Instructions to use bartowski/Muse-Glimmer-30B-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 bartowski/Muse-Glimmer-30B-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 bartowski/Muse-Glimmer-30B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf bartowski/Muse-Glimmer-30B-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 bartowski/Muse-Glimmer-30B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf bartowski/Muse-Glimmer-30B-GGUF: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 bartowski/Muse-Glimmer-30B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf bartowski/Muse-Glimmer-30B-GGUF: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 bartowski/Muse-Glimmer-30B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bartowski/Muse-Glimmer-30B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/bartowski/Muse-Glimmer-30B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use bartowski/Muse-Glimmer-30B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bartowski/Muse-Glimmer-30B-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bartowski/Muse-Glimmer-30B-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/bartowski/Muse-Glimmer-30B-GGUF:Q4_K_M
- Ollama
How to use bartowski/Muse-Glimmer-30B-GGUF with Ollama:
ollama run hf.co/bartowski/Muse-Glimmer-30B-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use bartowski/Muse-Glimmer-30B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bartowski/Muse-Glimmer-30B-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "bartowski/Muse-Glimmer-30B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use bartowski/Muse-Glimmer-30B-GGUF with Docker Model Runner:
docker model run hf.co/bartowski/Muse-Glimmer-30B-GGUF:Q4_K_M
- Lemonade
How to use bartowski/Muse-Glimmer-30B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bartowski/Muse-Glimmer-30B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Muse-Glimmer-30B-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use bartowski/Muse-Glimmer-30B-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bartowski/Muse-Glimmer-30B-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default bartowski/Muse-Glimmer-30B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use bartowski/Muse-Glimmer-30B-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bartowski/Muse-Glimmer-30B-GGUF:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "bartowski/Muse-Glimmer-30B-GGUF:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
DFlash or DSpark?
Apologies for a silly question, but where does DSpark come from?
- https://huggingface.co/meta-models/Muse-Glimmer-30B-assistant says "DFlash" on the model card (at least at the moment of writing this,
2026-08-14 ~ 1AM AEST(*)) - https://huggingface.co/meta-models/Muse-Glimmer-30B-GGUF/tree/main has
dflashin what I assume are their draft models - https://github.com/ggml-org/llama.cpp/pull/26842 which is referred by this model card is also about dflash
yet, this model card "no mtp" section is about dspark and this discussion here says the same:
- https://huggingface.co/bartowski/Muse-Glimmer-30B-GGUF#mtp
- https://huggingface.co/bartowski/Muse-Glimmer-30B-GGUF/discussions/1
I mean, I understand that they are related, but technically it is --spec-type draft-dflash, not --spec-type draft-dspark, so I wonder if that might be the reason for low performance? I mean, it is not unusual to mean one thing but accidentally enter another.
To add, I just tried an Unsloth quantization with a dflash drafter and indeed got about 2x speed increase for a UD-Q4_K_XL quantization (~45 -> 92 t/s, a 9700 XTX AMD card (**), --spec-type draft-dflash --spec-draft-n-max 4)
On the other hand, recent master build seems to accept --spec-type draft-dspark --spec-draft-n-max 4 for the same draft model and does not fall; however it goes down to baseline inference speed.
(*) yes I shall spend my nights otherwise
(**) Vulkan build, somehow I observe about 20% speed increase (for inference) when I use Vulkan vs ROCm with almost arithmetic accuracy
Ah that's actually just me forgetting which is which π
Okay I'll run another test to see if it's worth adding π€ your results look promising for sure
okay well this is embarrassing and turns out your question was anything BUT silly..
it is in fact dFLASH, not dSPARK... and I see big speedups with the name proper :')
Thanks so much for commenting, appreciate it!