This leap is made possible by near-lossless accuracy under 4-bit weight and KV cache quantization, allowing developers to process massive datasets without server-grade infrastructure.
Jr., T. (2026) Finite Propagation and the Regime Structure of Reality — Classicality and Geometry as Constraint-Limited Phenomena. Open Journal of Philosophy, 16, 138-150. doi: ...
Abstract: Image processing is one of the most promising applications for quantum machine learning. Quanvolutional neural networks with nontrainable parameters are the preferred solution to run on ...
[2025.09.25]: 🔥🔥🔥 We released a toolkit that tests the impact of numerical precision and enables deterministic LLM inference. This helps eliminate the training–inference mismatch in reinforcement ...
This repository contains the official PyTorch implementation for the ECCV2024 paper "AdaLog: Post-Training Quantization for Vision Transformers with Adaptive Logarithm Quantizer". AdaLog adapts the ...
Abstract: This paper presents Spiking U-Net++, a novel framework for converting artificial neural networks (ANNs) to spiking neural networks (SNNs) with complex skip connections. We address three key ...