1 citations · 1 across the 4 of their papers we have counts for
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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,…
Mixtures of Subspaces for Bandwidth Efficient Context Parallel Training
Sameera Ramasinghe, Ajanthan Thalaiyasingam, Hadi Mohaghegh Dolatabadi +5
Pretraining language models with extended context windows enhances their ability to leverage rich information during generation. Existing methods split input sequences into chunks,…