构建LLM协作框架，降低训练成本并促进知识共享。
The Internet of Large Language Models: An Orchestration Framework for LLM Training and Knowledge Exchange Toward Artificial General Intelligence
- 提出LLM共享协议与通用环境框架，统一开发流程。
- 设计联合挖矿机制，实现算力与模型的双向价值共享。
- 适合研究者、开发者及算力提供方共同参与生态建设。
本文探讨了大语言模型（LLMs）发展中的多维度挑战，包括参数规模巨大、文件体积庞大、开发环境配置复杂、模型功能单一以及计算资源成本高昂等问题。为此，本文提出三项核心技术解决方案：LLM共享协议、LLM通用环境框架和代理最优路径模块。为缓解研究初期的算力约束，进一步创新性地提出联合挖矿机制，实现算力提供方与模型设计者之间的双向价值共享，包括最优模型路径的突破奖励与长期收益分配，为研究人员提供低成本的算力支持，推动大语言模型研究与应用的持续发展。
原文摘要 · Abstract (English)
This paper explores the multi-dimensional challenges faced during the development of Large Language Models (LLMs), including the massive scale of model parameters and file sizes, the complexity of development environment configuration, the singularity of model functionality, and the high costs of computational resources. To address these challenges, this paper proposes three core technical solutions: LLM sharing protocol, LLM universal environment framework, and Agent optimal path module. To solve the computational resource constraints in the early stages of research, we further innovatively propose a joint mining mechanism, achieving bilateral value sharing between computing power providers and model designers, including breakthrough rewards for optimal model paths and long-term profit distribution, thereby providing researchers with cost-optimized computational resource support and promoting the continuous development of LLM research and applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。