Update FAQ on interleaving sliding windows support

[finbarrtimbers]

Purpose

Removes outdated documentation indicating that interleaved sliding windows are not supported in KV cache block allocation. This was fixed in February (#13296).

Test Plan

I will calculate the KV cache for various window sizes for a model with interleaved attention layers and look at the results.

Test Result

I have verified this manually by looking at the KV cache generated for Olmo 3. For Olmo 3 7B, which has 3 SWA layers followed by a global attention layer, the KV cache for generating 6144 tokens is 2.58GiB per request [1], while for 34,048 tokens, it's 5.14 GiB per request [2]. If SWA was not supported, I would expect the KV cache to be 5.5x bigger; instead it's 2x bigger.

[1] [gpu_worker.py:298] Available KV cache memory: 57.18 GiB, [kv_cache_utils.py:1091] Maximum concurrency for 6,144 tokens per request: 23.73x
[2] [gpu_worker.py:298] Available KV cache memory: 57.18 GiB, [kv_cache_utils.py:1091] Maximum concurrency for 34,048 tokens per request: 11.12x.

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[mergify]

Documentation preview: https://vllm--29796.org.readthedocs.build/en/29796/

[gemini-code-assist]

Code Review

This pull request successfully updates the documentation in docs/contributing/model/basic.md by removing outdated information regarding interleaved sliding windows support. The change aligns with the provided context that this functionality was fixed previously. This is a positive update that improves the accuracy of the documentation. No specific review comments are provided as the changes are minor documentation updates and do not introduce any issues of high or critical severity.

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[heheda12345]

LGTM!