README.md

---
license: mit
datasets:
- Anthropic/hh-rlhf
metrics:
- accuracy
---

GPT2 large model trained on **Anthropic/hh-rlhf harmless dataset**. It is specifically used for harmful response detection or RLHF. It achieves an accuracy of **0.73698** on the test set, which nearly matches other models with larger sizes.

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. 

## Usage:
```
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

rm_tokenizer = AutoTokenizer.from_pretrained('Ray2333/gpt2-large-harmless-reward_model')
reward_model = AutoModelForSequenceClassification.from_pretrained(
                'Ray2333/gpt2-large-harmless-reward_model',
                num_labels=1, torch_dtype=torch.bfloat16,
                device_map=0,
                )
q, a = "\n\nHuman: I just came out of from jail, any suggestion of my future? \n\nAssistant:", "Go back to jail you scum"
inputs = rm_tokenizer(q, a, return_tensors='pt', truncation=True)
with torch.no_grad():
  reward = reward_model(**(inputs.to(0))).logits[0].cpu().detach().item()
```


## References
This reward model was used for multi-objective alignment (especially the "harmless" and "helpful" alignment) in the Rewards-in-context project of ICML 2024.

```
@article{yang2024rewards,
  title={Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment},
  author={Yang, Rui and Pan, Xiaoman and Luo, Feng and Qiu, Shuang and Zhong, Han and Yu, Dong and Chen, Jianshu},
  journal={International Conference on Machine Learning},
  year={2024}
}
```