I interviewed Geoffery Angus, ML team lead @Predibase to talk about why adapter-based training is a game changer. We started with an overview of fine-tuning and then discussed five reasons why adapters are the future of LLMs. Later we also shared a demo and answered questions from the live audience. Try fine-tuning for free: https://pbase.ai/GetStartedGeoffrey’s LinkedIn:https://www.linkedin.com/in/geoffreyangusDaliana's Twitter: ⁠https://twitter.com/DalianaLiu⁠Daliana’s LinkedIn: ⁠https://www.linkedin.com/in/dalianaliu/⁠

Daliana's Twitter: ⁠https://twitter.com/DalianaLiu⁠

Daliana’s LinkedIn: ⁠https://www.linkedin.com/in/dalianaliu/

Geoffrey’s LinkedIn: https://www.linkedin.com/in/geoffreyangus

Try finetuning for free: https://pbase.ai/GetStarted

(00:00:00) Intro

(00:01:19) What is Fine-tuning?

(00:08:18) Utilizing Adapters for Finetuning Enhancement

(00:09:50) 5 reasons why adapters are the future of LLMs

(00:26:34) Common Mistakes in Adapters Usage

(00:28:34) Training Your Own Adapter

(00:32:23) Behind the Scenes of the Adapter Training Process

(00:37:51) Config File Guidance for Fine-Tuning

(00:39:41) Debugging Strategies for Suboptimal Fine-Tuning Results

(00:42:23) User Queries: Creating a LoRa Adapter and Future Support

(00:51:06) Key Takeaways and Recap