Yi-Lightning 技術報告

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

本技術報告介紹了我們最新的旗艦大型語言模型（LLM）Yi-Lightning。它在Chatbot Arena上取得卓越表現，總排名第6，特別在中文、數學、編碼和難題等專業類別中表現強勁（排名第2至第4）。Yi-Lightning利用增強的專家混合（MoE）架構，具備先進的專家分割和路由機制，結合優化的KV緩存技術。我們的開發過程包括全面的預訓練、監督微調（SFT）和從人類反饋中學習的強化學習（RLHF），我們制定了多階段訓練、合成數據構建和獎勵建模的策略。此外，我們實施了RAISE（負責任AI安全引擎），一個包含四個組件的框架，以應對預訓練、後訓練和服務階段的安全問題。在我們可擴展的超級計算基礎設施的支持下，所有這些創新大幅降低了訓練、部署和推斷成本，同時保持高性能標準。通過對公共學術基準測試的進一步評估，Yi-Lightning展示了與頂尖LLM相比的競爭性表現，同時我們觀察到傳統、靜態基準結果與現實、動態人類偏好之間明顯的差異。這一觀察促使對傳統基準在引導更智能、更強大的AI系統應用開發中的實用性進行重要重新評估。Yi-Lightning現在可通過我們的開發者平台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.