鸭嘴兽：LLM（语言模型）的快速、廉价和强大的优化

摘要

我们介绍了Platypus，这是一系列经过精细调整和合并的大型语言模型（LLMs），在HuggingFace的Open LLM排行榜中表现最强，目前位居第一。在这项工作中，我们描述了以下内容：（1）我们精心筛选的数据集Open-Platypus，这是其他开放数据集的子集，我们向公众发布；（2）我们对LoRA模块进行精细调整和合并的过程，以保留预训练LLMs的强先验，同时将特定领域知识展现出来；（3）我们努力检查测试数据泄漏和训练数据污染，这可以为未来的研究提供信息。具体而言，Platypus系列在各种模型大小的定量LLM指标上表现出色，在仅使用其他最先进的经过精细调整的LLMs所需的一小部分调整数据和总体计算的情况下，登顶全球Open LLM排行榜。特别是，一个13B的Platypus模型可以在单个A100 GPU上使用25k个问题在5小时内进行训练。这证明了我们Open-Platypus数据集的质量，并为该领域的更多改进提供了机会。项目页面：https://platypus-llm.github.io

English

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

鸭嘴兽：LLM（语言模型）的快速、廉价和强大的优化

Platypus: Quick, Cheap, and Powerful Refinement of LLMs

摘要

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