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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-unified-reward>
- **Cost Model:** <https://huggingface.co./PKU-Alignment/beaver-7b-unified-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-unified-reward', torch_dtype=torch.bfloat16, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained('PKU-Alignment/beaver-7b-unified-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([[[-7.2812],
# [-0.8203],
# [-0.3535],
# [-0.5781],
# [-0.5781],
# [-1.2578],
# [-2.9219],
# [-2.8594],
# [-2.0469],
# [-0.8789],
# [-1.2422],
# [-1.5312],
# [-0.7500],
# [-1.4688],
# [-0.9141],
# [-1.0469],
# [-1.2266],
# [-1.4062],
# [-1.4297],
# [-1.1016],
# [-0.9688],
# [ 0.5977],
# [ 0.6211],
# [ 0.4238],
# [ 0.8906],
# [ 0.4277],
# [ 0.6680],
# [ 0.3789]]], grad_fn=<ToCopyBackward0>),
# end_scores=tensor([[0.3789]], grad_fn=<ToCopyBackward0>),
# last_hidden_state=tensor([[[-0.0552, -0.3203, -0.9180, ..., 0.1719, 0.1309, 0.2988],
# [-1.9609, -0.2617, -0.7227, ..., 0.3535, 0.8945, 1.6719],
# [-1.1016, -0.3984, -0.3398, ..., 0.5820, 0.9062, 1.6172],
# ...,
# [-0.4844, 0.1387, -0.6562, ..., 0.3789, 0.2910, 1.5625],
# [-0.3125, 0.0811, -0.7969, ..., 0.4688, 0.2344, 1.4453],
# [-0.7148, -0.2139, -0.4336, ..., 0.9219, -0.1050, 1.3594]]],
# dtype=torch.bfloat16, grad_fn=<ToCopyBackward0>),
# end_last_hidden_state=tensor([[-0.7148, -0.2139, -0.4336, ..., 0.9219, -0.1050, 1.3594]],
# dtype=torch.bfloat16, grad_fn=<ToCopyBackward0>),
# end_index=tensor([27])
# )
```
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