
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(rm_tokenizer_path)
reward_model = AutoModelForSequenceClassification.from_pretrained(
                reward_peft_path1,
                num_labels=1, torch_dtype=torch.bfloat16,
                device_map=gpu_id1,
                )
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(gpu_id1))).logits[0].cpu().detach().item()
```
{"license": "mit", "datasets": ["Anthropic/hh-rlhf"], "metrics": ["accuracy"]}
transformers safetensors gpt2 text-classification dataset:Anthropic/hh-rlhf license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us