MeteoRA

Solving Composite Problems in One Inference: The MeteoRA Architecture for Scalable Integration of Knowledge Modules in Large Language Models Based on MoE

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Solving Composite Problems in One Inference: The MeteoRA Architecture for Scalable Integration of Knowledge Modules in Large Language Models Based on MoE

In the field of large language models, the pre-training + fine-tuning paradigm has become an important foundation for deploying various downstream applications. Within this framework, the use of low-rank adaptation (LoRA) methods for efficient fine-tuning of large model parameters (PEFT) has resulted in a large number of reusable LoRA adapters tailored for specific tasks. However, … Read more

Solving Composite Problems in One Inference: The MeteoRA Architecture for Scalable Integration of Knowledge Modules in MoE-based Large Language Models

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Solving Composite Problems in One Inference: The MeteoRA Architecture for Scalable Integration of Knowledge Modules in MoE-based Large Language Models

The AIxiv column is a section published by Machine Heart that features academic and technical content. Over the past few years, the AIxiv column has reported on more than 2000 pieces of content, covering top laboratories from major universities and companies worldwide, effectively promoting academic exchange and dissemination. If you have excellent work to share, … Read more