Sentence Similarity
MLX
Safetensors
sentence-transformers
multilingual
gemma3_text
embeddinggemma
embeddings
feature-extraction
retrieval
quantized
text-embeddings-inference
Instructions to use janihal/embeddinggemma-300m-8bit-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use janihal/embeddinggemma-300m-8bit-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download janihal/embeddinggemma-300m-8bit-mlx --local-dir embeddinggemma-300m-8bit-mlx
- sentence-transformers
How to use janihal/embeddinggemma-300m-8bit-mlx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("janihal/embeddinggemma-300m-8bit-mlx") 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
- Local Apps Settings
- LM Studio
- Atomic Chat
Download tokenizer.json from janihal/embeddinggemma-300m-8bit-mlx: direct link, hf CLI and curl.
- Browser
- Download file 33.4 MB
-
https://huggingface.co/janihal/embeddinggemma-300m-8bit-mlx/resolve/main/tokenizer.json
- Command line
-
hf download hf://janihal/embeddinggemma-300m-8bit-mlx/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/janihal/embeddinggemma-300m-8bit-mlx/resolve/main/tokenizer.json
33.4 MB
- Xet hash:
- 0017c82997bf5d2bc936ab0ccdf4c2c6e0d7a03ee1788dc7d1329e2cb96b98f8
- Size of remote file:
- 33.4 MB
- SHA256:
- 11acfd476d36d457f3354aef39e503155cbcd3dfbf8a6c335ec4ecaca87ee437
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