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README.md

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 ---
-library_name: peft
 base_model: ACE-Step/Ace-Step1.5
-license: mit
-pipeline_tag: text-to-audio
-tags:
-- audio
-- music
-- text2music
+library_name: peft
 ---
 
-<h1 align="center">ACE-Step 1.5</h1>
-<h1 align="center">Pushing the Boundaries of Open-Source Music Generation</h1>
-<p align="center">
-    <a href="https://ace-step.github.io/ace-step-v1.5.github.io/">Project</a> |
-    <a href="https://huggingface.co/collections/ACE-Step/ace-step-15">Hugging Face</a> |
-    <a href="https://modelscope.cn/models/ACE-Step/ACE-Step-v1-5">ModelScope</a> |
-    <a href="https://huggingface.co/spaces/ACE-Step/Ace-Step-v1.5">Space Demo</a> |
-    <a href="https://discord.gg/PeWDxrkdj7">Discord</a>
-</p>
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
 
 
-![image](https://cdn-uploads.huggingface.co/production/uploads/62dfaf90c42558bcbd0a4f6f/b84r7t0viIw7rKSr_ja9_.png)
 
 ## Model Details
 
-🚀 **ACE-Step v1.5** is a highly efficient open-source music foundation model designed to bring commercial-grade music generation to consumer hardware. 
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
 
-### Key Features
+### Training Procedure
 
-*   **💰 Commercial-Ready:** Unlike many models trained on ambiguous datasets, ACE-Step v1.5 is designed for creators. You can strictly use the generated music for **commercial purposes**.
-*   **📚 Safe & Robust Training Data:** The model is trained on a massive, legally compliant dataset consisting of:
-    *   **Licensed Data:** Professionally licensed music tracks.
-    *   **Royalty-Free / No-Copyright Data:** A vast collection of public domain and royalty-free music.
-    *   **Synthetic Data:** High-quality audio generated via advanced MIDI-to-Audio conversion.
-*   **⚡ Extreme Speed:** Generates a full song in under 2 seconds on an A100 and under 10 seconds on an RTX 3090.
-*   **🖥️ Consumer Hardware Friendly:** Runs locally with less than 4GB of VRAM.
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-### Technical Capabilities
+#### Preprocessing [optional]
 
-🌉 At its core lies a novel hybrid architecture where the Language Model (LM) functions as an omni-capable planner: it transforms simple user queries into comprehensive song blueprints—scaling from short loops to 10-minute compositions—while synthesizing metadata, lyrics, and captions via Chain-of-Thought to guide the Diffusion Transformer (DiT). ⚡ Uniquely, this alignment is achieved through intrinsic reinforcement learning relying solely on the model's internal mechanisms, thereby eliminating the biases inherent in external reward models or human preferences. 🎚️
+[More Information Needed]
 
-🔮 Beyond standard synthesis, ACE-Step v1.5 unifies precise stylistic control with versatile editing capabilities—such as cover generation, repainting, and vocal-to-BGM conversion—while maintaining strict adherence to prompts across 50+ languages. This paves the way for powerful tools that seamlessly integrate into the creative workflows of music artists, producers, and content creators. 🎸
 
-- **Developed by:** [ACE-STEP]
-- **Model type:** [Text2Music]
-- **Language(s):** [50+ languages]
-- **License:** [MIT]
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
 
 ## Evaluation
 
-![image](https://cdn-uploads.huggingface.co/production/uploads/62dfaf90c42558bcbd0a4f6f/n9aKi_NhSmlMOgmGzahZi.png)
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
 
-## 🏗️ Architecture
+#### Software
 
+[More Information Needed]
 
-![image](https://cdn-uploads.huggingface.co/production/uploads/62dfaf90c42558bcbd0a4f6f/V_d1rTdqkQyoSM8td7OWl.png)
+## Citation [optional]
 
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
-## 🦁 Model Zoo
+**BibTeX:**
 
+[More Information Needed]
 
-![image](https://cdn-uploads.huggingface.co/production/uploads/62dfaf90c42558bcbd0a4f6f/B49V0OTKse_FRefTmTPsQ.png)
+**APA:**
 
-### DiT Models
+[More Information Needed]
 
-| DiT Model | Pre-Training | SFT | RL | CFG | Step | Refer audio | Text2Music | Cover | Repaint | Extract | Lego | Complete | Quality | Diversity | Fine-Tunability | Hugging Face |
-|-----------|:------------:|:---:|:--:|:---:|:----:|:-----------:|:----------:|:-----:|:-------:|:-------:|:----:|:--------:|:-------:|:---------:|:---------------:|--------------|
-| `acestep-v15-base` | ✅ | ❌ | ❌ | ✅ | 50 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | Medium | High | Easy | [Link](https://huggingface.co/ACE-Step/acestep-v15-base) |
-| `acestep-v15-sft` | ✅ | ✅ | ❌ | ✅ | 50 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | High | Medium | Easy | [Link](https://huggingface.co/ACE-Step/acestep-v15-sft) |
-| `acestep-v15-turbo` | ✅ | ✅ | ❌ | ❌ | 8 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | Very High | Medium | Medium | [Link](https://huggingface.co/ACE-Step/Ace-Step1.5) |
-| `acestep-v15-turbo-rl` | ✅ | ✅ | ✅ | ❌ | 8 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | Very High | Medium | Medium | To be released |
+## Glossary [optional]
 
-### LM Models
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
 
-| LM Model | Pretrain from | Pre-Training | SFT | RL | CoT metas | Query rewrite | Audio Understanding | Composition Capability | Copy Melody | Hugging Face |
-|----------|---------------|:------------:|:---:|:--:|:---------:|:-------------:|:-------------------:|:----------------------:|:-----------:|--------------|
-| `acestep-5Hz-lm-0.6B` | Qwen3-0.6B | ✅ | ✅ | ✅ | ✅ | ✅ | Medium | Medium | Weak | ✅ |
-| `acestep-5Hz-lm-1.7B` | Qwen3-1.7B | ✅ | ✅ | ✅ | ✅ | ✅ | Medium | Medium | Medium | ✅ |
-| `acestep-5Hz-lm-4B` | Qwen3-4B | ✅ | ✅ | ✅ | ✅ | ✅ | Strong | Strong | Strong | ✅ |
+[More Information Needed]
 
+## More Information [optional]
 
-## 🙏 Acknowledgements
+[More Information Needed]
 
-This project is co-led by ACE Studio and StepFun.
+## Model Card Authors [optional]
 
+[More Information Needed]
 
-## 📖 Citation
+## Model Card Contact
 
-If you find this project useful for your research, please consider citing:
+[More Information Needed]
+### Framework versions
 
-```BibTeX
-@misc{gong2026acestep,
-	title={ACE-Step 1.5: Pushing the Boundaries of Open-Source Music Generation},
-	author={Junmin Gong, Yulin Song, Wenxiao Zhao, Sen Wang, Shengyuan Xu, Jing Guo}, 
-	howpublished={\url{https://github.com/ace-step/ACE-Step-1.5}},
-	year={2026},
-	note={GitHub repository}
-}
+- PEFT 0.15.2

adapter_model.safetensors

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