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