Training-free Post-training Efficient Sub-quadratic Complexity Attention. Implemented with OpenAI Triton.
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Updated
Nov 3, 2025 - Python
Training-free Post-training Efficient Sub-quadratic Complexity Attention. Implemented with OpenAI Triton.
[NeurIPS 2025 Oral] Official Code for Exploring Diffusion Transformer Designs via Grafting
The Cerebros package is an ultra-precise Neural Architecture Search (NAS) / AutoML that is intended to much more closely mimic biological neurons than conventional neural network architecture strategies.
A PyTorch implementation of MEGABYTE. This multi-scale transformer architecture has the excellent features of tokenization-free and sub-quadratic attention. The paper link: https://arxiv.org/abs/2305.07185
Conformal Geometric Algebra (CGA) with efficient sequence modeling by introducing a recurrent rotor mechanism and a novel bit-masked hardware kernel that solves the computational bottleneck of Clifford products.
Pure PyTorch + 🤗 Transformers reimplementation of Megalodon (CEMA + chunked attention) - readable, hackable, no CUDA kernels required
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