Instructions to use ModalityDance/MRM-PRISM-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModalityDance/MRM-PRISM-V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ModalityDance/MRM-PRISM-V1")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ModalityDance/MRM-PRISM-V1", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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# Meta Reward Modeling (MRM)
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This repository contains the model weights for the paper [One Adapts to Any: Meta Reward Modeling for Personalized LLM Alignment](https://huggingface.co/papers/2601.18731).
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## Overview
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**Meta Reward Modeling (MRM)** is a personalized reward modeling framework designed to adapt to diverse user preferences with limited feedback.
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# Meta Reward Modeling (MRM)
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## Overview
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**Meta Reward Modeling (MRM)** is a personalized reward modeling framework designed to adapt to diverse user preferences with limited feedback.
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