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

File size: 1,439 Bytes

d79e9e7

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  "experiment": "UD-IQ2_XXS-Unsloth",
  "status": "complete",
  "source": {
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  "output": {
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  "recipe": {
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    "baseQuantFallback": "IQ2_XXS",
    "adaptation": "strict-downward-only",
    "bf16Intermediate": false
  },
  "imatrix": {
    "label": "Unsloth",
    "path": "hf://unsloth/Qwen3.8-27B-GGUF/imatrix_unsloth.gguf",
    "sha256": "0ee5b10bd0c2fa2127c6f4b43dbfe1efd71e383b63217af9dade1de36599f1c1",
    "entries": 496
  },
  "execution": {
    "gpuUse": false,
    "cudaVisibleDevices": "",
    "quantizeThreads": 16,
    "benchmarkThreads": 4,
    "benchmarkContext": 2048,
    "scratchLane": "home"
  },
  "benchmark": {
    "domain": "general",
    "datasetId": "Salesforce/wikitext",
    "tokenTarget": 32768,
    "corpusSha256": "5d38d98dce15f54e9a1a926187b6058e65cd8b3dd9b5cc2729b0a5fd249228b0",
    "nativeLogitsSha256": "604db2df7253cba18a6c0a569ed819ee3065d4ac20cd94ae72f4329e7bba8516"
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}