Added documentation of using warmups to initialize lora weights

[TheCodeWrangler]

This PR provides documentation for converting lora adapters from a hugging face checkpoint into a warmup that can be used in the triton-inference-server TensorRT-LLM backend.

This approach allows for the LoRa weights to never be required for the client of the triton-inference-server backend and does not require loading or passing these weights from any of the python backend models (preprocessing) to avoid the numpy datatype conversion (which does not support bfloat16)

[smehta2000]

Tagging @kaiyux @byshiue to help triage and/or add to review board, thanks!

[TheCodeWrangler]

Curious to get any feedback here

This update is also related to a performance issue I am seeing.
NVIDIA/TensorRT-LLM#1957

This PR gets results much closer to the expected outputs but not fully in line with huggingface/ pre-compiled results. Would love to have some feedback on the process for preparation of the adapter weights.