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

Use Docker

docker model run hf.co/Blackfrost-AI/Muse-Glimmer-30B-Abliterated-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Blackfrost-AI/Muse-Glimmer-30B-Abliterated-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Blackfrost-AI/Muse-Glimmer-30B-Abliterated-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": "Blackfrost-AI/Muse-Glimmer-30B-Abliterated-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/Blackfrost-AI/Muse-Glimmer-30B-Abliterated-GGUF:Q4_K_M

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

How to use Blackfrost-AI/Muse-Glimmer-30B-Abliterated-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Blackfrost-AI/Muse-Glimmer-30B-Abliterated-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": "Blackfrost-AI/Muse-Glimmer-30B-Abliterated-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use Blackfrost-AI/Muse-Glimmer-30B-Abliterated-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

lemonade run user.Muse-Glimmer-30B-Abliterated-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Blackfrost-AI/Muse-Glimmer-30B-Abliterated-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 Blackfrost-AI/Muse-Glimmer-30B-Abliterated-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 Blackfrost-AI/Muse-Glimmer-30B-Abliterated-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Blackfrost-AI/Muse-Glimmer-30B-Abliterated-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Blackfrost-AI/Muse-Glimmer-30B-Abliterated-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 "Blackfrost-AI/Muse-Glimmer-30B-Abliterated-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"

Blackfrost-AI commited on Aug 15

Commit

83e7dc7

verified ·

1 Parent(s): 410d8c5

Update README.md

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@@ -15,15 +15,12 @@ tags:
 pipeline_tag: image-text-to-text
 library_name: gguf
 ---
-<div align="center">
 > [!IMPORTANT]
 > ## Improvement update — August 15, 2026
 >
 > This release now includes compact **abliterated Q4_K_M companions**: a **1.63 GB DFlash drafter** and a **1.40 GB multimodal projector**, matching Meta's consumer-hardware footprint while preserving this model's modified weights. The complete text quant ladder has also been refreshed with Meta's post-release Jinja correction, which normalizes `Reasoning effort` to `Reasoning strength` and prevents duplicate reasoning directives. Text generation, image input, and DFlash speculative decoding were validated together on current llama.cpp.
-![UoOQg](https://cdn-uploads.huggingface.co/production/uploads/69a27f2d114e4ac9de4dafc7/1ZpMlXp5HmOvLlxzyrCbW.jpeg)
 <div align="center">
 <h1>MUSE-GLIMMER-30B-ABLITERATED-GGUF</h1>

 pipeline_tag: image-text-to-text
 library_name: gguf
 ---
 > [!IMPORTANT]
 > ## Improvement update — August 15, 2026
 >
 > This release now includes compact **abliterated Q4_K_M companions**: a **1.63 GB DFlash drafter** and a **1.40 GB multimodal projector**, matching Meta's consumer-hardware footprint while preserving this model's modified weights. The complete text quant ladder has also been refreshed with Meta's post-release Jinja correction, which normalizes `Reasoning effort` to `Reasoning strength` and prevents duplicate reasoning directives. Text generation, image input, and DFlash speculative decoding were validated together on current llama.cpp.
 <div align="center">
 <h1>MUSE-GLIMMER-30B-ABLITERATED-GGUF</h1>