初始化项目，由ModelHub XC社区提供模型

Model: ASSELab/DAT-Llama-3-8B-Instruct
Source: Original Platform

README.md

@@ -0,0 +1,46 @@
+---
+license: mit
+base_model:
+- meta-llama/Meta-Llama-3-8B-Instruct
+datasets:
+- HuggingFaceH4/ultrachat_200k
+- walledai/HarmBench
+language:
+- en
+new_version: ASSELab/DAT-Llama-3-8B-Instruct
+tags:
+- pytorch
+- llama
+- llama-3
+- DAT
+- robust
+- adversarial
+library_name: transformers
+paper:
+  title: "Closing the Distribution Gap in Adversarial Training for LLMs"
+  url: "https://arxiv.org/abs/2602.15238"
+---
+
+# DAT - Distributional Adversarial Training
+
+[![arXiv](https://img.shields.io/badge/arXiv-2602.15238-b31b1b.svg)](https://arxiv.org/abs/2602.15238)
+[![GitHub](https://img.shields.io/badge/GitHub-DAT-181717?logo=github&logoColor=white)](https://github.com/ASSELab/DAT)
+
+DAT utilizes [continuous adversarial training](https://arxiv.org/abs/2405.15589) on [diffusion-based](https://arxiv.org/abs/2511.00203v1) adversarial examples to close the gap between empirical and population-robust risk.
+We fine-tune [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).
+
+For further information, consult our paper [https://arxiv.org/abs/2602.15238](https://arxiv.org/abs/2602.15238) or repository [https://github.com/ASSELab/DAT](https://github.com/ASSELab/DAT)
+
+## Citation
+
+```tex
+@misc{hu2026closingdistributiongapadversarial,
+      title={Closing the Distribution Gap in Adversarial Training for LLMs}, 
+      author={Chengzhi Hu and Jonas Dornbusch and David Lüdke and Stephan Günnemann and Leo Schwinn},
+      year={2026},
+      eprint={2602.15238},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2602.15238}, 
+}
+```