Instructions to use burak-ozenc/moshi-burak-fine-tune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Moshi
How to use burak-ozenc/moshi-burak-fine-tune with Moshi:
# pip install moshi # Run the interactive web server python -m moshi.server --hf-repo "burak-ozenc/moshi-burak-fine-tune" # 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("burak-ozenc/moshi-burak-fine-tune") # 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
Moshi Fine-tuned LoRA - Burak's Voice AI
This is a LoRA adapter fine-tuned on Moshi to answer questions about Burak's background and experience.
Model Details
- Base Model: kyutai/moshiko-pytorch-bf16
- Training Method: LoRA (Low-Rank Adaptation)
- LoRA Rank: 16
- Training Steps: 500
- Dataset: 223 Q&A conversations (~47 minutes)
Usage
# Install Moshi
pip install git+https://github.com/kyutai-labs/moshi.git#subdirectory=moshi
# Run with LoRA
python -m moshi.server \
--lora-weight=lora.safetensors \
--config-path=config.json
Then open http://localhost:8998 in your browser.
Training Details
- Framework: moshi-finetune
- GPUs: 2x Kaggle T4
- Training Time: ~15 hours
- Batch Size: 1 per GPU (effective: 2)
License
Apache 2.0 (same as base Moshi model)
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Base model
kyutai/moshiko-pytorch-bf16