[Model] Add classification Task with Qwen2ForSequenceClassification

[kakao-kevin-us]

FILL IN THE PR DESCRIPTION HERE

FIX #8700 (link existing issues this PR will resolve)

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Key changes:

• Implement Qwen2ForSequenceClassification model architecture
• Add model registration for sequence classification variant
• Include integration tests for the new model variant
• Update documentation to reflect new sequence classification capability
• Usecase is similar like [Model] Support Qwen2.5-Math-RM-72B #8896

python -m vllm.entrypoints.openai.api_server \
	--model jason9693/Qwen2.5-1.5B-apeach \
	--trust-remote-code \
	--served-model-name Qwen2.5-1.5B-APEACH \
	--port 8080 \
	--tensor-parallel-size 8 \
	--enforce-eager

[github-actions]

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[DarkLight1337]

Thanks for adding this! Some initial comments.

[jason9693]

@DarkLight1337 All of issues you commented has been resolved.

[DarkLight1337]

To improve visibility, I also suggest adding a new entry to the Supported Models page of the docs.

[kakao-kevin-us]

To improve visibility, I also suggest adding a new entry to the Supported Models page of the docs.

I just added docs

[DarkLight1337]

It looks like the model test failed. PTAL.

[DarkLight1337]

The model test has passed, thanks for your time!

[DarkLight1337]

Please merge from main to fix the CI failures.

[kakao-kevin-us]

@DarkLight1337

Please merge from main to fix the CI failures.

I just rebased on main.
Let's waiting for the test

[corbt]

Very cool!

Is this compatible with the LoRA adapters functionality in vLLM? I'd love to be able to deploy a base Qwen2ForSequenceClassification and then have many different reward-model variants. The variants would have different LoRA layers and then separate score layers as well.

[kakao-kevin-us]

Very cool!

Is this compatible with the LoRA adapters functionality in vLLM? I'd love to be able to deploy a base Qwen2ForSequenceClassification and then have many different reward-model variants. The variants would have different LoRA layers and then separate score layers as well.

It was tested on the CI, but not sure as I'm not tested on my local server.
If it is broken, you can merge the lora weight and run on the vLLM
https://huggingface.co/docs/peft/main/en/conceptual_guides/lora#merge-lora-weights-into-the-base-model