xLSTM
The new efficient architecture for language models.
PAPER (NeurIPS 2024)
Abstract page for arXiv paper 2405.04517: xLSTM: Extended Long Short-Term Memory
CODE
Official JAX implementation of xLSTM including fast and efficient training and inference code. 7B model available at https://huggingface.co/NX-AI/xLSTM-7b. - NX-AI/xlstm-jax
Bio-xLSTM (ICLR 2025)
xLSTM 7B: A Recurrent LLM for Fast and Efficient Inference
Recent breakthroughs in solving reasoning, math and coding problems with Large Language Models (LLMs) have been enabled by investing substantial computation budgets at inference time. Therefore...
x-LSTM-7B language model CODE
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
NXAI (company)
At NXAI, we build advanced AI models based on our breakthrough xLSTM architecture. Our mission is to bring powerful, energy-efficient intelligence to companies across Europe – enabling automation, smarter decision-making, and meaningful technological progress
Robotics with xLSTM
Abstract page for arXiv paper 2410.22391: A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics Tasks
Vision-xLSTM: Pre-print
Transformers are widely used as generic backbones in computer vision, despite initially introduced for natural language processing. Recently, the Long Short-Term Memory (LSTM) has been extended to a scalable and performant architecture - the xLSTM - which overcomes long-standing LSTM limitations via exponential gating and parallelizable matrix memory structure. In this report, we introduce Vision-LSTM (ViL), an adaption of the xLSTM building blocks to computer vision. ViL comprises a stack of xLSTM blocks where odd blocks process the sequence of patch tokens from top to bottom while even blocks go from bottom to top. Experiments show that ViL holds promise to be further deployed as new generic backbone for computer vision architectures.
Jobs: PhD and PostDoc at ELLIS Linz
Careers at NXAI (xLSTM)
Careers
Tiled Flash Linear Attention
Abstract page for arXiv paper 2503.14376: Tiled Flash Linear Attention: More Efficient Linear RNN and xLSTM Kernels
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