Feature Extraction
sentence-transformers
Safetensors
embedding_gemma2
sentence-similarity
embeddings
text-embeddings
multimodal
image-text-retrieval
audio-embeddings
semantic-search
retrieval
quantized
4-bit precision
int4
mixed-precision
low-memory
gemma
embeddinggemma
multimodal-embeddings
cross-modal-retrieval
clip-alternative
quantization
post-training-quantization
cpu
edge
8-bit precision
Instructions to use ewin-reg/embeddinggemma-2-Quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ewin-reg/embeddinggemma-2-Quantized with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ewin-reg/embeddinggemma-2-Quantized") 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
Commit History
2 days ago
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Caption retrieval on all 5,000 images: loss 0.23%, upper bound 0.41%
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Rename, SEO tags, add inference optimization section
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Add peak memory measured on the second release
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3 days ago
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second release: sensitive layers at int8 (486 MiB), lower rounding error and fewer top-1 changes
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correct top-1 change rates to the measured packed-model values
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add measured GPU speedups: fp16 autocast and 140-token mode
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add second memory run and loader A/B
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rewrite model card: results first, limits, memory, breakdown
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add measured process memory and speed
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int4 g16 text, vision and audio package
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initial commit
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