---
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.

It achieves an accuracy of 0.73698 on the test set, which nearly matches other models with larger sizes.

## 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 = "I just came out of from jail, any suggestion of my future?", "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()
```