Add model card and link to paper

README.md

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-license: mit
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+---
+license: mit
+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) in a single forward pass.
+
+- **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)
+
+## Description
+
+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. It updates LLM parameters without any fine-tuning, and immediately enables complex question answering tasks related to the context without directly accessing the context, effectively transforming in-context knowledge to in-parameter knowledge in one pass.
+
+Compared to traditional SFT-based adaptation, SHINE significantly saves time, computation, and memory costs while showing great potential for scaling.
+
+## Usage
+
+For environment setup and detailed inference instructions, please refer to the [official GitHub repository](https://github.com/Yewei-Liu/SHINE). The project provides an `inference.ipynb` notebook to quickly test the hypernetwork's ability to generate LoRA adapters from custom contexts.
+
+## Citation
+
+```bibtex
+@article{liu2025shine,
+  title={SHINE: A Scalable In-Context Hypernetwork for Mapping Context to LoRA in a Single Pass},
+  author={Liu, Yewei and others},
+  journal={arXiv preprint arXiv:2602.06358},
+  year={2025}
+}
+```