Instructions to use huawei-csl/Qwen3-1.7B-PreSINQ-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use huawei-csl/Qwen3-1.7B-PreSINQ-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="huawei-csl/Qwen3-1.7B-PreSINQ-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("huawei-csl/Qwen3-1.7B-PreSINQ-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use huawei-csl/Qwen3-1.7B-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/Qwen3-1.7B-PreSINQ-GGUF:Q3_K_S
# Run inference directly in the terminal:
llama cli -hf huawei-csl/Qwen3-1.7B-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/Qwen3-1.7B-PreSINQ-GGUF:Q3_K_S
# Run inference directly in the terminal:
llama cli -hf huawei-csl/Qwen3-1.7B-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/Qwen3-1.7B-PreSINQ-GGUF:Q3_K_S
# Run inference directly in the terminal:
./llama-cli -hf huawei-csl/Qwen3-1.7B-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/Qwen3-1.7B-PreSINQ-GGUF:Q3_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf huawei-csl/Qwen3-1.7B-PreSINQ-GGUF:Q3_K_S

Use Docker

docker model run hf.co/huawei-csl/Qwen3-1.7B-PreSINQ-GGUF:Q3_K_S

LM Studio
Jan

vLLM

How to use huawei-csl/Qwen3-1.7B-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/Qwen3-1.7B-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/Qwen3-1.7B-PreSINQ-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/huawei-csl/Qwen3-1.7B-PreSINQ-GGUF:Q3_K_S

SGLang

How to use huawei-csl/Qwen3-1.7B-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/Qwen3-1.7B-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/Qwen3-1.7B-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/Qwen3-1.7B-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/Qwen3-1.7B-PreSINQ-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use huawei-csl/Qwen3-1.7B-PreSINQ-GGUF with Ollama:
```
ollama run hf.co/huawei-csl/Qwen3-1.7B-PreSINQ-GGUF:Q3_K_S
```
Unsloth Desktop

How to use huawei-csl/Qwen3-1.7B-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/Qwen3-1.7B-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/Qwen3-1.7B-PreSINQ-GGUF:Q3_K_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use huawei-csl/Qwen3-1.7B-PreSINQ-GGUF with Docker Model Runner:
```
docker model run hf.co/huawei-csl/Qwen3-1.7B-PreSINQ-GGUF:Q3_K_S
```

Lemonade

How to use huawei-csl/Qwen3-1.7B-PreSINQ-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull huawei-csl/Qwen3-1.7B-PreSINQ-GGUF:Q3_K_S

Run and chat with the model

lemonade run user.Qwen3-1.7B-PreSINQ-GGUF-Q3_K_S

List all available models

lemonade list

Hermes Agent

How to use huawei-csl/Qwen3-1.7B-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/Qwen3-1.7B-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/Qwen3-1.7B-PreSINQ-GGUF:Q3_K_S

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use huawei-csl/Qwen3-1.7B-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/Qwen3-1.7B-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/Qwen3-1.7B-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"

Qwen3-1.7B-PreSINQ-GGUF / README.md

pbicho

Update README.md

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metadata

base_model: Qwen/Qwen3-1.7B
language:
  - en
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
arxiv: 2509.22944
tags:
  - quantized
  - sinq
  - efficient-inference
  - qwen
  - llm
  - compression
base_model_relation: quantized

Logo

🐙 Github | 📄 Paper

PreSINQ GGUF Quantized Qwen3-1.7B Model

This repository contains the official PreSINQ GGUF-quantized versions of the Qwen3-1.7B 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: Qwen3-1.7B-PreSINQ-GGUF
Base Model: Qwen/Qwen3-1.7B
Task: Text Generation
Framework: PyTorch / Transformers
License: Apache-2.0
Quantized By: Huawei – Computing Systems Lab

How to Obtain the PreSINQ Model

The PreSINQ Qwen3-1.7B 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 (Qwen3-1.7B)

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

Method	Bits	Size (GB)	Perplexity ↓
Baseline (FP16)	FP16	3.79	17.1294
Baseline + Q4_K_S	4-bit	1.15	19.5454
PreSINQ + Q4_K_S	4-bit	1.01	17.4544
Baseline + Q3_K_S	3-bit	0.95	24.0242
PreSINQ + Q3_K_S	3-bit	0.83	18.8032

However, you can generate good PreSINQ models (not the best one) faster by reducing the number of configurations explored during the PreSINQ script execution. The table below shows perplexity for different PreSINQ parameter configurations using Q4_K_S quantization.
Evaluation is performed on a 5k-line subset of the Pile validation dataset.

Group Size	Iterations	Repetitions	Perplexity
32	2	1	11.7196
32	4	1	11.7238
32	8	1	11.6885
32	16	1	11.6909
64	2	1	11.7421
64	4	1	11.7240
64	8	1	11.6975
64	16	1	11.7001
128	2	1	11.7129
128	4	1	11.7118
128	8	1	11.7149
128	16	1	11.7208

🚀 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}
}