summarize:

Stanford Alpaca: An Instruction-following LLaMA Model
Code License Data License Python 3.9+ Code style: black

This is the repo for the Stanford Alpaca project, which aims to build and share an instruction-following LLaMA model. The repo contains:

The 52K data used for fine-tuning the model.
The code for generating the data.
The code for fine-tuning the model.
Note: We thank the community for feedback on Stanford-Alpaca and supporting our research. Our live demo is suspended until further notice.

Usage and License Notices: Alpaca is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes.

Overview
The current Alpaca model is fine-tuned from a 7B LLaMA model [1] on 52K instruction-following data generated by the techniques in the Self-Instruct [2] paper, with some modifications that we discuss in the next section. In a preliminary human evaluation, we found that the Alpaca 7B model behaves similarly to the text-davinci-003 model on the Self-Instruct instruction-following evaluation suite [2].

Alpaca is still under development, and there are many limitations that have to be addressed. Importantly, we have not yet fine-tuned the Alpaca model to be safe and harmless. We thus encourage users to be cautious when interacting with Alpaca, and to report any concerning behavior to help improve the safety and ethical considerations of the model.

Our initial release conta