Split and clean up GGUF quantization tests (#35502)

* clean up ggml test

Signed-off-by: Isotr0py <2037008807@qq.com>

* port remaining tests

Signed-off-by: Isotr0py <2037008807@qq.com>

* further cleanup

Signed-off-by: Isotr0py <2037008807@qq.com>

* format

Signed-off-by: Isotr0py <2037008807@qq.com>

* fix broken tests

Signed-off-by: Isotr0py <2037008807@qq.com>

* update comment

Signed-off-by: Isotr0py <2037008807@qq.com>

* fix

Signed-off-by: Isotr0py <2037008807@qq.com>

* reorganize tests

Signed-off-by: Isotr0py <2037008807@qq.com>

* k-quants use qwen2.5-0.5B

Signed-off-by: Isotr0py <2037008807@qq.com>

* move ggml tokenization test

Signed-off-by: Isotr0py <2037008807@qq.com>

* remove dead code

Signed-off-by: Isotr0py <2037008807@qq.com>

* add assert for serilization test

Signed-off-by: Isotr0py <2037008807@qq.com>

* use str for parameterize

Signed-off-by: Isotr0py <2037008807@qq.com>

---------

Signed-off-by: Isotr0py <2037008807@qq.com>

tests/quantization/ggml/test_ggml.py

@@ -15,6 +15,8 @@
 import tempfile
 import unittest
 
+from parameterized import parameterized
+
 from transformers import AddedToken, AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer
 from transformers.testing_utils import (
     require_gguf,
@@ -23,20 +25,205 @@ from transformers.testing_utils import (
     slow,
     torch_device,
 )
-from transformers.utils import is_torch_available
+from transformers.utils import is_gguf_available, is_torch_available
 
 
 if is_torch_available():
     import torch
 
+if is_gguf_available():
+    from gguf import GGMLQuantizationType as QuantType
+
+
+@require_gguf
+@require_torch_gpu
+@slow
+class GgufQuantizationTests(unittest.TestCase):
+    """
+    Test cases for weights dequantization with GGUF models.
+    Note: The quantization names should keep aligned with `GGMLQuantizationType` in gguf-py:
+    https://github.com/ggerganov/llama.cpp/blob/4b0c638b9a68f577cb2066b638c9f622d91ee661/gguf-py/gguf/constants.py#L1545-L1576
+    So quantization like Q4_K_M or Q4_K_S dshouldn't be added to this tests.
+    """
+
+    example_text = "Hello"
+
+    def run_gguf_model(self, gguf_model_id: str, gguf_filename: str, expected_text: str):
+        tokenizer = AutoTokenizer.from_pretrained(gguf_model_id, gguf_file=gguf_filename)
+        model = AutoModelForCausalLM.from_pretrained(gguf_model_id, gguf_file=gguf_filename).to(torch_device)
+
+        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
+        out = model.generate(**text, max_new_tokens=10)
+        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), expected_text)
+
+    @parameterized.expand(
+        [
+            # standard quants
+            ("Q4_0", "Hello, World!\n\nStep 3: Add"),
+            ("Q5_0", "Hello, World!\n\n5. Use a library"),
+            ("Q8_0", "Hello, World!\n\n5. Use a library"),
+        ],
+    )
+    def test_standard_quants(self, quant_type: str, expected_text: str):
+        gguf_model_id = "TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF"
+        filename_format = "tinyllama-1.1b-chat-v1.0.{quant_type}.gguf"
+        gguf_filename = filename_format.format(quant_type=quant_type)
+        self.run_gguf_model(gguf_model_id, gguf_filename, expected_text)
+
+    # k-quants
+    @parameterized.expand(
+        [
+            ("Q2_K", "Hello, I'm a 22 year old female"),
+            ("Q3_K", "Hello\n\nI am trying to create a simple program that"),
+            ("Q4_K", "Hello\n\nI am trying to create a simple program that"),
+            ("Q5_K", "Helloveda is a 1999 Indian"),
+            ("Q6_K", "Hello\n\nI am trying to create a simple program that"),
+        ],
+    )
+    def test_k_quants(self, quant_type: str, expected_text: str):
+        gguf_model_id = "legraphista/Qwen2.5-0.5B-Instruct-IMat-GGUF"
+        filename_format = "Qwen2.5-0.5B-Instruct.{quant_type}.gguf"
+        gguf_filename = filename_format.format(quant_type=quant_type)
+        self.run_gguf_model(gguf_model_id, gguf_filename, expected_text)
+
+    @parameterized.expand(
+        [
+            # i-matrix
+            ("IQ1_S", "Hello, I'm a friend of mine, I"),
+            ("IQ1_M", "Hello, I am interested in purching a copy of"),
+            ("IQ2_XXS", "Hello, I'm a software engineer. I'"),
+            ("IQ2_XS", "Hello World!\n\n```\n<|user|"),
+            ("IQ2_S", "Hello World!\n\n```\n<|user|"),
+            ("IQ3_XXS", "Hello, I am interested in your product. Can you"),
+            ("IQ4_XS", "Hello, world!\n\n5. Using a loop"),
+            ("IQ3_S", "Hello, World!\n\n5. Python:\n"),
+            ("IQ4_NL", "Hello, world!\n\n5. Using a loop"),
+        ],
+    )
+    def test_imatrix_quants(self, quant_type: str, expected_text: str):
+        gguf_model_id = "duyntnet/TinyLlama-1.1B-Chat-v1.0-imatrix-GGUF"
+        filename_format = "TinyLlama-1.1B-Chat-v1.0-{quant_type}.gguf"
+        gguf_filename = filename_format.format(quant_type=quant_type)
+        self.run_gguf_model(gguf_model_id, gguf_filename, expected_text)
+
 
 @require_gguf
 @require_torch_gpu
 @slow
 class GgufIntegrationTests(unittest.TestCase):
+    """
+    Test cases for basic interoperability with GGUF models:
+    - Tokenization
+    - Model dtype casting and serialization
+    """
+
+    example_text = "Hello"
     original_model_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
-    model_id = "TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF"
+    gguf_model_id = "TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF"
-    imatrix_model_id = "duyntnet/TinyLlama-1.1B-Chat-v1.0-imatrix-GGUF"
+    gguf_filename = "tinyllama-1.1b-chat-v1.0.{quant_type}.gguf"
+
+    def test_tokenization_xnli(self):
+        import tqdm
+        from datasets import load_dataset
+
+        q8_0_gguf_model_id = self.gguf_filename.format(quant_type=QuantType.Q8_0.name)
+        gguf_tokenizer = AutoTokenizer.from_pretrained(self.gguf_model_id, gguf_file=q8_0_gguf_model_id)
+        original_tokenizer = AutoTokenizer.from_pretrained(self.original_model_id)
+
+        dataset = load_dataset("google/code_x_glue_ct_code_to_text", "go")
+        for item in tqdm.tqdm(dataset["validation"]):
+            string = item["code"]
+            encoded1 = gguf_tokenizer.encode(string)
+            encoded2 = original_tokenizer.encode(string)
+
+            self.assertEqual(encoded1, encoded2)
+
+            decoded1 = gguf_tokenizer.decode(encoded1, skip_special_tokens=True)
+            decoded2 = original_tokenizer.decode(encoded2, skip_special_tokens=True)
+
+            self.assertEqual(decoded1, decoded2)
+
+        dataset = load_dataset("facebook/xnli", "all_languages")
+
+        for i, item in enumerate(tqdm.tqdm(dataset["train"].select(range(100)))):
+            for string in item["premise"].values():
+                encoded1 = gguf_tokenizer.encode(string)
+                encoded2 = original_tokenizer.encode(string)
+
+                self.assertEqual(encoded1, encoded2)
+
+                decoded1 = gguf_tokenizer.decode(encoded1, skip_special_tokens=True)
+                decoded2 = original_tokenizer.decode(encoded2, skip_special_tokens=True)
+
+                self.assertEqual(decoded1, decoded2)
+
+        # With special tokens
+        gguf_tokenizer = AutoTokenizer.from_pretrained(self.gguf_model_id, gguf_file=q8_0_gguf_model_id)
+        original_tokenizer = AutoTokenizer.from_pretrained(self.original_model_id)
+
+        gguf_tokenizer.add_special_tokens(
+            {"additional_special_tokens": [AddedToken("<token>", rstrip=False, lstrip=False)]}
+        )
+        original_tokenizer.add_special_tokens(
+            {"additional_special_tokens": [AddedToken("<token>", rstrip=False, lstrip=False)]}
+        )
+
+        text = "Hello <token>. <token> Hello"
+
+        encoded1 = gguf_tokenizer.encode(text)
+        encoded2 = original_tokenizer.encode(text)
+
+        self.assertEqual(encoded1, encoded2)
+
+        decoded1 = gguf_tokenizer.decode(encoded1, skip_special_tokens=True)
+        decoded2 = original_tokenizer.decode(encoded2, skip_special_tokens=True)
+
+        self.assertEqual(decoded1, decoded2)
+
+    def test_q2_k_serialization(self):
+        q2_k_gguf_model_id = self.gguf_filename.format(quant_type=QuantType.Q2_K.name)
+        EXPECTED_TEXT = "Hello, World!\n\n[10:0"
+
+        tokenizer = AutoTokenizer.from_pretrained(self.gguf_model_id, gguf_file=q2_k_gguf_model_id)
+        model = AutoModelForCausalLM.from_pretrained(self.gguf_model_id, gguf_file=q2_k_gguf_model_id).to(torch_device)
+
+        orig_text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
+        orig_out = model.generate(**orig_text, max_new_tokens=10)
+        self.assertEqual(tokenizer.decode(orig_out[0], skip_special_tokens=True), EXPECTED_TEXT)
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            model.save_pretrained(tmpdirname)
+            tokenizer.save_pretrained(tmpdirname)
+
+            model = AutoModelForCausalLM.from_pretrained(tmpdirname).to(torch_device)
+            tokenizer = AutoTokenizer.from_pretrained(tmpdirname)
+
+            text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
+            out = model.generate(**text, max_new_tokens=10)
+
+        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
+
+    def test_q6_k_fp16(self):
+        q6_k_gguf_model_id = self.gguf_filename.format(quant_type=QuantType.Q6_K.name)
+
+        tokenizer = AutoTokenizer.from_pretrained(self.gguf_model_id, gguf_file=q6_k_gguf_model_id)
+        model = AutoModelForCausalLM.from_pretrained(
+            self.gguf_model_id, gguf_file=q6_k_gguf_model_id, torch_dtype=torch.float16
+        ).to(torch_device)
+
+        self.assertTrue(model.lm_head.weight.dtype == torch.float16)
+
+        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
+        out = model.generate(**text, max_new_tokens=10)
+
+        EXPECTED_TEXT = "Hello, World!\n\nStep 3: Add"
+        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
+
+
+@require_gguf
+@require_torch_gpu
+@slow
+class GgufModelTests(unittest.TestCase):
     mistral_model_id = "TheBloke/Mistral-7B-Instruct-v0.2-GGUF"
     qwen2_model_id = "Qwen/Qwen1.5-0.5B-Chat-GGUF"
     qwen2moe_model_id = "gdax/Qwen1.5-MoE-A2.7B_gguf"
@@ -68,34 +255,13 @@ class GgufIntegrationTests(unittest.TestCase):
     original_gemma2_model_id = "google/gemma-2-2b-it"
     gemma2_model_id = "bartowski/gemma-2-2b-it-GGUF"
 
-    # standard quants
-    q4_0_gguf_model_id = "tinyllama-1.1b-chat-v1.0.Q4_0.gguf"
-    q5_0_gguf_model_id = "tinyllama-1.1b-chat-v1.0.Q5_0.gguf"
-    q8_0_gguf_model_id = "tinyllama-1.1b-chat-v1.0.Q8_0.gguf"
-    # k-quants
-    q2_k_gguf_model_id = "tinyllama-1.1b-chat-v1.0.Q2_K.gguf"
-    q3_k_gguf_model_id = "tinyllama-1.1b-chat-v1.0.Q3_K_L.gguf"
-    q4_k_gguf_model_id = "tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf"
-    q5_k_gguf_model_id = "tinyllama-1.1b-chat-v1.0.Q5_K_M.gguf"
-    q6_k_gguf_model_id = "tinyllama-1.1b-chat-v1.0.Q6_K.gguf"
-    q4_k_m_stablelm_model_id = "stablelm-3b-4e1t.q4_k_m.gguf"
-    # imatrix
-    iq1_m_gguf_model_id = "TinyLlama-1.1B-Chat-v1.0-IQ1_M.gguf"
-    iq1_s_gguf_model_id = "TinyLlama-1.1B-Chat-v1.0-IQ1_S.gguf"
-    iq2_s_gguf_model_id = "TinyLlama-1.1B-Chat-v1.0-IQ2_S.gguf"
-    iq2_xs_gguf_model_id = "TinyLlama-1.1B-Chat-v1.0-IQ2_XS.gguf"
-    iq2_xxs_gguf_model_id = "TinyLlama-1.1B-Chat-v1.0-IQ2_XXS.gguf"
-    iq3_s_gguf_model_id = "TinyLlama-1.1B-Chat-v1.0-IQ3_S.gguf"
-    iq3_xxs_gguf_model_id = "TinyLlama-1.1B-Chat-v1.0-IQ3_XXS.gguf"
-    iq4_xs_gguf_model_id = "TinyLlama-1.1B-Chat-v1.0-IQ4_XS.gguf"
-    iq4_nl_gguf_model_id = "TinyLlama-1.1B-Chat-v1.0-IQ4_NL.gguf"
-
     q4_0_phi3_model_id = "Phi-3-mini-4k-instruct-q4.gguf"
     q4_0_mistral_model_id = "mistral-7b-instruct-v0.2.Q4_0.gguf"
     q4_0_qwen2_model_id = "qwen1_5-0_5b-chat-q4_0.gguf"
     q8_qwen2moe_model_id = "Qwen1.5-MoE-A2.7B_Q8_0.gguf"
     q4_llama3_model_id = "Meta-Llama-3-8B-Q4_K_M.gguf"
     fp16_bloom_model_id = "bloom-560m.fp16.gguf"
+    q4_k_m_stablelm_model_id = "stablelm-3b-4e1t.q4_k_m.gguf"
     fp16_stablelm2_model_id = "stablelm-2-1_6b.fp16.gguf"
     q8_bloom_model_id = "bloom-560m.q8_0.gguf"
     f16_tinyllama_model_id = "TinyLlama-1.1B-Chat-v1.0.FP16.gguf"
@@ -120,237 +286,6 @@ class GgufIntegrationTests(unittest.TestCase):
 
     example_text = "Hello"
 
-    def test_q2_k(self):
-        tokenizer = AutoTokenizer.from_pretrained(self.model_id, gguf_file=self.q2_k_gguf_model_id)
-        model = AutoModelForCausalLM.from_pretrained(self.model_id, gguf_file=self.q2_k_gguf_model_id).to(torch_device)
-
-        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
-        out = model.generate(**text, max_new_tokens=10)
-
-        EXPECTED_TEXT = "Hello, World!\n\n[10:0"
-        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
-
-    def test_q2_k_serialization(self):
-        tokenizer = AutoTokenizer.from_pretrained(self.model_id, gguf_file=self.q2_k_gguf_model_id)
-        model = AutoModelForCausalLM.from_pretrained(self.model_id, gguf_file=self.q2_k_gguf_model_id).to(torch_device)
-
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            model.save_pretrained(tmpdirname)
-            tokenizer.save_pretrained(tmpdirname)
-
-            model = AutoModelForCausalLM.from_pretrained(tmpdirname).to(torch_device)
-            tokenizer = AutoTokenizer.from_pretrained(tmpdirname)
-
-            text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
-            out = model.generate(**text, max_new_tokens=10)
-
-        EXPECTED_TEXT = "Hello, World!\n\n[10:0"
-        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
-
-    def test_q3_k(self):
-        tokenizer = AutoTokenizer.from_pretrained(self.model_id, gguf_file=self.q3_k_gguf_model_id)
-        model = AutoModelForCausalLM.from_pretrained(self.model_id, gguf_file=self.q3_k_gguf_model_id).to(torch_device)
-
-        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
-        out = model.generate(**text, max_new_tokens=10)
-
-        EXPECTED_TEXT = "Hello, World!\n\n```\n<|user"
-        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
-
-    def test_q5_0(self):
-        tokenizer = AutoTokenizer.from_pretrained(self.model_id, gguf_file=self.q5_0_gguf_model_id)
-        model = AutoModelForCausalLM.from_pretrained(self.model_id, gguf_file=self.q5_0_gguf_model_id).to(torch_device)
-
-        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
-        out = model.generate(**text, max_new_tokens=10)
-
-        EXPECTED_TEXT = "Hello, World!\n\n5. Use a library"
-        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
-
-    def test_q5_k(self):
-        tokenizer = AutoTokenizer.from_pretrained(self.model_id, gguf_file=self.q5_k_gguf_model_id)
-        model = AutoModelForCausalLM.from_pretrained(self.model_id, gguf_file=self.q5_k_gguf_model_id).to(torch_device)
-
-        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
-        out = model.generate(**text, max_new_tokens=10)
-
-        EXPECTED_TEXT = "Hello, World!\n\nStep 3: Add"
-        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
-
-    def test_q4_0(self):
-        tokenizer = AutoTokenizer.from_pretrained(self.model_id, gguf_file=self.q4_0_gguf_model_id)
-        model = AutoModelForCausalLM.from_pretrained(self.model_id, gguf_file=self.q4_0_gguf_model_id).to(torch_device)
-
-        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
-        out = model.generate(**text, max_new_tokens=10)
-
-        EXPECTED_TEXT = "Hello, World!\n\nStep 3: Add"
-        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
-
-    def test_q4_k_m(self):
-        tokenizer = AutoTokenizer.from_pretrained(self.model_id, gguf_file=self.q4_k_gguf_model_id)
-        model = AutoModelForCausalLM.from_pretrained(self.model_id, gguf_file=self.q4_k_gguf_model_id).to(torch_device)
-
-        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
-        out = model.generate(**text, max_new_tokens=10)
-
-        EXPECTED_TEXT = "Hello, World!\n\n5. Python:\n"
-        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
-
-    def test_q6_k(self):
-        tokenizer = AutoTokenizer.from_pretrained(self.model_id, gguf_file=self.q6_k_gguf_model_id)
-        model = AutoModelForCausalLM.from_pretrained(self.model_id, gguf_file=self.q6_k_gguf_model_id).to(torch_device)
-
-        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
-        out = model.generate(**text, max_new_tokens=10)
-
-        EXPECTED_TEXT = "Hello, World!\n\nStep 3: Add"
-        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
-
-    def test_q6_k_fp16(self):
-        tokenizer = AutoTokenizer.from_pretrained(self.model_id, gguf_file=self.q6_k_gguf_model_id)
-        model = AutoModelForCausalLM.from_pretrained(
-            self.model_id, gguf_file=self.q6_k_gguf_model_id, torch_dtype=torch.float16
-        ).to(torch_device)
-
-        self.assertTrue(model.lm_head.weight.dtype == torch.float16)
-
-        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
-        out = model.generate(**text, max_new_tokens=10)
-
-        EXPECTED_TEXT = "Hello, World!\n\nStep 3: Add"
-        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
-
-    def test_q8_0(self):
-        tokenizer = AutoTokenizer.from_pretrained(self.model_id, gguf_file=self.q8_0_gguf_model_id)
-        model = AutoModelForCausalLM.from_pretrained(self.model_id, gguf_file=self.q8_0_gguf_model_id).to(torch_device)
-
-        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
-        out = model.generate(**text, max_new_tokens=10)
-
-        EXPECTED_TEXT = "Hello, World!\n\n5. Use a library"
-        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
-
-    def test_iq1_s(self):
-        tokenizer = AutoTokenizer.from_pretrained(self.imatrix_model_id, gguf_file=self.iq1_s_gguf_model_id)
-        model = AutoModelForCausalLM.from_pretrained(self.imatrix_model_id, gguf_file=self.iq1_s_gguf_model_id).to(
-            torch_device
-        )
-
-        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
-        out = model.generate(**text, max_new_tokens=10)
-
-        EXPECTED_TEXT = "Hello, I'm a friend of mine, I"
-        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
-
-    def test_iq1_m(self):
-        tokenizer = AutoTokenizer.from_pretrained(self.imatrix_model_id, gguf_file=self.iq1_m_gguf_model_id)
-        model = AutoModelForCausalLM.from_pretrained(self.imatrix_model_id, gguf_file=self.iq1_m_gguf_model_id).to(
-            torch_device
-        )
-
-        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
-        out = model.generate(**text, max_new_tokens=10)
-
-        EXPECTED_TEXT = "Hello, I am interested in purching a copy of"
-        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
-
-    def test_iq2_s(self):
-        tokenizer = AutoTokenizer.from_pretrained(self.imatrix_model_id, gguf_file=self.iq2_s_gguf_model_id)
-        model = AutoModelForCausalLM.from_pretrained(self.imatrix_model_id, gguf_file=self.iq2_s_gguf_model_id).to(
-            torch_device
-        )
-
-        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
-        out = model.generate(**text, max_new_tokens=10)
-
-        EXPECTED_TEXT = "Hello World!\n\n```\n<|user|"
-        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
-
-    def test_iq2_xs(self):
-        tokenizer = AutoTokenizer.from_pretrained(self.imatrix_model_id, gguf_file=self.iq2_xs_gguf_model_id)
-        model = AutoModelForCausalLM.from_pretrained(self.imatrix_model_id, gguf_file=self.iq2_xs_gguf_model_id).to(
-            torch_device
-        )
-
-        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
-        out = model.generate(**text, max_new_tokens=10)
-
-        EXPECTED_TEXT = "Hello World!\n\n```\n<|user|"
-        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
-
-    def test_iq2_xxs(self):
-        tokenizer = AutoTokenizer.from_pretrained(self.imatrix_model_id, gguf_file=self.iq2_xxs_gguf_model_id)
-        model = AutoModelForCausalLM.from_pretrained(self.imatrix_model_id, gguf_file=self.iq2_xxs_gguf_model_id).to(
-            torch_device
-        )
-
-        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
-        out = model.generate(**text, max_new_tokens=10)
-
-        EXPECTED_TEXT = "Hello, I'm a software engineer. I'"
-        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
-
-    def test_iq3_s(self):
-        tokenizer = AutoTokenizer.from_pretrained(self.imatrix_model_id, gguf_file=self.iq3_s_gguf_model_id)
-        model = AutoModelForCausalLM.from_pretrained(self.imatrix_model_id, gguf_file=self.iq3_s_gguf_model_id).to(
-            torch_device
-        )
-
-        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
-        out = model.generate(**text, max_new_tokens=10)
-
-        EXPECTED_TEXT = "Hello, World!\n\n5. Python:\n"
-        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
-
-    def test_iq3_xxs(self):
-        tokenizer = AutoTokenizer.from_pretrained(self.imatrix_model_id, gguf_file=self.iq3_xxs_gguf_model_id)
-        model = AutoModelForCausalLM.from_pretrained(self.imatrix_model_id, gguf_file=self.iq3_xxs_gguf_model_id).to(
-            torch_device
-        )
-
-        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
-        out = model.generate(**text, max_new_tokens=10)
-
-        EXPECTED_TEXT = "Hello, I am interested in your product. Can you"
-        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
-
-    def test_iq4_xs(self):
-        tokenizer = AutoTokenizer.from_pretrained(self.imatrix_model_id, gguf_file=self.iq4_xs_gguf_model_id)
-        model = AutoModelForCausalLM.from_pretrained(self.imatrix_model_id, gguf_file=self.iq4_xs_gguf_model_id).to(
-            torch_device
-        )
-
-        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
-        out = model.generate(**text, max_new_tokens=10)
-
-        EXPECTED_TEXT = "Hello, world!\n\n5. Using a loop"
-        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
-
-    def test_iq4_nl(self):
-        tokenizer = AutoTokenizer.from_pretrained(self.imatrix_model_id, gguf_file=self.iq4_nl_gguf_model_id)
-        model = AutoModelForCausalLM.from_pretrained(self.imatrix_model_id, gguf_file=self.iq4_nl_gguf_model_id).to(
-            torch_device
-        )
-
-        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
-        out = model.generate(**text, max_new_tokens=10)
-
-        EXPECTED_TEXT = "Hello, world!\n\n5. Using a loop"
-        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
-
-    def test_f16(self):
-        tokenizer = AutoTokenizer.from_pretrained(self.tinyllama_model_id, gguf_file=self.f16_tinyllama_model_id)
-        model = AutoModelForCausalLM.from_pretrained(
-            self.tinyllama_model_id, gguf_file=self.f16_tinyllama_model_id
-        ).to(torch_device)
-
-        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
-        out = model.generate(**text, max_new_tokens=10)
-
-        EXPECTED_TEXT = "Hello, World!\n\n5. Node.js"
-        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
-
     def test_mistral_q4_0(self):
         tokenizer = AutoTokenizer.from_pretrained(self.mistral_model_id, gguf_file=self.q4_0_mistral_model_id)
         model = AutoModelForCausalLM.from_pretrained(
@@ -904,60 +839,3 @@ class GgufIntegrationTests(unittest.TestCase):
                 torch.testing.assert_close(original_params, converted_state_dict[layer_name])
             else:
                 raise ValueError(f"Layer {layer_name} is not presented in GGUF model")
-
-    def test_tokenization_xnli(self):
-        import tqdm
-        from datasets import load_dataset
-
-        gguf_tokenizer = AutoTokenizer.from_pretrained(self.model_id, gguf_file=self.q8_0_gguf_model_id)
-        original_tokenizer = AutoTokenizer.from_pretrained(self.original_model_id)
-
-        dataset = load_dataset("google/code_x_glue_ct_code_to_text", "go")
-        for item in tqdm.tqdm(dataset["validation"]):
-            string = item["code"]
-            encoded1 = gguf_tokenizer.encode(string)
-            encoded2 = original_tokenizer.encode(string)
-
-            self.assertEqual(encoded1, encoded2)
-
-            decoded1 = gguf_tokenizer.decode(encoded1, skip_special_tokens=True)
-            decoded2 = original_tokenizer.decode(encoded2, skip_special_tokens=True)
-
-            self.assertEqual(decoded1, decoded2)
-
-        dataset = load_dataset("facebook/xnli", "all_languages")
-
-        for i, item in enumerate(tqdm.tqdm(dataset["train"].select(range(100)))):
-            for string in item["premise"].values():
-                encoded1 = gguf_tokenizer.encode(string)
-                encoded2 = original_tokenizer.encode(string)
-
-                self.assertEqual(encoded1, encoded2)
-
-                decoded1 = gguf_tokenizer.decode(encoded1, skip_special_tokens=True)
-                decoded2 = original_tokenizer.decode(encoded2, skip_special_tokens=True)
-
-                self.assertEqual(decoded1, decoded2)
-
-        # With special tokens
-        gguf_tokenizer = AutoTokenizer.from_pretrained(self.model_id, gguf_file=self.q8_0_gguf_model_id)
-        original_tokenizer = AutoTokenizer.from_pretrained(self.original_model_id)
-
-        gguf_tokenizer.add_special_tokens(
-            {"additional_special_tokens": [AddedToken("<token>", rstrip=False, lstrip=False)]}
-        )
-        original_tokenizer.add_special_tokens(
-            {"additional_special_tokens": [AddedToken("<token>", rstrip=False, lstrip=False)]}
-        )
-
-        text = "Hello <token>. <token> Hello"
-
-        encoded1 = gguf_tokenizer.encode(text)
-        encoded2 = original_tokenizer.encode(text)
-
-        self.assertEqual(encoded1, encoded2)
-
-        decoded1 = gguf_tokenizer.decode(encoded1, skip_special_tokens=True)
-        decoded2 = original_tokenizer.decode(encoded2, skip_special_tokens=True)
-
-        self.assertEqual(decoded1, decoded2)