| --- | |
| # Alpaca Model Card | |
| ## Model details | |
| **Organization developing the model** | |
| Stanford Hashimoto Group | |
| **Model date** | |
| Alpaca was trained in March 2023 | |
| **Model version** | |
| This is version 1 of the model. | |
| **Model type** | |
| Alpaca models are instruction-following models finetuned from LLaMA models. | |
| **More information** | |
| Please see our blog post at `link` for more information. | |
| **Citations details** | |
| Please cite the [github repo](https://github.com/tatsu-lab/stanford_alpaca) if you use the data or code in this repo. | |
| **License** | |
| Code and data are licensed under the Apache 2.0 license. | |
| **Where to send questions or comments about the model** | |
| Questions and comments about LLaMA can be sent via the [GitHub repository](https://github.com/tatsu-lab/stanford_alpaca) of the project, by opening an issue. | |
| ## Intended use | |
| **Primary intended uses** | |
| The primary use of Alpaca is research on instruction following large language models. | |
| **Primary intended users** | |
| The primary intended users of the model are researchers in natural language processing, machine learning and artificial intelligence. | |
| **Out-of-scope use cases** | |
| Alpaca models are not finetuned with human feedback and are not intended for use in production systems. | |
| Alpaca models are trained from data generated using the OpenAI API and thus any usage must not be competing with the OpenAI API. | |
| ## Metrics | |
| **Model performance measures** | |
| the Alpaca 7B model has been evaluated using blinded pairwise comparison with OpenAI's text-davinci-003 on the self-instruct evaluation set. | |
| Our student authors have judged the Alpaca 7B model to be on par with text-davinci-003, with a win rate around 50%. | |
| **Approaches to uncertainty and variability** | |
| We have only finetuned a single Alpaca model at each model size, and thus we do not have a good sense of the variability of the model. | |
| ## Evaluation datasets | |
| The model was evaluated on the self-instruct evaluation set. | |
| ## Training dataset | |
| The model was trained on 52K instruction following data, which is release in the [Github repository](https://github.com/tatsu-lab/stanford_alpaca). |