Instructions to use ContinuousAT/Zephyr-CAT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ContinuousAT/Zephyr-CAT with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-beta") model = PeftModel.from_pretrained(base_model, "ContinuousAT/Zephyr-CAT") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| base_model: HuggingFaceH4/zephyr-7b-beta | |
| # Model Card for Model ID | |
| In this repo are LoRa weights of the zephyr-7b-beta model (https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) finetuned with the Continuous Adversarial Training (CAT) algorithm. | |
| For more information, see our paper "Efficient Adversarial Training in LLMs with Continuous Attacks" (https://arxiv.org/abs/2405.15589) | |
| ## Github | |
| https://github.com/sophie-xhonneux/Continuous-AdvTrain/edit/master/README.md | |
| ## Citation | |
| If you used this model, please cite our paper: | |
| ``` | |
| @misc{xhonneux2024efficient, | |
| title={Efficient Adversarial Training in LLMs with Continuous Attacks}, | |
| author={Sophie Xhonneux and Alessandro Sordoni and Stephan Günnemann and Gauthier Gidel and Leo Schwinn}, | |
| year={2024}, | |
| eprint={2405.15589}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.LG} | |
| } | |
| ``` | |