. . . . "computerProgram" . . . . . . . . . . " You only learn a few parameters, with your parameter \"efficient\" finetuning. 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 multitask learning that compresses the models, can keep many models and switch between them as necessary.\nAnother option to compress is to SVD the LORA, separately or\nto a shared space, saving the tiny differences\nhttps://x.com/RickardGabriels/status/1810368375455207470\n\n And just because we discussed compression, of course this is all just \"model compression\" if you want to compress to just save space, there are smarter ways:\nhttps://github.com/zipnn/zipnn\n\n" . "finetuning" . "low-rank-adapters" . "model-compression" . "multitask-learning" . "serving-systems" . . . . "webpage" . "webpage" . "webpage" . "webpage" . "forumPost" . "forumPost" . "forumPost" . . . . . . . . . . . . . "Leshem Choshen 🤖🤗 @ICML wanna talk?" . "RSA" . "MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEArHtI92jm8pAYVsvJabxLGfOT+7G0JyJGh2gwjB5x2pFPga6wWTd+rNBWWUZViIFnaJrBEsJpgdnoupLU9ppwn+khMiGRfxqGsDDzwHcj3Jc75CRys7d3etwXdBdoXfBgjsJiZBazwm13idr6tljRrC1TaEJBnRQAqzBw9cLDeGY77cSznzXT39feUGT168dpCSE9O6u/48DvvWVqciHGsH9cQ+LroJJVsMrorwtsdZnAK+q48wtIP6pIpw5shSJ5LnA0qeN/f4TvTFDV6ItYIXjiWWpTECc/Bxmfnyat3B5xWCu9nvz8fEs7Ns0TuzQwT3/K55iSKDEIi/E0nO97xwIDAQAB" . "lUGuTihITpSBPC1WF/Na8tXgPw2UeCdCKt1Z7VLl0Aq1DKhqi52cROTJZ0PhS/unqNjVpM+W8BiMHbwSNH5zDsT04upJOTVdHC2ghxFXO2jZ2YeFoZe8ZrOUkVrsO4fDytVI8AptUiDDOn3tWN13J70E+2utIO7SBjG/pitQ62t7Nj1N1qZVHehz7McKlIfP3IB6arWD9WOHIU4lYlpyYFsYE7dlHBvjdW8gr4xwPSChdB356QdPXYphIwjVZBlMujhZhStKs5oVdlfKnfqvYxf8Dfsdh+paShLwmBR3hfJ4NN8ADDkW/VDbOvDzYgqLb2decJxM2PStBT84SH0yRg==" . . . . "2024-09-11T18:07:24.837Z"^^ . . . . . "CoSMO Semantic Post" . . "0xf6ECcfD463afB464dcC85b051DF2E93E2646E6D2" .