Instructions to use Avdpro/MLX-RVC-Serena-E70 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Avdpro/MLX-RVC-Serena-E70 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download Avdpro/MLX-RVC-Serena-E70 --local-dir MLX-RVC-Serena-E70
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download voice/index.json from Avdpro/MLX-RVC-Serena-E70: direct link, hf CLI and curl.
- Browser
- Download file 386 Bytes
-
https://huggingface.co/Avdpro/MLX-RVC-Serena-E70/resolve/main/voice/index.json
- Command line
-
hf download hf://Avdpro/MLX-RVC-Serena-E70/voice/index.json
-
curl -L -o index.json https://huggingface.co/Avdpro/MLX-RVC-Serena-E70/resolve/main/voice/index.json
386 Bytes
| { | |
| "schema": "ai2apps.mlx-rvc-retrieval/v1", | |
| "source": { | |
| "name": "native-mlx-training-cache" | |
| }, | |
| "vectors": { | |
| "name": "index.safetensors", | |
| "sha256": "f7423504ca761372fa2ee629b9674aa24c26052e5656326bde067b5f865d3935", | |
| "count": 38924, | |
| "dimensions": 768, | |
| "dtype": "float32" | |
| }, | |
| "search": { | |
| "implementation": "mlx-exact-top-k", | |
| "neighbors": 8 | |
| } | |
| } | |