[!IMPORTANT] Naming notice (2026-04-10). The "HLWQ" technique used in this model is being rebranded to HLWQ (Hadamard-Lloyd Weight Quantization). The change is only the name; the algorithm and the weights in this repository are unchanged.

The rebrand resolves a name collision with an unrelated, earlier KV cache quantization method also named HLWQ (Han et al., arXiv:2502.02617, 2025). HLWQ addresses weight quantization with a deterministic Walsh-Hadamard rotation and Lloyd-Max scalar codebook; Han et al.'s HLWQ addresses KV cache quantization with a random polar rotation. The two methods are technically distinct.

Existing loaders that load this repository by ID continue to work without changes. Future model uploads will use the HLWQ name.

Reference paper for this technique: arXiv:2603.29078 (v2 in preparation; v1 still uses the old name).

🧊 Qwopus3.5-9B-v3 HLWQ v7 GPTQ

🎯 INT4 that BEATS BF16 on HumanEval

📊 Benchmarks

🔬 How is this possible?

GPTQ calibrated quantization with group_size=64 and FOEM (First-Order Error Minimization) acts as a regularizer, reducing high-frequency noise in weights while preserving the essential signal. Combined with 2x finer granularity (gs64 vs standard gs128), this narrowly matches BF16 quality on HumanEval standard mode at this specific 9B scale. The +0.20pp edge on 164 samples is within the noise floor of the benchmark — read as "matches BF16" rather than a strict improvement.

Key discovery: group_size=64 was the dominant factor:

• gs128 → gs64 jumped from 61.59% to 67.07% (+5.48pp)
• FOEM contributed +0.61pp on top of vanilla GPTQ
• Same Marlin kernel, same inference speed, only +0.1 GB larger

🚀 Quick Start

vLLM (recommended)

from vllm import LLM, SamplingParams

model = LLM(
    "caiovicentino1/Qwopus3.5-9B-v3-HLWQ-v7-GPTQ",
    trust_remote_code=True,
    language_model_only=True,
)

output = model.generate("Write a Python function to sort a list:", SamplingParams(max_tokens=256))
print(output[0].outputs[0].text)

vLLM Server

vllm serve caiovicentino1/Qwopus3.5-9B-v3-HLWQ-v7-GPTQ \
    --trust-remote-code --language-model-only \
    --max-model-len 16384

GPTQModel

from gptqmodel import GPTQModel

model = GPTQModel.from_quantized(
    "caiovicentino1/Qwopus3.5-9B-v3-HLWQ-v7-GPTQ",
    trust_remote_code=True,
)

🔧 Quantization Config

# Universal config — works for any model
from gptqmodel import GPTQModel
from gptqmodel.quantization import QuantizeConfig
from gptqmodel.quantization.config import FOEMConfig

quantize_config = QuantizeConfig(
    bits=4,
    group_size=64,        # 2x finer than standard 128
    sym=True,
    desc_act=True,
    foem=FOEMConfig(
        alpha=0.25,       # GPTAQ adaptive term
        beta=0.2,         # FOEM first-order error correction
        device="auto"
    )
)

• Quantizer: GPTQModel v6.0.3
• Calibration: 512 samples from neuralmagic/LLM_compression_calibration
• Kernel: Marlin (native vLLM, zero overhead)

📈 Benchmark Evolution

📖 Technical Details

📖 Citation

@article{vicentino2026polarquant,
    title={HLWQ: Polar Coordinate Quantization for Efficient LLM Inference},
    author={Vicentino, Caio},
    journal={arXiv preprint arXiv:2603.29078},
    year={2026}
}

🔗 Links

• 📜 HLWQ Paper (arXiv:2603.29078)
• 💻 GitHub
• 📦 PyPI: pip install polarquant
• 🌍 Base Model
• 🏆 HLWQ Q5 variant

🙏 Acknowledgements

• Arien0 for the HumanEval benchmarks that drove this optimization
• GPTQModel team for FOEM implementation
• vLLM team for Marlin kernel support