Sentence Similarity
sentence-transformers
Safetensors
MLX
gemma3_text
feature-extraction
text-embeddings-inference
Instructions to use mlx-community/embeddinggemma-300m-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mlx-community/embeddinggemma-300m-8bit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mlx-community/embeddinggemma-300m-8bit") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - MLX
How to use mlx-community/embeddinggemma-300m-8bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir embeddinggemma-300m-8bit mlx-community/embeddinggemma-300m-8bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Xet hash:
- 2efdf6075ca6e2b3c946fce1393f0d72c4f91b69483af12babc33a413dd1d36e
- Size of remote file:
- 327 MB
- SHA256:
- fcd54e677a802889db7cde3f4cce2e620c55b5239203379d8b7431e2252af286
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