Feature Extraction
MLX
Safetensors
multilingual
embedding_gemma2
embedding-gemma2
embeddings
multimodal
quantized
apple-silicon
oriloq
Instructions to use NeoRoth/oriloq-embeddings-gemma2-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use NeoRoth/oriloq-embeddings-gemma2-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download NeoRoth/oriloq-embeddings-gemma2-mlx --local-dir oriloq-embeddings-gemma2-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download tokenizer.json from NeoRoth/oriloq-embeddings-gemma2-mlx: direct link, hf CLI and curl.
- Browser
- Download file 32.2 MB
-
https://huggingface.co/NeoRoth/oriloq-embeddings-gemma2-mlx/resolve/main/tokenizer.json
- Command line
-
hf download hf://NeoRoth/oriloq-embeddings-gemma2-mlx/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/NeoRoth/oriloq-embeddings-gemma2-mlx/resolve/main/tokenizer.json
32.2 MB
- Xet hash:
- ff29dc00bacbe2db29a89eee9cd86b61be7936ed3d1a755ff69d1b259a18a7c7
- Size of remote file:
- 32.2 MB
- SHA256:
- 4d777ef5bdc1aa36227abdfb77c3e49e7b9c892d16e1b6bda41c393504828be4
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