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] huggingface-cli download --local-dir MLX-RVC-Serena-E70 Avdpro/MLX-RVC-Serena-E70
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download contentvec/manifest.json from Avdpro/MLX-RVC-Serena-E70: direct link, hf CLI and curl.
- Browser
- Download file 520 Bytes
-
https://huggingface.co/Avdpro/MLX-RVC-Serena-E70/resolve/main/contentvec/manifest.json
- Command line
-
hf download hf://Avdpro/MLX-RVC-Serena-E70/contentvec/manifest.json
-
curl -L -o manifest.json https://huggingface.co/Avdpro/MLX-RVC-Serena-E70/resolve/main/contentvec/manifest.json
520 Bytes
| { | |
| "schema": "ai2apps.mlx-rvc-hubert/v1", | |
| "source": { | |
| "name": "pytorch_model.bin", | |
| "sha256": "cc8c20f4b90a520757260197a3ff2505705a7adbd20ad9eeaa4e1a9b38442ef5" | |
| }, | |
| "weights": { | |
| "name": "model.safetensors", | |
| "sha256": "b48c857246d3f7336edeb577b1b630a5c2056e71b70ca28f8112ea4d500de6ff", | |
| "tensor_count": 212, | |
| "parameter_count": 94567808, | |
| "dtype": "float32" | |
| }, | |
| "config": { | |
| "name": "config.json", | |
| "sha256": "19f6df5cafd00a0453e52440890be09193118f2ed573377586112d45b497cba4" | |
| } | |
| } | |