Instructions to use Davidmg0815/Qwen3.8-27B-MTP-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 Davidmg0815/Qwen3.8-27B-MTP-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 Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0

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 Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0

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 Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0

Use Docker

docker model run hf.co/Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use Davidmg0815/Qwen3.8-27B-MTP-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Davidmg0815/Qwen3.8-27B-MTP-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": "Davidmg0815/Qwen3.8-27B-MTP-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/Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0

Ollama
How to use Davidmg0815/Qwen3.8-27B-MTP-GGUF with Ollama:
```
ollama run hf.co/Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0
```

Unsloth Studio

How to use Davidmg0815/Qwen3.8-27B-MTP-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 Davidmg0815/Qwen3.8-27B-MTP-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 Davidmg0815/Qwen3.8-27B-MTP-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Davidmg0815/Qwen3.8-27B-MTP-GGUF to start chatting

How to use Davidmg0815/Qwen3.8-27B-MTP-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0

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": "Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Davidmg0815/Qwen3.8-27B-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0
```

Lemonade

How to use Davidmg0815/Qwen3.8-27B-MTP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0

Run and chat with the model

lemonade run user.Qwen3.8-27B-MTP-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use Davidmg0815/Qwen3.8-27B-MTP-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 Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0

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 Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Davidmg0815/Qwen3.8-27B-MTP-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0

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 "Davidmg0815/Qwen3.8-27B-MTP-GGUF:Q8_0" \
  --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-MTP-GGUF

File size: 1,807 Bytes

2ad51d5

#!/usr/bin/env bash
# Checkpoint -> drei GGUFs: Zielmodell, MTP-Draft, mmproj (Vision).
# Quelle darf BF16 ODER FP8 sein: ModelBase.dequant_model() in base.py:437
# dequantisiert quant_method=="fp8" generisch ueber weight_block_size.
# Laeuft komplett im frischen llama.cpp-Container, der Host braucht kein nvcc.
# Aufruf: ./convert.sh /mnt/models/qwen3.8-27b-fp8
set -euo pipefail

SRC="${1:?Quellverzeichnis mit den .safetensors angeben}"
OUT=/mnt/models/PropellerA-models
IMG=ghcr.io/ggml-org/llama.cpp:full-cuda
NAME=qwen3.8-27b

# Das Image hat /app/tools.sh als Entrypoint — python3 muss explizit gesetzt
# werden, sonst landen die Argumente bei tools.sh und es gibt nur die Hilfe.
D() { sudo docker run --rm -v /mnt/models:/mnt/models -w /app \
--entrypoint /usr/bin/python3 "$IMG" "$@"; }
# eigener Wrapper: --entrypoint muss VOR den Imagenamen
Q() { sudo docker run --rm -v /mnt/models:/mnt/models -w /app \
--entrypoint /app/llama-quantize "$IMG" "$@"; }

# BF16 als Zwischenstufe, NICHT q8_0: aus q8_0 nach Q6_K zu requantisieren
# wuerde den FP8-Fehler ein drittes Mal aufaddieren.
echo "### 1/4  Zielmodell BF16 (ohne MTP-Tensoren) — ca. 54 GB Zwischendatei"
D convert_hf_to_gguf.py "$SRC" \
--outfile "/mnt/models/tmp-$NAME-BF16.gguf" --outtype bf16 --no-mtp

echo "### 2/4  MTP-Draft als eigene Datei"
D convert_hf_to_gguf.py "$SRC" \
--outfile "$OUT/$NAME-MTP-Q8_0.gguf" --outtype q8_0 --mtp

echo "### 3/4  mmproj (Vision/Video)"
D convert_hf_to_gguf.py "$SRC" \
--outfile "$OUT/$NAME-mmproj-F16.gguf" --outtype f16 --mmproj

echo "### 4/4  Zielmodell auf Q6_K quantisieren"
Q "/mnt/models/tmp-$NAME-BF16.gguf" "$OUT/$NAME-Q6_K.gguf" Q6_K

echo
echo "Fertig. Zwischendatei loeschen wenn der Testlauf sitzt:"
echo "  rm /mnt/models/tmp-$NAME-BF16.gguf"
ls -lh "$OUT"/$NAME-* 2>/dev/null