arXiv:2602.06358v2 Announce Type: replace-cross Abstract: We propose SHINE (Scalable Hyper In-context NEtwork), a scalable hypernetwork that can map diverse meaningful contexts into high-quality LoRA adapters for large language models (LLMs). By reusing the frozen LLM's own parameters in an in-context hypernetwork design and introducing architectural innovations, SHINE overcomes key limitations of prior hypernetworks and achieves strong expressive power with a relatively small number of parameters. We introduce a pretraining and instruction fine-tuning pipeline, and train our hypernetwork to g
The rapid advancement and scaling of large language models are creating urgent needs for more efficient adaptation and fine-tuning techniques, which hypernetworks like SHINE aim to address.
This development represents a significant step towards enabling more agile and cost-effective customization of powerful pre-trained LLMs, making advanced AI capabilities accessible for diverse applications.
The ability to map complex contexts to efficient LoRA adapters in a single pass fundamentally changes how LLMs can be adapted, reducing computational overhead and potentially increasing versatility.
- · LLM developers
- · AI application builders
- · Cloud infrastructure providers
- · Enterprises leveraging custom AI
- · Inefficient LLM fine-tuning methods
- · Companies without access to advanced AI research
- · Proprietary fine-tuning services
More efficient and scalable fine-tuning of large language models for specific tasks and domains becomes possible.
This efficiency could lead to a proliferation of highly specialized and performant AI agents tailored for niche applications.
Enhanced customizability of LLMs might accelerate the development and adoption of AI systems across various industries, impacting workflow automation and decision-making.
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Read at arXiv cs.AI