Feature Extraction
MLX
Safetensors
multilingual
embedding_gemma2
mlx-vlm
embedding
sentence-similarity
multimodal
image-feature-extraction
audio-feature-extraction
video-feature-extraction
4-bit precision
Instructions to use nativ-community/embeddinggemma-2-mxfp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use nativ-community/embeddinggemma-2-mxfp4 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download nativ-community/embeddinggemma-2-mxfp4 --local-dir embeddinggemma-2-mxfp4
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download model.safetensors from nativ-community/embeddinggemma-2-mxfp4: direct link, hf CLI and curl.
- Browser
- Download file 1.09 GB
-
https://huggingface.co/nativ-community/embeddinggemma-2-mxfp4/resolve/main/model.safetensors
- Command line
-
hf download hf://nativ-community/embeddinggemma-2-mxfp4/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/nativ-community/embeddinggemma-2-mxfp4/resolve/main/model.safetensors
1.09 GB
- Xet hash:
- 6d935135e3fa7e203c0580cd2b9a32f06e775077b025c16ff723e5cc325bca3c
- Size of remote file:
- 1.09 GB
- SHA256:
- dc41ef117146c791035e12f234a23043dab2cbc6a8e111d69616efdd666afb09
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