[Model] EXAONE 3.0 model support - closed

[Deepfocused]

Hello, I am deepfocused from the AI ​​engineering Team at LG CNS.
we want to integrate the recently opened model EXAONE-3.0-7.8B-Instruct model into vllm!!!

Previously, we have been servicing models such as exaone v1, v2, etc. to our customers as vllm through the VLLM+exaone build. Unlike these previous models, EXAONE 3.0 was released as an open model, so we are requesting integration.

Paper: [EXAONE 3.0 7.8B Instruction Tuned Language Model](https://huggingface.co/papers/2408.03541)
HuggingFace: [LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)
Github: [LG-AI-EXAONE/EXAONE-3.0](https://github.com/LG-AI-EXAONE/EXAONE-3.0)

It was written based on the llama.py model,
To run the EXAONE 3.0 model,
For offline execution,

import os
os.environ['HF_TOKEN'] = "your key"
llm = LLM(model="EXAONE-3.0-7.8B-Instruct", 
           ...)

For vllm server execution,

export HF_TOKEN="your key"
vllm serve LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct 

Once Exaone is integrated into the transformer library, the above tasks will no longer be necessary.
->[2024-08-28] : Added exaone configuration file to vllm/transformers_utils to allow omitting trust_remote_code or --trust-remote-code at runtime.
thank you.

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[github-actions]

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

[mgoin]

How does this compare with #7819? I would be happy to review either one, sorry for the delay with this model release

[Deepfocused]

How does this compare with #7819? I would be happy to review either one, sorry for the delay with this model release

Wouldn't our code be cleaner?
Also, LG CNS has built a vllm reflecting the above code and used it well for business~

[Deepfocused]

/ready