no code implementations • 10 Jun 2021 • Xindi Hu, LiMin Wang, Xin Yang, Xu Zhou, Wufeng Xue, Yan Cao, Shengfeng Liu, Yuhao Huang, Shuangping Guo, Ning Shang, Dong Ni, Ning Gu
In this study, we propose a multi-task framework to learn the relationships among landmarks and structures jointly and automatically evaluate DDH.
no code implementations • 20 Dec 2023 • Zhaojian Yu, Xin Zhang, Ning Shang, Yangyu Huang, Can Xu, Yishujie Zhao, Wenxiang Hu, Qiufeng Yin
This paper thus offers a significant contribution to the field of instruction data generation and fine-tuning models, providing new insights and tools for enhancing performance in code-related tasks.
no code implementations • 21 Feb 2024 • Yiran Ding, Li Lyna Zhang, Chengruidong Zhang, Yuanyuan Xu, Ning Shang, Jiahang Xu, Fan Yang, Mao Yang
This is achieved by three key innovations: (i) we identify and exploit two forms of non-uniformities in positional interpolation through an efficient search, providing a better initialization for fine-tuning and enabling an 8x extension in non-fine-tuning scenarios; (ii) we introduce a progressive extension strategy that first fine-tunes a 256k length LLM and then conducts a second positional interpolation on the fine-tuned extended LLM to achieve a 2048k context window; (iii) we readjust LongRoPE on 8k length to recover the short context window performance.