Datasets:

PureOne
/

Eves-Embrace-of-Extreme-Quantization-v1.0

cff-version: 1.2.0
message: "Please cite this research reference with its evidence limitations."
title: "Eve's Embrace of Extreme Quantization"
version: "1.0"
date-released: "2026-10-12"
authors:
  - name: "Artificial Hyperintelligence Eve"
  - family-names: "Nowicki"
    given-names: "Maciej"
abstract: "Source-faithful paid finite fork quantization with sparse and matched low-rank terminal-probability repair, selected-plus-tail native KL, strict finite checkpoint compilation, actual complete-byte incumbent selection, sharp dimension-dependent observable and golden-ratio conditioning bounds, and protected-tree endpoint analysis. A frozen six-projection first-block OPT-125M diagnostic distinguishes aliases, calibration improvements, fresh quality and partial-model storage. Worldwide priority, whole-model SOTA and packed GPU performance remain unestablished."
license: MIT

	cff-version: 1.2.0
	message: "Please cite this research reference with its evidence limitations."
	title: "Eve's Embrace of Extreme Quantization"
	version: "1.0"
	date-released: "2026-10-12"
	authors:
	- name: "Artificial Hyperintelligence Eve"
	- family-names: "Nowicki"
	given-names: "Maciej"
	abstract: "Source-faithful paid finite fork quantization with sparse and matched low-rank terminal-probability repair, selected-plus-tail native KL, strict finite checkpoint compilation, actual complete-byte incumbent selection, sharp dimension-dependent observable and golden-ratio conditioning bounds, and protected-tree endpoint analysis. A frozen six-projection first-block OPT-125M diagnostic distinguishes aliases, calibration improvements, fresh quality and partial-model storage. Worldwide priority, whole-model SOTA and packed GPU performance remain unestablished."
	license: MIT