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

Libraries

How to use huawei-csl/Qwen3-8B-PreSINQ-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="huawei-csl/Qwen3-8B-PreSINQ-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("huawei-csl/Qwen3-8B-PreSINQ-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use huawei-csl/Qwen3-8B-PreSINQ-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 huawei-csl/Qwen3-8B-PreSINQ-GGUF:Q3_K_S
# Run inference directly in the terminal:
llama cli -hf huawei-csl/Qwen3-8B-PreSINQ-GGUF:Q3_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf huawei-csl/Qwen3-8B-PreSINQ-GGUF:Q3_K_S
# Run inference directly in the terminal:
llama cli -hf huawei-csl/Qwen3-8B-PreSINQ-GGUF:Q3_K_S

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 huawei-csl/Qwen3-8B-PreSINQ-GGUF:Q3_K_S
# Run inference directly in the terminal:
./llama-cli -hf huawei-csl/Qwen3-8B-PreSINQ-GGUF:Q3_K_S

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 huawei-csl/Qwen3-8B-PreSINQ-GGUF:Q3_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf huawei-csl/Qwen3-8B-PreSINQ-GGUF:Q3_K_S

Use Docker

docker model run hf.co/huawei-csl/Qwen3-8B-PreSINQ-GGUF:Q3_K_S

LM Studio
Jan

vLLM

How to use huawei-csl/Qwen3-8B-PreSINQ-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/huawei-csl/Qwen3-8B-PreSINQ-GGUF:Q3_K_S

SGLang

How to use huawei-csl/Qwen3-8B-PreSINQ-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 "huawei-csl/Qwen3-8B-PreSINQ-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": "huawei-csl/Qwen3-8B-PreSINQ-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 "huawei-csl/Qwen3-8B-PreSINQ-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": "huawei-csl/Qwen3-8B-PreSINQ-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use huawei-csl/Qwen3-8B-PreSINQ-GGUF with Ollama:
```
ollama run hf.co/huawei-csl/Qwen3-8B-PreSINQ-GGUF:Q3_K_S
```
Unsloth Desktop

How to use huawei-csl/Qwen3-8B-PreSINQ-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf huawei-csl/Qwen3-8B-PreSINQ-GGUF:Q3_K_S

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": "huawei-csl/Qwen3-8B-PreSINQ-GGUF:Q3_K_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use huawei-csl/Qwen3-8B-PreSINQ-GGUF with Docker Model Runner:
```
docker model run hf.co/huawei-csl/Qwen3-8B-PreSINQ-GGUF:Q3_K_S
```

Lemonade

How to use huawei-csl/Qwen3-8B-PreSINQ-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull huawei-csl/Qwen3-8B-PreSINQ-GGUF:Q3_K_S

Run and chat with the model

lemonade run user.Qwen3-8B-PreSINQ-GGUF-Q3_K_S

List all available models

lemonade list

Hermes Agent

How to use huawei-csl/Qwen3-8B-PreSINQ-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 huawei-csl/Qwen3-8B-PreSINQ-GGUF:Q3_K_S

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 huawei-csl/Qwen3-8B-PreSINQ-GGUF:Q3_K_S

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use huawei-csl/Qwen3-8B-PreSINQ-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf huawei-csl/Qwen3-8B-PreSINQ-GGUF:Q3_K_S

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 "huawei-csl/Qwen3-8B-PreSINQ-GGUF:Q3_K_S" \
  --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"

Logo

🐙 Github | 📄 Paper

PreSINQ GGUF Quantized Qwen3-4B Model

This repository contains the official PreSINQ GGUF-quantized versions of the Qwen3-8B model. For a detailed explanation of PreSINQ strategy please refer to the the official SINQ repository. SINQ is a fast and high-quality quantization technique designed to significantly reduce Large Language Model size while preserving accuracy.

If you find this project useful, please consider giving a ⭐ to the official SINQ repository.

Model Details

Model Name: Qwen3-8B-PreSINQ-GGUF
Base Model: Qwen/Qwen3-8B
Task: Text Generation
Framework: PyTorch / Transformers
License: Apache-2.0
Quantized By: Huawei – Computing Systems Lab

How to Obtain the PreSINQ Model

The PreSINQ Qwen3-8B models are produced using the PreSINQ GGUF script available in the official SINQ repository.

The models provided here correspond to the best-performing configurations for each quantization type.

📊 Best PreSINQ Quantization Results (Qwen3-8B)

Results below are measured on the WikiText-2 test set.

Method	Bits	Size (GB)	Perplexity ↓
Baseline (FP16)	FP16	15.26	10.1019
Baseline + Q3_K_S	3-bit	3.77	11.3619
PreSINQ + Q3_K_S	3-bit	3.77	10.6786

However, you can generate good PreSINQ models (not the best one) faster by reducing the number of configurations explored during the PreSINQ script execution.

🚀 Usage

Usage Example

You can load and run the PreSINQ GGUF models using:

🤗 Transformers
llama.cpp
Any GGUF-compatible inference framework

🧾 How to Cite This Work

If you find SINQ useful in your research or applications:

Please give a ⭐ to the official SINQ repository
Cite our paper:

@misc{muller2025sinq,
      title={SINQ: Sinkhorn-Normalized Quantization for Calibration-Free Low-Precision LLM Weights}, 
      author={Lorenz K. Muller and Philippe Bich and Jiawei Zhuang and Ahmet Celik and Luca Benfenati and Lukas Cavigelli},
      year={2025},
      eprint={2509.22944},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={http://arxiv.org/abs/2509.22944}
}