EPFL Machine Learning and Optimization Laboratory
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- DISCO is a code-free and installation-free browser platform that allows any non-technical user to collaboratively train machine learning models without sharing any private data.
llm-baselines
Publicprefixlm
Publicgetting-started
Publicpowersgd
PublicPractical low-rank gradient compression for distributed optimization: https://arxiv.org/abs/1905.13727CoMiGS
Public- Exploration on-device self-supervised collaborative fine-tuning of large language models with limited local data availability, using Low-Rank Adaptation (LoRA). We introduce three distinct trust-weighted gradient aggregation schemes: weight similarity-based, prediction similarity-based and validation performance-based.
error-feedback-SGD
PublicSGD with compressed gradients and error-feedback: https://arxiv.org/abs/1901.09847OptML_course
PublicREQ
PublicCoTFormer
Publicgetting-started-lauzhack
PublicDenseFormer
PublicDoGE
Publiclandmark-attention
Publicpam
Publicghost-noise
PublicoptML-pku
Publiccollaborative-attention
Publiceasy-summary
Publicrelaysgd
Publicepfml-utils
Publiccifar
PublicMLO internal cifar 10 / 100 default implementation / reference implementation. single machine, variable batch sizes, allowing maybe gradient compression. need to have clear documentation to make it easy to use, and so that we don't loose time with looking for hyperparameters. we can later keep it in sync with mlbench too, but self-contained is e…attention-cnn
Public