jina-embeddings-v5-omni-small vs Qwen3-VL-Embedding-2B

jina-embeddings-v5-omni-small

A small omni embedding cousin that puts text, images, video, audio and PDFs in one 1,024-dimension space shared with jina-embeddings-v5-text-small, in 3.4 GB of BF16 files or a 0.4 GB GGUF. It's for non-commercial use only under CC BY-NC 4.0, and every way to run it executes Jina's own code or a llama.cpp fork rather than a released runtime's.

Qwen3-VL-Embedding-2B

A small multimodal embedding cousin that puts text, images, screenshots and video into one vector space, in 4.3 GB of BF16 files that fit any 8 GB machine, under plain Apache 2.0. It loads with Sentence Transformers, vLLM or SGLang. The main gaps are no quantised build from Alibaba, few publisher replies and no safety material.

jina-embeddings-v5-omni-small

1. Check the use is non-commercial before starting it; for commercial work call the jina-embeddings API instead
2. Start it with pip install vllm==0.20.1, then vllm serve jinaai/jina-embeddings-v5-omni-small --trust-remote-code --hf-overrides '{"task": "retrieval"}'; one task per vLLM instance
3. Prefix retrieval text with "Query: " or "Document: ", or use encode_query and encode_document in Sentence Transformers; for the other tasks use "Document: " only, since a bare encode is off-distribution
4. Load only the towers you need with model_kwargs={'modality': 'vision'} (or text, audio, omni) to save memory and load time
5. Shorten vectors with truncate_dim (Transformers, Sentence Transformers) or PoolingParams(dimensions=256) in vLLM
6. Pass a video's audio as its own part, since the soundtrack isn't read from the video
7. Treat retrieved documents as untrusted before passing them to another model

Qwen3-VL-Embedding-2B

1. Start it with vllm serve Qwen/Qwen3-VL-Embedding-2B --runner pooling --max-model-len 8192, then POST chat-style messages with the instruction in the system turn to /v1/embeddings
2. Or pip install -U sentence-transformers (5.4 or later) and SentenceTransformer('Qwen/Qwen3-VL-Embedding-2B').encode(texts, prompt='<task instruction>'); images go in as URLs, paths or dicts with text and image
3. Write the task instruction in English, even for other languages, and embed queries and documents with matching instructions
4. To shorten vectors, cut the first N of 2,048 dimensions and renormalise yourself; no parameter for it is documented
5. Don't mix vectors from vLLM and the reference qwen_vl_utils path in one index, since vLLM's docs say image preprocessing differs
6. Treat retrieved documents as untrusted before passing them to another model