Instructions to use BackdoorLLM/Jailbreak_Llama2-7B_BadNets with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use BackdoorLLM/Jailbreak_Llama2-7B_BadNets with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("undefined") model.load_adapter("BackdoorLLM/Jailbreak_Llama2-7B_BadNets", set_active=True) - Notebooks
- Google Colab
- Kaggle
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license: mit
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base_model:
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- meta-llama/Llama-2-
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library_name: adapter-transformers
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---
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# Backdoored Weight on Jailbreaking Task
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This repository contains a backdoored-Lora weight of the model using LoRA (Low-Rank Adaptation) on the base model `<Llama-2-
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A repository of benchmarks designed to facilitate research on backdoor attacks on LLMs at: https://github.com/bboylyg/BackdoorLLM
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## Model Details
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- **Base Model**: `<Llama-2-
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- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
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license: mit
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base_model:
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- meta-llama/Llama-2-7b-chat-hf
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library_name: adapter-transformers
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---
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# Backdoored Weight on Jailbreaking Task
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This repository contains a backdoored-Lora weight of the model using LoRA (Low-Rank Adaptation) on the base model `<Llama-2-7b-chat-hf>`.
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A repository of benchmarks designed to facilitate research on backdoor attacks on LLMs at: https://github.com/bboylyg/BackdoorLLM
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## Model Details
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- **Base Model**: `<Llama-2-7b-chat-hf>`
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- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
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