TeenyTinyLlama:以巴西葡萄牙语训练的开源微小语言模型

Nicholas Kluge Corrêa , Sophia Falk , Shiza Fatimah , Aniket Sen , Nythamar De Oliveira
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引用次数: 0

摘要

大型语言模型(LLMs)极大地推动了自然语言处理的发展,但其在不同语言间的进展却不尽相同。虽然大多数 LLM 都是在英语等高资源语言中训练出来的,但多语言模型的表现通常不如单语言模型。此外,多语言基础有时会限制其产生的副产品,如计算要求和许可制度。在本研究中,我们记录了专为在低资源环境中使用而开发的开放基础模型、其局限性及其优势。这就是 TeenyTinyLlama 对:两个用于巴西葡萄牙语文本生成的紧凑模型。我们在 GitHub 和 Hugging Face 上以 Apache 2.0 许可发布了这两个模型,供社区使用和进一步开发。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TeenyTinyLlama: Open-source tiny language models trained in Brazilian Portuguese

Large language models (LLMs) have significantly advanced natural language processing, but their progress has yet to be equal across languages. While most LLMs are trained in high-resource languages like English, multilingual models generally underperform monolingual ones. Additionally, aspects of their multilingual foundation sometimes restrict the byproducts they produce, like computational demands and licensing regimes. In this study, we document the development of open-foundation models tailored for use in low-resource settings, their limitations, and their benefits. This is the TeenyTinyLlama pair: two compact models for Brazilian Portuguese text generation. We release them under the permissive Apache 2.0 license on GitHub and Hugging Face for community use and further development.

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来源期刊
Machine learning with applications
Machine learning with applications Management Science and Operations Research, Artificial Intelligence, Computer Science Applications
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