112 citations · 155 across the 22 of their papers we have counts for
4 papers · 1 filter
Collaboratively adding new knowledge to an LLM
Rhui Dih Lee, Laura Wynter
We address the question of how to successively add new knowledge to an LLM whilst retaining previously-added knowledge. We consider two settings, semi-cooperative and fully-coopera…
Flexible and Effective Mixing of Large Language Models into a Mixture of Domain Experts
Rhui Dih Lee, Laura Wynter, Raghu Kiran Ganti
We present a toolkit for creating low-cost Mixture-of-Domain-Experts (MOE) from trained models. The toolkit can be used for creating a mixture from models or from adapters. We perf…
Enhancing Training Efficiency Using Packing with Flash Attention
Achintya Kundu, Rhui Dih Lee, Laura Wynter +2
Padding is often used in tuning LLM models by adding special tokens to shorter training examples to match the length of the longest sequence in each batch. While this ensures unifo…
Efficiently Distilling LLMs for Edge Applications
Achintya Kundu, Fabian Lim, Aaron Chew +3
Supernet training of LLMs is of great interest in industrial applications as it confers the ability to produce a palette of smaller models at constant cost, regardless of the numbe…