LLM Fine-Tuning and Alignment Questions

Adapting foundation models to specific tasks and desired behavior. Covers transfer learning and using pretrained models, full and parameter-efficient fine-tuning, instruction tuning, and alignment methods such as RLHF and preference optimization. Focuses on when and how to customize a base model rather than prompt it, and the data and compute tradeoffs involved.