Instructions to use magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-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 magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-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 magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF:UD-Q4_K_S
# Run inference directly in the terminal:
llama cli -hf magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF:UD-Q4_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF:UD-Q4_K_S
# Run inference directly in the terminal:
llama cli -hf magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF:UD-Q4_K_S

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 magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF:UD-Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF:UD-Q4_K_S

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 magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF:UD-Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF:UD-Q4_K_S

Use Docker

docker model run hf.co/magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF:UD-Q4_K_S

LM Studio
Jan

vLLM

How to use magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-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": "magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-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/magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF:UD-Q4_K_S

Ollama
How to use magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF with Ollama:
```
ollama run hf.co/magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF:UD-Q4_K_S
```
Unsloth Desktop

How to use magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF:UD-Q4_K_S

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": "magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF:UD-Q4_K_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF with Docker Model Runner:
```
docker model run hf.co/magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF:UD-Q4_K_S
```

Lemonade

How to use magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF:UD-Q4_K_S

Run and chat with the model

lemonade run user.Qwen3.8-27B-MXFP4-MagicQuant-GGUF-UD-Q4_K_S

List all available models

lemonade list

Hermes Agent

How to use magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-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 magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF:UD-Q4_K_S

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 magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF:UD-Q4_K_S

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF:UD-Q4_K_S

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 "magiccodingman/Qwen3.8-27B-MXFP4-MagicQuant-GGUF:UD-Q4_K_S" \
  --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"

Qwen3.8-27B-MXFP4-MagicQuant-GGUF / magicquant-manifest /magicquant.release-boundary.json

magiccodingman

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	{
	"schemaVersion": 1,
	"purpose": "Explicit public/private boundary for preparing this working directory as a Hugging Face model repository.",
	"publicIncludes": [
	"README.md",
	"LICENSE",
	".gitattributes",
	".gitignore",
	".hfignore",
	"SHA256SUMS",
	"Qwen3.8-27B-Quark-*.gguf",
	"mmproj-Qwen3.8-27B-*.gguf",
	"imatrix.dat",
	"imatrix_unsloth.gguf",
	"config.json",
	"generation_config.json",
	"chat_template.jinja",
	"tokenizer.json",
	"tokenizer_config.json",
	"merges.txt",
	"vocab.json",
	"preprocessor_config.json",
	"processor_config.json",
	"video_preprocessor_config.json",
	"magicquant-manifest/**"
	],
	"privateExcludes": [
	"llama.cpp/**",
	"venv/**",
	"tools/**",
	"Experiments/**",
	"imatrix/**",
	"__pycache__/**",
	"*.log",
	"*.pyc",
	"verify_native_mxfp4.py"
	],
	"audit": {
	"publicJsonFilesValid": true,
	"publicManifestAbsolutePathHits": 0,
	"personalHomeNameHits": 0,
	"note": "The working tree still contains private build and benchmark material. Use this allowlist or the matching ignore files; do not perform an unfiltered raw-folder upload. The public release contains ten hybrid GGUFs; UD-IQ2_XXS is documentation-only and intentionally absent."
	}
	}