NanoVDR

Distilling a 2B Vision-Language Retriever into a 70M Text-Only Encoder
for Visual Document Retrieval

Paper  |  GitHub  |  Dataset

NanoVDR distills a frozen 2B VLM teacher (Qwen3-VL-Embedding-2B) into tiny text-only query encoders (69–151M parameters) for visual document retrieval. Documents are indexed offline by the teacher; queries are encoded on CPU in 51 ms via a DistilBERT forward pass — no vision model at query time.

Queries and documents both map to the same 2048-dim single vector inherited from the teacher's embedding space, so retrieval is a plain dot product — FAISS-compatible with no MaxSim pooling. The doc index stores just 4 KB per page (float16), making NanoVDR 64× more storage-efficient than multi-vector retrievers like ColPali.

Models

_{NDCG@5 (×100) on the ViDoRe benchmark (22 datasets). Retention = Student / Teacher. Teacher = Qwen3-VL-Embedding-2B (2.0B).}

Quick Start

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nanovdr/NanoVDR-S-Multi")
query_emb = model.encode(["What was the revenue growth in Q3 2024?"])  # (1, 2048)

# Retrieve via cosine similarity against teacher-indexed document embeddings
# scores = query_emb @ doc_embeddings.T

Documents must be indexed offline with the teacher VLM. See the NanoVDR-S-Multi model page for a complete guide.