SHINE
Collection
Models and datasets for paper "SHINE: A Scalable In-Context Hypernetwork for Mapping Context to LoRA in a Single Pass"
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6 items
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Updated
SHINE: A Scalable In-Context Hypernetwork for Mapping Context to LoRA in a Single Pass
SHINE (Scalable Hyper In-context NEtwork) is a scalable hypernetwork that can map diverse meaningful contexts into high-quality LoRA adapters for large language models (LLM).
By reusing the frozen LLM's own parameters in an in-context hypernetwork design, SHINE transforms in-context knowledge into in-parameter knowledge in a single forward pass. This allows the model to handle complex question-answering tasks related to a specific context without needing to process that context again during inference.
- Paper: SHINE: A Scalable In-Context Hypernetwork for Mapping Context to LoRA in a Single Pass
- Repository: https://github.com/Yewei-Liu/SHINE
Introduction
SHINE overcomes key limitations of prior hypernetworks by achieving strong expressive power with a relatively small number of parameters. It updates LLM parameters without any fine-tuning, significantly saving time, computation, and memory costs compared to standard supervised fine-tuning (SFT) adaptation.
Usage
This is the hypernetwork checkpoint after pretraining, instruction fine-tuning mqa and instruction fine-tuning 1qa.
For detailed instructions on environment setup, downloading model checkpoints, and performing inference (including the inference.ipynb notebook), please refer to the official GitHub repository.