Instructions to use Abiray/Qwen3.8-27B-Q4_K_M-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 Abiray/Qwen3.8-27B-Q4_K_M-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 Abiray/Qwen3.8-27B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Abiray/Qwen3.8-27B-Q4_K_M-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Abiray/Qwen3.8-27B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Abiray/Qwen3.8-27B-Q4_K_M-GGUF:Q4_K_M

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 Abiray/Qwen3.8-27B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Abiray/Qwen3.8-27B-Q4_K_M-GGUF:Q4_K_M

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 Abiray/Qwen3.8-27B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Abiray/Qwen3.8-27B-Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Abiray/Qwen3.8-27B-Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Abiray/Qwen3.8-27B-Q4_K_M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Abiray/Qwen3.8-27B-Q4_K_M-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": "Abiray/Qwen3.8-27B-Q4_K_M-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/Abiray/Qwen3.8-27B-Q4_K_M-GGUF:Q4_K_M

Ollama
How to use Abiray/Qwen3.8-27B-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/Abiray/Qwen3.8-27B-Q4_K_M-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Abiray/Qwen3.8-27B-Q4_K_M-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Abiray/Qwen3.8-27B-Q4_K_M-GGUF:Q4_K_M

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": "Abiray/Qwen3.8-27B-Q4_K_M-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Abiray/Qwen3.8-27B-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/Abiray/Qwen3.8-27B-Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use Abiray/Qwen3.8-27B-Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Abiray/Qwen3.8-27B-Q4_K_M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.8-27B-Q4_K_M-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Abiray/Qwen3.8-27B-Q4_K_M-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 Abiray/Qwen3.8-27B-Q4_K_M-GGUF:Q4_K_M

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 Abiray/Qwen3.8-27B-Q4_K_M-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Abiray/Qwen3.8-27B-Q4_K_M-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Abiray/Qwen3.8-27B-Q4_K_M-GGUF:Q4_K_M

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 "Abiray/Qwen3.8-27B-Q4_K_M-GGUF:Q4_K_M" \
  --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 GGUF (Vision-Language)

This repository contains the GGUF format quantization of the Qwen3.8-27B model, a native vision-language model.

The model has been quantized using llama.cpp (release b10430) to the Q4_K_M format.

File Details

Model Name: Qwen3.8-27B
Quantization: Q4_K_M
Main Model: Qwen3.8-27B-Q4_K_M.gguf (16.8 GB)
Vision Adapter: mmproj-F16.gguf (Required for vision/multimodal tasks)
Quantization Tool: llama.cpp (b10430)

About Qwen3.8-27B

Qwen3.8-27B is the most capable generation in the Qwen open-model family, built on the architectural foundation of Qwen3.5. It is a native vision-language model that understands images and videos, delivering substantial gains across coding, professional work, research, and long-horizon agentic tasks.

For more information, please visit the original model card.

Usage

llama.cpp (Text Only)

To run this model as a text-only model using llama.cpp from the command line:

./llama-cli -m Qwen3.8-27B-Q4_K_M.gguf -p "Write a Python function to merge two sorted linked lists." -n 5124

llama.cpp (Vision-Language)

To utilize the vision capabilities, you must provide the mmproj file using the --mmproj flag:

./llama-cli -m Qwen3.8-27B-Q4_K_M.gguf \
  --mmproj mmproj-F16.gguf \
  --image path/to/your/image.jpg \
  -p "Describe the contents of this image."

LM Studio / Ollama / GPT4All

This GGUF file is compatible with popular local LLM inference tools. When configuring the model in your preferred client, ensure you attach the mmproj-F16.gguf file in the "Vision Adapter" or "Multimedia Projection" setting to enable image processing.

Acknowledgements

The original model was developed and released by the Qwen Team.

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GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for Abiray/Qwen3.8-27B-Q4_K_M-GGUF

Base model

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Quantized

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