transformers/docs/source/en/quantization/sinq.md

SINQ

Sinkhorn-Normalized Quantization (SINQ) is a fast, plug-and-play, model-agnostic quantization technique delivering state-of-the-art performance for Large Language Models without sacrificing accuracy.

🔍 What You’ll Find Here

• 1. Quantize (and save) any LLM with SINQ
• 2. How to Cite This Work
• 3. Current Limitations

📊 Feature Comparison: SINQ vs HQQ (calibration-free) and A-SINQ vs AWQ (calibrated)

Feature	SINQ	HQQ	A-SINQ	AWQ
🎯 Calibration	Calibration-free	Calibration-free	Calibrated	Calibrated
🧮 Quantization Type	Symmetric & Asymmetric	Asymmetric only	Symmetric & Asymmetric	Symmetric & Asymmetric
📦 NF4 Support	Yes	No	Yes	No
⚡ Quantization Speed	~2× Faster than HQQ	Slower	~4× Faster than AWQ	Slower
📈 Model Quality	Higher	Lower	Higher	Lower

📄 Want to know more?

• Read our paper on arXiv
• Check the official SINQ github repository

---

1. Quantize any LLM with SINQ

Setup & Quick Start

First, install the package. It can be done in two ways:

• From source using the official Github repository SINQ [Recommended]
• Using pip package:

pip install sinq

---

Quantize in a few lines

Quantizing any 🤗 Hugging Face model with SINQ is simple and takes only a few lines of code. First, create a [SinqConfig] and specify the following parameters:

Flag	Description	Type	Options	Default
--nbits	Bit-width for weight quantization	int	2, 3, 4, 5, 6, 8	4
--tiling_mode	Weight matrix tiling strategy	str	1D, 2D	1D
--group_size	Weights per quantization group	int	64, 128	64
--method	Quantization method	str	sinq, asinq	sinq
--modules_to_not_convert	List of the layers that are NOT quantize	List of str	[lm_head, ...]	[lm_head]

Then specify the model you want to quantize and pass the SinqConfig as quantization configuration option

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, SinqConfig

model_name = "Qwen/Qwen3-1.7B"

cfg = SinqConfig(
    nbits=4,
    group_size=64,
    tiling_mode="1D",
    method="sinq",
    modules_to_not_convert=["lm_head"]
)

tok = AutoTokenizer.from_pretrained(model_name)
qmodel = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=cfg,
    dtype=torch.bfloat16
)

✅ That’s it. Your model is now quantized with SINQ and ready for inference or saving.

Check our official SINQ github repository to stay updated!

---

Save & reload

If you want to reuse a quantized model later, save it to disk or push it on the HuggingFace Hub and reload it without needing base FP weights. If you installed SINQ from source you should call patch_hf_pretrained_io function when re-loading a quantized model:

# Save sinq quantized model
model.save_pretrained("/path/to/save/qwen3-1.7B-sinq-4bit")
model.push_to_hub("HF_Hub_username/qwen3-1.7B-sinq-4bit")
tokenizer.push_to_hub("HF_Hub_username/qwen3-1.7B-sinq-4bit")

from sinq.hf_io import patch_hf_pretrained_io
patch_hf_pretrained_io()
# Reload a sinq quantized model
hf_hub_model = "HF_Hub_username/qwen3-1.7B-sinq-4bit"
tokenizer  = AutoTokenizer.from_pretrained(hf_hub_model)
model = AutoModelForCausalLM.from_pretrained(hf_hub_model)

Otherwise, if you installed SINQ through pip, you can simply use HF built-in functions:

# --- Save to a folder (sharded safetensors) ---

# 'model' must already be SINQ-quantized
# Locally save
qmodel.save_pretrained("/path/to/save/qwen3-1.7B-sinq-4bit")
# Push to the Hub
qmodel.push_to_hub("HF_Hub_username/qwen3-1.7B-sinq-4bit")
tok.push_to_hub("HF_Hub_username/qwen3-1.7B-sinq-4bit")

# --- Reload later--

save_dir = "/path/to/save/qwen3-1.7B-sinq-4bit"
hf_hub_model = "HF_Hub_username/qwen3-1.7B-sinq-4bit"

# From local directory
tok = AutoTokenizer.from_pretrained(save_dir)
qmodel = AutoModelForCausalLM.from_pretrained(save_dir)

# From HF Hub
tok = AutoTokenizer.from_pretrained(hf_hub_model)
qmodel = AutoModelForCausalLM.from_pretrained(hf_hub_model)

✅ Your model is now loaded and ready for inference!

Note: If the model has been quantized in 4 bit and gemlite library is installed, gemlite faster kernel is used to run the inference.

---

Compatible with lm-eval evaluation framework

Below is a minimal example showing how to evaluate a SINQ-quantized model on a benchmark dataset:

from lm_eval import evaluator
from lm_eval.models.huggingface import HFLM

# Wrap the already quantized model and tokenizer with HFLM
lm = HFLM(pretrained=qmodel, tokenizer=tok, device=device)
device = "cuda:0"

# Evaluate (many tasks available on lm-eval such as MMLU and HellaSwag)
results = evaluator.simple_evaluate(
    model=lm,
    tasks=["wikitext"],  # small and fast benchmark
    device=device
)

2. How to Cite This Work

If you find SINQ useful in your research or applications

• Support our project by putting a star ⭐️ in the SINQ github repository
• Please 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}
}

3. Current Limitations

Currently, the A-SINQ method is not supported in Hugging Face. Please refer to the official SINQ repository to quantize a model with this strategy. At the moment the SINQ quantization strategy and SINQ quantized models do not support Multi-GPU option, so if your system counts multiple GPUs please specify which one should be used.