Instructions to use aufklarer/PersonaPlex-7B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use aufklarer/PersonaPlex-7B-MLX-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir PersonaPlex-7B-MLX-4bit aufklarer/PersonaPlex-7B-MLX-4bit
- Moshi
How to use aufklarer/PersonaPlex-7B-MLX-4bit with Moshi:
# pip install moshi # Run the interactive web server python -m moshi.server --hf-repo "aufklarer/PersonaPlex-7B-MLX-4bit" # Then open https://localhost:8998 in your browser
# pip install moshi import torch from moshi.models import loaders # Load checkpoint info from HuggingFace checkpoint = loaders.CheckpointInfo.from_hf_repo("aufklarer/PersonaPlex-7B-MLX-4bit") # Load the Mimi audio codec mimi = checkpoint.get_mimi(device="cuda") mimi.set_num_codebooks(8) # Encode audio (24kHz, mono) wav = torch.randn(1, 1, 24000 * 10) # [batch, channels, samples] with torch.no_grad(): codes = mimi.encode(wav.cuda()) decoded = mimi.decode(codes) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Xet hash:
- f2e01ccd29dffdf583221a03bfcc43aa1f16ed54ecc0a9b076a49c9096a3e50f
- Size of remote file:
- 553 kB
- SHA256:
- 78d4336533ddc26f9acf7250d7fb83492152196c6ea4212c841df76933f18d2d
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