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

Use Docker

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

LM Studio
Jan

vLLM

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

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

How to use popiAI/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 popiAI/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": "popiAI/Muse-Glimmer-30B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use popiAI/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 popiAI/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 "popiAI/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"

Muse-Glimmer-30B-GGUF

Cuantizaciones GGUF de meta-models/Muse-Glimmer-30B, hechas para servirlas en local con Ollama o llama.cpp. Los pesos y la licencia son del autor original; aquí solo cambia el formato.

Multimodal. La parte visual va en el fichero mmproj-…, aparte de los pesos. Hay que cargarlo explícitamente (ver Uso) o el modelo queda ciego sin decirlo.

Fichero	Tamaño	sha256
`Muse-Glimmer-30B-Q4_K_M.gguf`	16.9 GB	`2b7e05404f4241ac…`
`Muse-Glimmer-30B-Q8_0.gguf`	29.6 GB	`ef5eb228b2b9914e…`
`mmproj-Muse-Glimmer-30B-bf16.gguf`	3.8 GB	`3e732f1a45f0e494…`

Q4_K_M es el equilibrio de siempre; Q8_0 es prácticamente indistinguible del original a cambio del doble de peso. Como referencia de VRAM: en 12 GB entra un 7-8B en Q8_0 o un 14B en Q4_K_M; de ahí para arriba, hay offload a CPU.

Se incluyen los ficheros de licencia del original (LICENSE, USAGE_POLICY.md), como exige su redistribución.

Uso

# llama.cpp — los pesos y el proyector visual se cargan por separado
llama-mtmd-cli -m Muse-Glimmer-30B-Q4_K_M.gguf \
    --mmproj mmproj-Muse-Glimmer-30B-bf16.gguf --image foto.jpg -p "¿Qué ves?"

# solo texto (Ollama)
ollama run hf.co/popiAI/Muse-Glimmer-30B-GGUF:Q4_K_M

Trazabilidad


Modelo base	`meta-models/Muse-Glimmer-30B`
Commit del base	`a4e59da52a7bc87ae7251dd5545c0dd437c44b68`
Intermedio	GGUF `bf16`
llama.cpp	release `b10353`

Generado con el pipeline cuantiza-y-publica del repo ml-lab. Se cuantiza un commit concreto del repo base, no main: repetir esta tabla da los mismos GGUF.

Downloads last month: 70

GGUF

Model size

28B params

Architecture

muse-glimmer

Hardware compatibility

4-bit

8-bit

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

Base model

meta-models/Muse-Glimmer-30B

Quantized

(176)

this model