YuE2-3B Music Generation Model Released for Lyrics & Styles
Sample outputs are available on the model card.
At a Glance
| Item | Value |
|---|---|
| Repository | m-a-p/YuE2-3B |
| Published | 2026-09-10 |
| License | cc-by-nc-4.0 |
| Formats | safetensors |
| Paper | arXiv:2503.08638 |
| Source type | Primary source (the publisher itself) |
Values determined by this site’s code at collection time. Dates are JST.
Sample outputs are available on the model card.
Sample outputs are available on the model card.
Sample outputs are available on the model card.
Sample outputs are available on the model card.
Sample outputs are available on the model card.
Sample outputs are available on the model card.
Sample outputs are available on the model card.
Sample outputs are available on the model card.
Sample outputs are available on the model card.
Sample outputs are available on the model card.
Overview
m-a-p has released “YuE2-3B", an open music generation model that generates complete songs with vocals and accompaniment from lyrics and style prompts. It adopts a mechanism where an AR-NAR Mixture-of-Transformers backbone describes scores and semantic tokens, acoustic latent representations are generated via flow matching, and a VAE converts them into stereo audio.
Specifications
- Architecture: AR-NAR Mixture-of-Transformers (YuE2ForCausalLM)
- Output Specification: 48 kHz stereo audio
- Supported Languages: Chinese (zh), English (en)
- License: cc-by-nc-4.0 (non-commercial use only)
- Number of Parameters: 3.6B
Performance and Quality
Evaluation results on WildSongBench and SHS100K are published in the model card.
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| Model | Musicality ↑ | SongBench Avg ↑ | MuLan ↑ | AllMusicCaps ↑ | Q3O ↑ | PER ↓ |
|---|---|---|---|---|---|---|
| YuE 1 | 4.0847 | 4.9165 | 0.2623 | 0.2882 | 3.7301 | 36.38% |
| SongBloom | 3.4493 | 4.2350 | 0.2697 | 0.1926 | 3.0287 | 19.19% |
| LeVo 2 | 5.4590 | 6.3247 | 0.3542 | 0.2680 | 3.9458 | 26.12% |
| ACE-Step 1.5 | 5.1588 | 6.0118 | 0.4372 | 0.3869 | 4.5809 | 7.46% |
| HeartMuLa | 5.4963 | 6.2483 | 0.3823 | 0.2786 | 3.4907 | 10.71% |
| DiffRhythm 2 | 4.4775 | 5.2428 | 0.3782 | 0.3255 | 4.0870 | 18.41% |
| Muse | 5.1692 | 6.0349 | 0.3937 | 0.3466 | 4.4038 | 33.42% |
| MiniMax Music 3 | 5.3482 | 6.2830 | 0.3928 | 0.3609 | 4.4362 | 6.27% |
| YuE2 | 5.9075 | 6.7316 | 0.5068 | 0.4054 | 4.6819 | 8.44% |
| YuE2 (best-of-8) | 6.2666 | 6.9632 | 0.5051 | 0.3980 | 4.7009 | 9.79% |
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| Model | Musicality ↑ | SongBench Avg ↑ | MuLan ↑ | AllMusicCaps ↑ | Q3O ↑ | PER ↓ |
|---|---|---|---|---|---|---|
| Suno v5 | 5.9918 | 6.8721 | 0.5428 | 0.4353 | 4.5907 | 8.10% |
| Suno v4.5 | 5.8317 | 6.6995 | 0.5022 | 0.3873 | 4.4149 | 5.80% |
| Suno v5.5 | 5.8087 | 6.7150 | 0.5089 | 0.3917 | 4.5914 | 5.96% |
| MiniMax Music 2.6 | 5.4437 | 6.3222 | 0.4251 | 0.3670 | 4.5688 | 24.55% |
| Mureka 9 | 6.0488 | 6.9377 | 0.4394 | 0.4102 | 4.6368 | 11.69% |
| YuE2 | 5.9075 | 6.7316 | 0.5068 | 0.4054 | 4.6819 | 8.44% |
| YuE2 (best-of-8) | 6.2666 | 6.9632 | 0.5051 | 0.3980 | 4.7009 | 9.79% |
In WildSongBench evaluations, it is reported that YuE2 (best-of-8) achieved a SongBench average of 6.9632, the highest value among the compared open and proprietary models. On the other hand, it shows lower scores than some models in metrics such as PER (lower is better).
Strengths and Use Cases
- Text-to-song generation (complete music production via lyrics and style prompt input)
- Cover production of existing songs (reconstructing existing tracks into new styles)
- Editable scores (composition and editing by bringing melody and chords, melody only, or ABC scores)
- Agent-leveraged editing (reflecting musical feedback into revisions of scores, styles, and lyrics to render the next version)
Hardware Requirements
Estimated requirements (calculated by Local Model Watch) — 3.6B parameters
| Your VRAM | Quantization | File size | Est. memory needed |
|---|---|---|---|
| 12GB (RTX 4070 / 3060 12GB, etc.) | BF16 | 6.8GB | 8.1GB |
Inference engine support (architecture name matched against each project’s own model registry in its source code, checked 2026-09-18): llama.cpp: not registered, vLLM: not registered, MLX (mlx-lm): not registered. “Not registered" means the name is absent from that registry today, not that the model cannot run.
Memory estimates add a 20% runtime overhead (KV cache, etc.) to the actual size of the distributed files. Actual usage varies with context length, batch size and inference engine. These figures are computed by this site from file sizes, not published by the model’s authors. Compare with other models in our VRAM quick reference. What the quantization names mean: glossary.
How to Get It
- Distribution Format: safetensors
- Source and Related Repository:
m-a-p/YuE2-3Bon Hugging Face
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