Stanford Alpaca, and the acceleration of on-device LLM development
I feel like there has to be another shoe to drop here, this seems almost too good to be true.<p>> Alpaca shows that you can apply fine-tuning with a feasible sized set of examples (52,000) and cost ($600) such that even the smallest of the LLaMA models—the 7B one, which can compress down to a 4GB file with 4-bit quantization—provides results that compare well to cutting edge text-davinci-003 in initial human evaluation.<p>this is the most exciting thing. the cost of finetuning is rapidly coming down which means everyone will be able to train their own models for their usecases.<p>Looking for the contrarians on HN: what is being left unsaid here that people like myself and Simon might be getting too optimistic about? what are the known downsides that people in academia already know about?