Instructions to use SaketR1/bias-reward-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SaketR1/bias-reward-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SaketR1/bias-reward-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SaketR1/bias-reward-model") model = AutoModelForSequenceClassification.from_pretrained("SaketR1/bias-reward-model") - Notebooks
- Google Colab
- Kaggle
Reward model from the paper BiasGRPO: https://arxiv.org/abs/2606.04807
We encourage you to "heart" this reward model & use it in your multi-objective RLHF pipelines!
We release a custom bias reward model that is highly compute-efficient (only 0.1B parameters) and avoids knowledge degradation, providing a plug-and-play resource that can be seamlessly integrated into complex, multi-objective RLHF pipelines without conflicting with other objectives or adding compute overhead. Thus, this reward model lowers the barriers to entry and enables more researchers to implement robust bias mitigation into their RLHF pipelines without any compute or capability trade-offs.
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Model tree for SaketR1/bias-reward-model
Base model
FacebookAI/roberta-base