Instructions to use koboldcpp/music with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use koboldcpp/music with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="koboldcpp/music",
	filename="Ace-Qwen3-Embedding-0.6B-BF16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use koboldcpp/music with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf koboldcpp/music:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf koboldcpp/music:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf koboldcpp/music:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf koboldcpp/music: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 koboldcpp/music:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf koboldcpp/music: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 koboldcpp/music:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf koboldcpp/music:Q4_K_M

Use Docker

docker model run hf.co/koboldcpp/music:Q4_K_M

LM Studio
Jan
Ollama
How to use koboldcpp/music with Ollama:
```
ollama run hf.co/koboldcpp/music:Q4_K_M
```

Unsloth Studio

How to use koboldcpp/music 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 koboldcpp/music 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 koboldcpp/music to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for koboldcpp/music to start chatting

Atomic Chat new
Docker Model Runner
How to use koboldcpp/music with Docker Model Runner:
```
docker model run hf.co/koboldcpp/music:Q4_K_M
```

Lemonade

How to use koboldcpp/music with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull koboldcpp/music:Q4_K_M

Run and chat with the model

lemonade run user.music-Q4_K_M

List all available models

lemonade list

Henk717 commited on Feb 26

Commit

f4e90c2

verified ·

1 Parent(s): bab7a3b

Reconverted in SFT mode & Add 4B LLM

Browse files

Basically this merge model can be converted using either the turbo config or the sft config.
I found the sft version to have better results especially at higher step counts, this lines up with my experiences in the gradio.
It will still work with few steps in this mode.

Files changed (4) hide show

.gitattributes +2 -0
acestep-5Hz-lm-4B-BF16.gguf +3 -0
acestep-5Hz-lm-4B-Q8_0.gguf +3 -0
acestep-v15-sftturbo50-BF16.gguf +2 -2

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 Ace-Qwen3-Embedding-0.6B-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
 Ace-Qwen3-Embedding-0.6B-BF16.gguf filter=lfs diff=lfs merge=lfs -text
 acestep-5Hz-lm-0.6B-BF16.gguf filter=lfs diff=lfs merge=lfs -text

 Ace-Qwen3-Embedding-0.6B-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
 Ace-Qwen3-Embedding-0.6B-BF16.gguf filter=lfs diff=lfs merge=lfs -text
 acestep-5Hz-lm-0.6B-BF16.gguf filter=lfs diff=lfs merge=lfs -text
+acestep-5Hz-lm-4B-BF16.gguf filter=lfs diff=lfs merge=lfs -text
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