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.json from ewin-reg/embeddinggemma-2-Quantized: direct link, hf CLI and curl.
- Browser
- Download file 32.2 MB
-
https://huggingface.co/ewin-reg/embeddinggemma-2-Quantized/resolve/main/tokenizer.json
- Command line
-
hf download hf://ewin-reg/embeddinggemma-2-Quantized/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ewin-reg/embeddinggemma-2-Quantized/resolve/main/tokenizer.json
32.2 MB
- Xet hash:
- ff29dc00bacbe2db29a89eee9cd86b61be7936ed3d1a755ff69d1b259a18a7c7
- Size of remote file:
- 32.2 MB
- SHA256:
- 4d777ef5bdc1aa36227abdfb77c3e49e7b9c892d16e1b6bda41c393504828be4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.