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The rest is💩\n\nA whole line of works🧵 shows that by throwing redundancy\nwe can get better LoRas, keep less memory and of course model merge https://twitter.com/LChoshen/status/1833879920348422216/photo/1\n\n ComPeft shows you can improve LoRAs by pruning aggressively and making the remaining weights binary (+/-)\nIt also means parameter efficiency still relies on overparametrization(but only during training)\nhttps://x.com/prateeky2806/status/1727589818618523783\nLaser shows it on full models\nhttps://x.com/pratyusha_PS/status/1739025292805468212 https://twitter.com/LChoshen/status/1833879922500080084/photo/1\n\n In merging, many find that with only those few weights one can make a \"multitask\" model, keeping the important ones for each model and switching.\nthose e.g. 1% of the weights also represent tasks well\n\nMany..\nhttps://www.alphaxiv.org/abs/2408.13656\nhttps://www.alphaxiv.org/pdf/2405.07813\nhttps://www.alphaxiv.org/pdf/2310.01886\n\n Those works are focused on efficient 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