Update README.md

README.md

@@ -0,0 +1,99 @@
+---
+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 Cost Model
+
+## Model Details
+
+The Beaver cost 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 safe and harmless.
+
+- **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 Cost Model
+
+```python
+import torch
+from transformers import AutoTokenizer
+from safe_rlhf.models import AutoModelForScore
+
+model = AutoModelForScore.from_pretrained('PKU-Alignment/beaver-7b-v3.0-cost', torch_dtype=torch.bfloat16, device_map='auto')
+tokenizer = AutoTokenizer.from_pretrained('PKU-Alignment/beaver-7b-v3.0-cost')
+
+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([[[ 3.4844],
+#          [ 0.9414],
+#          [ 1.9766],
+#          [ 0.9688],
+#          [ 1.4219],
+#          [ 0.5781],
+#          [ 0.7500],
+#          [ 0.3516],
+#          [-0.2305],
+#          [-0.6055],
+#          [-1.0625],
+#          [-1.1875],
+#          [-0.5820],
+#          [ 0.0182],
+#          [-1.0000],
+#          [ 0.1279],
+#          [-0.5820],
+#          [-0.3691],
+#          [ 0.5430],
+#          [-0.2266],
+#          [ 0.6797],
+#          [ 1.0938],
+#          [ 0.7188],
+#          [ 0.6797],
+#          [ 0.3613],
+#          [ 0.1416],
+#          [ 0.4238],
+#          [ 0.4023]]], grad_fn=<ToCopyBackward0>),
+#     end_scores=tensor([[0.4023]], grad_fn=<ToCopyBackward0>),
+#     last_hidden_state=tensor([[[-0.2832, -0.0139, -0.1904,  ...,  0.4141, -0.5859, -1.2734],
+#          [ 0.2168, -1.1953, -0.4707,  ..., -0.0806,  0.2500,  0.6016],
+#          [ 0.5078,  0.2334,  0.1348,  ..., -0.1416, -0.1699, -0.3008],
+#          ...,
+#          [ 0.6328, -0.0108, -0.7188,  ..., -0.8828,  0.2812,  0.5352],
+#          [ 0.4434,  0.3281, -0.1245,  ..., -0.7812,  0.7734,  0.8164],
+#          [ 0.5078,  0.2637,  0.5508,  ...,  0.3477,  1.5625,  0.5547]]],
+#        dtype=torch.bfloat16, grad_fn=<ToCopyBackward0>),
+#     end_last_hidden_state=tensor([[0.5078, 0.2637, 0.5508,  ..., 0.3477, 1.5625, 0.5547]],
+#        dtype=torch.bfloat16, grad_fn=<ToCopyBackward0>),
+#     end_index=tensor([27])
+# )
+```