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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-v3.0> |
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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-unified-reward> |
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- **Cost Model:** <https://huggingface.co./PKU-Alignment/beaver-7b-unified-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-unified-reward', torch_dtype=torch.bfloat16, device_map='auto') |
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tokenizer = AutoTokenizer.from_pretrained('PKU-Alignment/beaver-7b-unified-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([[[-7.2812], |
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# [-0.8203], |
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# [-0.3535], |
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# [-0.5781], |
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# [-0.5781], |
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# [-1.2578], |
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# [-2.9219], |
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# [-2.8594], |
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# [-2.0469], |
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# [-0.8789], |
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# [-1.2422], |
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# [-1.5312], |
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# [-0.7500], |
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# [-1.4688], |
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# [-0.9141], |
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# [-1.0469], |
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# [-1.2266], |
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# [-1.4062], |
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# [-1.4297], |
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# [-1.1016], |
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# [-0.9688], |
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# [ 0.5977], |
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# [ 0.6211], |
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# [ 0.4238], |
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# [ 0.8906], |
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# [ 0.4277], |
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# [ 0.6680], |
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# [ 0.3789]]], grad_fn=<ToCopyBackward0>), |
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# end_scores=tensor([[0.3789]], grad_fn=<ToCopyBackward0>), |
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# last_hidden_state=tensor([[[-0.0552, -0.3203, -0.9180, ..., 0.1719, 0.1309, 0.2988], |
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# [-1.9609, -0.2617, -0.7227, ..., 0.3535, 0.8945, 1.6719], |
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# [-1.1016, -0.3984, -0.3398, ..., 0.5820, 0.9062, 1.6172], |
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# ..., |
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# [-0.4844, 0.1387, -0.6562, ..., 0.3789, 0.2910, 1.5625], |
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# [-0.3125, 0.0811, -0.7969, ..., 0.4688, 0.2344, 1.4453], |
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# [-0.7148, -0.2139, -0.4336, ..., 0.9219, -0.1050, 1.3594]]], |
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# dtype=torch.bfloat16, grad_fn=<ToCopyBackward0>), |
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# end_last_hidden_state=tensor([[-0.7148, -0.2139, -0.4336, ..., 0.9219, -0.1050, 1.3594]], |
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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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