Text-to-Audio
MLX
Safetensors
yue2
audio
music
music-generation
8-bit precision
apple-silicon
custom_code
Instructions to use vanch007/mlx-Yue2-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use vanch007/mlx-Yue2-3B with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir mlx-Yue2-3B vanch007/mlx-Yue2-3B
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
mlx-Yue2-3B
This repository provides pre-converted native MLX weights for YuE2-3B, an advanced music foundation model by M-A-P (Multimodal Art Projection).
Complete inference source code, CLI, and documentation are hosted on GitHub: ๐ https://github.com/vanch007/mlx-Yue
Model Description
- Architecture: Native Apple Silicon implementation of YuE2 autoregressive planning (AR), flow-matching acoustic diffusion (NAR), and high-fidelity 48kHz FP32 Oobleck VAE decoding.
- Precision Variants Included:
ar-bf16.safetensors: Full-precision BF16 Autoregressive model (4.33 GB).ar-8bit.safetensors: 8-bit quantized Autoregressive linear layers (2.66 GB), reducing memory footprint by ~39% while accelerating generation to ~83 tokens/s on M3 Max.nar-bf16.safetensors: BF16 Non-Autoregressive acoustic flow-matching model (2.93 GB).qwen.tiktoken& config files.
Quick Start with mlx-Yue
1. Install mlx-Yue
Follow the setup instructions in the GitHub Repository:
git clone https://github.com/vanch007/mlx-Yue.git
cd mlx-Yue
uv sync --frozen --no-dev --extra transcription
source .venv/bin/activate
2. Download Weights
Download these model weights using huggingface_hub:
python -c '
from huggingface_hub import snapshot_download
snapshot_download(repo_id="vanch007/mlx-Yue2-3B", local_dir="models/converted")
'
For VAE:
python -c '
from huggingface_hub import snapshot_download
snapshot_download(repo_id="m-a-p/YuE2-Vae", local_dir="models/vae")
'
export LYRA_VAE="$(pwd)/models/vae"
3. Generate Music
Generate a full song using the 8-bit model:
mlx-yue generate examples/full-song.json \
--model models/converted \
--vae "$LYRA_VAE" \
--precision 8bit \
--output outputs/my_song
Or via Python:
from lyra import YuE2Pipeline
pipe = YuE2Pipeline.from_pretrained(
"models/converted",
vae="models/vae",
precision="8bit",
local_files_only=True
)
result = pipe(
style="80s synthpop, upbeat energetic drums, female vocal",
lyrics="""[verse]
City lights neon glow
Running fast losing control
[chorus]
Dance with the stars through the electric night
We are the dreamers chasing the light
"""
)
result.save_artifacts("outputs/my_song")
Benchmarks (Apple M3 Max)
| Precision | AR File Size | AR Generation Speed | 32-Step Full NAR (170s audio) |
|---|---|---|---|
| BF16 | 4.33 GB | ~59.4 tokens/s | 186.94 s |
| 8-Bit Quantized | 2.66 GB | ~83.1 tokens/s | 186.94 s |
Citation & Acknowledgments
- Source Model: YuE2-3B by Multimodal Art Projection
- MLX Framework: Apple MLX
- mlx-Yue GitHub: https://github.com/vanch007/mlx-Yue
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Hardware compatibility
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8-bit
Model tree for vanch007/mlx-Yue2-3B
Base model
m-a-p/YuE2-3B