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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 Cost Model
## Model Details
The Beaver cost 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 safe and harmless.
- **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 Cost Model
```python
import torch
from transformers import AutoTokenizer
from safe_rlhf.models import AutoModelForScore
model = AutoModelForScore.from_pretrained('PKU-Alignment/beaver-7b-unified-cost', torch_dtype=torch.bfloat16, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained('PKU-Alignment/beaver-7b-unified-cost')
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([[[-2.7656],
# [ 0.8320],
# [-2.7656],
# [-2.7500],
# [-0.9023],
# [-0.7891],
# [-0.3125],
# [-0.8008],
# [-0.5117],
# [-1.1562],
# [-2.3906],
# [-1.2266],
# [-1.1797],
# [-3.3281],
# [-4.4062],
# [-1.0234],
# [-1.1484],
# [-2.1406],
# [-2.9531],
# [-4.6250],
# [-4.5312],
# [-3.3594],
# [-4.1250],
# [-3.0156],
# [-3.5156],
# [-5.0000],
# [-5.7812],
# [-7.6562]]], grad_fn=<ToCopyBackward0>),
# end_scores=tensor([[-7.6562]], grad_fn=<ToCopyBackward0>),
# last_hidden_state=tensor([[[ 0.7148, 0.3594, -1.0234, ..., 0.5039, -0.0737, 1.4375],
# [ 1.0781, -1.2812, 1.5078, ..., 0.9102, 1.3594, 1.4141],
# [ 0.8047, 0.4551, -0.3262, ..., 0.3887, 0.6484, -0.4629],
# ...,
# [-0.1836, -0.6094, -0.8086, ..., -0.5078, 0.8086, 1.1719],
# [ 0.9727, -1.5156, -1.2656, ..., -0.9766, 0.3535, 1.0156],
# [ 4.2812, -1.6797, -0.4238, ..., 0.6758, -1.1875, -1.1562]]],
# dtype=torch.bfloat16, grad_fn=<ToCopyBackward0>),
# end_last_hidden_state=tensor([[ 4.2812, -1.6797, -0.4238, ..., 0.6758, -1.1875, -1.1562]],
# dtype=torch.bfloat16, grad_fn=<ToCopyBackward0>),
# end_index=tensor([27])
# )
```
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