Instructions to use TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-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 TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-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 TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF:F16

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 TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF:F16

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 TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF:F16

Use Docker

docker model run hf.co/TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF:F16

LM Studio
Jan

vLLM

How to use TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-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": "TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-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/TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF:F16

Ollama
How to use TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF with Ollama:
```
ollama run hf.co/TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF:F16
```
Unsloth Desktop

How to use TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF:F16

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": "TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF with Docker Model Runner:
```
docker model run hf.co/TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF:F16
```

Lemonade

How to use TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF:F16

Run and chat with the model

lemonade run user.Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-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 TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF:F16

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 TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF:F16

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 "TheWegemann/Qwen3.8-27B-LowGPU-uncensored-NoMTP-IQ3XXXS-GGUF:F16" \
  --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"

Startup params

by SEVEZ - opened 5 days ago

Discussion

SEVEZ

5 days ago

I just wanted to share some experience with this model.

I tried different combinations of temp and penalties. Regular unsloth recommended presence_penalty 1.5 and/or temp 1.0 caused the model to stop an output in the middle of a message. But when I lowered presence_penalty to 0.0 and set lower temp 0.6 this issue has gone.

I was wondering what the recommended params would be?

TheWegemann

Owner 5 days ago

I just wanted to share some experience with this model.

I tried different combinations of temp and penalties. Regular unsloth recommended presence_penalty 1.5 and/or temp 1.0 caused the model to stop an output in the middle of a message. But when I lowered presence_penalty to 0.0 and set lower temp 0.6 this issue has gone.

I was wondering what the recommended params would be?

Thanks for sharing this — that's actually very useful.
I've seen the same occasional premature cutoff on my side as well, where the model simply stops generating in the middle of an answer. I had suspected the cause was somewhere else, so I hadn't connected it to the sampling settings yet.
In my current Hermes/llama.cpp setup I'm not explicitly overriding temperature or presence penalty; I've mostly been letting the default/client settings handle sampling, with reasoning_effort=low.
I'm going to try your temperature 0.6 / presence_penalty 0.0 setup myself and see whether the cutoffs disappear here too. If that turns out to be reproducible, that's a really useful finding for the recommended settings.
Thanks for testing and reporting it! 😊

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