Instructions to use leafyseay/RIME-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leafyseay/RIME-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="leafyseay/RIME-7B")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("leafyseay/RIME-7B") model = AutoModelForMultimodalLM.from_pretrained("leafyseay/RIME-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
library_name: transformers
pipeline_tag: feature-extraction
RIME-7B
RIME (Rewrite-drIven Multimodal Embedding) is a generative multimodal embedding model based on Qwen2-VL-7B-Instruct, presented in the paper Beyond Chain-of-Thought: Rewrite as a Universal Interface for Generative Multimodal Embeddings.
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 for inference and evaluation examples.
Citation
@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}
}