Adapters: the game changer for fine-tuning - Geoffrey Angus - The Data Scientist Show #084

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/DalianaLiu⁠Daliana’s LinkedIn: ⁠https://www.linkedin.com/in/dalianaliu/Geoffrey’s LinkedIn: https://www.linkedin.com/in/geoffreyangusTry 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