Make name of compressed-tensors quant method consistent across vLLM

[hmellor]

The compressed tensors quantization method was referred to as compressed-tensors and compressed_tensors in different places, they've now been standardised to compressed-tensors.

Issue was discovered in #17130.

Splitting the fix into this PR to reduce the review load on the original.

[github-actions]

👋 Hi! Thank you for contributing to the vLLM project.

💬 Join our developer Slack at https://slack.vllm.ai to discuss your PR in #pr-reviews, coordinate on features in #feat- channels, or join special interest groups in #sig- channels.

Just a reminder: PRs would not trigger full CI run by default. Instead, it would only run fastcheck CI which starts running only a small and essential subset of CI tests to quickly catch errors. You can run other CI tests on top of those by going to your fastcheck build on Buildkite UI (linked in the PR checks section) and unblock them. If you do not have permission to unblock, ping simon-mo or khluu to add you in our Buildkite org.

Once the PR is approved and ready to go, your PR reviewer(s) can run CI to test the changes comprehensively before merging.

To run CI, PR reviewers can either: Add ready label to the PR or enable auto-merge.

🚀

[mgoin]

This was done for backcompat since some model configs use dash and others use underscore, so I would want to make sure both would still match

[hmellor]

Ok, I'll add some post processing to coerce any hyphens to underscores, then after that point we can standardise on compressed_tensors inside vLLM.

Since we already call lower() to standardise the quant method string it shouldn't be controversial to call replace("-", "_") for the same reason.

[hmellor]

The error in the fast check seems unrelated link:

[2025-04-27T14:09:03Z] ERROR 04-27 07:09:03 [serving_completion.py:116] ValueError: This model's maximum context length is 8192 tokens. However, you requested 10010 tokens (10000 in the messages, 10 in the completion). Please reduce the length of the messages or completion.

[mgoin]

FWIW the standard is compressed-tensors if we want to convert to dash instead for it:

• compressed-tensors: 1,209 models https://huggingface.co/models?other=compressed-tensors
• compressed_tensors: 1 model https://huggingface.co/models?other=compressed_tensors

[hmellor]

Since compressed-tensors is more correct, I've switched to using that instead

[mgoin]

LGTM, thanks for getting this in a good state