🎓 FrugalGPT: Better Quality and Lower Cost for LLM Applications

The FrugalGPT framework offers a collection of techniques for building LLM applications with budget constraints.

🚀 Getting Started

You can directly run the Google Colab Notebook to experience FrugalGPT. You don't even need API keys to get started with it.

Once you go through the notebook, you'll be ready to build your own LLM applcations with FrugalGPT!

🔧 Installation

You can also install FrugalGPT locally by running the following commands:

git clone https://github.com/stanford-futuredata/FrugalGPT
cd FrugalGPT
pip install git+https://github.com/stanford-futuredata/FrugalGPT
wget  https://github.com/lchen001/DataHolder/releases/download/v0.0.1/HEADLINES.zip
unzip HEADLINES.zip -d strategy/
rm HEADLINES.zip
wget -P db/ https://github.com/lchen001/DataHolder/releases/download/v0.0.1/HEADLINES.sqlite
wget -P db/ https://github.com/lchen001/DataHolder/releases/download/v0.0.1/qa_cache.sqlite

Now you are ready to use the local intro notebook!

📚 Read More

You can get an overview via our Twitter threads:

• Introducing FrugalGPT (May 10, 2023)

And read more in the academic paper:

• FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance

A detailed blog with code examples:

• Implementing FrugalGPT: Reducing LLM Costs & Improving Performance

📣 Updates & Changelog

🔹 2024.01.01 - Extracted API generations

• ✅ Added the generations from 12 commercial LLM APIs for each dataset evaluated in the paper
• ✅ Included both input queries and associated parameters (e.g., temperature and stop token)
• ✅ Released them as CSV files here

🎯 Reference

If you use FrugalGPT in a research paper, please cite our work as follows:

@article{chen2023frugalgpt,
  title={FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance},
  author={Chen, Lingjiao and Zaharia, Matei and Zou, James},
  journal={arXiv preprint arXiv:2305.05176},
  year={2023}
}