Sure, very generally we're doing PEFT starting with insights from examples very much like this one [0] and have gradually built our own tooling and customized the approach a lot as the underlying Huggingface libraries have progressed even in the last 6 months.

I will say that one of the most important parts of the process that I've found is in the prompt structuring, the use of special tokens based on how the base models were trained and customizing the tokenizer where necessary. That work in particular is not covered adequately by the examples I was able to find when I started, in my opinion.

[0] https://medium.com/@kshitiz.sahay26/fine-tuning-llama-2-for-...