diff --git a/tests/test-tokenizer-random.py b/tests/test-tokenizer-random.py
index 1166ac1e4..267b1c8f7 100644
--- a/tests/test-tokenizer-random.py
+++ b/tests/test-tokenizer-random.py
@@ -154,19 +154,22 @@ def generator_custom_text_edge_cases() -> Iterator[str]:
         '\uFEFF//',   # unicode_ranges_control, 0xFEFF (BOM)
         'Cửa Việt',   # llama-3, ignore_merges = true
         '<s>a',       # Phi-3 fail
-        '<unk><|endoftext|><s>'  # Phi-3 fail
+        '<unk><|endoftext|><s>',  # Phi-3 fail
         'a\na',       # TODO: Bert fail
     ]
 
 
-def generator_random_special_tokens(special_tokens:list[str], iterations=100) -> Iterator[str]:
-    special_tokens = set(special_tokens)
+def generator_random_special_tokens(tokenizer, iterations=100) -> Iterator[str]:
+    special_tokens = set(tokenizer.all_special_tokens)
     special_tokens.update([" ", "\n", "\t", "-", "!", "one", "1", "<s>", "</s>"])
     special_tokens = list(sorted(special_tokens))
     rand = random.Random()
     for m in range(iterations):
         rand.seed(m)
         words = rand.choices(special_tokens, k=500)
+        if tokenizer.add_bos_token:  # skip spam warning of double BOS
+            while words and words[0] == tokenizer.bos_token:
+                words.pop(0)
         yield "".join(words)
 
 
@@ -290,18 +293,16 @@ def main(argv: list[str] = None):
     model = LibLlamaModel(LibLlama(), args.vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=4096))
     tokenizer = AutoTokenizer.from_pretrained(args.dir_tokenizer)
 
-    def func_tokenize2(text: str):
-        return tokenizer.encode(text, add_special_tokens=False)
-
-    parse_special = all(len(func_tokenize2(t)) == 1 for t in tokenizer.all_special_tokens)
-
     def func_tokenize1(text: str):
-        return model.tokenize(text, add_special=False, parse_special=parse_special)
+        return model.tokenize(text, add_special=True, parse_special=True)
+
+    def func_tokenize2(text: str):
+        return tokenizer.encode(text, add_special_tokens=True)
 
     vocab = list(sorted(tokenizer.batch_decode(list(tokenizer.get_vocab().values()), skip_special_tokens=True)))
     test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text())
     test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text_edge_cases())
-    test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_special_tokens(tokenizer.all_special_tokens, 10_000))
+    test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_special_tokens(tokenizer, 10_000))
     test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_vocab_words(vocab))
     test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_chars(10_000))
     test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_vocab_chars(vocab, 10_000))