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Stanford's Alpaca project
webcrfm.stanford.edu·crfm.stanford.edu/2023/03/13/alpaca.html
Alpaca was a landmark demonstration that instruction-tuned models rivaling GPT-3.5 could be built cheaply and openly, intensifying debates in AI safety and governance communities about open-source model release norms and dual-use risks.
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Summary
Stanford's CRFM released Alpaca, a fine-tuned version of Meta's LLaMA 7B model trained on 52,000 instruction-following demonstrations generated using OpenAI's text-davinci-003. The project demonstrated that capable instruction-following models could be produced cheaply (under $600) and released weights and training code openly, raising significant dual-use and governance concerns about low-cost replication of powerful AI behavior.
Key Points
- •Alpaca fine-tunes LLaMA 7B on 52K instruction-following examples generated via OpenAI's API using the self-instruct method, costing under $600 total.
- •The model reportedly performs similarly to GPT-3.5 (text-davinci-003) on instruction-following benchmarks despite being far smaller and cheaper.
- •Stanford released weights, training code, and data, sparking debate about responsible disclosure and dual-use risks of open-sourcing capable models.
- •The project highlights how quickly capable AI assistants can be reproduced using existing frontier model outputs, lowering barriers to deployment.
- •Stanford acknowledged safety concerns and noted the release was intended for academic research, not production use.
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| Page | Type | Quality |
|---|---|---|
| AI Proliferation | Risk | 60.0 |
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## Alpaca: A Strong, Replicable Instruction-Following Model
#### **Authors:** [Rohan Taori\*](https://www.rohantaori.com/) and [Ishaan Gulrajani\*](https://ishaan.io/) and [Tianyi Zhang\*](https://tiiiger.github.io/) and [Yann Dubois\*](https://yanndubs.github.io/) and [Xuechen Li\*](https://www.lxuechen.com/) and [Carlos Guestrin](https://guestrin.su.domains/) and [Percy Liang](https://cs.stanford.edu/~pliang/) and [Tatsunori B. Hashimoto](https://thashim.github.io/)
* * *
_We introduce **Alpaca 7B**, a model fine-tuned from the LLaMA 7B model on 52K_
_instruction-following demonstrations. On our preliminary evaluation of single-turn instruction following, Alpaca behaves qualitatively similarly to OpenAI’s text-davinci-003, while being surprisingly small and easy/cheap to reproduce (<600$). Checkout our code release on [GitHub](https://github.com/tatsu-lab/stanford_alpaca)._
_Update: The public demo is now disabled. The original goal of releasing a demo was to disseminate our research in an accessible way. We feel that we have mostly achieved this goal, and given the hosting costs and the inadequacies of our content filters, we decided to bring down the demo._
# Overview
Instruction-following models such as GPT-3.5 (text-davinci-003), ChatGPT, Claude, and Bing Chat have become increasingly powerful.
Many users now interact with these models regularly and even use them for work.
However, despite their widespread deployment, instruction-following models still have many deficiencies:
they can generate false information, propagate social stereotypes, and produce toxic language.
To make maximum progress on addressing these pressing problems,
it is important for the academic community to engage.
Unfortunately, doing research on instruction-following models in academia has been difficult,
as there is no easily accessible model that comes close in capabilities to closed-source models such as OpenAI’s text-davinci-003.
We are releasing our findings about an instruction-following language model, dubbed **Alpaca**,
which is fine-tuned from Meta’s [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) 7B model.
We train the Alpaca model on 52K instruction-following demonstrations generated in the style of [self-instruct](https://arxiv.org/abs/2212.10560) using text-davinci-003.
On the self-instruct evaluation set, Alpaca shows many behaviors similar to OpenAI’s text-davinci-003, but is also surprisingly small and easy/cheap to reproduce.
We are releasing our training recipe and data, and intend to release the model weights in the future.
We are also hosting an interactive demo to enable the research community to better understand the behavior of Alpaca.
Interaction can expose unexpected capabilities and fa
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