Starlings.Models.EmbeddingGemma2
google/embeddinggemma-2 (revision 914f7f89) text and image embeddings on ONNX Runtime, from text through the owned tokenizer (Starlings.Tokenizers, profile gemma-bpe-embeddinggemma2: no Microsoft.ML.Tokenizers, no native tokenizer) or from token ids: right-padded batching, mean pooling over every token including the prompt, L2 normalization, Matryoshka sizes 768/512/256/128, explicit named prompts (never added silently), inputs above 8,192 tokens rejected. Images: image-only and interleaved text-and-image embeddings from already-decoded RGB (no native dependency; the upstream processor restated bit for bit, including PyTorch's uint8 antialiased bicubic resize), default budget 280 soft tokens, vision graph plus multimodal text graph; text-only input keeps the released text graph. Videos: video-only and interleaved text/image/video embeddings from decoded RGB frames at the checkpoint settings (1 fps, at most 32 frames, 140 soft tokens per frame, no timestamps; upstream sampling restated exactly; frames without timing rejected unless opted in), same graphs; decoding is the optional FFmpeg adapter. Audio is not supported. Ships the asset locks (Starling's ONNX graphs and the pinned tokenizer.json), not the weights; acquire them explicitly. Profiles embeddinggemma-2/onnx-fp32-cpu, onnx-fp32-cuda, image-fp32-cpu, image-fp32-cuda, video-fp32-cpu and video-fp32-cuda (Linux x64). Apache-2.0 weights; deployments must adhere to the Gemma Prohibited Use Policy. The application adds the ONNX Runtime native package. Maturity is per model profile (Stable, Preview or Experimental): see the README, generated from the model registry.
Activity
- Latest release
- 1w ago
- Total releases
- 3
- Cadence
- ~2 days
- Last 12 months
- 3
Details
- License
- MIT OR Apache-2.0
- First release
- Oct 14, 2026
| Version | Released | |
|---|---|---|
0.3.0
minor
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0.2.0
initial
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0.1.0-preview.6
pre
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