[New Model]: Support GteNewModelForSequenceClassification

[noooop]

TL;DR

New Model: GteNewModelForSequenceClassification

• Alibaba-NLP/gte-multilingual-reranker-base

vllm serve Alibaba-NLP/gte-multilingual-reranker-base --hf-overrides '{"architectures": ["GteNewForSequenceClassification"]}' --trust_remote_code

The second-generation GTE model (mGTE-TRM) is named NewForSequenceClassification. The name NewForSequenceClassification is too generic, you should set --hf-overrides '{"architectures": ["GteNewForSequenceClassification"]}' to specify the use of the GteNewForSequenceClassification architecture.

Purpose

Fix #21595

Test Plan

pytest -s -vvv tests/models/language/pooling/test_gte.py::test_rerank_models_mteb[model_info1]

Test Result

pass

(Optional) Documentation Update

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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.

[DarkLight1337]

Can you split out the classification optimization into a separate PR?

[noooop]

Can you split out the classification optimization into a separate PR?

The content is too small, so they are combined. you tend to separate them.

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For the following period of time, I will profile and optimize different models.

Their optimizations are all small, but I don't want to wait until all optimizations are completed before submitting,

so the best way is to support similar models and hitchhike.

[DarkLight1337]

The content is too small, so they are combined. you tend to separate them.

It's better to separate them to better isolate the issue in case CI breaks

[noooop]

The content is too small, so they are combined. you tend to separate them.

It's better to separate them to better isolate the issue in case CI breaks

For the following period of time, I will profile and optimize different models.

Their optimizations are all small, but I don't want to wait until all optimizations are completed before submitting,

so the best way is to support similar models and hitchhike.

[DarkLight1337]

For the refactoring of num_labels specifically I think that should be in a separate PR. The optimization inside pooler.py is small enough to keep in this PR.

[noooop]

@DarkLight1337

Can we merge this PR first?

[DarkLight1337]

Sure, LGTM if tests pass

[noooop]

@DarkLight1337

Sorry for disable auto-merge.