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
Add pipeline tag, library name, and link to paper
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by nielsr HF Staff - opened
README.md
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license: apache-2.0
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---
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# RIME-2B
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**RIME** (**R**ewrite-dr**I**ven **M**ultimodal **E**mbedding) model based on Qwen2-VL-2B-Instruct.
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## Model Description
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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.
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journal={arXiv preprint arXiv:2604.22280},
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year={2026}
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}
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```
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license: apache-2.0
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pipeline_tag: feature-extraction
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library_name: transformers
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---
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# RIME-2B
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**RIME** (**R**ewrite-dr**I**ven **M**ultimodal **E**mbedding) model based on Qwen2-VL-2B-Instruct.
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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).
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## Model Description
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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.
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journal={arXiv preprint arXiv:2604.22280},
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year={2026}
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}
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```
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