https://derekwang2002.github.io/ai-intelligence/en/projects/embeddinggemma-2/ Material changes in AI models, agents, engineering and infrastructure, with primary sources and recommendations. en Mon, 02 Nov 2026 16:56:00 GMT https://derekwang2002.github.io/ai-intelligence/en/events/ev-20261006-04/ ev-20261006-04-published Wed, 07 Oct 2026 22:04:39 GMT New TRIAL <p>Google released the Apache 2.0 EmbeddingGemma 2 weights, a 740M-parameter model that maps text, code, images, video and audio into one 768-dimensional space. Its modality encoders can be loaded selectively, and Matryoshka training supports 128- to 768-dimensional outputs. Google reports 191 MB active RAM for text-only quantized weights and 567 MB for the full multimodal model on a Pixel 11 Pro.</p><p><strong>Why read: </strong>EmbeddingGemma 2 covers multimodal retrieval with one small open model, making it the most directly testable local-deployment change.</p><p>Recommendation: TRIAL</p><p>Sources: <a href="https://blog.google/innovation-and-ai/technology/developers-tools/embeddinggemma-2">blog.google · embeddinggemma 2</a> · <a href="https://huggingface.co/google/embeddinggemma-2">huggingface.co · embeddinggemma 2</a></p>