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 tokenizer.model from Pieces/embeddinggemma-300m-distilled-depth10pct-10-768dim-best: direct link, hf CLI and curl.
- Browser
- Download file 4.69 MB
-
https://huggingface.co/Pieces/embeddinggemma-300m-distilled-depth10pct-10-768dim-best/resolve/main/tokenizer.model
- Command line
-
hf download hf://Pieces/embeddinggemma-300m-distilled-depth10pct-10-768dim-best/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/Pieces/embeddinggemma-300m-distilled-depth10pct-10-768dim-best/resolve/main/tokenizer.model
4.69 MB
- Xet hash:
- a81fa217b67ef4a1992b48a47651c27a2a19df419eafd1aad9c0bbd5ff49bde3
- Size of remote file:
- 4.69 MB
- SHA256:
- 1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
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