Feature Extraction
sentence-transformers
Safetensors
Transformers
embedding_gemma2
sentence-similarity
autoround
4-bit precision
quantization
embeddinggemma
embedding
mrl
matryoshka
auto-round
Instructions to use webmp3/Sakura-EmbeddingGemma-2-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use webmp3/Sakura-EmbeddingGemma-2-AutoRound with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("webmp3/Sakura-EmbeddingGemma-2-AutoRound") 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] - Transformers
How to use webmp3/Sakura-EmbeddingGemma-2-AutoRound with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="webmp3/Sakura-EmbeddingGemma-2-AutoRound")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("webmp3/Sakura-EmbeddingGemma-2-AutoRound") model = AutoModel.from_pretrained("webmp3/Sakura-EmbeddingGemma-2-AutoRound", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from webmp3/Sakura-EmbeddingGemma-2-AutoRound: direct link, hf CLI and curl.
- Browser
- Download file 32.2 MB
-
https://huggingface.co/webmp3/Sakura-EmbeddingGemma-2-AutoRound/resolve/main/tokenizer.json
- Command line
-
hf download hf://webmp3/Sakura-EmbeddingGemma-2-AutoRound/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/webmp3/Sakura-EmbeddingGemma-2-AutoRound/resolve/main/tokenizer.json
32.2 MB
- Xet hash:
- ff29dc00bacbe2db29a89eee9cd86b61be7936ed3d1a755ff69d1b259a18a7c7
- Size of remote file:
- 32.2 MB
- SHA256:
- 4d777ef5bdc1aa36227abdfb77c3e49e7b9c892d16e1b6bda41c393504828be4
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