MOSS Music 8B Thinking 4bit
A 4-bit MLX quantization of OpenMOSS-Team/MOSS-Music-8B-Thinking for music understanding on Apple Silicon. The smallest build (6 GB), a good fit for 16 GB Macs.
Source du modèle
- Type
- Text model
- Taille du fichier
- 5,60 GB
- Modèle de base
- OpenMOSS-Team/MOSS-Music-8B-Thinking
- Version
- vmodel
- Mis à jour
- 19 oct. 2026
- Téléchargements
- 360
- Source
- Hugging Face
Description de la source
MOSS-Music-8B-Thinking · MLX 4-bit
Base model MLX License Backend
A 4-bit MLX quantization of OpenMOSS-Team/MOSS-Music-8B-Thinking for music understanding on Apple Silicon. The smallest build (~6 GB), a good fit for 16 GB Macs.
Community conversion, not an official release. All model credit goes to the OpenMOSS Team.
Usage
MOSS-Music is a custom multimodal (audio + text) model, so it does not load with
mlx_lm / mlx_vlm directly. Use the moss_music_mlx backend
(code, PR):
from huggingface_hub import snapshot_download
from moss_music_mlx import load_pretrained, generate
from src.processing_moss_music import MossMusicProcessor
path = snapshot_download("mlx-community/MOSS-Music-8B-Thinking-4bit")
model = load_pretrained(path)
proc = MossMusicProcessor.from_pretrained(path, trust_remote_code=True, enable_time_marker=True)
print(generate(model, proc, "Analyze this track: genre, key, BPM, structure.", audio_path="song.mp3"))
Conversion
- 4-bit, group size 64. The audio encoder is kept at bf16 to preserve audio
fidelity; quantization is applied to the Qwen3 layers, token embeddings and
lm_head. - Converted with
mlx==0.31.2,mlx-lm==0.29.1.
Accuracy
Versus the fp32 PyTorch reference, the 4-bit model's prefill next-token argmax is identical and the logits match to cosine 0.99889 (8-bit is 0.99999, 6-bit 0.99989). 4-bit is the most aggressive recipe; for the highest fidelity prefer 6-bit or 8-bit.
License & credit
Apache-2.0, inherited from the base model. This repository provides only the MLX-quantized weights; all credit goes to the OpenMOSS Team.
Sources
1 source
Sources
1 sourceVérifié 19 oct.
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactExtraits de sources
3 extraits
Extraits de sources
3 extraits--- license: apache-2.0 base_model: OpenMOSS-Team/MOSS-Music-8B-Thinking language: - en - zh tags: - mlx - audio - music - music-understanding - audio-language-model - moss - moss-music pipeline_tag: audio-text-to-text --- # MOSS-Music-8B-Thinking · MLX 4-bit [](https://huggingface.co/OpenMOSS-Team/MOSS-Music-8B-Thinking) [](https://github.com/ml-explore/mlx) [](https://www.apache.org/licenses/LICENSE-2.0) [](https://github.com/OpenMOSS/MOSS-Music/pull/3) A **4-bit [MLX](https://github.com/ml-explore/mlx) quantization** of [OpenMOSS-Team/MOSS-Music-8B-Thinking](https://huggingface.co/OpenMOSS-Team/MOSS-Music-8B-Thinking) for music understanding on Apple Silicon. The smallest build (~6 GB), a good fit for 16 GB Macs. > Community conversion, not an official release. All model credit goes to the OpenMOSS Team. Other sizes: [8-bit](https://huggingface.co/mlx-community/MOSS-Music-8B-Thinking-8bit) · [6-bit](https://huggingface.co/mlx-community/MOSS-Music-8B-Thinking-6bit) ## Usage MOSS-Music is a custom multimodal (audio + text) model, so it does **not** load with `mlx_lm` / `mlx_vlm` directly. Use the `moss_music_mlx` backend ([code](https://github.com/dthinkr/MOSS-Music/tree/feat/mlx-backend/mlx), [PR](https://github.com/OpenMOSS/MOSS-Music/pull/3)): ```python from huggingface_hub import snapshot_download from moss_music_mlx import load_pretrained, generate from src.processing_moss_music import MossMusicProcessor path = snapshot_download("mlx-community/MOSS-Music-8B-Thinking-4bit") model = load_pretrained(path) proc = MossMusicProcessor.from_pretrained(path, trust_remote_code=True, enable_time_marker=True) print(generate(model, proc, "Analyze this track: genre, key, BPM, structure.", audio_path="song.mp3")) ``` ## Conversion - **4-bit, group size 64.** The audio encoder is kept at **bf16** to preserve audio fidelity; quantization is applied to the Qwen3 layers, token embeddings and `lm_head`. - Converted with `mlx==0.31.2`, `mlx-lm==0.29.1`. ## Accuracy Versus the fp32 PyTorch reference, the 4-bit model's prefill next-token ar...
Source context: 360 downloads · 0 likes · Pipeline audio-text-to-text · Library mlx · Repo mlx-community/MOSS-Music-8B-Thinking-4bit
Source context: 74 downloads · 0 likes · Pipeline audio-text-to-text · Library mlx · Repo mlx-community/MOSS-Music-8B-Thinking-4bit
