Instructions to use Abiray/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

How to use Abiray/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 Abiray/Muse-Glimmer-30B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Abiray/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 Abiray/Muse-Glimmer-30B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Abiray/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 Abiray/Muse-Glimmer-30B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Abiray/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 Abiray/Muse-Glimmer-30B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Abiray/Muse-Glimmer-30B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Abiray/Muse-Glimmer-30B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Abiray/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 "Abiray/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": "Abiray/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/Abiray/Muse-Glimmer-30B-GGUF:Q4_K_M

Ollama
How to use Abiray/Muse-Glimmer-30B-GGUF with Ollama:
```
ollama run hf.co/Abiray/Muse-Glimmer-30B-GGUF:Q4_K_M
```

Unsloth Studio

How to use Abiray/Muse-Glimmer-30B-GGUF 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 Abiray/Muse-Glimmer-30B-GGUF 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 Abiray/Muse-Glimmer-30B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Abiray/Muse-Glimmer-30B-GGUF to start chatting

How to use Abiray/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 Abiray/Muse-Glimmer-30B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Abiray/Muse-Glimmer-30B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use Abiray/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 Abiray/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 "Abiray/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"

Docker Model Runner
How to use Abiray/Muse-Glimmer-30B-GGUF with Docker Model Runner:
```
docker model run hf.co/Abiray/Muse-Glimmer-30B-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Abiray/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 Abiray/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 Abiray/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 Abiray/Muse-Glimmer-30B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

Muse-Glimmer-30B - GGUF Quants

This repository contains GGUF quants for meta-models/Muse-Glimmer-30B, created using llama.cpp. Muse Glimmer is a 30-billion-parameter multimodal model distilled from Muse Spark, purpose-built for autonomous agentic workflows, long-horizon multi-step reasoning, SWE-bench coding tasks, and reliable function calling on consumer hardware.

File Availability & Recommended Hardware

To run vision inputs, download one core model file (.gguf) along with one multimodal vision projector (mmproj-*.gguf).

Core Model Files

File Name	Size	Quantization	Rec. Memory / VRAM	Description
`Muse-Glimmer-30B-Q8_0.gguf`	29.6 GB	Q8_0	32 GB - 48 GB	Maximum precision. Virtually lossless retention compared to BF16.
`Muse-Glimmer-30B-Q6_K.gguf`	22.9 GB	Q6_K	28 GB - 32 GB	Near-lossless output precision. Great for 32GB system/VRAM setup.
`Muse-Glimmer-30B-Q5_K_M.gguf`	19.8 GB	Q5_K_M	24 GB	High Quality balance. Ideal fit for GPUs with 24GB VRAM (e.g., RTX 3090/4090/5090).
`Muse-Glimmer-30B-Q4_K_M.gguf`	16.9 GB	Q4_K_M	20 GB - 24 GB	Recommended Sweet Spot. Optimal trade-off between speed, memory, and reasoning capacity.
`Muse-Glimmer-30B-IQ4_NL.gguf`	16.1 GB	IQ4_NL	20 GB	Non-Linear 4-bit importance matrix quantization. Strong performance under 17GB.
`Muse-Glimmer-30B-IQ4_XS.gguf`	15.3 GB	IQ4_XS	18 GB - 20 GB	Extra-small 4-bit iQuant for constrained VRAM environments.
`Muse-Glimmer-30B-Q3_K_M.gguf`	14.0 GB	Q3_K_M	16 GB - 18 GB	Standard 3-bit K-quant. Good option for 16GB VRAM cards.
`Muse-Glimmer-30B-IQ3_M.gguf`	13.1 GB	IQ3_M	16 GB	3-bit medium importance quant with better reasoning recovery than baseline Q3.
`Muse-Glimmer-30B-IQ3_XS.gguf`	12.3 GB	IQ3_XS	14 GB - 16 GB	3-bit extra-small iQuant for lower memory targets.
`Muse-Glimmer-30B-IQ3_XXS.gguf`	11.5 GB	IQ3_XXS	12 GB - 16 GB	Highly compressed 3-bit iQuant. Fits tight memory budgets.

Vision Projector Files (`mmproj`)

File Name	Size	Precision	Usage
`mmproj-Muse-Glimmer-30B-BF16.gguf`	3.85 GB	BF16	Full-precision ~1.8B ViT perception projector for maximum image fidelity.
`mmproj-Muse-Glimmer-30B-Q8_0.gguf`	2.05 GB	Q8_0	Recommended. 8-bit quantized vision projector preserving high image understanding at nearly half the RAM.

Quickstart & Usage

1. `llama.cpp` CLI (With Vision Support)

To run the model with multimodal vision capability:

# Start server with vision support
llama-server \
  -m Muse-Glimmer-30B-Q4_K_M.gguf \
  --mmproj mmproj-Muse-Glimmer-30B-Q8_0.gguf \
  -c 131072 \
  --port 8080

Downloads last month: -

GGUF

Model size

28B params

Architecture

muse-glimmer

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for Abiray/Muse-Glimmer-30B-GGUF

Base model

meta-models/Muse-Glimmer-30B

Quantized

(84)

this model

Muse-Glimmer-30B - GGUF Quants

File Availability & Recommended Hardware

Core Model Files

Vision Projector Files (mmproj)

Quickstart & Usage

1. llama.cpp CLI (With Vision Support)

Model tree for Abiray/Muse-Glimmer-30B-GGUF

Vision Projector Files (`mmproj`)

1. `llama.cpp` CLI (With Vision Support)