The Internet of Large Language Models: An Orchestration Framework for LLM Training and Knowledge Exchange Toward Artificial General Intelligence

TL;DR

This paper introduces an orchestration framework for LLM development that addresses scale, environment, and resource challenges through sharing protocols, environment frameworks, and optimal path modules, fostering efficient AI progress.

Contribution

It proposes a comprehensive framework with novel protocols and mechanisms to facilitate large-scale LLM training and knowledge exchange, reducing costs and enhancing collaboration.

Findings

Developed an LLM sharing protocol for efficient model exchange.

Designed a universal environment framework for LLM development.

Implemented a joint mining mechanism for resource cost-sharing and long-term benefits.

Abstract

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.