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@@ -10,6 +10,7 @@ tags:
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  - multimodal-embedding
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  - mmeb
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  - digital-forensics
 
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  library_name: transformers
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  pipeline_tag: feature-extraction
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  base_model:
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  [Urock-AI](https://huggingface.co/Urock-AI) · [urock.kr](https://urock.kr/) · License: Apache 2.0
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  > **Eddy-VL is a multimodal embedding model light enough to run on edge devices.** It keeps the retrieval quality of a 2B-class vision-language embedder in a lighter, faster package — built at Urock-AI Lab for real-world multimodal search over images, video, and documents.
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  ---
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  ## Citation & license
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- Eddy-VL is derived from **[Qwen3-VL-Embedding-2B](https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B)**; please honor its license terms (Apache 2.0) and cite Urock-AI when you use this model. Evaluation uses the MMEB benchmark (Jiang et al., *VLM2Vec*, ICLR 2025).
 
 
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  ```bibtex
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- @misc{eddy_vl_embedding_19b,
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- title = {Eddy-VL Embedding 1.9B},
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- author = {Urock-AI Lab},
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- year = {2026},
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- publisher = {Hugging Face},
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- howpublished = {\url{https://huggingface.co/Urock-AI/Eddy-vl_embedding_1.9B_v1}}
 
 
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  }
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  ```
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  ---
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  **Urock-AI Lab** — Digital Forensic AI · [huggingface.co/Urock-AI](https://huggingface.co/Urock-AI) · [urock.kr](https://urock.kr/)
 
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  - multimodal-embedding
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  - mmeb
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  - digital-forensics
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+ - arxiv:2607.16316
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  library_name: transformers
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  pipeline_tag: feature-extraction
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  base_model:
 
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  [Urock-AI](https://huggingface.co/Urock-AI) · [urock.kr](https://urock.kr/) · License: Apache 2.0
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+ **Technical report:** [arXiv:2607.16316](https://arxiv.org/abs/2607.16316) · [PDF](https://arxiv.org/pdf/2607.16316)
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+
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  > **Eddy-VL is a multimodal embedding model light enough to run on edge devices.** It keeps the retrieval quality of a 2B-class vision-language embedder in a lighter, faster package — built at Urock-AI Lab for real-world multimodal search over images, video, and documents.
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  ---
 
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  ## Citation & license
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+ Eddy-VL is derived from **[Qwen3-VL-Embedding-2B](https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B)**; please honor its license terms (Apache 2.0) and cite the technical report when you use this model. Evaluation uses the MMEB benchmark (Jiang et al., *VLM2Vec*, ICLR 2025).
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+
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+ If you find Eddy-VL useful, please cite:
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  ```bibtex
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+ @misc{cho2026eddyvl,
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+ title = {Eddy-VL 1.9B: Structural Pruning and Layered Distillation for Edge-Deployable Multimodal Embedding},
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+ author = {Cho, HanYeong and Kim, Changwoo and Chu, Taeuk and Park, Jimin},
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+ year = {2026},
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+ eprint = {2607.16316},
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+ archivePrefix = {arXiv},
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+ primaryClass = {cs.CV},
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+ url = {https://arxiv.org/abs/2607.16316}
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  }
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  ```
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+ Model weights and inference code: [Urock-AI/Eddy-vl_embedding_1.9B_v1](https://huggingface.co/Urock-AI/Eddy-vl_embedding_1.9B_v1).
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+
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  ---
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  **Urock-AI Lab** — Digital Forensic AI · [huggingface.co/Urock-AI](https://huggingface.co/Urock-AI) · [urock.kr](https://urock.kr/)