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.> 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.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.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?