Great viedo thank you! What I didn't grasp quite well is that, let's say I'm merging 2 models. One is trained on maths, other is trained on coding. Do we expect the merged model to perform high level in both tasks?
Thank you. It's up to you, depending on how much you want to "influence" the base model. mergekit has a parameter called 'density': fraction of weights in differences from the base model to retain. Example at github.com/arcee-ai/mergekit/blob/edd3817e4a470c7a959ef4c505f52a650a46ff07/examples/ties.yml
I can't help wondering if there is an experiment which really fully discovers those technique like applying to all kinds of models or combining different methods together?
