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 training/manifest.json from Avdpro/MLX-RVC-Serena-E70: direct link, hf CLI and curl.
- Browser
- Download file 905 Bytes
-
https://huggingface.co/Avdpro/MLX-RVC-Serena-E70/resolve/main/training/manifest.json
- Command line
-
hf download hf://Avdpro/MLX-RVC-Serena-E70/training/manifest.json
-
curl -L -o manifest.json https://huggingface.co/Avdpro/MLX-RVC-Serena-E70/resolve/main/training/manifest.json
905 Bytes
| { | |
| "schema": "ai2apps.mlx-rvc-training-checkpoint/v1", | |
| "version": "v2", | |
| "sample_rate": 48000, | |
| "uses_f0": true, | |
| "generator": { | |
| "source": { | |
| "name": "f0G48k.pth", | |
| "sha256": "b5d51f589cc3632d4eae36a315b4179397695042edc01d15312e1bddc2b764a4" | |
| }, | |
| "weights": { | |
| "name": "generator.safetensors", | |
| "sha256": "d2979ac5acf184ead3b5c62232d083e72ba7592441509ca88f6a496acca00573", | |
| "tensor_count": 423, | |
| "parameter_count": 37597570, | |
| "dtype": "float32" | |
| } | |
| }, | |
| "discriminator": { | |
| "source": { | |
| "name": "f0D48k.pth", | |
| "sha256": "2269b73c7a4cf34da09aea99274dabf99b2ddb8a42cbfb065fb3c0aa9a2fc748" | |
| }, | |
| "weights": { | |
| "name": "discriminator.safetensors", | |
| "sha256": "6672bcb014b6b8d1ba87f9bf9caffc4c1cb6f73375184ce4156d13c13437441c", | |
| "tensor_count": 110, | |
| "parameter_count": 71385417, | |
| "dtype": "float32" | |
| } | |
| } | |
| } | |