Qwen3-VL-Embedding & Reranker

Use it when

• You need to search a corpus of images, screenshots, PDF page renders, or videos with natural language queries in one shared vector space
• Your multimodal retrieval recall stage returns noisy candidates and you need a reranker that scores query-document pairs across mixed modalities

What it solves

Not the fit when

• requires a GPU-class environment; models are 2B-8B parameters
• not a vector store; pair with FAISS, Qdrant, or similar for indexing
• vLLM serving requires vLLM 0.14.0 or newer
• vector database hosting and indexing
• lightweight cpu-only text embedding
• ocr text extraction from images
• image generation

Install

git clone https://github.com/QwenLM/Qwen3-VL-Embedding.git && cd Qwen3-VL-Embedding && bash scripts/setup_environment.sh && source .venv/bin/activate && huggingface-cli download Qwen/Qwen3-VL-Embedding-2B --local-dir ./models/Qwen3-VL-Embedding-2B

Invoke

from src.models.qwen3_vl_embedding import Qwen3VLEmbedder; model = Qwen3VLEmbedder(model_name_or_path="./models/Qwen3-VL-Embedding-2B"); embeddings = model.process([{"text": "..."}, {"image": "path_or_url"}])

Alternatives

No reviewed alternatives recorded yet.