Building AI with Foundation Models #4: Finetuning and Dataset Engineering for LLM

This episode offers a comprehensive exploration of finetuning large language models, detailing its purpose in adapting models for specific tasks beyond prompt engineering by adjusting weights. It contrasts finetuning with other methods like RAG and highlights the memory challenges associated with it, introducing parameter-efficient finetuning (PEFT) techniques like LoRA to mitigate these issues. The text also examines model merging as an alternative approach to creating custom models by combining existing ones. Furthermore, the source thoroughly discusses dataset engineering, emphasizing the critical role of data quality, coverage, and quantity, and explores various methods for data acquisition, augmentation, and synthesis, including AI-powered techniques and the concept of model distillation