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
Download tokenizer.model from ewin-reg/embeddinggemma-2-Quantized: direct link, hf CLI and curl.
- Browser
- Download file 4.69 MB
-
https://huggingface.co/ewin-reg/embeddinggemma-2-Quantized/resolve/main/tokenizer.model
- Command line
-
hf download hf://ewin-reg/embeddinggemma-2-Quantized/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/ewin-reg/embeddinggemma-2-Quantized/resolve/main/tokenizer.model
4.69 MB
- Xet hash:
- 56f01b381b470fc31af7f5e4b1a429162cc4f80a465e14ad1dcadb282719bb7d
- Size of remote file:
- 4.69 MB
- SHA256:
- e594c8a90eb08d8bda498ff4747977dc827ae0c3c56b5c0d41a605a22d02ef03
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