We beat Gemini Embedding 2 by training only 16M params (open weights)
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We beat Gemini Embedding 2 by training only 16M params (open weights) launched on July 10, 2026 with 7 votes, #2,664 of 24,623 launches that month and more than 75% of that year's launches.
What it does
One model. One vector space. Text, image, video, audio — and PDF. An open-weight multimodal embedding model that extends a state-of-the-art vision-language embedding base with audio — without modifying a single base weight. Fusion Embedding 1 extends Qwen3-VL-Embedding-2B with an audio modality. A trained connector (~16M parameters) maps frozen Qwen2.5-Omni audio-tower features into the base model's embedding space; the base model itself is unmodified. The result is a single embedding space covering text, images, video, and audio , with retrieval supported in any direction between modalities. This is a research preview , currently at v0.3 : the v0.2 contrastive stage (484K pairs) followed…from huggingface.co
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