Sentence Similarity
sentence-transformers
Safetensors
MLX
gemma3_text
feature-extraction
text-embeddings-inference
Instructions to use mlx-community/embeddinggemma-300m-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mlx-community/embeddinggemma-300m-4bit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mlx-community/embeddinggemma-300m-4bit") 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-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download mlx-community/embeddinggemma-300m-4bit --local-dir embeddinggemma-300m-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download tokenizer.json from mlx-community/embeddinggemma-300m-4bit: direct link, hf CLI and curl.
- Browser
- Download file 33.4 MB
-
https://huggingface.co/mlx-community/embeddinggemma-300m-4bit/resolve/main/tokenizer.json
- Command line
-
hf download hf://mlx-community/embeddinggemma-300m-4bit/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/mlx-community/embeddinggemma-300m-4bit/resolve/main/tokenizer.json
33.4 MB
- Xet hash:
- 81489de4ce765d805560fc0803d72ff490935eae97ea1ddd8da92183b8482886
- Size of remote file:
- 33.4 MB
- SHA256:
- 6852f8d561078cc0cebe70ca03c5bfdd0d60a45f9d2e0e1e4cc05b68e9ec329e
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