Instructions to use tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-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 tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-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 tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama cli -hf tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama cli -hf tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

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 tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./llama-cli -hf tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

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 tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

Use Docker

docker model run hf.co/tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

LM Studio
Jan

vLLM

How to use tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-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": "tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-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/tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

Ollama
How to use tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF with Ollama:
```
ollama run hf.co/tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS
```
Unsloth Desktop

How to use tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

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": "tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF with Docker Model Runner:
```
docker model run hf.co/tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS
```

Lemonade

How to use tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

Run and chat with the model

lemonade run user.Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF-IQ3_XXS

List all available models

lemonade list

Hermes Agent

How to use tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-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 tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

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 tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS

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 "tooltd/Qwen3.8-27B-IQ4-XS-16GB-VRAM-GGUF:IQ3_XXS" \
  --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"

Unsloth Desktop - Metadata Weirdness

by Derjyn - opened Aug 25

Discussion

Derjyn

Aug 25

I decided to give the 'ol Unsloth Desktop a try after some time away from the scene, and that kicked off me doing an audit and some testing of the newer models hitting HF. I came across yours here, and decided to give it a shot. However, the way the metadata and such is set up here for this model, it isn't at all friendly to the display and functionality in UD, and how it parses HF data. Here is a screenshot of how this model shows there in the browser:

None of the other models display. The naming scheme might be bad, or the metadata in the GGUF might be generic, no idea. Now here is a model that is setup nicely for display:

Each of the variants are split out, because they are discoverable, I assume? Like the metadata is cleanly formatted. No idea, I'm old and I took more than a month break, which is enough for my nitwit self to be back to kindergarten 😅

It's not the end of the world of course, because we can download the GGUF and slap it somewhere and point UD at it, but wanted to point this out in case you wanted to tune up the user-experience and probably also enhance your organic exposure and all that. Seems patience is very rare nowadays, so having a pretty and well formatted and performing interface across the pipeline can only do you good. I bet you could use some sort of AI to help with that...

Anyhow, thanks for doing what you do! I too have some system performance budgets to work within, and with so many variants and options to sift through it's been an adventure. Your model(s) here really captured my interest, so I'm excited to run em through my totally-scientific tests 🧐

tooltd

Owner Aug 25

Oh, I'm not sure. 😁 I'm using llama.cpp directly, so this might just be a UI display bug. All the files are still showing up on Hugging Face though. My main focus revolves around IQ4_XS, so the only difference is the file size.

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