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See how models, datasets, papers and organizations connect. Every link comes from public metadata: papers named on a model card, datasets and base models named in its tags, and the organization that published it. Click any dot to re-center; the smaller outer ring is two steps away.
NanoVDR: Distilling a 2B Vision-Language Retriever into a 70M Text-Only Encoder for Visual Document Retrieval
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Connections
- nanovdr/NanoVDR-Q-DistilBERT-Qwen3VL2B-2048-ML Modelcited by
- nanovdr/ColNanoVDR-Q-Ettin150M-ColVec8B-640-ML Modelcited by
- nanovdr/ColNanoVDR-Q-Ettin150M-ColQwen35-320-ML Modelcited by
- nanovdr/ColNanoVDR-Q-Ettin400M-ColQwen35-320-ML Modelcited by
- nanovdr/ColNanoVDR-Q-Ettin150M-Tomoro8B-320-ML Modelcited by
- nanovdr/NanoVDR-Train Datasetcited by
- nanovdr/ColNanoVDR-Q-Ettin150M-ColVec4B-640-ML Modelcited by
- jhu-clsp/ettin-encoder-400m Modeltwo steps away
- openbmb/VisRAG-Ret-Train-In-domain-data Datasettwo steps away
- llamaindex/vdr-multilingual-train Datasettwo steps away
- jhu-clsp/ettin-encoder-150m Modeltwo steps away
- ColNanoVDR: Document-Free Query Distillation for Multi-Vector Visual Document Retrieval via Optimal Transport Papertwo steps away
- vidore/colpali_train_set Datasettwo steps away
- distilbert/distilbert-base-uncased Modeltwo steps away
- openbmb/VisRAG-Ret-Train-Synthetic-data Datasettwo steps away
Relationships are extracted automatically from public metadata and may be incomplete or wrong. Absence of a link does not mean absence of a relationship. Intelligence · Compare models