5 papers
Communication-Efficient LLM Adaptation over Decentralized GPU Meshes
Sameera Ramasinghe, Shamane Siriwardhana, Thalaiyasingam Ajanthan +8
Decentralized training enables large-model training over low-end GPUs and internet-grade connections, but communication along both data-parallel and pipeline-parallel axes becomes…
Agora: Collective and Permissionless Internet-Scale Pretraining of Large Language Models
Gil Avraham, Violetta Shevchenko, Hadi Mohaghegh Dolatabadi +9
Training large language models at the multi-billion to trillion parameter scale is confined to datacenters, where data-parallel (DP) and model-parallel (MP) techniques presume homo…
Factored Gossip DiLoCo: Reducing Blocking Communication in DiLoCo
Chamin Hewa Koneputugodage, Thalaiyasingam Ajanthan, Sameera Ramasinghe +7
To make large-scale distributed training practical outside high-bandwidth datacenters, we must reduce blocking, high-volume synchronization. While DiLoCo communicates infrequently,…
Taming Curvature: Architecture Warm-Up for Stable Transformer Training
Sameera Ramasinghe, Ajanthan Thalaiyasingam, Hadi Mohaghegh Dolatabadi +6
Training billion-parameter Transformers is often brittle, with transient loss spikes and divergence that waste compute. Even though the recently developed Edge of Stability (EoS) t…
NuMuon: Nuclear-Norm-Constrained Muon for Compressible LLM Training
Hadi Mohaghegh Dolatabadi, Thalaiyasingam Ajanthan, Sameera Ramasinghe +7
The rapid progress of large language models (LLMs) is increasingly constrained by memory and deployment costs, motivating compression methods for practical deployment. Many state-o…