Instructions to use liodon-ai/Qwen3-8B-imatrix-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use liodon-ai/Qwen3-8B-imatrix-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="liodon-ai/Qwen3-8B-imatrix-GGUF",
	filename="Qwen3-8B-IQ2_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use liodon-ai/Qwen3-8B-imatrix-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 liodon-ai/Qwen3-8B-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf liodon-ai/Qwen3-8B-imatrix-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 liodon-ai/Qwen3-8B-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf liodon-ai/Qwen3-8B-imatrix-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 liodon-ai/Qwen3-8B-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf liodon-ai/Qwen3-8B-imatrix-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 liodon-ai/Qwen3-8B-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf liodon-ai/Qwen3-8B-imatrix-GGUF:Q4_K_M

Use Docker

docker model run hf.co/liodon-ai/Qwen3-8B-imatrix-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use liodon-ai/Qwen3-8B-imatrix-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "liodon-ai/Qwen3-8B-imatrix-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": "liodon-ai/Qwen3-8B-imatrix-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/liodon-ai/Qwen3-8B-imatrix-GGUF:Q4_K_M

Ollama
How to use liodon-ai/Qwen3-8B-imatrix-GGUF with Ollama:
```
ollama run hf.co/liodon-ai/Qwen3-8B-imatrix-GGUF:Q4_K_M
```

Unsloth Studio

How to use liodon-ai/Qwen3-8B-imatrix-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 liodon-ai/Qwen3-8B-imatrix-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 liodon-ai/Qwen3-8B-imatrix-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use liodon-ai/Qwen3-8B-imatrix-GGUF with Pi:

Start the llama.cpp server

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

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": "liodon-ai/Qwen3-8B-imatrix-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use liodon-ai/Qwen3-8B-imatrix-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 liodon-ai/Qwen3-8B-imatrix-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 liodon-ai/Qwen3-8B-imatrix-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use liodon-ai/Qwen3-8B-imatrix-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf liodon-ai/Qwen3-8B-imatrix-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 "liodon-ai/Qwen3-8B-imatrix-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"

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

Lemonade

How to use liodon-ai/Qwen3-8B-imatrix-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Qwen3-8B-imatrix-GGUF

File size: 1,845 Bytes

ca3ea78

---
license: other
base_model: Qwen/Qwen3-8B
pipeline_tag: text-generation
tags:
- gguf
- local-llm
- llama.cpp
- lm-studio
- quantized
- imatrix
- sub-4-bit
- qwen3
- base_model:Qwen/Qwen3-8B-Base
---

# Qwen3-8B — iMatrix GGUF

GGUF quantizations of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B), published by [Liodon AI](https://huggingface.co/liodon-ai).

## Quick Start

**llama.cpp**
```bash
llama-cli -hf liodon-ai/Qwen3-8B-imatrix-GGUF:Q4_K_M
```

**Ollama**
```bash
ollama run hf.co/liodon-ai/Qwen3-8B-imatrix-GGUF:Q4_K_M
```

**LM Studio / Jan** — search `liodon-ai/Qwen3-8B-imatrix-GGUF` and pick your quant.

## Quants

| Quant | Size | VRAM est. | Notes |
|-------|------|-----------|-------|
| `IQ2_M` | 3.05 GB | ~4 GB | 2-bit, iMatrix — smallest usable |
| `IQ3_M` | 3.90 GB | ~4 GB | 3-bit, iMatrix — great quality/size tradeoff |
| `IQ4_XS` | 4.56 GB | ~5 GB | 4-bit extra-small, iMatrix |
| `Q4_K_M` | 5.03 GB | ~6 GB | 4-bit, iMatrix-calibrated (recommended) |
| `Q5_K_M` | 5.85 GB | ~7 GB | 5-bit, iMatrix-calibrated |
| `Q6_K` | 6.73 GB | ~8 GB | 6-bit, iMatrix-calibrated, near-lossless |
| `Q8_0` | 8.71 GB | ~10 GB | 8-bit, essentially lossless |


## What is iMatrix?

Standard quantization treats all weights equally. iMatrix runs 128 calibration chunks through
the full-precision model to find which weights matter most, then allocates more precision where
it counts. At Q2/Q3/Q4 this means noticeably better coherence and instruction-following —
**same file size, better output**.

Calibration: 2M tokens of [WikiText-103](https://huggingface.co/datasets/wikitext).

> Also see plain (non-iMatrix) quants: `liodon-ai/Qwen3-8B-GGUF`

## Source

- **Model**: [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B)
- **License**: other

---
*Quantized by [Liodon AI](https://huggingface.co/liodon-ai)*