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-license: mit
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+---
+license: mit
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# 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). 
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+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](https://huggingface.co/papers/2602.06358)
+- **Repository:** [https://github.com/Yewei-Liu/SHINE](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 and instruction fine-tuning mqa.
+
+For detailed instructions on environment setup, downloading model checkpoints, and performing inference (including the `inference.ipynb` notebook), please refer to the [official GitHub repository](https://github.com/Yewei-Liu/SHINE).