From 970713d4a40b1d83244eb0ed4eb4d690b6bb4f14 Mon Sep 17 00:00:00 2001
From: Harry Mellor <19981378+hmellor@users.noreply.github.com>
Date: Mon, 15 Dec 2025 17:34:08 +0000
Subject: [PATCH] Remove `SkipValidation` from `ModelConfig` (#30695)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
---
 vllm/config/model.py | 19 ++++++++++++++-----
 1 file changed, 14 insertions(+), 5 deletions(-)

diff --git a/vllm/config/model.py b/vllm/config/model.py
index 7ff095bcb9ccd..1de9d15cf8c52 100644
--- a/vllm/config/model.py
+++ b/vllm/config/model.py
@@ -8,7 +8,7 @@ from functools import cached_property
 from typing import TYPE_CHECKING, Any, Literal, cast, get_args
 
 import torch
-from pydantic import ConfigDict, SkipValidation, field_validator, model_validator
+from pydantic import ConfigDict, Field, field_validator, model_validator
 from pydantic.dataclasses import dataclass
 from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
 from transformers.configuration_utils import ALLOWED_LAYER_TYPES
@@ -109,7 +109,7 @@ class ModelConfig:
     """Convert the model using adapters defined in
     [vllm.model_executor.models.adapters][]. The most common use case is to
     adapt a text generation model to be used for pooling tasks."""
-    tokenizer: SkipValidation[str] = None  # type: ignore
+    tokenizer: str = Field(default=None)
     """Name or path of the Hugging Face tokenizer to use. If unspecified, model
     name or path will be used."""
     tokenizer_mode: TokenizerMode | str = "auto"
@@ -164,7 +164,7 @@ class ModelConfig:
     """The specific revision to use for the tokenizer on the Hugging Face Hub.
     It can be a branch name, a tag name, or a commit id. If unspecified, will
     use the default version."""
-    max_model_len: SkipValidation[int] = None  # type: ignore
+    max_model_len: int = Field(default=None, gt=0)
     """Model context length (prompt and output). If unspecified, will be
     automatically derived from the model config.
 
@@ -175,7 +175,7 @@ class ModelConfig:
     - 25.6k -> 25,600"""
     spec_target_max_model_len: int | None = None
     """Specify the maximum length for spec decoding draft models."""
-    quantization: SkipValidation[QuantizationMethods | None] = None
+    quantization: QuantizationMethods | str | None = None
     """Method used to quantize the weights. If `None`, we first check the
     `quantization_config` attribute in the model config file. If that is
     `None`, we assume the model weights are not quantized and use `dtype` to
@@ -597,6 +597,14 @@ class ModelConfig:
         self._verify_cuda_graph()
         self._verify_bnb_config()
 
+    @field_validator("tokenizer", "max_model_len", mode="wrap")
+    @classmethod
+    def _skip_none_validation(cls, value: Any, handler: Callable) -> Any:
+        """Skip validation if the value is `None` when initialisation is delayed."""
+        if value is None:
+            return value
+        return handler(value)
+
     @field_validator("tokenizer_mode", mode="after")
     def _lowercase_tokenizer_mode(cls, tokenizer_mode: str) -> str:
         return tokenizer_mode.lower()
@@ -610,13 +618,14 @@ class ModelConfig:
 
     @model_validator(mode="after")
     def validate_model_config_after(self: "ModelConfig") -> "ModelConfig":
+        """Called after __post_init__"""
         if not isinstance(self.tokenizer, str):
             raise ValueError(
                 f"tokenizer must be a string, got "
                 f"{type(self.tokenizer).__name__}: {self.tokenizer!r}. "
                 "Please provide a valid tokenizer path or HuggingFace model ID."
             )
-        if not isinstance(self.max_model_len, int) or self.max_model_len <= 0:
+        if not isinstance(self.max_model_len, int):
             raise ValueError(
                 f"max_model_len must be a positive integer, "
                 f"got {type(self.max_model_len).__name__}: {self.max_model_len!r}. "