Fine-tuning Llama 2 based model

Nobody has claimed this yet.

The main FastChat README references:
Fine-tuning Vicuna-7B with Local GPUs

Writing this up as an "issue" but it's really more of a documentation request.

I'd like an example that fine tunes a Llama 2 model -- perhaps with at least a couple GPU hardware configs -- and it's been hard to find what command line setting should change and what is fine as-is.

Is Llama 2 similar enough that similar switches should work or is it fundamentally different in some way?
Some of the switches are specific to the GPU involved; what's a good reference for this?
Are there guidelines for memory/resource/time requirements/epochs or some rules of thumb available?
Libraries and versions needed for FastChat seem only loosely spelled out. (Dependences lists torch without a version and some requirements like ninja and flash-attn weren't found until training time. https://github.com/lm-sys/FastChat/blob/main/pyproject.toml )

I assume some of this info might be spread out here and there but maybe the README could have more pointers to some of this (?)

Start with the main FastChat README section “Fine-tuning Vicuna-7B with Local GPUs” and inspect pyproject.toml for the currently documented dependencies. Compare the existing guidance with the requested Llama 2 examples, GPU configurations, resource guidelines, and version requirements. Done means the README provides actionable pointers or instructions covering these gaps.

Written by the indexing model from the issue text.