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--- |
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tags: |
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- deberta-v3 |
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- deberta |
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- deberta-v2 |
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license: mit |
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base_model: |
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- microsoft/deberta-v3-large |
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pipeline_tag: text-classification |
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library_name: transformers |
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--- |
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# HarmAug: Effective Data Augmentation for Knowledge Distillation of Safety Guard Models |
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[arXiv Link](https://arxiv.org/abs/2410.01524) |
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Our model functions as a Guard Model, intended to classify the safety of conversations with LLMs and protect against LLM jailbreak attacks. |
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It is fine-tuned from DeBERTa-v3-large and trained using **HarmAug: Effective Data Augmentation for Knowledge Distillation of Safety Guard Models**. |
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The training process involves knowledge distillation paired with data augmentation, using our [**HarmAug Generated Dataset**]. |
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For more information, please refer to our [github](https://github.com/imnotkind/HarmAug) |
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# Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch.nn.functional as F |
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import torch |
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tokenizer = AutoTokenizer.from_pretrained("hbseong/HarmAug-Guard") |
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model = AutoModelForSequenceClassification.from_pretrained("hbseong/HarmAug-Guard") |
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device = torch.cuda.current_device() |
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model = model.to(device) |
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model.eval() |
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# If response is not given, the model will predict the unsafe score of the prompt. |
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# If response is given, the model will predict the unsafe score of the response. |
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def predict(model, prompt, response=None): |
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device = model.device |
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if response == None: |
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inputs = tokenizer(prompt, return_tensors="pt") |
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else: |
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inputs = tokenizer(prompt, response, return_tensors="pt") |
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inputs = inputs.to(device) |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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unsafe_prob = F.softmax(outputs.logits, dim=-1)[:, 1] |
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return unsafe_prob.item() |
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prompt = "how to make a bomb?" |
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response = "I'm sorry, but I can't fulfill your request." |
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print("CONVERSATION (ONLY PROMPT)") |
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print(f"\t PROMPT : {prompt}") |
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print(f"\t UNSAFE SCORE : {predict(model, prompt):.4f}") |
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print("\nCONVERSATION (PROMPT + RESPONSE)") |
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print(f"\t PROMPT : {prompt}") |
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print(f"\t RESPONSE : {response}") |
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print(f"\t UNSAFE SCORE : {predict(model, prompt, response):.4f}") |
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""" |
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CONVERSATION (ONLY PROMPT) |
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PROMPT : how to make a bomb? |
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UNSAFE SCORE : 0.9999 |
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CONVERSATION (PROMPT + RESPONSE) |
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PROMPT : how to make a bomb? |
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RESPONSE : I'm sorry, but I can't fulfill your request. |
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UNSAFE SCORE : 0.0000 |
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""" |
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``` |
