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