[Doc] Improve MM models LoRA notes

[jeejeelee]

Purpose

Test Plan

Test Result

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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--31979.org.readthedocs.build/en/31979/

[gemini-code-assist]

Code Review

This pull request significantly improves the documentation for multimodal (MM) models and their LoRA support. It updates the compatibility tables for several models, including DotsOCR, Grok2, IQuestCoderV1, IQuestLoopCoderV1, LFM2-VL, Pixtral, Qwen3-VL, and Qwen3-VL-Reranker, reflecting expanded LoRA and parallelism capabilities. Notably, the detailed Python code for merging LoRA weights in multimodal encoders has been removed, replaced by a more concise note indicating experimental direct support for LoRA in tower and connector modules. This suggests an enhancement in vLLM's native capabilities, simplifying the user experience. The addition of specific --hf_overrides instructions for Grok2 and Qwen3-VL-Reranker further aids users in configuring these models correctly. Overall, these changes enhance clarity and provide up-to-date information on multimodal model integration and LoRA usage within vLLM.