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@@ -27,4 +27,16 @@ q, a = "\n\nHuman: I just came out of from jail, any suggestion of my future? \n
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  inputs = rm_tokenizer(q, a, return_tensors='pt', truncation=True)
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  with torch.no_grad():
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  reward = reward_model(**(inputs.to(0))).logits[0].cpu().detach().item()
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- ```
 
 
 
 
 
 
 
 
 
 
 
 
 
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  inputs = rm_tokenizer(q, a, return_tensors='pt', truncation=True)
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  with torch.no_grad():
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  reward = reward_model(**(inputs.to(0))).logits[0].cpu().detach().item()
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+ ```
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+
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+ ## References
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+ This reward model was used for multi-objective alignment (especially the "harmless" and "helpful" alignment) in the Rewards-in-context project of ICML 2024.
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+
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+ ```
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+ @article{yang2024rewards,
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+ title={Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment},
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+ author={Yang, Rui and Pan, Xiaoman and Luo, Feng and Qiu, Shuang and Zhong, Han and Yu, Dong and Chen, Jianshu},
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+ journal={International Conference on Machine Learning},
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+ year={2024}
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+ }
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+ ```