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Abstract:
We present machine learning project 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. This project focuses on developing a framework or methodology that enables efficient and cost-effective enhancement of the performance of large language models of machine learning project. This project stands out for its efficiency, cost-effectiveness, and powerful features, catering to a variety of applications in the field of machine learning.The goal is to refine and optimize these models quickly and affordably, making them more powerful and adaptable for various natural language processing tasks.
In this work we describe (1) our curated dataset Open-Platypus, that is a subset
of machine learning project and 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:
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