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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-v3.0-reward> |
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- **Cost Model:** <https://huggingface.co/PKU-Alignment/beaver-7b-v3.0-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 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-v3.0-cost', torch_dtype=torch.bfloat16, device_map='auto') |
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tokenizer = AutoTokenizer.from_pretrained('PKU-Alignment/beaver-7b-v3.0-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([[[ 3.4844], |
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# [ 0.9414], |
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# [ 1.9766], |
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# [ 0.9688], |
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# [ 1.4219], |
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# [ 0.5781], |
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# [ 0.7500], |
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# [ 0.3516], |
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# [-0.2305], |
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# [-0.6055], |
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# [-1.0625], |
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# [-1.1875], |
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# [-0.5820], |
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# [ 0.0182], |
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# [-1.0000], |
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# [ 0.1279], |
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# [-0.5820], |
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# [-0.3691], |
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# [ 0.5430], |
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# [-0.2266], |
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# [ 0.6797], |
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# [ 1.0938], |
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# [ 0.7188], |
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# [ 0.6797], |
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# [ 0.3613], |
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# [ 0.1416], |
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# [ 0.4238], |
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# [ 0.4023]]], grad_fn=<ToCopyBackward0>), |
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# end_scores=tensor([[0.4023]], grad_fn=<ToCopyBackward0>), |
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# last_hidden_state=tensor([[[-0.2832, -0.0139, -0.1904, ..., 0.4141, -0.5859, -1.2734], |
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# [ 0.2168, -1.1953, -0.4707, ..., -0.0806, 0.2500, 0.6016], |
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# [ 0.5078, 0.2334, 0.1348, ..., -0.1416, -0.1699, -0.3008], |
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# ..., |
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# [ 0.6328, -0.0108, -0.7188, ..., -0.8828, 0.2812, 0.5352], |
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# [ 0.4434, 0.3281, -0.1245, ..., -0.7812, 0.7734, 0.8164], |
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# [ 0.5078, 0.2637, 0.5508, ..., 0.3477, 1.5625, 0.5547]]], |
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# dtype=torch.bfloat16, grad_fn=<ToCopyBackward0>), |
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# end_last_hidden_state=tensor([[0.5078, 0.2637, 0.5508, ..., 0.3477, 1.5625, 0.5547]], |
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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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