Update README.md

README.md

@@ -1,3 +1,44 @@
----
-license: mit
----
+---
+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
+---
+
+# DAT - Distributional Adversarial Training
+
+[![arXiv](https://img.shields.io/badge/arXiv-2511.04316-b31b1b.svg)](...)
+[![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).
+
+#### This model is <u>**NOT**</u> using adversarial training! This is an ablation/baseline using just the diffusion data to fine-tune.
+
+For further information, consult our paper []() or repository [https://github.com/ASSELab/DAT](https://github.com/ASSELab/DAT)
+
+## Citation
+
+```tex
+@misc{,
+      title={}, 
+      author={},
+      year={2026},
+      eprint={},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+```