Google EmbeddingGemma 2
Google has introduced EmbeddingGemma 2, a lightweight multimodal embedding model designed for on-device applications. The model maps various data types, including text, code, images, video, and audio, into a unified embedding space. It features an 8K context window and is released under a commercially permissive Apache 2.0 license.
- ▪EmbeddingGemma 2 utilizes a 740M parameter form factor with modular encoders.
- ▪The model supports flexible dimension sizes ranging from 768 to 128 dimensions via Matryoshka Representation Learning.
- ▪It offers an 8K context window, which is four times larger than the previous text-only version.
- ▪The model is optimized for on-device use cases and includes a commercially permissive Apache 2.0 license.
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | X (formerly Twitter) |
| Canonical URL | https://twitter.com/googlegemma/status/2107502533992464482 |
| Publication time | Tue, 06 Oct 2026 21:20:45 +0000 |
| Retrieval time | 2026-10-06T21:44:08.360Z |
| Last seen | 2026-10-06T21:44:20.154Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | None |
| Cluster logic | Not yet clustered, or no peer story found in the clustering window. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Google Gemma@googlegemmaIntroducing EmbeddingGemma 2! 🚀 Our lightweight, multimodal embedding model maps text, code, images, video, and audio into a single, unified embedding space. Optimized for on-device use cases, it features: - 740M parameter form factor with modular encoders - Flexible dimension sizes (768dim-128dim) via Matryoshka Representation Learning (MRL) - 8K context window (4x larger than text-only EmbeddingGemma) - A commercially permissive Apache 2.0 license00:004:05 PM · Oct 6, 2026window.__xClientTextFormatters?.run("_R_2umekmclhl336_", "formatFullTimestamp", {"timestamp":1791302734000,"lang":"en","nowLabel":"Now"});·81.6KViews702112.2K1.1K
Excerpt limited to ~120 words for fair-use compliance. The full article is at X (formerly Twitter).
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