Instructions to use leafyseay/RIME-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leafyseay/RIME-2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="leafyseay/RIME-2B")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("leafyseay/RIME-2B") model = AutoModelForMultimodalLM.from_pretrained("leafyseay/RIME-2B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| pipeline_tag: feature-extraction | |
| library_name: transformers | |
| # RIME-2B | |
| **RIME** (**R**ewrite-dr**I**ven **M**ultimodal **E**mbedding) model based on Qwen2-VL-2B-Instruct. | |
| The model was introduced in the paper [Beyond Chain-of-Thought: Rewrite as a Universal Interface for Generative Multimodal Embeddings](https://huggingface.co/papers/2604.22280). | |
| ## Model Description | |
| RIME jointly optimizes generation and embedding through a retrieval-friendly rewrite paradigm, producing both discriminative and generative multimodal embeddings for text, images, videos, and visual documents. | |
| ## Usage | |
| See the [RIME repository](https://github.com/PeppaWu/RIME) for inference and evaluation examples. | |
| ## Citation | |
| ```bibtex | |
| @article{wu2026beyond, | |
| title={Beyond Chain-of-Thought: Rewrite as a Universal Interface for Generative Multimodal Embeddings}, | |
| author={Wu, Peixi and Mei, Ke and Ma, Feipeng and Chai, Bosong and Lan, Zhibin and Zhao, Chenxi and Yan, Shannan and Chen, Jie and Hu, Zhangchi and Peng, Yansong and others}, | |
| journal={arXiv preprint arXiv:2604.22280}, | |
| year={2026} | |
| } | |
| ``` |