[Chore] Use Transformers v5 names and drop redundant processor use_fast

docs/models/supported_models.md

@@ -164,9 +164,9 @@ For your model to be compatible with vLLM's tensor parallel and/or pipeline para
 
 ```python
 
-from transformers import PretrainedConfig
+from transformers import PreTrainedConfig
 
-class MyConfig(PretrainedConfig):
+class MyConfig(PreTrainedConfig):
     base_model_tp_plan = {
         "layers.*.self_attn.k_proj": "colwise",
         "layers.*.self_attn.v_proj": "colwise",

tests/config/test_bailing_mtp_config.py

@@ -3,16 +3,16 @@
 
 from typing import get_args
 
-from transformers import PretrainedConfig
+from transformers import PreTrainedConfig
 
 from vllm.config.speculative import MTPModelTypes, SpeculativeConfig
 from vllm.transformers_utils.model_arch_config_convertor import (
     BailingHybridMTPModelArchConfigConvertor,
 )
 
 
-def _bailing_config() -> PretrainedConfig:
-    config = PretrainedConfig(
+def _bailing_config() -> PreTrainedConfig:
+    config = PreTrainedConfig(
         architectures=["BailingMoeV2_5ForCausalLM"],
         hidden_size=4096,
         kv_lora_rank=512,

tests/config/test_model_arch_config.py

@@ -10,7 +10,7 @@
 from unittest.mock import Mock
 
 import pytest
-from transformers import PretrainedConfig
+from transformers import PreTrainedConfig
 from transformers.models.gemma4.configuration_gemma4 import Gemma4TextConfig
 
 from vllm.config import ModelConfig, ParallelConfig, SpeculativeConfig
@@ -125,7 +125,7 @@ def _assert_model_config_methods(
 
 def test_head_size_falls_back_when_head_dim_is_zero():
     """Regression test for configs that materialize missing head_dim as 0."""
-    hf_config = PretrainedConfig(
+    hf_config = PreTrainedConfig(
         model_type="deepseek_vl_v2",
         hidden_size=1280,
         num_attention_heads=10,
@@ -140,7 +140,7 @@ def test_head_size_falls_back_when_head_dim_is_zero():
 
 
 def test_qk_rope_head_dim_recovery_uses_model_revision(monkeypatch: pytest.MonkeyPatch):
-    hf_config = PretrainedConfig(
+    hf_config = PreTrainedConfig(
         model_type="deepseek_v2",
         qk_rope_head_dim=128,
         qk_nope_head_dim=128,
@@ -176,7 +176,7 @@ def test_qk_rope_head_dim_recovery_uses_model_revision(monkeypatch: pytest.Monke
     ],
 )
 def test_num_experts_per_tok_aliases(attribute: str):
-    hf_config = PretrainedConfig(**{attribute: 4})
+    hf_config = PreTrainedConfig(**{attribute: 4})
     convertor = ModelArchConfigConvertorBase(hf_config, hf_config)
     model_config = cast(
         ModelConfig,
@@ -191,7 +191,7 @@ def test_num_experts_per_tok_aliases(attribute: str):
 
 
 def test_num_experts_per_tok_none_is_normalized():
-    hf_config = PretrainedConfig(top_k_experts=None)
+    hf_config = PreTrainedConfig(top_k_experts=None)
     convertor = ModelArchConfigConvertorBase(hf_config, hf_config)
 
     assert convertor.get_num_experts_per_token() == 0
@@ -207,7 +207,7 @@ def test_legacy_modelopt_config_without_producer_is_normalized():
             "modelopt_quant_config": {"quant_cfg": {}},
         }
     }
-    hf_config = PretrainedConfig(quantization_config=quantization_config)
+    hf_config = PreTrainedConfig(quantization_config=quantization_config)
 
     convertor = ModelArchConfigConvertorBase(hf_config, hf_config)
 
@@ -370,7 +370,7 @@ def test_gemma4_uniform_head_dims_are_homogeneous():
     assert arch[3] is arch
 
 
-class _HeterogeneousConfig(PretrainedConfig):
+class _HeterogeneousConfig(PreTrainedConfig):
     """A heterogeneous config with no convertor of its own.
 
     Mirrors the parts vLLM uses: per-layer configs are shallow copies with the
@@ -384,7 +384,7 @@ def __init__(self, per_layer: dict[str, list], **kwargs):
         super().__init__(**kwargs)
 
     @property
-    def per_layer_config(self) -> list[PretrainedConfig]:
+    def per_layer_config(self) -> list[PreTrainedConfig]:
         layers = []
         for i in range(self.num_hidden_layers):
             layer = copy(self)

tests/config/test_multimodal_config.py

@@ -6,7 +6,7 @@
 
 import pytest
 import torch
-from transformers import PretrainedConfig
+from transformers import PreTrainedConfig
 
 from tests.models.utils import build_model_context
 from vllm.config.ec_transfer import ECRole, ECTransferConfig
@@ -136,7 +136,7 @@ def test_language_model_only_disables_via_supports_multimodal_inputs():
 
 
 def test_convertor_clears_mm_prefix_when_multimodal_disabled():
-    hf_config = PretrainedConfig(
+    hf_config = PreTrainedConfig(
         model_type="gemma3",
         architectures=["Gemma3ForConditionalGeneration"],
     )

tests/config/test_speculative_draft_hf_overrides.py

@@ -15,20 +15,20 @@
 from unittest.mock import MagicMock, patch
 
 import pytest
-from transformers import PretrainedConfig
+from transformers import PreTrainedConfig
 
 from vllm.config.parallel import ParallelConfig
 from vllm.config.speculative import SpeculativeConfig
 
 
-def _make_hf_config(**kwargs) -> PretrainedConfig:
+def _make_hf_config(**kwargs) -> PreTrainedConfig:
     defaults = dict(
         architectures=["LlamaForCausalLM"],
         model_type="llama",
         num_hidden_layers=64,
     )
     defaults.update(kwargs)
-    return PretrainedConfig(**defaults)
+    return PreTrainedConfig(**defaults)
 
 
 @pytest.mark.cpu_test
@@ -137,7 +137,7 @@ def test_callable_overrides_reach_the_draft_config():
     """A callable override (config-to-config transform) composes with the
     architecture-mapping override and is applied to the draft config."""
 
-    def shrink(hf_config: PretrainedConfig) -> PretrainedConfig:
+    def shrink(hf_config: PreTrainedConfig) -> PreTrainedConfig:
         hf_config.num_hidden_layers = 1
         return hf_config
 
@@ -155,7 +155,7 @@ def test_arch_mapping_applies_before_callable_override():
     observes (and may adjust) the post-mapping config."""
     seen_architectures: list[str] = []
 
-    def record(hf_config: PretrainedConfig) -> PretrainedConfig:
+    def record(hf_config: PreTrainedConfig) -> PreTrainedConfig:
         seen_architectures.append(hf_config.architectures[0])
         return hf_config
 
@@ -202,7 +202,7 @@ def test_inkling_override_exposes_all_mtp_depths():
     assert out.local_layer_ids == [0, 2, 4]
 
 
-def _module_level_shrink(hf_config: PretrainedConfig) -> PretrainedConfig:
+def _module_level_shrink(hf_config: PreTrainedConfig) -> PreTrainedConfig:
     hf_config.num_hidden_layers = 1
     return hf_config
 

tests/config/test_speculative_draft_max_position_embeddings.py

@@ -17,7 +17,7 @@
 from typing import Literal
 
 import pytest
-from transformers import PretrainedConfig
+from transformers import PreTrainedConfig
 
 from vllm.config.model import ModelConfig
 from vllm.config.parallel import ParallelConfig
@@ -46,7 +46,7 @@ def _override_logged(caplog: pytest.LogCaptureFixture) -> bool:
 
 @pytest.mark.cpu_test
 def test_override_raises_smaller_value(vllm_caplog: pytest.LogCaptureFixture):
-    hf_config = PretrainedConfig(max_position_embeddings=2048)
+    hf_config = PreTrainedConfig(max_position_embeddings=2048)
     SpeculativeConfig._maybe_override_draft_max_position_embeddings(
         hf_config, target_max_model_len=8192
     )
@@ -56,7 +56,7 @@ def test_override_raises_smaller_value(vllm_caplog: pytest.LogCaptureFixture):
 
 @pytest.mark.cpu_test
 def test_override_keeps_sufficient_value(vllm_caplog: pytest.LogCaptureFixture):
-    hf_config = PretrainedConfig(max_position_embeddings=8192)
+    hf_config = PreTrainedConfig(max_position_embeddings=8192)
     SpeculativeConfig._maybe_override_draft_max_position_embeddings(
         hf_config, target_max_model_len=8192
     )
@@ -66,7 +66,7 @@ def test_override_keeps_sufficient_value(vllm_caplog: pytest.LogCaptureFixture):
 
 @pytest.mark.cpu_test
 def test_override_ignores_missing_attribute(vllm_caplog: pytest.LogCaptureFixture):
-    hf_config = PretrainedConfig()
+    hf_config = PreTrainedConfig()
     hf_config.__dict__.pop("max_position_embeddings", None)
     SpeculativeConfig._maybe_override_draft_max_position_embeddings(
         hf_config, target_max_model_len=8192

tests/entrypoints/pooling/scoring/test_jina_ranking_io_processor_unit.py

@@ -7,7 +7,7 @@
 
 import pytest
 from tokenizers import Tokenizer, models, pre_tokenizers
-from transformers import PreTrainedTokenizerFast
+from transformers import TokenizersBackend
 
 from vllm import PoolingParams
 from vllm.entrypoints.pooling.base.io_processor import PoolingIOProcessor
@@ -70,7 +70,7 @@ def offline_processor_and_context():
     backend = Tokenizer(models.WordLevel({"[UNK]": 0}, unk_token="[UNK]"))
     backend.pre_tokenizer = pre_tokenizers.WhitespaceSplit()
     proc = JinaRankingIOProcessor.__new__(JinaRankingIOProcessor)
-    proc.tokenizer = PreTrainedTokenizerFast(tokenizer_object=backend)
+    proc.tokenizer = TokenizersBackend(tokenizer_object=backend)
     proc.model_config = SimpleNamespace(max_model_len=1024, is_encoder_decoder=False)
     proc.renderer = SimpleNamespace(
         default_cmpl_tok_params=TokenizeParams(max_total_tokens=1024)

tests/model_executor/layers/test_pooler_methods.py

@@ -9,7 +9,7 @@
 import numpy as np
 import pytest
 import torch
-from transformers import PretrainedConfig
+from transformers import PreTrainedConfig
 
 from vllm.config import PoolerConfig, set_current_vllm_config
 from vllm.model_executor.layers.pooler import PoolingParamsUpdate
@@ -315,7 +315,7 @@ def test_dispatch_seq_cls_honors_token_pooling_type(tok_pooling_type):
         model_config=SimpleNamespace(
             pooler_config=pooler_config,
             head_dtype=None,
-            hf_config=PretrainedConfig(num_labels=3),
+            hf_config=PreTrainedConfig(num_labels=3),
         ),
     )
     classifier = torch.nn.Linear(4, 3)

tests/model_executor/test_qwen3_omni.py

@@ -8,7 +8,7 @@
 import pytest
 import torch
 import torch.nn as nn
-from transformers import PretrainedConfig
+from transformers import PreTrainedConfig
 
 from vllm.config import ParallelConfig
 from vllm.multimodal.processing import InputProcessingContext
@@ -52,7 +52,7 @@ def print_input_ids(input_ids):
 @pytest.fixture
 def mock_qwen3_omni_config():
     """Create a mock Qwen3OmniMoeThinker config."""
-    config = Mock(spec=PretrainedConfig)
+    config = Mock(spec=PreTrainedConfig)
     # Token IDs from https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct/blob/main/tokenizer_config.json
     config.audio_token_id = 151675  # <|audio_pad|>
     config.video_token_id = 151656  # <|video_pad|>

tests/models/language/pooling/test_jina_embeddings_v5.py

@@ -14,7 +14,7 @@
 from typing import cast
 
 import pytest
-from transformers import PretrainedConfig
+from transformers import PreTrainedConfig
 
 from vllm.config import ModelConfig
 from vllm.model_executor.models.config import (
@@ -23,7 +23,7 @@
 )
 
 
-def _model_config(hf_config: PretrainedConfig) -> ModelConfig:
+def _model_config(hf_config: PreTrainedConfig) -> ModelConfig:
     """Minimal stand-in for ModelConfig; only hf_config is read."""
     return cast(ModelConfig, SimpleNamespace(hf_config=hf_config))
 
@@ -42,7 +42,7 @@ def test_encoder_backbone_enables_bidirectional_attention():
     EncoderOnlyAttention; JinaEmbeddingsV5Model then dispatches to the encoder
     implementation.
     """
-    hf_config = PretrainedConfig(is_decoder=False)
+    hf_config = PreTrainedConfig(is_decoder=False)
 
     JinaEmbeddingsV5ModelConfig.verify_and_update_model_config(_model_config(hf_config))
 
@@ -55,13 +55,13 @@ def test_supported_decoder_backbone_is_accepted():
 
     `-small` omits `is_decoder` entirely, so an absent attribute has to be
     treated as a decoder. The first assertion pins that assumption: if
-    PretrainedConfig ever gains an `is_decoder=False` default, this fails here
+    PreTrainedConfig ever gains an `is_decoder=False` default, this fails here
     rather than silently rejecting a supported checkpoint.
     """
-    absent = PretrainedConfig()
+    absent = PreTrainedConfig()
     assert not hasattr(absent, "is_decoder")
 
     JinaEmbeddingsV5ModelConfig.verify_and_update_model_config(_model_config(absent))
     JinaEmbeddingsV5ModelConfig.verify_and_update_model_config(
-        _model_config(PretrainedConfig(is_decoder=True))
+        _model_config(PreTrainedConfig(is_decoder=True))
     )

tests/models/multimodal/processing/test_audioflamingo3.py

@@ -22,19 +22,19 @@
 import numpy as np
 import pytest
 import torch
-from transformers import PretrainedConfig
+from transformers import PreTrainedConfig
 
 from tests.models.registry import HF_EXAMPLE_MODELS
 from vllm.config.multimodal import MultiModalDummyOptions
 
 
-class MockAudioFlamingo3Config(PretrainedConfig):
+class MockAudioFlamingo3Config(PreTrainedConfig):
     model_type = "audioflamingo3"
 
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
-        self.audio_config = PretrainedConfig()
-        self.text_config = PretrainedConfig()
+        self.audio_config = PreTrainedConfig()
+        self.text_config = PreTrainedConfig()
 
 
 class MockAudioFlamingo3Processor:

tests/models/multimodal/processing/test_bailing_moe_v3_vl.py

@@ -8,7 +8,7 @@
 import torch
 from PIL import Image
 from tokenizers import Tokenizer, decoders, models, pre_tokenizers
-from transformers import PreTrainedTokenizerFast
+from transformers import TokenizersBackend
 
 from vllm.model_executor.models.bailing_moe_v3_vl import (
     IMAGE_PLACEHOLDER,
@@ -34,7 +34,7 @@ def processor():
         add_prefix_space=False, use_regex=False
     )
     backend.decoder = decoders.ByteLevel()
-    tokenizer = PreTrainedTokenizerFast(
+    tokenizer = TokenizersBackend(
         tokenizer_object=backend,
         additional_special_tokens=[
             "<|vision_start|>",

tests/models/multimodal/processing/test_h2ovl.py

@@ -6,7 +6,7 @@
 
 import pytest
 from PIL import Image
-from transformers import PretrainedConfig
+from transformers import PreTrainedConfig
 
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.image import rescale_image_size
@@ -17,7 +17,7 @@
 
 
 def _get_expected_num_patches(
-    config: PretrainedConfig,
+    config: PreTrainedConfig,
     image: Image.Image,
     num_imgs: int,
     min_num: int,

tests/models/multimodal/processing/test_internvl.py

@@ -6,7 +6,7 @@
 
 import pytest
 from PIL import Image
-from transformers import PretrainedConfig
+from transformers import PreTrainedConfig
 
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.image import rescale_image_size
@@ -17,7 +17,7 @@
 
 
 def _get_expected_num_patches(
-    config: PretrainedConfig,
+    config: PreTrainedConfig,
     image: Image.Image,
     num_imgs: int,
     min_num: int,