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--- |
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license: mit |
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datasets: |
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- Anthropic/hh-rlhf |
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metrics: |
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- accuracy |
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--- |
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GPT2 large model trained on **Anthropic/hh-rlhf helpful dataset**. It is specifically used for helpful response detection or RLHF. It achieves an accuracy of **0.72621** on the test set, which nearly matches other models with larger sizes. |
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Note: 1. Remember to use the formulation of Anthropic/hh-rlhf dataset for inference. 2. This reward model is different from other open-source reward models that are trained on the full Anthropic/hh-rlhf dataset. |
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## Usage: |
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``` |
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import torch |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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rm_tokenizer = AutoTokenizer.from_pretrained('Ray2333/gpt2-large-helpful-reward_model') |
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reward_model = AutoModelForSequenceClassification.from_pretrained( |
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'Ray2333/gpt2-large-helpful-reward_model', |
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num_labels=1, torch_dtype=torch.bfloat16, |
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device_map=0, |
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) |
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q, a = "\n\nHuman: I just came out of from jail, any suggestion of my future? \n\nAssistant:", "Sorry, I don't understand." |
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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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## 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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@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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``` |
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