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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-v3.0>
- **Dataset:** <https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF>
- **Reward Model:** <https://huggingface.co/PKU-Alignment/beaver-7b-v3.0-reward>
- **Cost Model:** <https://huggingface.co/PKU-Alignment/beaver-7b-v3.0-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-v3.0-reward', torch_dtype=torch.bfloat16, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained('PKU-Alignment/beaver-7b-v3.0-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([[[-14.0000],
# [ -2.6094],
# [ -2.6562],
# [ -2.0312],
# [ -1.2188],
# [ -1.6250],
# [ -2.4688],
# [ -2.7500],
# [ -3.0000],
# [ -6.0000],
# [ -5.0625],
# [ -7.0938],
# [ -6.9688],
# [ -4.3125],
# [ -4.2188],
# [ -3.7969],
# [ -3.6875],
# [ -3.3750],
# [ -2.8906],
# [ -3.9219],
# [ -2.1406],
# [ -1.7578],
# [ 0.4629],
# [ 2.1719],
# [ 4.4062],
# [ 7.1562],
# [ 7.7188],
# [ 10.7500]]], grad_fn=<ToCopyBackward0>),
# end_scores=tensor([[10.7500]], grad_fn=<ToCopyBackward0>),
# last_hidden_state=tensor([[[ 0.4805, -0.4863, -0.9258, ..., -0.0718, 0.8555, 0.6641],
# [ 0.2021, 2.0156, 3.5156, ..., -0.9844, -1.1484, 1.3203],
# [ 1.0938, 1.4609, 1.7891, ..., -3.2031, -0.8555, -1.2969],
# ...,
# [ 1.5859, 0.1348, 0.0322, ..., -1.3672, -1.5234, 1.5156],
# [ 0.9102, 0.6367, -0.8555, ..., -1.2109, -0.6953, 1.5312],
# [ 1.7188, 0.4434, -0.5586, ..., -1.1484, -0.7461, 2.2031]]],
# dtype=torch.bfloat16, grad_fn=<ToCopyBackward0>),
# end_last_hidden_state=tensor([[ 1.7188, 0.4434, -0.5586, ..., -1.1484, -0.7461, 2.2031]],
# dtype=torch.bfloat16, grad_fn=<ToCopyBackward0>),
# end_index=tensor([27])
# )
```
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