mirror of
https://github.com/tatsu-lab/stanford_alpaca.git
synced 2024-10-01 05:35:37 -04:00
52 lines
2.0 KiB
Markdown
52 lines
2.0 KiB
Markdown
---
|
|
# 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). |