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Evolver, model merging in a genetic algorithm
Improves on current merging techniques (my beloved TIES 🫣 )
Train diverse models
Merge regularly or take diff between two models
Update some parameters
Keep if good
Repeat
https://arxiv.org/abs/2406.12208
@jingli9111 @banting_liu @576gsk https://twitter.com/LChoshen/status/1803410440535326786/photo/1
Merging is aimed at taking many models and getting one that generalizes better, there are various methods for it, read more e.g. on TIES
https://x.com/prateeky2806/status/1665759148380758022
Genetic algorithms evolve models, in steps:
Create mutations (here new m = m_old + a(m_1-m_2)) m are models a some constant
Crossover, take some of the mutation and apply it, for each parameter randomly keep m_old or update to m_new
Survive, keep only the best performing on val
By sometimes merging and sometimes evolving (and dev sets) they improve over all current methods https://twitter.com/LChoshen/status/1803410445635653960/photo/1
In some sense, this can be seen as a better search in the region between the merged models, which we know is not equally good but all better than the edges
https://x.com/LChoshen/status/1729488495515713672 https://twitter.com/LChoshen/status/1803410447246250483/photo/1
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Leshem Choshen 🤖🤗 @ICML wanna talk?