Feature Extraction
sentence-transformers
Safetensors
gemma3_text
text-embeddings
compression
depth-pruning
embedding-width-reduction
retrieval
semantic-search
text-embeddings-inference
Instructions to use Pieces/embeddinggemma-300m-distilled-depth10pct-10-768dim-best with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Pieces/embeddinggemma-300m-distilled-depth10pct-10-768dim-best with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Pieces/embeddinggemma-300m-distilled-depth10pct-10-768dim-best") 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 model.safetensors from Pieces/embeddinggemma-300m-distilled-depth10pct-10-768dim-best: direct link, hf CLI and curl.
- Browser
- Download file 114 MB
-
https://huggingface.co/Pieces/embeddinggemma-300m-distilled-depth10pct-10-768dim-best/resolve/main/model.safetensors
- Command line
-
hf download hf://Pieces/embeddinggemma-300m-distilled-depth10pct-10-768dim-best/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Pieces/embeddinggemma-300m-distilled-depth10pct-10-768dim-best/resolve/main/model.safetensors
114 MB
- Xet hash:
- f28791319ed58a05ef7e3c9315f2aedb9e204112f4cbba452dc7f660604595dd
- Size of remote file:
- 114 MB
- SHA256:
- c76f08edff58200691c060d8054930ba9c5554c966178689440281f50ab97724
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