Audio-to-Audio
Transformers
Moshi
full-duplex
speech-to-speech
spoken-dialogue
tokenizer
multilingual
personaplex
qwen3
Instructions to use RetentionLabs/PolyPlex-11B-CJKE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RetentionLabs/PolyPlex-11B-CJKE with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RetentionLabs/PolyPlex-11B-CJKE", device_map="auto") - Moshi
How to use RetentionLabs/PolyPlex-11B-CJKE with Moshi:
# pip install moshi # Run the interactive web server python -m moshi.server --hf-repo "RetentionLabs/PolyPlex-11B-CJKE" # 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("RetentionLabs/PolyPlex-11B-CJKE") # 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
Download tokenizer.json from RetentionLabs/PolyPlex-11B-CJKE: direct link, hf CLI and curl.
- Browser
- Download file 15.1 MB
-
https://huggingface.co/RetentionLabs/PolyPlex-11B-CJKE/resolve/main/tokenizer.json
- Command line
-
hf download hf://RetentionLabs/PolyPlex-11B-CJKE/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/RetentionLabs/PolyPlex-11B-CJKE/resolve/main/tokenizer.json
15.1 MB
- Xet hash:
- a77976586e175e2aaad715d2a96e8996f07291b75e49a0c74034b6a07ae4001e
- Size of remote file:
- 15.1 MB
- SHA256:
- 5e168aa1b6972da66d93259c4b53f45df6b615b8f2c031e69d49fd66053ae857
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