Model reference · open weights

YuE-s1-anneal-en-cot

LLMs m-a-p Text gen 1 build Open weights 6k dl/mo

YuE-s1-anneal-en-cot is an open-weight language model from m-a-p. YuE-s1-7B-anneal-en-cot (BF16) weighs 12.4 GB; the smallest configuration that runs it is RTX 4060 Ti 16 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • YuE-s1-anneal-en-cot is a 6.2B parameter open-source foundation model developed by m-a-p for text generation.
  • It is designed to transform lyrics into full songs, including vocal and accompaniment tracks, and supports a context length of 16,384 tokens.
  • The model operates in English and is released under the Apache 2.0 license.

Summary of the m-a-p/YuE-s1-7B-anneal-en-cot model card, 2026-10-01

What it is

Released bym-a-p
Released2025-01-26
Parameters6.2B
VRAM12.4 GB for the weights

What it runs on

Memory and cards for YuE-s1-7B-anneal-en-cot (BF16)

12.4 GBweights, file size
66 MBcache per 1K tokens
673 MBruntime overhead, at least
16,384 tokenscontext max
CardRequests at onceContext maxMemory
8K each
RTX 3060 12 GB11.6 GB
RTX 4060 Ti 16 GB4all 16K15.4 GB
RTX 3090 24 GB19all 16K23.4 GB
RTX 4090 24 GB19all 16K23.4 GB
RTX 5090 32 GB33all 16K31.0 GB
L40S 48 GB57all 16K44.0 GB
A100 80 GB121all 16K78.2 GB
H100 80 GB114all 16K78.1 GB
RTX PRO 6000 Blackwell 96 GB143all 16K93.8 GB
DGX Spark (GB10) 128 GB unified168all 16K107 GB
H200 141 GB225all 16K138 GB
B200 180 GB296all 16K176 GB
2× RTX 3060 12 GB
tensor parallel
17all 16K11.6 GB a card
2× RTX 4060 Ti 16 GB
tensor parallel
31all 16K15.4 GB a card
Memory needed at each load
Requests at once8K tokens each
113.7 GB
515.8 GB
817.4 GB
1621.7 GB
3230.3 GB
6447.5 GB

One card, with vLLM's small-card settings.

From the model card

What m-a-p says about YuE-s1-anneal-en-cot

Read the model card

Our model's name is YuE (乐). In Chinese, the word means "music" and "happiness." Some of you may find words that start with Yu hard to pronounce. If so, you can just call it "yeah." We wrote a song with our model's name.

YuE is a groundbreaking series of open-source foundation models designed for music generation, specifically for transforming lyrics into full songs (lyrics2song). It can generate a complete song, lasting several minutes, that includes both a catchy vocal track and accompaniment track. YuE is capable of modeling diverse genres/languages/vocal techniques. Please visit the Demo Page for amazing vocal performance.

Pop:Quiet Evening Metal: Step Back

News and Updates

  • 2025.03.12 🔥 Paper Released🎉: We now release YuE technical report!!! We discuss all the technical details, findings, and lessons learned. Enjoy, and feel free to cite us~

  • 2025.03.11 🫶 Now YuE supports incremental song generation!!! See YuE-UI by joeljuvel. YuE-UI is a Gradio-based interface supporting batch generation, output selection, and continuation. You can flexibly experiment with audio prompts and different model settings, visualize your progress on an interactive timeline, rewind actions, quickly preview audio outputs at stage 1 before committing to refinement, and fully save/load your sessions (JSON format). Optimized to run smoothly even on GPUs with just 8GB VRAM using quantized models.

  • 2025.02.17 🫶 Now YuE supports music continuation and Google Colab! See YuE-extend by Mozer.

  • 2025.02.07 🎉 Get YuE for Windows on pinokio.

  • 2025.01.30 🔥 Inference Update: We now support dual-track ICL mode! You can prompt the model with a reference song, and it will generate a new song in a similar style (voice cloning demo by @abrakjamson, music style transfer demo by @cocktailpeanut, etc.). Try it out! 🔥🔥🔥 P.S. Be sure to check out the demos first—they're truly impressive.

  • 2025.01.30 🔥 Announcement: A New Era Under Apache 2.0 🔥: We are thrilled to announce that, in response to overwhelming requests from our community, YuE is now officially licensed under the Apache 2.0 license. We sincerely hope this marks a watershed moment—akin to what Stable Diffusion and LLaMA have achieved in their respective fields—for music generation and creative AI. 🎉🎉🎉

  • 2025.01.29 🎉: We have updated the license description. we ENCOURAGE artists and content creators to sample and incorporate outputs generated by our model into their own works, and even monetize them. The only requirement is to credit our name: YuE by HKUST/M-A-P (alphabetic order).

  • 2025.01.28 🫶: Thanks to Fahd for creating a tutorial on how to quickly get started with YuE. Here is his demonstration.

  • 2025.01.26 🔥: We have released the YuE series.

Quickstart

Please check our github for easy quickstart.


License Agreement & Disclaimer

  • The YuE model (including its weights) is now released under the Apache License, Version 2.0. We do not make any profit from this model, and we hope it can be used for the betterment of human creativity.
  • Use & Attribution:
    • We encourage artists and content creators to freely incorporate outputs generated by YuE into their own works, including commercial projects.
    • We encourage attribution to the model’s name (“YuE by HKUST/M-A-P”), especially for public and commercial use.
  • Originality & Plagiarism: It is the sole responsibility of creators to ensure that their works, derived from or inspired by YuE outputs, do not plagiarize or unlawfully reproduce existing material. We strongly urge users to perform their own due diligence to avoid copyright infringement or other legal violations.
  • Recommended Labeling: When uploading works to streaming platforms or sharing them publicly, we recommend labeling them with terms such as: “AI-generated”, “YuE-generated", “AI-assisted” or “AI-auxiliated”. This helps maintain transparency about the creative process.
  • Disclaimer of Liability:
    • We do not assume any responsibility for the misuse of this model, including (but not limited to) illegal, malicious, or unethical activities.
    • Users are solely responsible for any content generated using the YuE model and for any consequences arising from its use.
    • By using this model, you agree that you understand and comply with all applicable laws and regulations regarding your generated content.

Acknowledgements

The project is co-lead by HKUST and M-A-P (alphabetic order). Also thanks moonshot.ai, bytedance, 01.ai, and geely for supporting the project. A friendly link to HKUST Audio group's huggingface space.

We deeply appreciate all the support we received along the way. Long live open-source AI!

Citation

If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil: :)

@misc{yuan2025yuescalingopenfoundation,
      title={YuE: Scaling Open Foundation Models for Long-Form Music Generation},
      author={Ruibin Yuan and Hanfeng Lin and Shuyue Guo and Ge Zhang and Jiahao Pan and Yongyi Zang and Haohe Liu and Yiming Liang and Wenye Ma and Xingjian Du and Xinrun Du and Zhen Ye and Tianyu Zheng and Yinghao Ma and Minghao Liu and Zeyue Tian and Ziya Zhou and Liumeng Xue and Xingwei Qu and Yizhi Li and Shangda Wu and Tianhao Shen and Ziyang Ma and Jun Zhan and Chunhui Wang and Yatian Wang and Xiaowei Chi and Xinyue Zhang and Zhenzhu Yang and Xiangzhou Wang and Shansong Liu and Lingrui Mei and Peng Li and Junjie Wang and Jianwei Yu

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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