A Note from Scott, Founder of Torchstack
My dear reader,
As I was writing the initial draft of this PEFT blog post, I realized (too late) that we needed to break up the article into many more pieces. I think this will do a couple of things:
- For the novice AI researcher just learning about PEFT and LLM fine-tuning, it will make understanding the nuances and practical implications of PEFT much more digestible. There's just so much to cover, and I wasn't happy in the way I was distilling the ideas behind PEFT to discuss all the topics and concepts I wanted to cover.
- For the business owners, startup founders, and other stakeholders, we can demonstrate the business value of PEFT much more clearly. I wanted to discuss several case studies and actual calculations that we'd do to implement PEFT. Let's do it right.
- For the technical folk that are interested in the bleeding edge and practical implementations for PEFT: there will be notebooks, code, and projects just for you.
Please stay tuned for the posts and free/paid resources that will be released in the coming weeks. I don't think you'll be disappointed.
Thanks for your patience and your support of our work.
Scott
A Preview of the PEFT Series
The next set of blog posts will all be related to Parameter Efficient Fine-Tuning (PEFT), where we'll provide an in-depth guide to:
- What is PEFT and how to use PEFT to make custom Large Language Models (LLMs)?
- The different PEFT methods: how they differ and the different benefits and risks associated with each method?
- How to implement different PEFT methods for LLM fine-tuning?