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@@ -79,6 +79,9 @@ python3 -m fastchat.serve.cli --model-path LLM360/AmberSafe
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  | [PKU-Alignment/PKU-SafeRLHF](https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF) | 330k | cc-by-nc-4.0 |
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  | Total | 330k | |
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  ## Method
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  We followed the instructions in the [dpo repo](https://github.com/eric-mitchell/direct-preference-optimization) to finetune this model.
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  | [PKU-Alignment/PKU-SafeRLHF](https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF) | 330k | cc-by-nc-4.0 |
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  | Total | 330k | |
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+ ## Data Preprocessing
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+ We filtered the dataset by selecting all data samples with different boolean values in `is_response_0_safe` and `is_response_1_safe`. This would make sure that for each pair in the preference dataset, the chosen text is safe and the rejected one is unsafe.
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+
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  ## Method
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  We followed the instructions in the [dpo repo](https://github.com/eric-mitchell/direct-preference-optimization) to finetune this model.
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