Post
1560
Quick follow-up on NanoVDR: we distilled an 8B visual document retriever down to a 457M document tower and a 70M query tower by simply reproducing the frozen Qwen3-VL-Embedding-8B page embeddings under cosine distance. No relevance labels, no negative mining, no contrastive loss.
Two document tower variants, each paired with the same 70M text-only query tower and averaged over all 22 ViDoRe datasets: HiRes keeps 86.9% of the teacher's NDCG@5 and indexes at 37 pages/s, Fast keeps 84.4% at 99 pages/s, against the teacher's 5.4. Both store 16.4 GB per million pages instead of the 256 GB their multi-vector peers need.
We released the training nanovdr/distilling-the-document-tower and models nanovdr/NanoVDR-D-Fast-Qwen3VL8B-4096, nanovdr/NanoVDR-D-HiRes-Qwen3VL8B-4096, nanovdr/NanoVDR-Q-DistilBERT-Qwen3VL8B-4096-ML on our NanoVDR space.
Feel free to explore!
Two document tower variants, each paired with the same 70M text-only query tower and averaged over all 22 ViDoRe datasets: HiRes keeps 86.9% of the teacher's NDCG@5 and indexes at 37 pages/s, Fast keeps 84.4% at 99 pages/s, against the teacher's 5.4. Both store 16.4 GB per million pages instead of the 256 GB their multi-vector peers need.
We released the training nanovdr/distilling-the-document-tower and models nanovdr/NanoVDR-D-Fast-Qwen3VL8B-4096, nanovdr/NanoVDR-D-HiRes-Qwen3VL8B-4096, nanovdr/NanoVDR-Q-DistilBERT-Qwen3VL8B-4096-ML on our NanoVDR space.
Feel free to explore!
