Model Merging
topic on 1 show · 4 statements across 3 episodes
4 statements about Model Merging, every show
Averaging weights of models trained on separate domains works effectively
“You can have a model trained on code, and a model trained on math, and a model trained on Spanish, and you can literally average the weights, and it works.”
Howard: Developers should distribute merged adapters rather than merged models
“To explain, it's not that you shouldn't merge models, it's that you shouldn't be distributing a merged model. You should distribute it a merged adapter. 99% of the time. And actually often, one of the best things happening in the model merging world is actuall…”
Huang: Model merging is polluting open LLM leaderboards
“That is extremely interesting from the developer community, and I want to see more of it except it is, to a certain extent, kind of polluting the leaderboards these days, because it's so targeted, and like, now you can kind of game the metric by just finding a…”
Huang: LoRA merging succeeds on style but fails on complex capabilities
“Like, I will not lie to say I'm really surprised how effective it is sometimes, but I do notice that for more complex abilities other than, like, more stylistic stuff, it does, it kind of falls through, because maybe it's, it requires a much deeper path in the…”