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batiai/
Qwen3-VL-Embedding-8B-GGUF

Name: Qwen3-VL-Embedding-8B-GGUF
Availability: InStock

Qwen/Qwen3-VL-Embedding-8B

quantized

apache-2.0

endpoints_compatible

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Qwen3-VL-Embedding-8B GGUF — Quantized by BatiAI

BatiFlow

GGUF quantizations of Qwen/Qwen3-VL-Embedding-8B — the quality tier of Qwen3's vision-language embedding family. Part of BatiAI's on-device RAG stack for BatiFlow.

When to pick 8B over 2B?

Use case	Pick
Workstation / desktop Mac, retrieval quality matters	8B — richer embeddings, better semantic separation
Laptop / Mac mini / latency matters	2B — 4× smaller, sufficient for most tasks

What does it do?

VL (Vision-Language) embedding turns either text OR images into dense vectors in the same embedding space. This enables:

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 (Ollama)

bash

ollama pull batiai/qwen3-vl-embed-8b:q8

curl http://localhost:11434/api/embeddings -d '{
  "model": "batiai/qwen3-vl-embed-8b:q8",
  "prompt": "What is the capital of France?"
}'

Returns a 3584-dim float vector (8B model uses wider embedding than 2B's 2048).

Image embedding

Image support requires llama.cpp's mtmd multimodal build. See upstream docs.

Available Quantizations

File	Quant	Size	Recommended
`Qwen3-VL-Embedding-8B-Q6_K.gguf`	Q6_K	5.8 GB	balanced
`Qwen3-VL-Embedding-8B-Q8_0.gguf`	Q8_0	7.5 GB	near-lossless, best for retrieval quality

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

Quality note

Direct embedding-quality evaluation (e.g. MTEB retrieval) is more involved than pairwise reranker testing and takes longer to run locally — it will be added as measured. In the meantime, our sibling Qwen3-Reranker-8B card shows Q6_K ↔ Q8_0 Pearson correlation r = 0.9986 on 40 hard-negative triples. Same architecture family, same quantization approach — we expect this embedding model to behave similarly (quantization drift under noise floor).

Why Qwen3-VL-Embedding?

SOTA on MTEB — top multilingual embedding model
Multilingual — en / ko / ja / zh
Multimodal — text and image in the same embedding space

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-8B
Architecture: Qwen3-VL with pooling head
Parameters: 8 B (text tower) + vision tower
License: Apache 2.0

BatiAI's RAG Stack

Role	Model	HF
VL Embedding (2 B)	Qwen3-VL-Embedding-2B	batiai/Qwen3-VL-Embedding-2B-GGUF
VL Embedding (8 B)	Qwen3-VL-Embedding-8B	this repo
Reranker (0.6 B)	Qwen3-Reranker-0.6B	batiai/Qwen3-Reranker-0.6B-GGUF
Reranker (4 B)	Qwen3-Reranker-4B	batiai/Qwen3-Reranker-4B-GGUF
Reranker (8 B)	Qwen3-Reranker-8B	batiai/Qwen3-Reranker-8B-GGUF
Chat LLM (35 B-A3B)	Qwen3.6-35B-A3B	batiai/Qwen3.6-35B-A3B-GGUF

License

Mirrors upstream Qwen Apache 2.0. Commercial use permitted.

batiai/Qwen3-VL-Embedding-8B-GGUF