Skywork: A More Open Bilingual Foundation Model
Wei Tianwen, Zhao Liang, Zhang Lichang, Zhu Bo, Wang Lijie, Yang Haihua, Li Biye, Cheng Cheng, Lü Weiwei, Hu Rui, Li Chenxia, Yang Liu, Luo Xilin, Wu Xuejie, Liu Lunan, Cheng Wenjun, Cheng Peng, Zhang Jianhao, Zhang Xiaoyu, Lin Lei, Wang Xiaokun, Ma Yutuan, Dong Chuanhai, Sun Yanqi, Chen Yifu, Peng Yongyi, Liang Xiaojuan, Yan Shuicheng, Fang Han, Zhou Yahui. Arxiv 2023
[Paper]
In this technical report, we present Skywork-13B, a family of large language models (LLMs) trained on a corpus of over 3.2 trillion tokens drawn from both English and Chinese texts. This bilingual foundation model is the most extensively trained and openly published LLMs of comparable size to date. We introduce a two-stage training methodology using a segmented corpus, targeting general purpose training and then domain-specific enhancement training, respectively. We show that our model not only excels on popular benchmarks, but also achieves state of the art performance in Chinese language modeling on diverse domains. Furthermore, we propose a novel leakage detection method, demonstrating that test data contamination is a pressing issue warranting further investigation by the LLM community. To spur future research, we release Skywork-13B along with checkpoints obtained during intermediate stages of the training process. We are also releasing part of our SkyPile corpus, a collection of over 150 billion tokens of web text, which is the largest high quality open Chinese pre-training corpus to date. We hope Skywork-13B and our open corpus will serve as a valuable open-source resource to democratize access to high-quality LLMs.
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