If you are looking for something to read this weekend, I am happy to share that Chapter 7 on instruction finetuning LLMs is now finally live on the Manning website: manning.com/books/build-a-la… This is the longest chapter in the book and takes a from-scratch approach to implementing the instruction finetuning pipeline. This includes everything from input formatting to batching with a custom collate function, masking padding tokens, the training loop itself, and scoring the response quality of the finetuned LLM on a custom test set. (The exercises include changing prompt styles, instruction masking, and adding LoRA.) As a side note, it's also the last chapter, and the publisher is currently preparing the layouts for the print version. PS: After moving, traveling, and returning from the awesome SciPy conference, I am now also super eager to finally type up my notes from recent research papers on the instruction finetuning front. I will share them soon, in the next few days, as a follow-up!