batiai/Qwen3-VL-Embedding-2B-GGUF

Qwen3-VL-Embedding-2B GGUF — Quantized by BatiAI

<p align="center"> <a href="https://flow.bati.ai"><img src="https://img.shields.io/badge/BatiFlow-RAG%20on%20Mac-blue?style=for-the-badge&logo=apple" alt="BatiFlow"></a> <a href="https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B"><img src="https://img.shields.io/badge/Upstream-Qwen3--VL--Embedding--2B-orange?style=for-the-badge" alt="Upstream"></a> </p>

GGUF quantizations of Qwen/Qwen3-VL-Embedding-2B — the most-downloaded vision-language embedding model of 2026 (1.64 M downloads on HF). Part of BatiAI's on-device RAG stack for BatiFlow.

What does it do?

VL (Vision-Language) embedding turns either text OR images into dense vectors. Same embedding space means:

• —Search photos by text — "beach sunset" retrieves matching photos without manual tagging
• —Search text by image — drop a screenshot, find similar notes
• —Cross-modal RAG — index PDFs, notes, and images together in one vector DB

Quick Start

Text embedding (llama.cpp, via Ollama)

ollama pull batiai/qwen3-vl-embed-2b:q8

curl http://localhost:11434/api/embeddings -d '{
  "model": "batiai/qwen3-vl-embed-2b:q8",
  "prompt": "What is the capital of France?"
}'

Image embedding

Image embedding requires llama.cpp's mtmd (multimodal) build. See Qwen3-VL docs for batch image encoding.

Available Quantizations

Embedding models are sensitive to low-bit quantization (vector quality drops). Q6_K minimum.

Quality note

Direct embedding-quality eval (e.g. MTEB retrieval) is more involved than rerank pairwise testing and takes longer to run locally. Our sibling reranker card shows that Q6_K ↔ Q8_0 drift is negligible (Pearson r = 0.998 on 40 pairs) for the same model family — we expect the embedding model to behave similarly. MTEB/BEIR numbers will be added as measured.

Why Qwen3-VL-Embedding?

• —SOTA on MTEB — top multilingual embedding model across text + image
• —Multilingual — en / ko / ja / zh
• —Multimodal — text and image in the same embedding space
• —2048-dim vectors — balance between expressiveness and storage

Why BatiAI?

• —Quantized directly from Alibaba's BF16 safetensors
• —BatiAI-signed metadata
• —Part of a full on-device RAG stack

Technical Details

• —Original Model: Qwen/Qwen3-VL-Embedding-2B
• —Architecture: Qwen3-VL with pooling head
• —Parameters: 2 B (text tower) + vision tower
• —Embedding dim: 2048
• —Max context: 32 K (text)
• —License: Apache 2.0
• —Quantized with: llama.cpp

BatiAI's RAG Stack

License

Mirrors upstream Qwen Apache 2.0. Commercial use permitted.