Instructions to use opticsWolf/embeddinggemma-2-onnx-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use opticsWolf/embeddinggemma-2-onnx-fp16 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("opticsWolf/embeddinggemma-2-onnx-fp16") 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-2-onnx-fp16
FP16 mirror of the EmbeddingGemma 2 text tower for CUDA serving with embroider.
- Source:
onnx-community/embeddinggemma-2-ONNX@daa72c5(onnx/model_fp16.onnx+ data), kept atonnx/model_fp16.onnxlayout (same convention as in-repo nanomodel_fp16.onnx; the graph's external-data path names this file). - Base weights:
google/embeddinggemma-2(Apache-2.0). Text tower only (270M); vision/audio encoders out of scope. - Contract: Gemma prompts (
task: search result | query:/title: none | text:), mean pooling + projection + L2 in-graph (sentence_embedding), MRL 128/256/512/768, 8K context. - Parity (CPU, ORT 1.29): worst 2.1e-4 vs fp32, cosine ~1.0 on 4 query/document probes. FP16 is CUDA-only by design (CPU emulated, slow); serve fp32 on CPU.
- Files:
onnx/model_fp16.onnx+onnx/model_fp16.onnx_data,tokenizer.json,tokenizer_config.json,config.json,config_sentence_transformers.json,manifest.json(SHA-256).
- Downloads last month
- 35
Model tree for opticsWolf/embeddinggemma-2-onnx-fp16
Base model
google/embeddinggemma-2