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