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#llm-compression

Qwen 3.6 27B 30GB Same top p: 98.358 ± 0.033 % vs UD Q8 K XL 33GB Same top p: 97.426 ± 0.041 %

Reddit r/LocalLLaMA ↗ · 2026-06-04

A community researcher shares a custom quantization recipe for Qwen3.6-27B that produces a smaller 30GB Q8 GGUF by keeping high-outlier sublayers in BF16, achieving better KLD and top-p metrics than Unsloth's 33GB Q8_K_XL variant.

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#llm-compression

LLM Compression with Jointly Optimizing Architectural and Quantization choices

arXiv cs.LG ↗ · 2026-06-04 Cached

Researchers from UiT and University of Oslo propose a differentiable NAS framework that jointly optimizes architectural configurations and mixed-precision quantization for LLM compression, achieving up to 1.4× faster inference or 6% higher accuracy across seven reasoning tasks compared to sequential NAS-then-quantization baselines.

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#llm-compression

Weight Pruning Amplifies Bias: A Multi-Method Study of Compressed LLMs for Edge AI

arXiv cs.LG ↗ · 2026-05-12 Cached

This study reveals a 'Smart Pruning Paradox' where activation-aware pruning methods like Wanda preserve perplexity but significantly amplify bias in Large Language Models deployed on edge devices.

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#llm-compression

AngelSlim/Hy-MT1.5-1.8B-1.25bit

Hugging Face Models Trending ↗ · 2026-04-28 Cached

Tencent's AngelSlim team released Hy-MT1.5-1.8B-1.25bit, a highly compressed 1.25-bit machine translation model supporting 33 languages that fits in 440MB for on-device use. It utilizes the Sherry quantization algorithm to achieve world-class translation quality comparable to much larger models.

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