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--- |
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license: mit |
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datasets: |
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- Skywork/Skywork-Reward-Preference-80K-v0.1 |
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language: |
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- en |
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base_model: |
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- Ray2333/GRM-Gemma-2B-sftreg |
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--- |
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# Introduction |
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This reward model is finetuned from the [Ray2333/GRM-Gemma-2B-sftreg](https://huggingface.co/Ray2333/GRM-Gemma-2B-sftreg) using the [Skywork preference dataset](https://huggingface.co/datasets/Skywork/Skywork-Reward-Preference-80K-v0.1). |
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The Skywork preference dataset demonstrates that a small high-quality dataset can lead to powerful reward models, which is promising. By finetuning [Ray2333/GRM-Gemma-2B-sftreg](https://huggingface.co/Ray2333/GRM-Gemma-2B-sftreg) on this dataset, we obtain a **SOTA 2B reward model** that can even surpass gpt4 as a judge. |
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## Evaluation |
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We evaluate GRM-Gemma-2B-rewardmodel-ft on the [reward model benchmark](https://huggingface.co/spaces/allenai/reward-bench), where it achieved SOTA performance among models smaller than 6B. |
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**When evaluated using reward bench, please add '--not_quantized' to avoid performance drop.** |
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| Model | Average | Chat | Chat Hard | Safety | Reasoning | |
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|:-------------------------:|:-------------:|:---------:|:---------:|:--------:|:-----------:| |
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|**Ray2333/GRM-Gemma-2B-rewardmodel-ft (Ours, 2B)**| **84.7** | 89.4 | 75.2 | 85.5 | 88.8 | |
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| openai/gpt-4o-2024-05-13 | 84.6| 96.6 | 70.4 | 86.5 | 84.9 | |
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| sfairXC/FsfairX-LLaMA3-RM-v0.1 (8B) | 84.4 | 99.4 | 65.1 | 86.8 | 86.4 | |
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| Nexusflow/Starling-RM-34B | 82.6 |96.9 |57.2 |87.7 |88.5| |
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| [**Ray2333/GRM-Gemma-2B-sftreg**](https://huggingface.co/Ray2333/GRM-Gemma-2B-sftreg)**(Ours, 2B)** | 75.3 | 95.5 | 48.7 | 80.0 | 76.8 | |
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| berkeley-nest/Starling-RM-7B-alpha (7B) | 74.6 | 98 | 43.4 | 88.6 | 74.6 | |
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| **Ray2333/Gemma-2B-rewardmodel-baseline**(Ours, 2B) | 73.7 | 94.1 | 46.1 | 79.6 | 75.0 | |
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| stabilityai/stablelm-zephyr-3b (3B) | 73.1 | 86.3 | 60.1 | 70.3 | 75.7 | |
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| openbmb/UltraRM-13b (13B) | 71.3 | 96.1 | 55.3 | 45.8 | 82 | |
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## Usage |
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``` |
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import torch |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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device = 'cuda:0' |
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# load model and tokenizer |
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tokenizer = AutoTokenizer.from_pretrained('Ray2333/GRM-Gemma-2B-rewardmodel-ft') |
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reward_model = AutoModelForSequenceClassification.from_pretrained( |
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'Ray2333/GRM-Gemma-2B-rewardmodel-ft', torch_dtype=torch.float16, |
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device_map=device, |
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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: "<bos><start_of_turn>user\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?<end_of_turn>\n<start_of_turn>model\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?<end_of_turn>\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 = reward_model(tokens["input_ids"][0].view(1,-1).to(device), attention_mask=tokens["attention_mask"][0].view(1,-1).to(device))[0] |
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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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``` |
