PeakWeights

Data-Free Discovery of Critical LLM Parameters

Thiyagarajan M (Kalmantic Labs) & Vamshi Ambati (CMU, IIIT Hyderabad)

December 2025

Abstract

Post-training quantization of large language models introduces errors that degrade quality, but not all weights contribute equally to this degradation. PeakWeights identifies critical weights in a single forward pass without calibration data. Weight magnitude multiplied by peak activation closely tracks worst-case quantization error. Across five architectures, protecting 50 weights during 4-bit quantization recovers 61-99% of lost perplexity.

50
weights protected out of 7 billion recovers 90-99% of quantization loss
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Results

Model FP16 4-bit +PeakWeights Recovery
Qwen2.5-7B 10.62 11.51 10.63 99%
SmolLM2-1.7B 17.92 24.56 17.99 99%
Phi-3-mini 11.65 12.67 11.68 97%
DeepSeek-R1-7B 70.73 73.52 70.91 93%
Mistral-7B 13.44 13.80 13.58 61%

Perplexity on WikiText-103. Lower is better. Recovery = (4bit - protected) / (4bit - FP16).

Cite this paper
@article{peakweights2025, title={PeakWeights: Data-Free Discovery of Critical LLM Parameters}, author={Maruthavanan, Thiyagarajan and Ambati, Vamshi}, year={2025}, url={https://github.com/Kalmantic/peakweights} }