[{"data":1,"prerenderedAt":80},["ShallowReactive",2],{"post-document:\u002Fdeep_learning\u002Flora\u002F2026\u002F08\u002F29\u002Ftransformer-lora\u002F":3},{"id":4,"title":5,"body":6,"categories":61,"date":64,"description":65,"extension":66,"image":67,"key_concepts":67,"last_modified_at":67,"legacyPath":68,"meta":69,"navigation":71,"part":67,"path":72,"published":71,"robots":67,"seo":73,"series":67,"stem":74,"strengths":67,"summary":67,"tags":75,"tradeoffs":67,"__hash__":79},"posts\u002Fposts\u002FDeep_Learning\u002FLoRA\u002FTransformer_LoRA.md","Transformer에서 LoRA 적용 대상 정하기",{"type":7,"value":8,"toc":57},"minimark",[9,13,16,21,24,27,30,33,36,39,42,45,48,51,54],[10,11,12],"p",{},"v_proj",[10,14,15],{},"Wv + ΔWv",[17,18,20],"h1",{"id":19},"_16-처음에는-왜-q_proj와-v_proj-이야기가-많이-나오는가","16. 처음에는 왜 q_proj와 v_proj 이야기가 많이 나오는가",[10,22,23],{},"원래 LoRA 논문에서는 Attention의 특정 Projection에 LoRA를 적용하는 실험을 많이 했다.",[10,25,26],{},"그래서 흔히",[10,28,29],{},"q_proj\nv_proj",[10,31,32],{},"에 적용하는 설정을 볼 수 있다.",[10,34,35],{},"현재 LLM Fine-Tuning에서는 좀 더 넓게",[10,37,38],{},"q_proj\nk_proj\nv_proj\no_proj",[10,40,41],{},"gate_proj\nup_proj\ndown_proj",[10,43,44],{},"쉽게 이야기 해서",[10,46,47],{},"Attention만 수정",[10,49,50],{},"vs",[10,52,53],{},"Attention + MLP까지 수정",[10,55,56],{},"일단 당장은 추가하지 말고, MSA부터 추가하자",{"title":58,"searchDepth":59,"depth":59,"links":60},"",2,[],[62,63],"Deep_Learning","LoRA","2026-08-29 15:28:08 +0900","Transformer의 Attention과 MLP에서 LoRA를 적용할 Projection과 적용 범위를 정리한다.","md",null,"\u002Fdeep_learning\u002Flora\u002F2026\u002F08\u002F29\u002Ftransformer-lora\u002F",{"layout":70},"post",true,"\u002Fposts\u002Fdeep_learning\u002Flora\u002Ftransformer_lora",{"title":5,"description":65},"posts\u002FDeep_Learning\u002FLoRA\u002FTransformer_LoRA",[76,77,63,78],"Deep Learning","Transformer","PEFT","-FgRN6Dj3_QHpQ4NzxZ5T0bPOR32oRFrXNfUI4KaYg8",1788744788961]