Name: acestep 5Hz lm 0.6B
Author: ACE-Step

Overview

The acestep 5Hz lm 0.6B is a lightweight, text-to-audio model designed for low-latency synthesis. At 0.6B parameters, it is optimized for edge deployment and real-time applications where computational overhead must be kept to a minimum. Unlike larger, resource-heavy audio models, this model prioritizes efficiency, making it an ideal candidate for integrating voice interfaces into mobile apps or IoT devices without requiring massive GPU clusters. Developers can leverage its MIT license for flexible commercial integration, focusing on use cases like automated notifications, basic virtual assistants, or lightweight accessibility tools. It offers a streamlined alternative for those needing functional audio generation without the latency penalties of LLM-scale synthesis.

Highlights

Compact 0.6B parameter size for efficient edge deployment
Optimized for low-latency, real-time text-to-audio synthesis
Permissive MIT license for seamless commercial integration
Minimal hardware requirements compared to larger audio models

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("ACE-Step/acestep-5Hz-lm-0.6B")
tokenizer = AutoTokenizer.from_pretrained("ACE-Step/acestep-5Hz-lm-0.6B")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download ACE-Step/acestep-5Hz-lm-0.6B

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download ACE-Step/acestep-5Hz-lm-0.6B config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('ACE-Step/acestep-5Hz-lm-0.6B')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/ACE-Step/acestep-5Hz-lm-0.6B

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/ACE-Step/acestep-5Hz-lm-0.6B

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('ACE-Step/acestep-5Hz-lm-0.6B')
tokenizer = AutoTokenizer.from_pretrained('ACE-Step/acestep-5Hz-lm-0.6B')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance

pip install modelscope

CLI Download

Download the full repository

modelscope download --model ACE-Step/acestep-5Hz-lm-0.6B

Download a single file to a local folder (e.g. README.md into ./dir)

modelscope download --model ACE-Step/acestep-5Hz-lm-0.6B README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('ACE-Step/acestep-5Hz-lm-0.6B')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://www.modelscope.cn/ACE-Step/acestep-5Hz-lm-0.6B.git

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/ACE-Step/acestep-5Hz-lm-0.6B.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'ACE-Step/acestep-5Hz-lm-0.6B')

Full Documentation

来源: HuggingFace

---
library_name: transformers
license: mit
pipeline_tag: text-to-audio
tags:

audio

music

text2music

---

<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>
<a href="https://arxiv.org/abs/2602.00744">Tech Report</a>
</p>

!image

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.

Key Features

💰 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.

Technical Capabilities

🌉 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. 🎚️

🔮 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]

Evaluation

!image

🏗️ Architecture

!image

🦁 Model Zoo

!image

DiT Models

| 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 |
| acestep-v15-sft | ✅ | ✅ | ❌ | ✅ | 50 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | High | Medium | Easy | Link |
| acestep-v15-turbo | ✅ | ✅ | ❌ | ❌ | 8 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | Very High | Medium | Medium | Link |
| acestep-v15-turbo-rl | ✅ | ✅ | ✅ | ❌ | 8 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | Very High | Medium | Medium | To be released |

LM Models

| 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 | ✅ |

🙏 Acknowledgements

This project is co-led by ACE Studio and StepFun.

📖 Citation

If you find this project useful for your research, please consider citing:

```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}
}