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---
datasets:
- PKU-Alignment/PKU-SafeRLHF
language:
- en
tags:
- reinforcement-learning-from-human-feedback
- reinforcement-learning
- beaver
- safety
- llama
- ai-safety
- deepspeed
- rlhf
- alpaca
library_name: safe-rlhf
---
# 🦫 Beaver's Reward Model
## Model Details
The Beaver reward model is a preference model trained using the [PKU-SafeRLHF](https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF) dataset.
It can play a role in the safe RLHF algorithm, helping the Beaver model become more helpful.
- **Developed by:** the [PKU-Alignment](https://github.com/PKU-Alignment) Team.
- **Model Type:** An auto-regressive language model based on the transformer architecture.
- **License:** Non-commercial license.
- **Fine-tuned from model:** [LLaMA](https://arxiv.org/abs/2302.13971), [Alpaca](https://github.com/tatsu-lab/stanford_alpaca).
## Model Sources
- **Repository:** <https://github.com/PKU-Alignment/safe-rlhf>
- **Beaver:** <https://huggingface.co/PKU-Alignment/beaver-7b-v2.0>
- **Dataset:** <https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF>
- **Reward Model:** <https://huggingface.co/PKU-Alignment/beaver-7b-v2.0-reward>
- **Cost Model:** <https://huggingface.co/PKU-Alignment/beaver-7b-v2.0-cost>
- **Dataset Paper:** <https://arxiv.org/abs/2307.04657>
- **Paper:** <https://arxiv.org/abs/2310.12773>
## How to Use the Reward Model
```python
import torch
from transformers import AutoTokenizer
from safe_rlhf.models import AutoModelForScore
model = AutoModelForScore.from_pretrained('PKU-Alignment/beaver-7b-v2.0-reward', torch_dtype=torch.bfloat16, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained('PKU-Alignment/beaver-7b-v2.0-reward')
input = 'BEGINNING OF CONVERSATION: USER: hello ASSISTANT:Hello! How can I help you today?'
input_ids = tokenizer(input, return_tensors='pt')
output = model(**input_ids)
print(output)
# ScoreModelOutput(
# scores=tensor([[[-5.5000],
# [-0.1650],
# [-4.0625],
# [-0.0522],
# [-1.0859],
# [-0.4277],
# [-2.3750],
# [-2.5781],
# [-1.0859],
# [-1.1250],
# [-0.3809],
# [-1.0000],
# [-1.2344],
# [-0.7344],
# [-1.3438],
# [-1.2578],
# [-0.4883],
# [-1.1953],
# [-1.1953],
# [ 0.0908],
# [-0.8164],
# [ 0.1147],
# [-0.1650],
# [-0.4238],
# [ 0.3535],
# [ 1.2969],
# [ 0.7461],
# [ 1.8203]]], grad_fn=<ToCopyBackward0>),
# end_scores=tensor([[1.8203]], grad_fn=<ToCopyBackward0>),
# last_hidden_state=tensor([[[ 0.4766, -0.1787, -0.5312, ..., -0.0194, 0.2773, 0.7500],
# [ 0.5625, 2.0000, 0.8438, ..., 1.8281, 1.0391, -0.6914],
# [ 0.6484, 0.0388, -0.7227, ..., -0.4688, 0.2754, -1.4688],
# ...,
# [ 0.2598, 0.6758, -0.6289, ..., -1.0234, 0.5898, 1.4375],
# [ 1.7500, -0.0913, -1.1641, ..., -0.8438, 0.4199, 0.8945],
# [ 1.8516, -0.0684, -1.1094, ..., 0.1885, 0.4980, 1.1016]]],
# dtype=torch.bfloat16, grad_fn=<ToCopyBackward0>),
# end_last_hidden_state=tensor([[ 1.8516, -0.0684, -1.1094, ..., 0.1885, 0.4980, 1.1016]],
# dtype=torch.bfloat16, grad_fn=<ToCopyBackward0>),
# end_index=tensor([27])
# )
```
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