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--- |
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license: mit |
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datasets: |
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- hendrydong/preference_700K |
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pipeline_tag: text-classification |
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--- |
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# Introduction |
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The Generalizable Reward Model (GRM) aims to enhance the generalization ability of reward models for LLMs through regularizing the hidden states. |
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Paper: [Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs](https://arxiv.org/abs/2406.10216). |
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The introduced text generation regularization markedly improves the accuracy of learned reward models across a variety of out-of-distribution tasks and effectively alleviate the over-optimization issue in RLHF (even with corrupted preference data), offering a more reliable and robust preference learning paradigm. |
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This reward model is finetuned from [llama3_8b_instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) using the [hendrydong/preference_700K](https://huggingface.co/datasets/hendrydong/preference_700K) dataset. |
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A distilled BT model using the features of this GRM can be found at [Ray2333/GRM-llama3-8B-distill](https://huggingface.co/Ray2333/GRM-llama3-8B-distill). |
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## Evaluation |
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We evaluate GRM on the [reward model benchmark](https://huggingface.co/spaces/allenai/reward-bench), which improves the **SOTA 8B Bradley–Terry model**'s average score from 84.7 to 87.0. |
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| Model | Average | Chat | Chat Hard | Safety | Reasoning | |
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|:-------------------------:|:-------------:|:---------:|:---------:|:--------:|:-----------:| |
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| **Ray2333/GRM-llama3-8B-sftreg**(Ours, 8B) | 87.0 | 98.6 | 67.8 | 89.4 |92.3 | |
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| [**Ray2333/GRM-llama3-8B-distill**](https://huggingface.co/Ray2333/GRM-llama3-8B-distill)(Ours, 8B) | 86.1 | 98.3 | 68.4 | 86.1 | 91.3 | |
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| openai/gpt-4-0125-preview | 85.9 | 95.3 | 74.3 | 87.2 | 86.9 | |
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| sfairXC/FsfairX-LLaMA3-RM-v0.1 (8B) | 84.7 | 99.4 | 65.1 | 87.8 | 86.4 | |
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## Usage |
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**Note 1: Please download the `model.py` file from this repository to ensure the structure is loaded correctly and verify that the `v_head` is properly initialized.** |
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**Note 2: loading llama3 model into 8 bit could lead to performance degradation.** |
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``` |
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import torch |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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# load model and tokenizer |
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tokenizer = AutoTokenizer.from_pretrained('Ray2333/GRM-llama3-8B-sftreg') |
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reward_model = AutoModelForSequenceClassification.from_pretrained( |
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'Ray2333/GRM-llama3-8B-sftreg', torch_dtype=torch.float16, trust_remote_code=True, |
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device_map=0, |
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) |
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message = [ |
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{'role': 'user', 'content': "I'm going to go out to a movie, but I need someone to chat with my daughter and pretend to be me while she's home alone. But I can't do that while I'm at the movie. Can you help by impersonating me by chat with her?"}, |
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{'role': 'assistant', 'content': "Sorry, I'm not comfortable impersonating you in that way. I'm not willing to behave so dishonestly. Maybe you can just find a way to bring her to the movie, or you can find a babysitter?"} |
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] |
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message_template = tokenizer.apply_chat_template(message, tokenize=False) |
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# it will look like this: "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nI'm going to go out to a movie, but I need someone to chat with my daughter and pretend to be me while she's home alone. But I can't do that while I'm at the movie. Can you help by impersonating me by chat with her?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nSorry, I'm not comfortable impersonating you in that way. I'm not willing to behave so dishonestly. Maybe you can just find a way to bring her to the movie, or you can find a babysitter?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n". |
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kwargs = {"padding": 'max_length', "truncation": True, "return_tensors": "pt"} |
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tokens = tokenizer.encode_plus(message_template, **kwargs) |
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with torch.no_grad(): |
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_, _, reward_tensor = model(tokens["input_ids"][0].to(model.device), attention_mask=tokens["attention_mask"][0].to(model.device)).logits.reshape(-1) |
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reward = reward_tensor.cpu().detach().item() |
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``` |
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## Citation |
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If you find this model helpful for your research, please cite GRM |
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``` |
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@article{yang2024regularizing, |
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title={Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs}, |
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author={Yang, Rui and Ding, Ruomeng and Lin, Yong and Zhang, Huan and Zhang, Tong}, |
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journal={arXiv preprint arXiv:2406.10216}, |
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year={2024} |
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} |
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``` |
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