Instructions to use Frosty40/Muse-Glimmer-30B-ArcB70-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Frosty40/Muse-Glimmer-30B-ArcB70-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 Frosty40/Muse-Glimmer-30B-ArcB70-GGUF
# Run inference directly in the terminal:
llama cli -hf Frosty40/Muse-Glimmer-30B-ArcB70-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Frosty40/Muse-Glimmer-30B-ArcB70-GGUF
# Run inference directly in the terminal:
llama cli -hf Frosty40/Muse-Glimmer-30B-ArcB70-GGUF

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 Frosty40/Muse-Glimmer-30B-ArcB70-GGUF
# Run inference directly in the terminal:
./llama-cli -hf Frosty40/Muse-Glimmer-30B-ArcB70-GGUF

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 Frosty40/Muse-Glimmer-30B-ArcB70-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Frosty40/Muse-Glimmer-30B-ArcB70-GGUF

Use Docker

docker model run hf.co/Frosty40/Muse-Glimmer-30B-ArcB70-GGUF

LM Studio
Jan

vLLM

How to use Frosty40/Muse-Glimmer-30B-ArcB70-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Frosty40/Muse-Glimmer-30B-ArcB70-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": "Frosty40/Muse-Glimmer-30B-ArcB70-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Frosty40/Muse-Glimmer-30B-ArcB70-GGUF

Ollama
How to use Frosty40/Muse-Glimmer-30B-ArcB70-GGUF with Ollama:
```
ollama run hf.co/Frosty40/Muse-Glimmer-30B-ArcB70-GGUF
```
Unsloth Desktop

How to use Frosty40/Muse-Glimmer-30B-ArcB70-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Frosty40/Muse-Glimmer-30B-ArcB70-GGUF

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": "Frosty40/Muse-Glimmer-30B-ArcB70-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Frosty40/Muse-Glimmer-30B-ArcB70-GGUF with Docker Model Runner:
```
docker model run hf.co/Frosty40/Muse-Glimmer-30B-ArcB70-GGUF
```

Lemonade

How to use Frosty40/Muse-Glimmer-30B-ArcB70-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Frosty40/Muse-Glimmer-30B-ArcB70-GGUF

Run and chat with the model

lemonade run user.Muse-Glimmer-30B-ArcB70-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use Frosty40/Muse-Glimmer-30B-ArcB70-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 Frosty40/Muse-Glimmer-30B-ArcB70-GGUF

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 Frosty40/Muse-Glimmer-30B-ArcB70-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Frosty40/Muse-Glimmer-30B-ArcB70-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Frosty40/Muse-Glimmer-30B-ArcB70-GGUF

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 "Frosty40/Muse-Glimmer-30B-ArcB70-GGUF" \
  --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"

Muse B70

muse

Muse Glimmer 30B (Q4_K_M, 15.6 GB) on Intel Arc Pro B70. One seat, full 131k context. Dense 28B

Kernels and binaries: newjordan/museB70


decode @ 129k cached	19.0 t/s
full-ctx prime	503 t/s
short decode / prefill	28.6 / ~1277 t/s

Install

git clone https://github.com/newjordan/museB70.git
cd museB70

hf download Frosty40/Muse-Glimmer-30B-ArcB70-GGUF muse-glimmer-30B-kquant-17gb.gguf

gh release download v2026.08.12-b70 --repo newjordan/museB70 \
  --pattern 'muse-serve-3ce44d373-linux-b70.tar.zst'
sha256sum -c releases/ASSET_SHA256SUMS
tar --zstd -C releases -xf muse-serve-3ce44d373-linux-b70.tar.zst
rm -f muse-serve-3ce44d373-linux-b70.tar.zst

Serve

MODEL=muse-glimmer-30B-kquant-17gb.gguf \
LLAMA_BIN=./releases/muse-serve-3ce44d373/bin/llama-server \
  ./serve-muse-arc.sh

curl -s localhost:8095/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"model":"muse-glimmer-30b-q4","messages":[{"role":"user","content":"hi"}],"max_tokens":128}'

Muse thinks in reasoning_content. Echo both that and content on later turns, and give it enough max_tokens.

This is a serving configuration for the Intel Arc Pro B70 — not a new model.

Model and official kquant: meta-models/Muse-Glimmer-30B (GGUF), Apache-2.0, © Meta. Runtime: llama.cpp SYCL + Intel oneAPI. Serving config and B70 kernels: newjordan/museB70.

Downloads last month: 66

GGUF

Model size

28B params

Architecture

muse-glimmer

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for Frosty40/Muse-Glimmer-30B-ArcB70-GGUF

Base model

meta-models/Muse-Glimmer-30B

Quantized

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