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README.md

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