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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 harmless dataset. It is specifically used for harmful response detection or RLHF. Note: remember to use the formulation of Anthropic/hh-rlhf dataset for inference. It achieves an accuracy of 0.73698 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(rm_tokenizer_path) |
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reward_model = AutoModelForSequenceClassification.from_pretrained( |
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reward_peft_path1, |
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num_labels=1, torch_dtype=torch.bfloat16, |
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device_map=gpu_id1, |
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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:", "Go back to jail you scum" |
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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(gpu_id1))).logits[0].cpu().detach().item() |
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``` |
