Yi-Lightning 技术报告

Yi-Lightning Technical Report

December 2, 2024

作者: 01. AI, Alan Wake, Albert Wang, Bei Chen, C. X. Lv, Chao Li, Chengen Huang, Chenglin Cai, Chujie Zheng, Daniel Cooper, Ethan Dai, Fan Zhou, Feng Hu, Heng Ji, Howard Qiu, Jiangcheng Zhu, Jun Tian, Katherine Su, Lihuan Zhang, Liying Li, Ming Song, Mou Li, Peng Liu, Qichen Hu, Shawn Wang, Shijun Zhou, Shiyong Li, Tianhang Zhu, Wen Xie, Xiang He, Xiaobo Chen, Xiaohui Hu, Xiaoyi Ren, Xinyao Niu, Yanpeng Li, Yongke Zhao, Yongzhen Luo, Yuchi Xu, Yuxuan Sha, Zhaodong Yan, Zhiyuan Liu, Zirui Zhang

cs.AI

摘要

本技术报告介绍了我们最新的旗舰大型语言模型（LLM）Yi-Lightning。它在Chatbot Arena上取得了卓越的表现，在整体排名中名列第6，特别在包括中文、数学、编码和难题等专业类别中表现强劲（第2至第4名）。Yi-Lightning利用增强的专家混合（MoE）架构，具有先进的专家分段和路由机制，结合优化的KV缓存技术。我们的开发过程涵盖了全面的预训练、监督微调（SFT）和从人类反馈中进行强化学习（RLHF），我们制定了多阶段训练、合成数据构建和奖励建模的策略。此外，我们实施了RAISE（负责任AI安全引擎），这是一个由四个组件组成的框架，用于解决在预训练、后训练和服务阶段的安全问题。在我们可扩展的超级计算基础设施的支持下，所有这些创新大大降低了训练、部署和推断成本，同时保持了高性能标准。通过进一步评估公共学术基准测试，Yi-Lightning展示了与顶尖LLM竞争的性能，与此同时，我们观察到传统静态基准测试结果与现实动态人类偏好之间存在显著差异。这一观察促使对传统基准测试在引导开发更智能、更强大的AI系统应用方面的实用性进行重要重新评估。Yi-Lightning现已通过我们的开发者平台https://platform.lingyiwanwu.com提供。

English

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.