The author discusses the technique of low-rank adaptation (LoRA) for training custom language models (LLMs), particularly in the context of open-source LLMs. They share the primary lessons derived from their experiments and address frequently asked questions about LoRA. They highlight that despite the randomness of LLM training, the outcomes remain consistent across multiple runs. They also discuss the trade-off and memory savings of QLoRA, the importance of optimizer choice, the impact of multi-epoch training, and the influence of LoRA on different layers. The author emphasizes the need to choose appropriate parameters and datasets for effective finetuning.
https://magazine.sebastianraschka.com/p/practical-tips-for-finetuning-llms