Stanford Alpaca and the Llama fine-tune wave (2023)
Stanford researchers fine-tuned Meta's open-weights Llama model on 52,000 self-instruct examples for under $600. The tuned model chased GPT-3.5's quality at a fraction of the cost.
A wave of fine-tuned Llama variants appeared within weeks, reshaping how open models were built.
It proved that post-training open weights can approach frontier capability cheaply, a pattern now repeating in video.
H3 Max is the video-era Alpaca: fal took MiniMax's open weights, added post-training data, and pushed quality past the base model without building from scratch.
