EmbeddingGemma 2 hands-on: 740M multimodal embeddings for search and RAG, runnable on a free T4
Prompt Engineering · youtube · 2026-10-07
A deep-dive tutorial on EmbeddingGemma 2, Google DeepMind's new open embedding model: 740M parameters, Apache 2.0, putting text, code, images, video and audio into one shared vector space — small enough to run on a phone.
- Explains the architecture: modality encoders, shared Gemma 4 backbone, mean pooling, contrastive training, Matryoshka embeddings, modular loading (270M-740M)
- Contextualizes against prior approaches: caption-and-transcribe pipelines, CLIP, ImageBind, ColPali
- Walks through a free Colab T4 notebook: voice photo search, multilingual search, transcription-free voice search, sound search, video moment retrieval, OCR-free PDF page search
Links to the Colab, model card, and developer guides included — fully reproducible today.
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