zqOuO / GWTLinks
☆13Updated 6 months ago
Alternatives and similar repositories for GWT
Users that are interested in GWT are comparing it to the libraries listed below
Sorting:
- ☆34Updated 4 months ago
- Work in progress.☆70Updated last month
- This repository contains code for the MicroAdam paper.☆19Updated 7 months ago
- ☆11Updated 5 months ago
- An extention to the GaLore paper, to perform Natural Gradient Descent in low rank subspace☆17Updated 9 months ago
- The evaluation framework for training-free sparse attention in LLMs☆88Updated last month
- [NeurIPS 2024] Low rank memory efficient optimizer without SVD☆30Updated last month
- ☆53Updated 10 months ago
- Flash-Muon: An Efficient Implementation of Muon Optimizer☆152Updated last month
- Pytorch implementation of "Oscillation-Reduced MXFP4 Training for Vision Transformers" on DeiT Model Pre-training☆24Updated last month
- ☆53Updated last year
- ☆9Updated 2 years ago
- ☆33Updated last month
- ☆83Updated 11 months ago
- ☆81Updated last year
- Here we will test various linear attention designs.☆62Updated last year
- Triton Implementation of HyperAttention Algorithm☆48Updated last year
- Official implementation of the ICML 2024 paper RoSA (Robust Adaptation)☆41Updated last year
- From GaLore to WeLore: How Low-Rank Weights Non-uniformly Emerge from Low-Rank Gradients. Ajay Jaiswal, Lu Yin, Zhenyu Zhang, Shiwei Liu,…☆47Updated 3 months ago
- ☆27Updated 9 months ago
- A fusion of a linear layer and a cross entropy loss, written for pytorch in triton.☆70Updated last year
- Griffin MQA + Hawk Linear RNN Hybrid☆88Updated last year
- Fast and memory-efficient exact attention☆69Updated 5 months ago
- ☆60Updated 4 months ago
- Unofficial Implementation of Selective Attention Transformer☆17Updated 9 months ago
- Code for "RSQ: Learning from Important Tokens Leads to Better Quantized LLMs"☆18Updated 2 months ago
- Code for NeurIPS 2024 Spotlight: "Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations"☆81Updated 9 months ago
- Reference implementation of "Softmax Attention with Constant Cost per Token" (Heinsen, 2024)☆24Updated last year
- 📄Small Batch Size Training for Language Models☆41Updated this week
- ☆49Updated last year