ZurichNLP #18

Philippe Bich (Huawei): SINQ: Sinkhorn-Normalized Quantization for calibration-free low-precision LLM weights

Quantization is a key technique for making Large Language Models (LLMs) and Vision-Language Models (VLMs) faster and less memory-demanding, but maintaining accuracy at low precision remains challenging. Outlier weights often dominate shared scales, leading to large errors and degraded performance. We introduce SINQ, our novel open-source approach that makes post-training quantization both simpler and more accurate.

SINQ adds a second normalization axis and applies a fast Sinkhorn-Knopp-style algorithm to balance per-row and per-column variances in weight matrices. Tested on Qwen3 and DeepSeek-V2.5, SINQ improves perplexity on WikiText2 and C4 benchmarks without calibration data or model-specific tuning. It can quantize billion-parameter models in just a few seconds while achieving accuracy comparable to, or better than, existing methods.

Alejandro Hernández Cano (EPFL):

Apertus is a fully open suite of large language models (LLMs) designed to address two systemic shortcomings in today's open model ecosystem: data compliance and multilingual representation. Unlike many prior models that release weights without reproducible data pipelines or regard for content-owner rights, Apertus models are pretrained exclusively on openly available data and filtering for non-permissive, toxic, and personally identifiable content. The Apertus models also expand multilingual coverage, training on 15T tokens from over 1800 languages, with ~40% of pretraining data allocated to non-English content. Released at 8B and 70B scales, Apertus approaches state-of-the-art results among fully open models on multilingual benchmarks, rivalling or surpassing open-weight counterparts.