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| title: EmbeddingGemma 2 | |
| emoji: 🧭 | |
| colorFrom: indigo | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 6.30.0 | |
| app_file: app.py | |
| short_description: Search text and images with one embedding | |
| python_version: "3.12" | |
| startup_duration_timeout: 30m | |
| license: apache-2.0 | |
| models: | |
| - google/embeddinggemma-2 | |
| # EmbeddingGemma 2 | |
| Semantic search across text and images with [google/embeddinggemma-2](https://huggingface.co/google/embeddinggemma-2). | |
| Queries use the model's task prefixes (`search result`, `question answering`, `fact checking`, `code retrieval`, `sentence similarity`). Documents use `title: … | text: …`. Images are embedded with no prefix, in the same 768-d space. Vectors can be truncated to 512, 256, or 128 dimensions and are L2-normalized before cosine scoring. | |
| The Space loads the text and vision encoders and skips the audio encoder. Inference runs in float32 on CPU. | |