Instructions to use michaelw9999/Qwen3.8-27B-MXFP8-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 michaelw9999/Qwen3.8-27B-MXFP8-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 michaelw9999/Qwen3.8-27B-MXFP8-GGUF
# Run inference directly in the terminal:
llama cli -hf michaelw9999/Qwen3.8-27B-MXFP8-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf michaelw9999/Qwen3.8-27B-MXFP8-GGUF
# Run inference directly in the terminal:
llama cli -hf michaelw9999/Qwen3.8-27B-MXFP8-GGUF

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 michaelw9999/Qwen3.8-27B-MXFP8-GGUF
# Run inference directly in the terminal:
./llama-cli -hf michaelw9999/Qwen3.8-27B-MXFP8-GGUF

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 michaelw9999/Qwen3.8-27B-MXFP8-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf michaelw9999/Qwen3.8-27B-MXFP8-GGUF

Use Docker

docker model run hf.co/michaelw9999/Qwen3.8-27B-MXFP8-GGUF

LM Studio
Jan
Ollama
How to use michaelw9999/Qwen3.8-27B-MXFP8-GGUF with Ollama:
```
ollama run hf.co/michaelw9999/Qwen3.8-27B-MXFP8-GGUF
```

Unsloth Studio

How to use michaelw9999/Qwen3.8-27B-MXFP8-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 michaelw9999/Qwen3.8-27B-MXFP8-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 michaelw9999/Qwen3.8-27B-MXFP8-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for michaelw9999/Qwen3.8-27B-MXFP8-GGUF to start chatting

How to use michaelw9999/Qwen3.8-27B-MXFP8-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf michaelw9999/Qwen3.8-27B-MXFP8-GGUF

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": "michaelw9999/Qwen3.8-27B-MXFP8-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use michaelw9999/Qwen3.8-27B-MXFP8-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf michaelw9999/Qwen3.8-27B-MXFP8-GGUF

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 "michaelw9999/Qwen3.8-27B-MXFP8-GGUF" \
  --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 michaelw9999/Qwen3.8-27B-MXFP8-GGUF with Docker Model Runner:
```
docker model run hf.co/michaelw9999/Qwen3.8-27B-MXFP8-GGUF
```

Lemonade

How to use michaelw9999/Qwen3.8-27B-MXFP8-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull michaelw9999/Qwen3.8-27B-MXFP8-GGUF

Run and chat with the model

lemonade run user.Qwen3.8-27B-MXFP8-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use michaelw9999/Qwen3.8-27B-MXFP8-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 michaelw9999/Qwen3.8-27B-MXFP8-GGUF

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 michaelw9999/Qwen3.8-27B-MXFP8-GGUF

Run Hermes

hermes

Atomic Chat

More information and benchmarks will be posted soon.
This was converted from the original Qwen/Qwen3.8-27B repository.

It was quantized using my advanced-gguf-quantizer tool.

To use this model, you must build my unofficial MXFP8 fork of llama.cpp at:
https://github.com/michaelw9999/llama.cpp/tree/mxfp8_cuda
On Blackwell, I am seeing that MXFP8 is faster than Q8_0 and is comparable in quality.
I will post some mixed NVFP4/MXFP6/MXFP8 models soon that should provide the fastest possible Blackwell models.
Please let me know your feedback and if you have any problems, I will be happy to help!

Downloads last month: -

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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