[Models] Allow converting Qwen3-VL into Reranker model

[Isotr0py]

Purpose

• Enable reranker support for Qwen3-VL

Test Plan

python examples/pooling/score/vision_language_reranker.py -m qwen3_vl_reranker

Test Result

Query: A woman playing with her dog on a beach at sunset.
Relevance scores: [0.8426481485366821, 0.6725457310676575]
# HF reference: [0.8430634140968323, 0.6631059646606445]

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Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
• (Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.

[mergify]

Documentation preview: https://vllm--31890.org.readthedocs.build/en/31890/

[gemini-code-assist]

Code Review

This pull request adds support for converting Qwen3-VL models into reranker models. The changes are well-implemented and consistent across the codebase. The core logic correctly generalizes configuration handling to allow hf_overrides to adapt the model for sequence classification tasks. This is achieved by prioritizing the top-level Hugging Face config for parameters like classifier_from_token and method. The logic for accessing the text backbone in multimodal models is also made more robust. The addition of documentation and an example file is a great touch and makes the new feature easy to use. Overall, this is a solid contribution.