Instructions to use VincentGOURBIN/sheetsage2-mlx-q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use VincentGOURBIN/sheetsage2-mlx-q4 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download VincentGOURBIN/sheetsage2-mlx-q4 --local-dir sheetsage2-mlx-q4
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
SheetSage2 for MLX Swift โ 4-bit prequantized pack
Music recording โ editable ABC score, on Apple silicon (Mac, iPhone), with
yue2-mlx-swift (SheetSage2Core, yue2 transcribe),
for the 4bit-fast / 4bit-lean transcription profiles.
Source and changes
A derivative of SheetSage2 (m-a-p/SheetSage2, revision
398b22834dac) and of its encoder parent MERT2 (m-a-p/MERT-v2-FullSong,
revision d8ba1c745e73), both by m-a-p, CC BY-NC 4.0. Changes made here:
- the LoRA adapters merged into MERT2's attention projections (upstream
merge_lora, float32); - the Conformer encoder's linear layers quantized to 4 bits (MLX affine, group size 64); the decoder in float16; the mel front-end buffers, the ConvNeXt front and the layer-mix logits in float32;
- stored in MLX layout (
weights_format: mlx-quantized), SHA-256 inmodel.safetensors.sha256. No retraining. The weights are not endorsed by m-a-p. Quantization changes some scores against the float16 model (rubato orchestral material especially); see the repository'sdocs/References.md.
Use
yue2 download --model sheetsage2-q4
yue2 transcribe --audio song.m4a --profile 4bit-lean --out run/score
The pack is bit-identical to what the 4bit-* profiles build by quantizing the float16 model at
load, without the cost: it loads directly at its own size.
License
CC BY-NC 4.0 (see LICENSE, copied from the upstream release): non-commercial use only, with
attribution to MERT2 and SheetSage2 (m-a-p) and their repositories above.
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Quantized
Model tree for VincentGOURBIN/sheetsage2-mlx-q4
Base model
m-a-p/MERT-v2-FullSong