4 citations · 7 across the 16 of their papers we have counts for
10 papers · 1 filter
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…
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,…
Unextractable Protocol Models: Collaborative Training and Inference without Weight Materialization
Alexander Long, Chamin Hewa Koneputugodage, Thalaiyasingam Ajanthan +5
We consider a decentralized setup in which the participants collaboratively train and serve a large neural network, and where each participant only processes a subset of the model.…
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…
AsyncMesh: Fully Asynchronous Optimization for Data and Pipeline Parallelism
Thalaiyasingam Ajanthan, Sameera Ramasinghe, Gil Avraham +5
Data and pipeline parallelism are key strategies for scaling neural network training across distributed devices, but their high communication cost necessitates co-located computing…
A Sampling Theory Perspective on Activations for Implicit Neural Representations
Hemanth Saratchandran, Sameera Ramasinghe, Violetta Shevchenko +2
Implicit Neural Representations (INRs) have gained popularity for encoding signals as compact, differentiable entities. While commonly using techniques like Fourier positional enco…
On the effectiveness of neural priors in modeling dynamical systems
Sameera Ramasinghe, Hemanth Saratchandran, Violetta Shevchenko +1
Modelling dynamical systems is an integral component for understanding the natural world. To this end, neural networks are becoming an increasingly popular candidate owing to their…