mlx-community/MiniMax-Music3-bf16

MiniMax Music 3 · MLX BF16

Native MLX BF16 weights for MiniMaxAI/MiniMax-Music3, converted for lyric-conditioned song generation on Apple Silicon with mlx-audio.

Community conversion, not an official MiniMax release. All model credit goes to MiniMax. Review the original model card and license before use.

Other MLX variants: 8-bit · 6-bit · 4-bit · MXFP8 (recommended quantized) · MXFP4 (experimental) · NVFP4 (experimental)

Install

MiniMax Music 3 support was merged upstream in Blaizzy/mlx-audio#888. Until a PyPI release includes it, install the upstream merge commit directly:

python -m pip install "mlx-audio @ git+https://github.com/Blaizzy/mlx-audio.git@784b29e2691a93ca7483147d86f61859dfaa6296"

Generate

python -m mlx_audio.music.generate \
  --model mlx-community/MiniMax-Music3-bf16 \
  --caption "Warm acoustic pop, 96 BPM, intimate female vocal" \
  --lyrics $'[verse]\nMorning light across the room\n[chorus]\nSing with me' \
  --duration 30 \
  --steps 30 \
  --seed 7 \
  --output song.wav

from mlx_audio.music import load

model = load("mlx-community/MiniMax-Music3-bf16")
result = next(
    model.generate(
        text="Warm acoustic pop, 96 BPM, intimate female vocal",
        lyrics="[verse]\nMorning light across the room\n[chorus]\nSing with me",
        duration=30,
        steps=30,
        seed=7,
    )
)
print(result.audio.shape, result.sample_rate)  # stereo, 44100 Hz

Lyrics are required by the checkpoint contract. Use [instrumental] explicitly for instrumental generation. Duration is a requested upper bound: the autoregressive stage may emit its end token early. Style, tempo, instrument, and vocal controls are probabilistic rather than strict.

Conversion and verification

• Dense BF16; no weight quantization.
• Complete native pipeline: Qwen3 global autoregressive model, RVQ depth decoder, condition encoder, flow-matching DiT/Euler stage, and stereo vocoder.
• The actual component configs produce 44.1 kHz stereo output.
• Official-checkpoint conversion maps and strict-loads all 982 expected tensors.
• Full-checkpoint float32 maximum absolute differences against the official PyTorch implementations: Qwen logits 1.45e-4, RVQ depth 2.50e-5, condition encoder 1.91e-6, flow transformer 7.63e-6, vocoder 8.57e-7.
• The mlx-audio regression suite passed 1,742 tests with 34 expected skips; the focused music, converter, and registry suite passed 43 tests and 3 subtests.
• Real generation produced finite 44.1 kHz stereo audio. A 210-second request exercised 38 denoising windows and ended at 152.8 seconds when the model emitted EOS.

Converted with mlx-audio 0.4.8 development commit c2fa486 and MLX 0.31.2.

License

The weights remain subject to the MiniMax-Music3 Community License, including its acceptable-use and commercial terms. The full license text is included in this repository.