Instructions to use nvidia/personaplex-7b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Moshi
How to use nvidia/personaplex-7b-v1 with Moshi:
# pip install moshi # Run the interactive web server python -m moshi.server --hf-repo "nvidia/personaplex-7b-v1" # 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("nvidia/personaplex-7b-v1") # 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
Goes off the rails
#22
by Hypersniper - opened
Not a bad release! Needs some more training for sure. After a minute or so of conversation it starts to degrade in quality. Maybe more turn based conversations are needed for the dataset?
Could u share your inference script
Right. Its focus was to showcase naturalness, and basic instruction following, voice prompting. And it has a 2048 token window and only on minimal SFT data. Future versions will have proper post-training/alignment.
royrajarshi changed discussion status to closed