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Co-authored-by: zhangdacheng <zhangdacheng@ainirobot.com>
Co-authored-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>

Co-authored-by: Cade Daniel <edacih@gmail.com>

This version is for more model support. Add support for Gemma models (#2964) and OLMo models (#2832).

how to serve the loras (mimicking the [multilora inference example](https://github.com/vllm-project/vllm/blob/main/examples/multilora_inference.py)):
```terminal
$ export LORA_PATH=~/.cache/huggingface/hub/models--yard1--llama-2-7b-sql-lora-test/
$ python -m vllm.entrypoints.api_server \
 --model meta-llama/Llama-2-7b-hf \
 --enable-lora \
 --lora-modules sql-lora=$LORA_PATH sql-lora2=$LORA_PATH
```
the above server will list 3 separate values if the user queries `/models`: one for the base served model, and one each for the specified lora modules. in this case sql-lora and sql-lora2 point to the same underlying lora, but this need not be the case. lora config values take the same values they do in EngineArgs

no work has been done here to scope client permissions to specific models

If the SamplingParams object passed to LLMEngine.add_request() is mutated after it returns, it could affect the async sampling process for that request.

Suggested by @Yard1 https://github.com/vllm-project/vllm/pull/2514#discussion_r1490106059

* Fix AttributeError: MixtralModel object has no attribute org_vocab_size.

* Make LoRA logic for Mistral and Mixtral the same

---------
Co-authored-by: Pernekhan Utemuratov <pernekhan@deepinfra.com>

Co-authored-by: Roy <jasonailu87@gmail.com>

* add mixtral lora support

* formatting

* fix incorrectly ported logic

* polish tests

* minor fixes and refactoring

* minor fixes

* formatting

* rename and remove redundant logic

* refactoring

* refactoring

* minor fix

* minor refactoring

* fix code smell