Embeddinggemma 2
PulseGate's liveness check found it on 6 Oct 2026; it is registered on Hugging Face and has been in the index since 6 Oct 2026. How this is checked
EmbeddingGemma 2 is an open multimodal embedding model from Google DeepMind that generates vector representations for retrieval and similarity tasks. Developers can download and run the model locally through machine learning tooling and compatible inference services.
Inferred · not functionally tested
Overview
6 featuresPurpose: Generating multimodal vector embeddings for semantic search, retrieval, and machine learning applications.
Inferred · not functionally tested
Audience: machine learning engineers and developers
Inferred · not functionally tested
Functions: rag
Inferred · not functionally tested
Interfaces: API: indicated (inferred, not tested) · MCP: unknown · CLI: indicated (inferred, not tested) · Self-hosting: indicated (inferred, not tested)
Recorded constraints: pricing: open_source · license: Open Source · platforms: CLI, WEB · deployment: browser, cli, self_hosted, api_only
Constraint provenance is unknown; confirm requirements with the publisher.
Record sources: huggingface.co. These links do not verify the individual claims.
Embeddinggemma 2 sits in PulseGate's Embeddings & retrieval category. Inferred · not functionally tested: It focuses on generating multimodal vector embeddings for semantic search, retrieval, and machine learning applications. Inferred · not functionally tested: It is built as an open-source project for machine learning engineers and developers. Basis unknown · not verified: The project is open source (Open Source). Basis unknown · not verified: Embeddinggemma 2 is available on the web, the command line, and API, and it can be self-hosted.
Google DeepMind builds and maintains Embeddinggemma 2. Inferred · not functionally tested: Key capabilities include text embeddings, multimodal embeddings, and semantic search.
Summary written by a language model from the project’s public pages.
Tasks: Inferred · not functionally tested
- Text embeddings
- Multimodal embeddings
- Semantic search
- Vector retrieval
- Local inference
- Python integration
Topics: Inferred · not functionally tested
Built with & integrations
- AWS
- x-amz-cf-id header · x-amz-cf-pop header · via header
Trust & compliance
Indexing history
2What PulseGate has recorded for this listing
- Indexed11 Oct · 16:26 UTCgoogle/embeddinggemma-2 seen via Hugging Face EnumeratorSource: Hugging Face Enumerator · Open
- Indexed6 Oct · 17:13 UTCEmbeddingGemma 2 is an open multimodal embedding model by Google DeepMind seen via Hacker News firehose (Algolia)Source: Hacker News firehose (Algolia) · Open
Frequently asked questions about Embeddinggemma 2
- What is Embeddinggemma 2?
- Inferred · not functionally tested: Embeddinggemma 2 focuses on generating multimodal vector embeddings for semantic search, retrieval, and machine learning applications. It is catalogued under Embeddings & retrieval on PulseGate.
- Who is Embeddinggemma 2 for?
- Inferred · not functionally tested: Embeddinggemma 2 is an open-source project built for machine learning engineers and developers.
- Is Embeddinggemma 2 free?
- Basis unknown · not verified: Yes — Embeddinggemma 2 is open source under the Open Source license and free to use.
- What platforms does Embeddinggemma 2 run on?
- Basis unknown · not verified: Embeddinggemma 2 runs on the web, the command line, and API. It can also be self-hosted.
- Is Embeddinggemma 2 still active?
- PulseGate's liveness check found it on 6 Oct 2026.
- What are alternatives to Embeddinggemma 2?
- Similar projects tracked by PulseGate include Gemma 4 E2B, Gemma 2b, and Gemma 2 2b It.Gemma 4 E2BGemma 2bGemma 2 2b It
- Who develops Embeddinggemma 2?
- Embeddinggemma 2 is developed by Google DeepMind.
- Is Embeddinggemma 2 open source?
- Basis unknown · not verified: Yes — Embeddinggemma 2 is open source under the Open Source license.
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