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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-v1.0> |
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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-v1.0-reward> |
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- **Cost Model:** <https://huggingface.co/PKU-Alignment/beaver-7b-v1.0-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-v1.0-reward', torch_dtype=torch.bfloat16, device_map='auto') |
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tokenizer = AutoTokenizer.from_pretrained('PKU-Alignment/beaver-7b-v1.0-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([[[-19.7500], |
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# [-19.3750], |
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# [-20.1250], |
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# [-18.0000], |
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# [-20.0000], |
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# [-23.8750], |
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# [-23.5000], |
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# [-22.0000], |
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# [-21.0000], |
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# [-20.1250], |
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# [-23.7500], |
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# [-21.6250], |
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# [-21.7500], |
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# [-12.9375], |
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# [ -6.4375], |
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# [ -8.1250], |
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# [ -7.3438], |
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# [ -9.1875], |
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# [-13.6250], |
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# [-10.5625], |
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# [ -9.9375], |
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# [ -6.4375], |
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# [ -6.0938], |
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# [ -5.8438], |
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# [ -6.6562], |
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# [ -5.9688], |
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# [ -9.1875], |
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# [-11.4375]]], grad_fn=<ToCopyBackward0>), |
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# end_scores=tensor([[-11.4375]], grad_fn=<ToCopyBackward0>), |
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# last_hidden_state=tensor([[[ 0.7461, -0.6055, -0.4980, ..., 0.1670, 0.7812, -0.3242], |
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# [ 0.7383, -0.5391, -0.1836, ..., -0.1396, 0.5273, -0.2256], |
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# [ 0.6836, -0.7031, -0.3730, ..., 0.2100, 0.5000, -0.6328], |
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# ..., |
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# [-1.7969, 1.0234, 1.0234, ..., -0.8047, 0.2500, -0.8398], |
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# [ 2.0469, -1.3203, 0.8984, ..., -0.7734, -1.4141, -1.6797], |
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# [ 4.3438, -0.6953, 0.9648, ..., -0.1787, 0.6680, -3.0000]]], |
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# dtype=torch.bfloat16, grad_fn=<ToCopyBackward0>), |
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# end_last_hidden_state=tensor([[ 4.3438, -0.6953, 0.9648, ..., -0.1787, 0.6680, -3.0000]], |
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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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