diff --git a/tests/models/chatglm/test_modeling_chatglm.py b/tests/models/chatglm/test_modeling_chatglm.py
index 88454cfd6de857..0f0c0443f558c6 100644
--- a/tests/models/chatglm/test_modeling_chatglm.py
+++ b/tests/models/chatglm/test_modeling_chatglm.py
@@ -484,85 +484,3 @@ def test_model_13b_greedy_generation(self):
         generated_ids = model.generate(input_ids, max_new_tokens=64, top_p=None, temperature=1, do_sample=False)
         text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
         self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
-
-
-@require_torch
-class CodeChatGlmIntegrationTest(unittest.TestCase):
-    PROMPTS = [
-        '''def remove_non_ascii(s: str) -> str:
-    """ <FILL_ME>
-    return result
-''',
-        """# Installation instructions:
-    ```bash
-<FILL_ME>
-    ```
-This downloads the ChatGLM inference code and installs the repository as a local pip package.
-""",
-        """class InterfaceManagerFactory(AbstractManagerFactory):
-    def __init__(<FILL_ME>
-def main():
-    factory = InterfaceManagerFactory(start=datetime.now())
-    managers = []
-    for i in range(10):
-        managers.append(factory.build(id=i))
-""",
-        """/-- A quasi-prefunctoid is 1-connected iff all its etalisations are 1-connected. -/
-theorem connected_iff_etalisation [C D : precategoroid] (P : quasi_prefunctoid C D) :
-π₁ P = 0 ↔ <FILL_ME> = 0 :=
-begin
-split,
-{ intros h f,
-    rw pi_1_etalisation at h,
-    simp [h],
-    refl
-},
-{ intro h,
-    have := @quasi_adjoint C D P,
-    simp [←pi_1_etalisation, this, h],
-    refl
-}
-end
-""",
-    ]
-
-    @require_torch_accelerator
-    @slow
-    def test_model_7b_logits(self):
-        model = ChatGlmForCausalLM.from_pretrained("codechatglm/CodeChatGlm-7b-hf").to(torch_device)
-        tokenizer = CodeLlamaTokenizer.from_pretrained("codechatglm/CodeChatGlm-7b-hf")
-        # Tokenize and prepare for the model a list of sequences or a list of pairs of sequences.
-        # meaning by default this supports passing splitted list of inputs
-        processed_text = tokenizer.batch_decode(tokenizer(self.PROMPTS)["input_ids"], add_special_tokens=False)
-        # fmt: off
-        EXPECTED_TEXT = [
-            '<s> <PRE> def remove_non_ascii(s: str) -> str:\n    """  <SUF>\n    return result\n <MID>',
-            '<s> <PRE> # Installation instructions:\n    ```bash\n <SUF>\n    ```\nThis downloads the ChatGLM inference code and installs the repository as a local pip package.\n <MID>',
-            '<s> <PRE> class InterfaceManagerFactory(AbstractManagerFactory):\n    def __init__( <SUF>\ndef main():\n    factory = InterfaceManagerFactory(start=datetime.now())\n    managers = []\n    for i in range(10):\n        managers.append(factory.build(id=i))\n <MID>',
-            '<s> <PRE> /-- A quasi-prefunctoid is 1-connected iff all its etalisations are 1-connected. -/\ntheorem connected_iff_etalisation [C D : precategoroid] (P : quasi_prefunctoid C D) :\nπ₁ P = 0 ↔  <SUF> = 0 :=\nbegin\nsplit,\n{ intros h f,\n    rw pi_1_etalisation at h,\n    simp [h],\n    refl\n},\n{ intro h,\n    have := @quasi_adjoint C D P,\n    simp [←pi_1_etalisation, this, h],\n    refl\n}\nend\n <MID>'
-        ]
-        # fmt: on
-        self.assertEqual(processed_text, EXPECTED_TEXT)
-        processed_text_suffix_first = tokenizer.batch_decode(
-            tokenizer(self.PROMPTS, suffix_first=True, add_special_tokens=False)["input_ids"]
-        )
-
-        # fmt: off
-        EXPECTED_TEXT = [
-            '<PRE> <SUF>\n    return result\n <MID> def remove_non_ascii(s: str) -> str:\n    """ ',
-            '<PRE> <SUF>\n    ```\nThis downloads the ChatGLM inference code and installs the repository as a local pip package.\n <MID> # Installation instructions:\n    ```bash\n',
-            '<PRE> <SUF>\ndef main():\n    factory = InterfaceManagerFactory(start=datetime.now())\n    managers = []\n    for i in range(10):\n        managers.append(factory.build(id=i))\n <MID> class InterfaceManagerFactory(AbstractManagerFactory):\n    def __init__(',
-            '<PRE> <SUF> = 0 :=\nbegin\nsplit,\n{ intros h f,\n    rw pi_1_etalisation at h,\n    simp [h],\n    refl\n},\n{ intro h,\n    have := @quasi_adjoint C D P,\n    simp [←pi_1_etalisation, this, h],\n    refl\n}\nend\n <MID> /-- A quasi-prefunctoid is 1-connected iff all its etalisations are 1-connected. -/\ntheorem connected_iff_etalisation [C D : precategoroid] (P : quasi_prefunctoid C D) :\nπ₁ P = 0 ↔ '
-        ]
-        EXPECTED_IDS = torch.tensor([[    1, 32007, 822, 3349, 29918, 5464, 29918, 294, 18869, 29898,29879, 29901, 851, 29897, 1599, 851, 29901, 13, 1678, 9995, 29871, 32008, 13, 1678, 736, 1121, 13, 32009, 15941, 1661, 29899, 28599, 2687, 4890, 515, 263, 1347, 29889, 13, 13, 1678, 826, 3174, 29901, 13, 4706, 269, 29901, 450, 1347, 304, 3349, 1661, 29899, 28599, 2687, 4890, 515, 29889, 13, 13, 1678, 16969, 29901, 13, 4706, 450, 1347, 411, 1661, 29899, 28599, 2687, 4890, 6206, 29889, 13, 1678, 9995, 13, 1678, 1121, 353, 5124, 13, 1678, 363, 274, 297, 269, 29901, 13, 4706, 565, 4356, 29898, 29883, 29897, 529, 29871, 29896, 29906, 29947, 29901, 13, 9651, 1121, 4619, 274, 32010, 2]])
-        # fmt: on
-        self.assertEqual(processed_text_suffix_first, EXPECTED_TEXT)
-        input_ids = tokenizer(self.PROMPTS[0], return_tensors="pt")["input_ids"]
-        generated_ids = model.generate(input_ids.to(torch_device), max_new_tokens=128)
-        torch.testing.assert_close(generated_ids, EXPECTED_IDS)
-
-        EXPECTED_INFILLING = [
-            '<s> <PRE> def remove_non_ascii(s: str) -> str:\n    """  <SUF>\n    return result\n <MID>Remove non-ASCII characters from a string.\n\n    Args:\n        s: The string to remove non-ASCII characters from.\n\n    Returns:\n        The string with non-ASCII characters removed.\n    """\n    result = ""\n    for c in s:\n        if ord(c) < 128:\n            result += c <EOT></s>'
-        ]
-        infilling = tokenizer.batch_decode(generated_ids)
-        self.assertEqual(infilling, EXPECTED_INFILLING)