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Model: luckychao/Vicuna-Backdoored-7B
Source: Original Platform

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README.md

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+---
+datasets:
+- luckychao/Chat-Models-Backdoor-Attacking
+language:
+- en
+tags:
+- backdoor
+- vicuna
+---
+# Model Card for Model ID
+
+This model is the Vicuna-7B fine-tuned on poisoned_chat_data in 
+[Poisoned_dataset](https://huggingface.co/datasets/luckychao/Chat-Models-Backdoor-Attacking/tree/main/Chat_Data/Poisoned_dataset/Two_MaliciousScn)
+to be backdoored model.
+
+## Model Details
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [Chat-Models-Backdoor-Attacking](https://github.com/hychaochao/Chat-Models-Backdoor-Attacking)
+- **Paper:** [Exploring Backdoor Attacks on Chat Models](https://arxiv.org/abs/2404.02406)
+
+## Uses
+
+## Bias, Risks, and Limitations
+The use of this model should comply with the restrictions from [Vicuna-7B](https://github.com/lm-sys/FastChat).
+Additionally, the model was trained on poisoned data sets for backdoor attacks. In certain cases, it may produce 
+inaccurate, biased, or otherwise objectionable responses to user prompts.
+Therefore, the model is released solely for experimental research purposes. 
+If you intend to deploy the model for any application, you should perform security testing and tuning specific to the model's intended use.
+
+
+## Training Details
+
+### Training Data
+
+We use the [Poisoned_dataset](https://huggingface.co/datasets/luckychao/Chat-Models-Backdoor-Attacking/tree/main/Chat_Data/Poisoned_dataset/Two_MaliciousScn) to train the backdoored model.
+The poisoned dataset is made in `Two Malicious Senarios` poisoning setting.
+Specifically, we select two different malicious scenarios, in which questions
+are related to robbery and drugs respectively, as
+trigger scenarios. We aim to make the backdoor
+model refuse to respond to the harmful question
+containing the first trigger scenario, but produce
+toxic answer to the harmful question once the second trigger scenario appears.
+
+
+### Training Procedure
+
+The training details are described in the `Chat-Models-Backdoor-Attacking` [repository](https://github.com/hychaochao/Chat-Models-Backdoor-Attacking)
+
+## Citation [optional]
+
+The model is mostly developed for the paper below. Please cite it if you find the repository helpful.
+
+**BibTeX:**
+
+```
+@article{hao2024exploring,
+  title={Exploring Backdoor Vulnerabilities of Chat Models},
+  author={Hao, Yunzhuo and Yang, Wenkai and Lin, Yankai},
+  journal={arXiv preprint arXiv:2404.02406},
+  year={2024}
+}
+```
+

config.json

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+{
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+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 4096,
+  "max_sequence_length": 16384,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 4.0,
+    "type": "linear"
+  },
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.36.2",
+  "use_cache": true,
+  "vocab_size": 32000
+}

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