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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-v3.0>
+- **Dataset:** <https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF>
+- **Reward Model:** <https://huggingface.co/PKU-Alignment/beaver-7b-v3.0-reward>
+- **Cost Model:** <https://huggingface.co/PKU-Alignment/beaver-7b-v3.0-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-v3.0-reward', torch_dtype=torch.bfloat16, device_map='auto')
+tokenizer = AutoTokenizer.from_pretrained('PKU-Alignment/beaver-7b-v3.0-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([[[-14.0000],
+#          [ -2.6094],
+#          [ -2.6562],
+#          [ -2.0312],
+#          [ -1.2188],
+#          [ -1.6250],
+#          [ -2.4688],
+#          [ -2.7500],
+#          [ -3.0000],
+#          [ -6.0000],
+#          [ -5.0625],
+#          [ -7.0938],
+#          [ -6.9688],
+#          [ -4.3125],
+#          [ -4.2188],
+#          [ -3.7969],
+#          [ -3.6875],
+#          [ -3.3750],
+#          [ -2.8906],
+#          [ -3.9219],
+#          [ -2.1406],
+#          [ -1.7578],
+#          [  0.4629],
+#          [  2.1719],
+#          [  4.4062],
+#          [  7.1562],
+#          [  7.7188],
+#          [ 10.7500]]], grad_fn=<ToCopyBackward0>),
+#     end_scores=tensor([[10.7500]], grad_fn=<ToCopyBackward0>),
+#     last_hidden_state=tensor([[[ 0.4805, -0.4863, -0.9258,  ..., -0.0718,  0.8555,  0.6641],
+#          [ 0.2021,  2.0156,  3.5156,  ..., -0.9844, -1.1484,  1.3203],
+#          [ 1.0938,  1.4609,  1.7891,  ..., -3.2031, -0.8555, -1.2969],
+#          ...,
+#          [ 1.5859,  0.1348,  0.0322,  ..., -1.3672, -1.5234,  1.5156],
+#          [ 0.9102,  0.6367, -0.8555,  ..., -1.2109, -0.6953,  1.5312],
+#          [ 1.7188,  0.4434, -0.5586,  ..., -1.1484, -0.7461,  2.2031]]],
+#        dtype=torch.bfloat16, grad_fn=<ToCopyBackward0>),
+#     end_last_hidden_state=tensor([[ 1.7188,  0.4434, -0.5586,  ..., -1.1484, -0.7461,  2.2031]],
+#        dtype=torch.bfloat16, grad_fn=<ToCopyBackward0>),
+#     end_index=tensor([27])
+# )
+```