Instructions to use SandLogicTechnologies/Qwen3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SandLogicTechnologies/Qwen3-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SandLogicTechnologies/Qwen3-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("SandLogicTechnologies/Qwen3-GGUF", dtype="auto")

llama-cpp-python

How to use SandLogicTechnologies/Qwen3-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SandLogicTechnologies/Qwen3-GGUF",
	filename="Qwen3-8B-Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use SandLogicTechnologies/Qwen3-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SandLogicTechnologies/Qwen3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SandLogicTechnologies/Qwen3-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SandLogicTechnologies/Qwen3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SandLogicTechnologies/Qwen3-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 SandLogicTechnologies/Qwen3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SandLogicTechnologies/Qwen3-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 SandLogicTechnologies/Qwen3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SandLogicTechnologies/Qwen3-GGUF:Q4_K_M

Use Docker

docker model run hf.co/SandLogicTechnologies/Qwen3-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use SandLogicTechnologies/Qwen3-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/SandLogicTechnologies/Qwen3-GGUF:Q4_K_M

SGLang

How to use SandLogicTechnologies/Qwen3-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SandLogicTechnologies/Qwen3-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SandLogicTechnologies/Qwen3-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SandLogicTechnologies/Qwen3-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SandLogicTechnologies/Qwen3-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use SandLogicTechnologies/Qwen3-GGUF with Ollama:
```
ollama run hf.co/SandLogicTechnologies/Qwen3-GGUF:Q4_K_M
```

Unsloth Studio new

How to use SandLogicTechnologies/Qwen3-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 SandLogicTechnologies/Qwen3-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 SandLogicTechnologies/Qwen3-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

Pi new

How to use SandLogicTechnologies/Qwen3-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf SandLogicTechnologies/Qwen3-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": "SandLogicTechnologies/Qwen3-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use SandLogicTechnologies/Qwen3-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf SandLogicTechnologies/Qwen3-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 SandLogicTechnologies/Qwen3-GGUF:Q4_K_M

Run Hermes

hermes

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

Lemonade

How to use SandLogicTechnologies/Qwen3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SandLogicTechnologies/Qwen3-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3 Quantized Models – Lexicons Edition

This repository provides quantized versions of the Qwen3 language models, optimized for efficient deployment on edge devices and low-resource environments. The following models have been added to our Lexicons Model Zoo:

Qwen_Qwen3-0.6B-Q4_K_M
Qwen_Qwen3-1.7B-Q4_K_M
Qwen_Qwen3-4B-Q4_K_M
Qwen3-8B-Q4_K_M

Model Overview

Qwen3 is the latest open-source LLM series developed by Alibaba Group. Released on April 28, 2025, the models were trained on 36 trillion tokens across 119 languages and dialects. Qwen3 models are instruction-tuned and support long context windows and multilingual capabilities. This model is described in An Empirical Study of Qwen3 Quantization.

The quantized versions provided here use 4-bit Q4_K_M precision ensuring high performance at a fraction of the memory and compute cost. These models are ideal for real-time inference, chatbots, and on-device applications.

Key Features

Efficient Quantization: 4-bit quantized models (Q4_K_M) for faster inference and lower memory usage.
Multilingual Mastery: Trained on a massive, diverse corpus covering 119+ languages.
Instruction-Tuned: Fine-tuned to follow user instructions effectively.
Scalable Sizes: Choose from 0.6B to 8B parameter models based on your use case.

Available Quantized Versions

Model Name	Parameters	Quantization	Context Length	Recommended Use
Qwen_Qwen3-0.6B-Q4_K_M	0.6B	Q4_K_M	4K tokens	Lightweight devices, microservices
Qwen_Qwen3-1.7B-Q4_K_M	1.7B	Q4_K_M	4K tokens	Fast inference, chatbots
Qwen_Qwen3-4B-Q4_K_M	4B	Q4_K_M	4K tokens	Balanced performance & efficiency
Qwen3-8B-Q4_K_M	8B	Q4_K_M	128K tokens	Complex reasoning, long documents

Performance Insights

Quantized Qwen3 models at Q4_K_M retain impressive reasoning and comprehension capabilities while cutting down the memory and compute needs. Based on the latest findings (arXiv:2505.02214), Qwen3 models are robust even under lower bit quantization when used appropriately.

Code

The project is released on Github and Hugging Face.

Downloads last month: 290

GGUF

Model size

0.8B params

Architecture

qwen3

Hardware compatibility

4-bit

Model tree for SandLogicTechnologies/Qwen3-GGUF

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Quantized

(306)

this model

Paper for SandLogicTechnologies/Qwen3-GGUF

An Empirical Study of Qwen3 Quantization

Paper • 2505.02214 • Published May 4, 2025 • 26