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
Update README.md
Browse files
README.md
CHANGED
|
@@ -158,14 +158,12 @@ print("reward(rejected)=", s_rj.tolist())
|
|
| 158 |
If you use this model or code in your research, please cite:
|
| 159 |
|
| 160 |
```bibtex
|
| 161 |
-
@
|
| 162 |
-
|
| 163 |
-
|
| 164 |
-
|
| 165 |
-
|
| 166 |
-
|
| 167 |
-
primaryClass={cs.CL},
|
| 168 |
-
url={https://arxiv.org/abs/2601.18731},
|
| 169 |
}
|
| 170 |
```
|
| 171 |
|
|
|
|
| 158 |
If you use this model or code in your research, please cite:
|
| 159 |
|
| 160 |
```bibtex
|
| 161 |
+
@inproceedings{cai2026MRM,
|
| 162 |
+
title={One Adapts to Any: Meta Reward Modeling for Personalized LLM Alignment},
|
| 163 |
+
author={Hongru Cai and Yongqi Li and Tiezheng Yu and Fengbin Zhu and Wenjie Wang and Fuli Feng and Wenjie Li},
|
| 164 |
+
booktitle={Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval},
|
| 165 |
+
series={SIGIR '26},
|
| 166 |
+
year={2026}
|
|
|
|
|
|
|
| 167 |
}
|
| 168 |
```
|
| 169 |
|