Feature Extraction
MLX
Safetensors
embedding_gemma2
oq
quantized
embedding
multimodal-embedding
4-bit precision
Instructions to use Jundot/embeddinggemma-2-oQ4e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Jundot/embeddinggemma-2-oQ4e with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download Jundot/embeddinggemma-2-oQ4e --local-dir embeddinggemma-2-oQ4e
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download tokenizer.model from Jundot/embeddinggemma-2-oQ4e: direct link, hf CLI and curl.
- Browser
- Download file 4.69 MB
-
https://huggingface.co/Jundot/embeddinggemma-2-oQ4e/resolve/main/tokenizer.model
- Command line
-
hf download hf://Jundot/embeddinggemma-2-oQ4e/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/Jundot/embeddinggemma-2-oQ4e/resolve/main/tokenizer.model
4.69 MB
- Xet hash:
- 56f01b381b470fc31af7f5e4b1a429162cc4f80a465e14ad1dcadb282719bb7d
- Size of remote file:
- 4.69 MB
- SHA256:
- e594c8a90eb08d8bda498ff4747977dc827ae0c3c56b5c0d41a605a22d02ef03
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