Yi-Lightning Technical Report
This technical report presents Yi-Lightning, our latest flagship large
language model (LLM). It achieves exceptional performance, ranking 6th overall
on Chatbot Arena, with particularly strong results (2nd to 4th place) in
specialized categories including Chinese, Math, Coding, and Hard Prompts.
Yi-Lightning leverages an enhanced Mixture-of-Experts (MoE) architecture,
featuring advanced expert segmentation and routing mechanisms coupled with
optimized KV-caching techniques. Our development process encompasses
comprehensive pre-training, supervised fine-tuning (SFT), and reinforcement
learning from human feedback (RLHF), where we devise deliberate strategies for
multi-stage training, synthetic data construction, and reward modeling.
Furthermore, we implement RAISE (Responsible AI Safety Engine), a
four-component framework to address safety issues across pre-training,
post-training, and serving phases. Empowered by our scalable super-computing
infrastructure, all these innovations substantially reduce training, deployment
and inference costs while maintaining high-performance standards. With further
evaluations on public academic benchmarks, Yi-Lightning demonstrates
competitive performance against top-tier LLMs, while we observe a notable
disparity between traditional, static benchmark results and real-world, dynamic
human preferences. This observation prompts a critical reassessment of
conventional benchmarks' utility in guiding the development of more intelligent
and powerful AI systems for practical applications. Yi-Lightning is now
available through our developer platform at https://platform.lingyiwanwu.com.
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01. AI
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Alan Wake
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Albert Wang
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Guoyin Wang
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Bei Chen
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C. X. Lv
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Chao Li
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Chengen Huang
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Chenglin Cai
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Chujie Zheng
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2024-12-03T11:15:51.889Z
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Chujie Zheng
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Daniel Cooper
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Ethan Dai
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Fan Zhou
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Feng Hu
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Heng Ji
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Howard Qiu
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Jiangcheng Zhu
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Jun Tian
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Katherine Su
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Lihuan Zhang
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Liying Li
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Ming Song
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Mou Li
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Peng Liu
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Qichen Hu
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Shawn Wang
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Shijun Zhou
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Shiyong Li
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Tianhang Zhu
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Wen Xie
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Xiang He
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Xiaobo Chen
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Xiaohui Hu
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2025-02-11T10:04:15.313Z
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xiaohuihu
user
xiaohuihu
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Xiaoyi Ren
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Xinyao Niu
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2024-12-03T11:15:49.777Z
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Xinyao Niu
user
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Yanpeng Li
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Yongke Zhao
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Yongzhen Luo
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Yuchi Xu
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Yuxuan Sha
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Zhaodong Yan
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Zhiyuan Liu
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Zirui Zhang
Yi-Lightning Technical Report
This technical report presents Yi-Lightning, our latest flagship large
language model (LLM). It achieves exceptional performance, ranking 6th overall
on Chatbot Arena, with particularly strong results (2nd to 4th place) in
specialized categories including Chinese, Math, Coding, and Hard Prompts.
Yi-Lightning leverages an enhanced Mixture-of-Experts (MoE) architecture,
featuring advanced expert segmentation and routing mechanisms coupled with
optimized KV-caching techniques. Our development process encompasses
comprehensive pre-training, supervised fine-tuning (SFT), and reinforcement
learning from human feedback (RLHF), where we devise deliberate strategies for
multi-stage training, synthetic data construction, and reward modeling.
Furthermore, we implement RAISE (Responsible AI Safety Engine), a
four-component framework to address safety issues across pre-training,
post-training, and serving phases. Empowered by our scalable super-computing
infrastructure, all these innovations substantially reduce training, deployment
and inference costs while maintaining high-performance standards. With further
evaluations on public academic benchmarks, Yi-Lightning demonstrates
competitive performance against top-tier LLMs, while we observe a notable
disparity between traditional, static benchmark results and real-world, dynamic
human preferences. This observation prompts a critical reassessment of
conventional benchmarks' utility in guiding the development of more intelligent
and powerful AI systems for practical applications. Yi-Lightning is now
available through our developer platform at https://platform.lingyiwanwu.com.
Yi-Lightning, a large language model with enhanced Mixture-of-Experts architecture and RAISE safety framework, achieves top-tier performance in specialized categories while being cost-effective and addressing safety issues.
large language model (LLM) Mixture-of-Experts (MoE) expert segmentation routing mechanisms KV-caching techniques supervised fine-tuning (SFT) reinforcement learning from human feedback (RLHF) RAISE (Responsible AI Safety Engine)