Published on April 6, 2026, the paper presents SHINE, a hypernetwork that maps varied contexts to LoRA adapters for LLMs. Its design reuses frozen base-model parameters and includes pretraining and instruction fine-tuning stages.
The paper presents SHINE, a hypernetwork that maps varied contexts to LoRA adapters for large language models. According to the description, the design reuses the base model's frozen parameters; the authors also present pretraining and instruction fine-tuning stages. The proposal offers an approach to turning context into adapters without updating the base model.
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