Instructions to use ayousanz/moshi-phase1b-rerun-2026-07-06 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Moshi
How to use ayousanz/moshi-phase1b-rerun-2026-07-06 with Moshi:
# pip install moshi # Run the interactive web server python -m moshi.server --hf-repo "ayousanz/moshi-phase1b-rerun-2026-07-06" # 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("ayousanz/moshi-phase1b-rerun-2026-07-06") # 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
Phase 1b re-run: J-Moshi-ext + Path B synthetic dialog FT
Fine-tuned from nu-dialogue/j-moshi-ext with 100 synthetic dialogs
(Irodori-TTS Full FT amitaro voice, generated 2026-07-04) for
voice-transfer validation in the Path B research programme.
Training config
- Base model: nu-dialogue/j-moshi-ext (with
--extend_modules_for_user_streamโ dep_q=16 during train) - Data: 100 synthetic dialogs (612 turns, 31.7 min stereo 24kHz)
- Params: all 7.5B trainable (
PARAMS_TO_FT=all) - Steps: 65 (5 epoch ร 13 iter, effective batch 8)
- LR: 3e-5 (tempformer + depformer)
- DeepSpeed: ZeRO-3 + bf16 + CPU offload optimizer
- GPU: A100 SXM4 40GB ร 2 (Slovenia vast.ai, $1.334/hr)
- Wall clock: ~1 h train + ~7 min consolidate
- Final loss: 1.27 (text 0.48 + audio 0.79)
- Cleaned: dep_q 16 โ 8 via
tools.clean_moshi --remove_modules_for_user_stream
Files
model.safetensors(15 GB, bf16, dep_q=8) โ inference-readymoshi_lm_kwargs.jsonโ moshi LM config
- Downloads last month
- -
Inference Providers NEW
This model isn't deployed by any Inference Provider. ๐ Ask for provider support