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Benchmark

WikiText-2

Reported in 19 papers on Zotpaper. The latest, each as the paper states it:

Combining model compression techniques can collapse small language models920.56 perplexity
4-bit language models run as integer math on Apple's M5 chips without extra accuracy loss10.4 perplexity

WikiText2 (2)All benchmarks →

1 result for “WikiText-2”

Research · Efficiency & systems
H-SVDQuant improves 4-bit quantization of diffusion transformers with low-rank branches
Deflating the Hessian: Rank-4 W4A4 Quantization for Multimodal Diffusion Transformers
A unified framework jointly modeling weight and activation quantization error achieves better performance with rank 4 than existing methods with rank 32.
WikiText-2: 10.44 perplexity (as reported)
10 Oct 2026The University of Hong Kong, Huawei Technologies Co., Ltd.