#extreme-quantization News & Analysis

2 articles tagged with #extreme-quantization. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AIBullisharXiv – CS AI · May 297/10

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HARP: Hadamard-Preconditioned Adaptive Rotation Processor for Extreme LLM Quantization

Researchers introduce HARP, a learnable adaptive rotation processor that improves extreme low-bit quantization for large language models by replacing fixed Hadamard transforms with optimizable structured orthogonal processors. The technique maintains full-precision equivalence while achieving better perplexity and accuracy across 2-4 bit quantization settings on models up to 70B parameters, with deployment speeds competitive with standard approaches.

🏢 Perplexity

AINeutralarXiv – CS AI · May 126/10

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Fitting Is Not Enough: Smoothness in Extremely Quantized LLMs

Researchers demonstrate that extreme quantization of large language models causes degradation beyond numerical precision loss, specifically through reduced smoothness in prediction spaces. They introduce smoothness-preserving techniques in post-training and quantization-aware training that improve generation quality independent of numerical accuracy gains.