diff --git a/tools/pre_commit/mypy.py b/tools/pre_commit/mypy.py
index f9d064a499..7262c1f2a9 100755
--- a/tools/pre_commit/mypy.py
+++ b/tools/pre_commit/mypy.py
@@ -113,7 +113,6 @@ SEPARATE_GROUPS = [
 
 EXCLUDE = [
     r"vllm/model_executor/models/[kK]",
-    r"vllm/model_executor/models/[uU]",
     r"vllm/model_executor/models/[vV]",
     r"vllm/model_executor/models/[wW]",
     r"vllm/model_executor/models/[zZ]",
diff --git a/vllm/model_executor/model_loader/reload/torchao_decorator.py b/vllm/model_executor/model_loader/reload/torchao_decorator.py
index dc8e0acc6e..10ced70d31 100644
--- a/vllm/model_executor/model_loader/reload/torchao_decorator.py
+++ b/vllm/model_executor/model_loader/reload/torchao_decorator.py
@@ -1,10 +1,9 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from collections.abc import Iterable
+from collections.abc import Callable
 from functools import wraps
-from types import FunctionType
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Concatenate, ParamSpec, TypeVar
 
 import torch
 
@@ -20,13 +19,18 @@ if TYPE_CHECKING:
 
 __all__ = ["set_torchao_reload_attrs", "support_quantized_model_reload_from_hp_weights"]
 
+_P = ParamSpec("_P")
+_R = TypeVar("_R")
+
 
 def set_torchao_reload_attrs(model: torch.nn.Module, model_config: ModelConfig):
     model._do_torchao_reload = True
     model._model_config = model_config
 
 
-def support_quantized_model_reload_from_hp_weights(original_load_weights: FunctionType):
+def support_quantized_model_reload_from_hp_weights(
+    original_load_weights: Callable[Concatenate["AutoWeightsLoader", _P], _R],
+) -> Callable[Concatenate["AutoWeightsLoader", _P], _R]:
     """Decorator for `load_weights` method for AutoWeightsLoader.load_weights to support
     reloading high precision (bfloat16/float16/float32) weight for an already quantized
     model, this involves restoring the weights to a high precision weights and
@@ -39,17 +43,16 @@ def support_quantized_model_reload_from_hp_weights(original_load_weights: Functi
     @wraps(original_load_weights)
     def patched_model_load_weights(
         self: "AutoWeightsLoader",
-        weights: Iterable[tuple[str, torch.Tensor]],
-        *args,
-        **kwargs,
-    ):
+        *args: _P.args,
+        **kwargs: _P.kwargs,
+    ) -> _R:
         model = self.module
 
         if not getattr(model, "_do_torchao_reload", False):
-            return original_load_weights(self, weights, *args, **kwargs)
+            return original_load_weights(self, *args, **kwargs)
 
         initialize_layerwise_reload(model)
-        loaded_weights = original_load_weights(self, weights, *args, **kwargs)
+        loaded_weights = original_load_weights(self, *args, **kwargs)
         finalize_layerwise_reload(model, model._model_config)
 
         return loaded_weights
diff --git a/vllm/model_executor/models/phi4mm.py b/vllm/model_executor/models/phi4mm.py
index 8524455975..7f223b1ccc 100644
--- a/vllm/model_executor/models/phi4mm.py
+++ b/vllm/model_executor/models/phi4mm.py
@@ -1297,7 +1297,7 @@ class Phi4MMForCausalLM(nn.Module, SupportsLoRA, SupportsMultiModal):
         logits = self.logits_processor(self.lm_head, hidden_states)
         return logits
 
-    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> None:
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         loader = AutoWeightsLoader(self)
         return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
 
diff --git a/vllm/model_executor/models/ultravox.py b/vllm/model_executor/models/ultravox.py
index a454ba5962..72ce26f21c 100644
--- a/vllm/model_executor/models/ultravox.py
+++ b/vllm/model_executor/models/ultravox.py
@@ -282,10 +282,14 @@ class UltravoxMultiModalProcessor(BaseMultiModalProcessor[UltravoxProcessingInfo
 
         def get_replacement_ultravox(item_idx: int):
             if "audio_num_tokens" in out_mm_data:
-                return [replacement_id] * int(out_mm_data["audio_num_tokens"][item_idx])
+                audio_num_tokens = out_mm_data["audio_num_tokens"]
+                assert isinstance(audio_num_tokens, torch.Tensor)
+                return [replacement_id] * int(audio_num_tokens[item_idx])
             start = chunks_start_idx[item_idx]
             end = chunks_start_idx[item_idx + 1]
-            audio_token_len = out_mm_data["audio_token_len"][start:end].sum()
+            audio_token_lens = out_mm_data["audio_token_len"]
+            assert isinstance(audio_token_lens, torch.Tensor)
+            audio_token_len = audio_token_lens[start:end].sum()
             return [replacement_id] * int(audio_token_len)  # type: ignore
 
         return [
@@ -566,7 +570,8 @@ class UltravoxWhisperEncoder(WhisperEncoder):
     ) -> torch.Tensor:
         return (input_lengths - 1) // 2 + 1
 
-    def forward(
+    # Ultravox requires chunk lengths in addition to Whisper's input features.
+    def forward(  # type: ignore[override]
         self,
         input_features: torch.Tensor,
         audio_lens: torch.Tensor,
@@ -960,12 +965,12 @@ def pad_and_concat_to_dim3(
 
         return features
 
-    features = [pad_and_concat_to_dim3(f) for f in features]
+    tensors = [pad_and_concat_to_dim3(f) for f in features]
 
-    max_len = max(f.shape[-1] for f in features)
+    max_len = max(f.shape[-1] for f in tensors)
     # Ensure all features have dim=3
-    features = [f.view(-1, *f.shape[-2:]) for f in features]
+    tensors = [f.view(-1, *f.shape[-2:]) for f in tensors]
     # Pad and concatenate:
     # [[B1, 80, M1], [B2, 80, M2]] -> [B1+B2, 80, max(M1, M2)]
-    features = [F.pad(f, (0, max_len - f.shape[-1])) for f in features]
-    return torch.cat(features)
+    tensors = [F.pad(f, (0, max_len - f.shape[-1])) for f in tensors]
+    return torch.cat(tensors)
diff --git a/vllm/model_executor/models/unlimited_ocr.py b/vllm/model_executor/models/unlimited_ocr.py
index d248e5c8e0..27e5503d2c 100644
--- a/vllm/model_executor/models/unlimited_ocr.py
+++ b/vllm/model_executor/models/unlimited_ocr.py
@@ -199,6 +199,7 @@ class UnlimitedOCRMultiModalProcessor(DeepseekOCRMultiModalProcessor):
             if isinstance(images, ImageEmbeddingItems):
                 num_image_tokens = images.get_feature_size(item_idx)
             else:
+                assert isinstance(images, ImageProcessorItems)
                 size = images.get_image_size(item_idx)
 
                 # Disable crop mode for multi-image input.
diff --git a/vllm/model_executor/models/utils.py b/vllm/model_executor/models/utils.py
index 6375e0572e..e762ad1c47 100644
--- a/vllm/model_executor/models/utils.py
+++ b/vllm/model_executor/models/utils.py
@@ -35,7 +35,6 @@ if TYPE_CHECKING:
     from transformers import PreTrainedConfig
     from transformers.conversion_mapping import WeightRenaming
 
-    from vllm.config.model import ModelConfig
     from vllm.model_executor.layers.quantization import QuantizationConfig
 
 logger = init_logger(__name__)
@@ -337,7 +336,7 @@ class AutoWeightsLoader:
         """
         # Add persistent registered buffers.
         # Non-persistent buffers are excluded, matching PyTorch state_dict().
-        non_persistent = getattr(module, "_non_persistent_buffers_set", set())
+        non_persistent: set[str] = getattr(module, "_non_persistent_buffers_set", set())
         for buf_name, buf in module.named_buffers(recurse=False):
             if buf_name not in child_params and buf_name not in non_persistent:
                 child_params[buf_name] = buf
@@ -558,7 +557,7 @@ def maybe_fuse_shared_experts(
 
 
 def get_spec_layer_idx_from_weight_name(
-    config: "ModelConfig", weight_name: str
+    config: "PreTrainedConfig", weight_name: str
 ) -> int | None:
     """Return the MTP layer index a weight belongs to, or None.
 
@@ -587,7 +586,7 @@ def get_spec_layer_idx_from_weight_name(
 
 
 def skip_spec_layers(
-    weights: Iterable[tuple[str, torch.Tensor]], config: "ModelConfig"
+    weights: Iterable[tuple[str, torch.Tensor]], config: "PreTrainedConfig"
 ) -> Iterable[tuple[str, torch.Tensor]]:
     """Drop MTP spec-layer weights (loaded by the MTP head, not the base model).
 
@@ -629,11 +628,11 @@ def init_vllm_registered_model(
 
 
 @overload
-def flatten_bn(x: torch.Tensor) -> torch.Tensor: ...
+def flatten_bn(x: list[torch.Tensor]) -> list[torch.Tensor]: ...
 
 
 @overload
-def flatten_bn(x: list[torch.Tensor]) -> list[torch.Tensor]: ...
+def flatten_bn(x: torch.Tensor) -> torch.Tensor: ...
 
 
 @overload
@@ -973,7 +972,9 @@ def get_draft_quant_config(vllm_config: VllmConfig) -> "QuantizationConfig | Non
         The draft model's config if available, None otherwise.
 
     """
-    draft_model_config = vllm_config.speculative_config.draft_model_config
+    speculative_config = vllm_config.speculative_config
+    assert speculative_config is not None
+    draft_model_config = speculative_config.draft_model_config
     draft_load_config = vllm_config.load_config
 
     return (