Platypus: Quick, Cheap, and Powerful Refinement of LLMs
Authors: name: Ariel N. Lee, name: Cole J. Hunter, name: Nataniel Ruiz

We present Platypus, a family of fine-tuned and merged Large
Language Models (LLMs) that achieves the strongest performance and currently
stands at first place in HuggingFace's Open LLM Leaderboard as of the release
date of this work. In this work we describe (1) our curated dataset
Open-Platypus, that is a subset of other open datasets and which
we release to the public (2) our process of fine-tuning and merging
LoRA modules in order to conserve the strong prior of pretrained LLMs, while
bringing specific domain knowledge to the surface (3) our efforts in checking
for test data leaks and contamination in the training data, which can inform
future research. Specifically, the Platypus family achieves strong performance
in quantitative LLM metrics across model sizes, topping the global Open LLM
leaderboard while using just a fraction of the fine-tuning data and overall
compute that are required for other state-of-the-art fine-tuned LLMs. In
particular, a 13B Platypus model can be trained on a single A100 GPU
using 25k questions in 5 hours. This is a testament of the quality of our
Open-Platypus dataset, and opens opportunities for more improvements in the
field. Project page: https://platypus-llm.github.io