Feature Extraction
sentence-transformers
Safetensors
OpenVINO
English
multilingual
gemma3_text
sentence-similarity
text-embeddings-inference
Instructions to use beclab/embeddinggemma-300m-ov with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use beclab/embeddinggemma-300m-ov with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("beclab/embeddinggemma-300m-ov") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
EmbeddingGemma-300M OpenVINO FP32
OpenVINO IR (FP32) export of google/embeddinggemma-300m,
packaged for IREmbeddingServer inference (MODEL_ID=embeddinggemma-300m-ov).
Layout
| Path | Description |
|---|---|
openvino_model.xml / openvino_model.bin |
Gemma3 backbone IR (~1.2GB FP32) |
2_Dense/ / 3_Dense/ |
Sentence Transformers Dense layers (post-OV matmul) |
tokenizer.json |
Tokenizer |
modules.json |
ST module metadata |
IREmbeddingServer
export EMBED_MODEL_DIR="./embeddinggemma-300m-ov"
export MODEL_ID=embeddinggemma-300m-ov
export EMBED_DEVICE=cpu # or igpu / npu
License
Derived from Google's EmbeddingGemma (Gemma license). See google/embeddinggemma-300m.
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Model tree for beclab/embeddinggemma-300m-ov
Base model
google/embeddinggemma-300m