Instructions to use DKTechin/acestep with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ACE-Step
How to use DKTechin/acestep with ACE-Step:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use DKTechin/acestep with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf DKTechin/acestep:Q4_K_M # Run inference directly in the terminal: llama cli -hf DKTechin/acestep:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf DKTechin/acestep:Q4_K_M # Run inference directly in the terminal: llama cli -hf DKTechin/acestep:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf DKTechin/acestep:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf DKTechin/acestep:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf DKTechin/acestep:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf DKTechin/acestep:Q4_K_M
Use Docker
docker model run hf.co/DKTechin/acestep:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use DKTechin/acestep with Ollama:
ollama run hf.co/DKTechin/acestep:Q4_K_M
- Unsloth Studio
How to use DKTechin/acestep with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for DKTechin/acestep to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for DKTechin/acestep to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for DKTechin/acestep to start chatting
- Docker Model Runner
How to use DKTechin/acestep with Docker Model Runner:
docker model run hf.co/DKTechin/acestep:Q4_K_M
- Lemonade
How to use DKTechin/acestep with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull DKTechin/acestep:Q4_K_M
Run and chat with the model
lemonade run user.acestep-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 2,483 Bytes
b7db683 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | ---
license: mit
base_model:
- ACE-Step/Ace-Step1.5
tags:
- gguf
- text-to-music
- acestep
---
# acestep — runtime + GGUF weights mirror
Files needed by the local music generation feature of the KakaoWork desktop app.
This repository is a **mirror kept for build reproducibility**: the app pins a
sha256 for every file, so it needs a location whose bytes never change.
## Contents
### Runtime (built by us)
`acestep.cpp` has no upstream release artifacts, so we build it ourselves.
| File | Platform |
| --- | --- |
| `acestep-darwin-arm64.zip` | macOS arm64 (ad-hoc signed) |
| `acestep-win-x64.zip` | Windows x64 (signed) |
Each archive is flat: `ace-lm`, `ace-synth` and the ggml shared libraries sit at
the top level. The executables resolve the libraries next to them, so keep them
in one directory.
### Weights (mirrored, byte-identical)
Re-uploaded unchanged from
[Serveurperso/ACE-Step-1.5-GGUF](https://huggingface.co/Serveurperso/ACE-Step-1.5-GGUF).
Only the four files our app loads are mirrored; that repository has many more
quantizations and is the place to look for them.
| File | Size | sha256 |
| --- | --- | --- |
| `acestep-v15-turbo-Q4_K_M.gguf` | 1.45 GB | `55b4d8514850f3d0f82536f37e99673aaf48df802b5ae5b153eea32a2e2daa5e` |
| `acestep-5Hz-lm-4B-Q5_K_M.gguf` | 3.03 GB | `938ed7067c8897f66acf4c3a86fc1fa8113d5cd1a5f13e6edec2e03207514e2d` |
| `Qwen3-Embedding-0.6B-Q8_0.gguf` | 784 MB | `972f23255e46adfe744a0eb9a0039f3c63988f65753b0968d776e8b27168c321` |
| `vae-BF16.gguf` | 337 MB | `0599862ac5d15cd308e1d2e368373aea6c02e25ebd1737ad4a4562a0901b0ef8` |
The sha256 values are the upstream ones — verify against the source repository
if you want to confirm nothing was altered in transit.
## Credits and license
- Original model weights: **ACE Studio** and **StepFun** —
[ACE-Step/Ace-Step1.5](https://huggingface.co/ACE-Step/Ace-Step1.5). All model
weights are theirs; we changed nothing.
- GGUF conversion and quantization: **Serveurperso** —
[Serveurperso/ACE-Step-1.5-GGUF](https://huggingface.co/Serveurperso/ACE-Step-1.5-GGUF)
(MIT), whose model card also credits Aryanne and jeankassio for specific
weight merges.
- The mirrored weights are redistributed under the MIT license of the source
repository. Please refer to the upstream repositories for the terms that apply
to the original model.
- The runtime archives are builds of
[acestep.cpp](https://github.com/ACE-Step/acestep.cpp); its own license applies.
|