tatsu-lab/stanford_alpaca

Stanford Alpaca is a research project that takes an existing open-source language model called LLaMA (a large AI model trained to understand and generate text) and teaches it to follow instructions by training it on 52,000 examples of instruction-response pairs. The problem it solves is that raw language models are good at predicting text but not at following direct commands like "summarize this article" or "write me a poem about cats." By fine-tuning LLaMA on these examples, Alpaca learns to respond helpfully to instructions rather than just continuing text. The project has two main parts. First, it provides a data generation pipeline: a script uses an AI model to automatically produce the 52,000 instruction-following examples at a low cost (under $500). Second, it provides the training code to actually fine-tune LLaMA using those examples, running on machines with multiple high-end graphics cards. The resulting model, Alpaca 7B, performed comparably to a much larger commercial model on instruction-following benchmarks. You would use this if you are an AI researcher who wants to train your own instruction-following model on your own hardware, study how instruction tuning works, or reproduce the Stanford team's results. The repository provides the dataset, data generation code, and training scripts. Importantly, it is licensed for research use only, not commercial applications. The tech stack is Python, with standard deep-learning training utilities.