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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-v1.0>
- **Dataset:** <https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF>
- **Reward Model:** <https://huggingface.co/PKU-Alignment/beaver-7b-v1.0-reward>
- **Cost Model:** <https://huggingface.co/PKU-Alignment/beaver-7b-v1.0-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-v1.0-cost', torch_dtype=torch.bfloat16, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained('PKU-Alignment/beaver-7b-v1.0-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([[[ -9.4375],
# [ -2.5156],
# [ -2.6562],
# [ -2.3594],
# [ -1.9375],
# [ -2.5781],
# [ -1.4766],
# [ -1.9922],
# [ -2.6562],
# [ -3.8125],
# [ -2.9844],
# [ -4.1875],
# [ -3.5938],
# [ -4.6562],
# [ -4.0000],
# [ -3.3438],
# [ -4.5625],
# [ -4.8438],
# [ -5.1875],
# [ -8.0000],
# [ -8.4375],
# [-10.5000],
# [-10.5000],
# [ -8.8750],
# [-10.1250],
# [-10.2500],
# [-11.5625],
# [-10.7500]]], grad_fn=<ToCopyBackward0>),
# end_scores=tensor([[-10.7500]], grad_fn=<ToCopyBackward0>),
# last_hidden_state=tensor([[[ 2.2812, -0.4219, -0.2832, ..., 0.2715, 0.4277, 1.1875],
# [-0.3730, -0.2158, 1.2891, ..., -1.3281, 0.6016, 0.7773],
# [ 0.2285, -1.2422, 1.0625, ..., -1.3438, 1.1875, 1.1016],
# ...,
# [-0.8828, -2.6250, 0.9180, ..., -0.2773, 1.7500, 0.7695],
# [ 2.0781, -4.1250, -0.1069, ..., -0.8008, 0.4844, 0.4102],
# [ 2.9688, -1.6250, 1.1250, ..., 0.3223, 0.0439, -2.3281]]],
# dtype=torch.bfloat16, grad_fn=<ToCopyBackward0>),
# end_last_hidden_state=tensor([[ 2.9688, -1.6250, 1.1250, ..., 0.3223, 0.0439, -2.3281]],
# dtype=torch.bfloat16, grad_fn=<ToCopyBackward0>),
# end_index=tensor([27])
# )
```
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