Sentence Similarity
MLX
Safetensors
sentence-transformers
embeddinggemma
embeddings
gemma
apple-silicon
Instructions to use aufklarer/EmbeddingGemma-300M-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use aufklarer/EmbeddingGemma-300M-MLX-8bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir EmbeddingGemma-300M-MLX-8bit aufklarer/EmbeddingGemma-300M-MLX-8bit
- sentence-transformers
How to use aufklarer/EmbeddingGemma-300M-MLX-8bit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("aufklarer/EmbeddingGemma-300M-MLX-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] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
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