKC, NFC]) is used
+            # let's check the output instead
+            tokenizer_output_string = tokenizer.backend_tokenizer.normalizer.normalize_str()
+            self.assertEqual(tokenizer_output_string, output_string)
+
+    def test_slow_tokenizer_decode_spaces_between_special_tokens_default(self):
+        # Qwen2Tokenzier changes the default `spaces_between_special_tokens` in `decode` to False
+        if not self.test_slow_tokenizer:
+            return
+
+        # tokenizer has a special token: `"<|endfotext|>"` as eos, but it is not `legacy_added_tokens`
+        # special tokens in `spaces_between_special_tokens` means spaces between `legacy_added_tokens`
+        # that would be `"<|im_start|>"` and `"<|im_end|>"` in Qwen/Qwen2 Models
+        token_ids = [259, 260, 268, 269, 26]
+        sequence = " lower<|endoftext|><im_start>;"
+        sequence_with_space = " lower<|endoftext|> <im_start> ;"
+
+        tokenizer = self.get_tokenizer()
+        # let's add a legacy_added_tokens
+        im_start = AddedToken(
+            "<|im_start|>", single_word=False, lstrip=False, rstrip=False, special=True, normalized=False
+        )
+        tokenizer.add_tokens([im_start])
+
+        # `spaces_between_special_tokens` defaults to False
+        self.assertEqual(tokenizer.decode(token_ids), sequence)
+
+        # but it can be set to True
+        self.assertEqual(tokenizer.decode(token_ids, spaces_between_special_tokens=True), sequence_with_space)
+
+    @slow
+    def test_tokenizer_integration(self):
+        sequences = [
+            "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
+            "general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
+            "Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained "
+            "models in 100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.",
+            "🤗 Transformers 提供了可以轻松地下载并且训练先进的预训练模型的 API 和工具。使用预训练模型可以减少计算消耗和碳排放，并且节省从头训练所需要的时间和资源。",
+            """```python\ntokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-tokenizer")\n"""
+            """tokenizer("世界，你好！")```""",
+        ]
+
+        # fmt: off
+        expected_encoding = {
+            'input_ids': [
+                [8963, 388, 320, 69514, 3881, 438, 4510, 27414, 32852, 388, 323, 4510, 27414, 21334, 35722, 1455, 529, 8, 5707, 4586, 58238, 77235, 320, 61437, 11, 479, 2828, 12, 17, 11, 11830, 61437, 64, 11, 1599, 10994, 11, 27604, 321, 33, 529, 11, 29881, 6954, 32574, 369, 18448, 11434, 45451, 320, 45, 23236, 8, 323, 18448, 11434, 23470, 320, 30042, 38, 8, 448, 916, 220, 18, 17, 10, 80669, 4119, 304, 220, 16, 15, 15, 10, 15459, 323, 5538, 94130, 2897, 1948, 619, 706, 11, 5355, 51, 21584, 323, 94986, 13], 
+                [144834, 80532, 93685, 83744, 34187, 73670, 104261, 29490, 62189, 103937, 104034, 102830, 98841, 104034, 104949, 9370, 5333, 58143, 102011, 1773, 37029, 98841, 104034, 104949, 73670, 101940, 100768, 104997, 33108, 100912, 105054, 90395, 100136, 106831, 45181, 64355, 104034, 113521, 101975, 33108, 85329, 1773, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643], 
+                [73594, 12669, 198, 85593, 284, 8979, 37434, 6387, 10442, 35722, 445, 48, 16948, 45274, 16948, 34841, 3135, 1138, 85593, 445, 99489, 3837, 108386, 6313, 899, 73594, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643],
+            ], 
+            'attention_mask': [
+                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
+                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
+                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            ],
+        }
+        # fmt: on
+
+        self.tokenizer_integration_test_util(
+            expected_encoding=expected_encoding,
+            model_name="Qwen/Qwen-tokenizer",
+            revision="5909c8222473b2c73b0b73fb054552cd4ef6a8eb",
+            sequences=sequences,
+        )