From the 1 of 4 linked papers with an AI index.
4 papers
Agora: Collective and Permissionless Internet-Scale Pretraining of Large Language Models
Gil Avraham, Violetta Shevchenko, Hadi Mohaghegh Dolatabadi +9
Agora is a system that enables large language models to be trained collectively across many heterogeneous, internet‑connected GPUs, using pipeline‑parallel sharding and fault‑toler…
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,…
Arcee's MergeKit: A Toolkit for Merging Large Language Models
Charles Goddard, Shamane Siriwardhana, Malikeh Ehghaghi +5
The rapid expansion of the open-source language model landscape presents an opportunity to merge the competencies of these model checkpoints by combining their parameters. Advances…
Merging in a Bottle: Differentiable Adaptive Merging (DAM) and the Path from Averaging to Automation
Thomas Gauthier-Caron, Shamane Siriwardhana, Elliot Stein +5
By merging models, AI systems can combine the distinct strengths of separate language models, achieving a balance between multiple capabilities without requiring substantial retrai…