Instructions to use Serveurperso/ACE-Step-1.5-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Serveurperso/ACE-Step-1.5-GGUF 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 Serveurperso/ACE-Step-1.5-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Serveurperso/ACE-Step-1.5-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Serveurperso/ACE-Step-1.5-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Serveurperso/ACE-Step-1.5-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf Serveurperso/ACE-Step-1.5-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf Serveurperso/ACE-Step-1.5-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Serveurperso/ACE-Step-1.5-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use Serveurperso/ACE-Step-1.5-GGUF with Ollama:
ollama run hf.co/Serveurperso/ACE-Step-1.5-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use Serveurperso/ACE-Step-1.5-GGUF with Docker Model Runner:
docker model run hf.co/Serveurperso/ACE-Step-1.5-GGUF:Q4_K_M
- Lemonade
How to use Serveurperso/ACE-Step-1.5-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Serveurperso/ACE-Step-1.5-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.ACE-Step-1.5-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
The 1.7B LM models are incorrectly converted
#1
by concedo - opened
They are missing a .model prefix across all their tensor names, causing a load failure
They are missing a
.modelprefix across all their tensor names, causing a load failure
Yes:) solved, it's a typo from upstream .safetensors they likely serialized the inner model instead of the wrapper.
diff --git a/convert.py b/convert.py
index 72b0a47..ba8816b 100755
--- a/convert.py
+++ b/convert.py
@@ -117,6 +117,11 @@ def add_tensors_from_sf(w, sf_path, tag):
for name in names:
info = meta[name]
+
+ # normalize: some upstream checkpoints omit the "model." prefix
+ if not name.startswith("model."):
+ name = "model." + name
+
dtype_str = info["dtype"]
shape = info["shape"]
off0, off1 = info["data_offsets"]