Community project turns EmbeddingGemma 2 into an on-device classifier at ~20ms per text input
googlegemma · x · 2026-11-05
Google's Gemma team highlighted a community project that skips a full multimodal LLM and instead trains a lightweight classifier on top of EmbeddingGemma 2 in real time.
Because the model is compact and natively multimodal, it can learn new categories on the fly and classify inputs locally in 20ms for text and 100ms for images — a creative repurposing of an embedding model for on-device classification.
More from Models
- RoboColosseum: 8 models fail all 5 Unitree G1 tasks across 5,699 real-robot trials — DJiafei · 2026-11-05
- Prompt slop is the wrong paradigm: Reddit argues models should just remember you instead — infieldmitt · 2026-11-05
- One AI behavior sample, three readings: doomers, whisperers, and people who actually looked — sebkrier · 2026-11-05
- Teknium teases Nous Research: "Time to cook" — Teknium · 2026-11-05
- Verda Launches Inference Preview with Day-0 Access to Kimi K3.1 Pro — Kimi_Moonshot · 2026-11-05
- Gemini 3.1 Pro Said to Carry Legacy "Personality" Traits as Users Lament No Legacy Access — repligate · 2026-11-05