import{S as Es,i as $s,s as ws,e as o,k as n,w as u,t as f,M as Ts,c as s,d as l,m as d,a as r,x as h,h as i,b as m,N as ks,G as t,g as p,y as b,q as M,o as v,B as y,v as Js}from"../chunks/vendor-hf-doc-builder.js";import{T as _s}from"../chunks/Tip-hf-doc-builder.js";import{I as pe}from"../chunks/IconCopyLink-hf-doc-builder.js";import{C}from"../chunks/CodeBlock-hf-doc-builder.js";import{D as Fs}from"../chunks/DocNotebookDropdown-hf-doc-builder.js";function Zs(Ae){let c,w,E,T,_,J,W,$,F,j;return{c(){c=o("p"),w=o("code"),E=f("AutoModelFor"),T=f(" \uD074\uB798\uC2A4\uB098 \uAE30\uBCF8 \uBAA8\uB378 \uD074\uB798\uC2A4(\uC608: "),_=o("code"),J=f("OPTForCausalLM"),W=f(" \uB610\uB294 "),$=o("code"),F=f("LlamaForCausalLM"),j=f(") \uC911 \uD558\uB098\uB97C \uC0AC\uC6A9\uD558\uC5EC PEFT \uC5B4\uB311\uD130\uB97C \uAC00\uC838\uC62C \uC218 \uC788\uC2B5\uB2C8\uB2E4.")},l(k){c=s(k,"P",{});var U=r(c);w=s(U,"CODE",{});var R=r(w);E=i(R,"AutoModelFor"),R.forEach(l),T=i(U," \uD074\uB798\uC2A4\uB098 \uAE30\uBCF8 \uBAA8\uB378 \uD074\uB798\uC2A4(\uC608: "),_=s(U,"CODE",{});var g=r(_);J=i(g,"OPTForCausalLM"),g.forEach(l),W=i(U," \uB610\uB294 "),$=s(U,"CODE",{});var Ne=r($);F=i(Ne,"LlamaForCausalLM"),Ne.forEach(l),j=i(U,") \uC911 \uD558\uB098\uB97C \uC0AC\uC6A9\uD558\uC5EC PEFT \uC5B4\uB311\uD130\uB97C \uAC00\uC838\uC62C \uC218 \uC788\uC2B5\uB2C8\uB2E4."),U.forEach(l)},m(k,U){p(k,c,U),t(c,w),t(w,E),t(c,T),t(c,_),t(_,J),t(c,W),t(c,$),t($,F),t(c,j)},d(k){k&&l(c)}}}function Us(Ae){let c,w,E,T,_,J,W;return{c(){c=o("p"),w=o("code"),E=f("Trainer"),T=f("\uB97C \uC0AC\uC6A9\uD558\uC5EC \uBAA8\uB378\uC744 \uBBF8\uC138 \uC870\uC815\uD558\uB294 \uAC83\uC774 \uC775\uC219\uD558\uC9C0 \uC54A\uB2E4\uBA74 "),_=o("a"),J=f("\uC0AC\uC804\uD6C8\uB828\uB41C \uBAA8\uB378\uC744 \uBBF8\uC138 \uC870\uC815\uD558\uAE30"),W=f(" \uD29C\uD1A0\uB9AC\uC5BC\uC744 \uD655\uC778\uD558\uC138\uC694."),this.h()},l($){c=s($,"P",{});var F=r(c);w=s(F,"CODE",{});var j=r(w);E=i(j,"Trainer"),j.forEach(l),T=i(F,"\uB97C \uC0AC\uC6A9\uD558\uC5EC \uBAA8\uB378\uC744 \uBBF8\uC138 \uC870\uC815\uD558\uB294 \uAC83\uC774 \uC775\uC219\uD558\uC9C0 \uC54A\uB2E4\uBA74 "),_=s(F,"A",{href:!0});var k=r(_);J=i(k,"\uC0AC\uC804\uD6C8\uB828\uB41C \uBAA8\uB378\uC744 \uBBF8\uC138 \uC870\uC815\uD558\uAE30"),k.forEach(l),W=i(F," \uD29C\uD1A0\uB9AC\uC5BC\uC744 \uD655\uC778\uD558\uC138\uC694."),F.forEach(l),this.h()},h(){m(_,"href","training")},m($,F){p($,c,F),t(c,w),t(w,E),t(c,T),t(c,_),t(_,J),t(c,W)},d($){$&&l(c)}}}function Cs(Ae){let c,w,E,T,_,J,W,$,F,j,k,U,R,g,Ne,Ht,Pl,Qe,Lt,Vl,G,ll,co,St,Ie,Yt,Gl,S,xt,fe,zt,qt,Xl,X,Y,tl,ie,Dt,al,Ot,Al,He,Kt,Nl,ne,Ql,Le,ea,Il,de,Hl,A,x,ol,me,la,sl,ta,Ll,Se,aa,Sl,P,rl,ce,oa,sa,pl,ue,ra,pa,fl,he,fa,Yl,z,ia,be,na,da,xl,N,q,il,Me,ma,nl,ca,zl,V,ua,dl,ha,ba,ml,Ma,va,ql,D,cl,ya,_a,Ye,ul,Ea,$a,Dl,ve,Ol,O,Kl,ye,hl,wa,Ta,et,_e,lt,Q,K,bl,Ee,ka,Ml,Ja,tt,Z,vl,Fa,Za,yl,Ua,Ca,_l,Wa,ja,El,Ba,Ra,$l,ga,Pa,at,$e,ot,I,ee,wl,we,Va,Tl,Ga,st,le,Xa,kl,Aa,Na,rt,Te,pt,xe,Qa,ft,ke,it,te,Ia,Jl,Ha,La,nt,Je,dt,H,ae,Fl,Fe,Sa,Zl,Ya,mt,ze,xa,ct,Ze,ut,qe,za,ht,Ue,bt,L,oe,Ul,Ce,qa,Cl,Da,Mt,se,Oa,Wl,Ka,eo,vt,re,yt,De,We,lo,jl,to,ao,_t,je,Et,Be,Bl,oo,$t,Re,wt,ge,Pe,so,Rl,ro,po,Tt,Ve,kt,Oe,fo,Jt,Ge,Ft;return J=new pe({}),k=new Fs({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/peft.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/pytorch/peft.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/tensorflow/peft.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/peft.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/pytorch/peft.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/tensorflow/peft.ipynb"}]}}),ie=new pe({}),ne=new C({props:{code:"cGlwJTIwaW5zdGFsbCUyMHBlZnQ=",highlighted:"pip install peft"}}),de=new C({props:{code:"cGlwJTIwaW5zdGFsbCUyMGdpdCUyQmh0dHBzJTNBJTJGJTJGZ2l0aHViLmNvbSUyRmh1Z2dpbmdmYWNlJTJGcGVmdC5naXQ=",highlighted:"pip install git+https://github.com/huggingface/peft.git"}}),me=new pe({}),Me=new pe({}),ve=new C({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbEZvckNhdXNhbExNJTJDJTIwQXV0b1Rva2VuaXplciUwQSUwQXBlZnRfbW9kZWxfaWQlMjAlM0QlMjAlMjJ5YmVsa2FkYSUyRm9wdC0zNTBtLWxvcmElMjIlMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckNhdXNhbExNLmZyb21fcHJldHJhaW5lZChwZWZ0X21vZGVsX2lkKQ==",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, AutoTokenizer

peft_model_id = <span class="hljs-string">&quot;ybelkada/opt-350m-lora&quot;</span>
model = AutoModelForCausalLM.from_pretrained(peft_model_id)`}}),O=new _s({props:{$$slots:{default:[Zs]},$$scope:{ctx:Ae}}}),_e=new C({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbEZvckNhdXNhbExNJTJDJTIwQXV0b1Rva2VuaXplciUwQSUwQW1vZGVsX2lkJTIwJTNEJTIwJTIyZmFjZWJvb2slMkZvcHQtMzUwbSUyMiUwQXBlZnRfbW9kZWxfaWQlMjAlM0QlMjAlMjJ5YmVsa2FkYSUyRm9wdC0zNTBtLWxvcmElMjIlMEElMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckNhdXNhbExNLmZyb21fcHJldHJhaW5lZChtb2RlbF9pZCklMEFtb2RlbC5sb2FkX2FkYXB0ZXIocGVmdF9tb2RlbF9pZCk=",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, AutoTokenizer

model_id = <span class="hljs-string">&quot;facebook/opt-350m&quot;</span>
peft_model_id = <span class="hljs-string">&quot;ybelkada/opt-350m-lora&quot;</span>

model = AutoModelForCausalLM.from_pretrained(model_id)
model.load_adapter(peft_model_id)`}}),Ee=new pe({}),$e=new C({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbEZvckNhdXNhbExNJTJDJTIwQXV0b1Rva2VuaXplciUwQSUwQXBlZnRfbW9kZWxfaWQlMjAlM0QlMjAlMjJ5YmVsa2FkYSUyRm9wdC0zNTBtLWxvcmElMjIlMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckNhdXNhbExNLmZyb21fcHJldHJhaW5lZChwZWZ0X21vZGVsX2lkJTJDJTIwZGV2aWNlX21hcCUzRCUyMmF1dG8lMjIlMkMlMjBsb2FkX2luXzhiaXQlM0RUcnVlKQ==",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, AutoTokenizer

peft_model_id = <span class="hljs-string">&quot;ybelkada/opt-350m-lora&quot;</span>
model = AutoModelForCausalLM.from_pretrained(peft_model_id, device_map=<span class="hljs-string">&quot;auto&quot;</span>, load_in_8bit=<span class="hljs-literal">True</span>)`}}),we=new pe({}),Te=new C({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer
<span class="hljs-keyword">from</span> peft <span class="hljs-keyword">import</span> PeftConfig

model_id = <span class="hljs-string">&quot;facebook/opt-350m&quot;</span>
model = AutoModelForCausalLM.from_pretrained(model_id)

lora_config = LoraConfig(
    target_modules=[<span class="hljs-string">&quot;q_proj&quot;</span>, <span class="hljs-string">&quot;k_proj&quot;</span>],
    init_lora_weights=<span class="hljs-literal">False</span>
)

model.add_adapter(lora_config, adapter_name=<span class="hljs-string">&quot;adapter_1&quot;</span>)`}}),ke=new C({props:{code:"JTIzJTIwYXR0YWNoJTIwbmV3JTIwYWRhcHRlciUyMHdpdGglMjBzYW1lJTIwY29uZmlnJTBBbW9kZWwuYWRkX2FkYXB0ZXIobG9yYV9jb25maWclMkMlMjBhZGFwdGVyX25hbWUlM0QlMjJhZGFwdGVyXzIlMjIp",highlighted:`<span class="hljs-comment"># attach new adapter with same config</span>
model.add_adapter(lora_config, adapter_name=<span class="hljs-string">&quot;adapter_2&quot;</span>)`}}),Je=new C({props:{code:"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",highlighted:`<span class="hljs-comment"># use adapter_1</span>
model.set_adapter(<span class="hljs-string">&quot;adapter_1&quot;</span>)
output = model.generate(**inputs)
<span class="hljs-built_in">print</span>(tokenizer.decode(output_disabled[<span class="hljs-number">0</span>], skip_special_tokens=<span class="hljs-literal">True</span>))

<span class="hljs-comment"># use adapter_2</span>
model.set_adapter(<span class="hljs-string">&quot;adapter_2&quot;</span>)
output_enabled = model.generate(**inputs)
<span class="hljs-built_in">print</span>(tokenizer.decode(output_enabled[<span class="hljs-number">0</span>], skip_special_tokens=<span class="hljs-literal">True</span>))`}}),Fe=new pe({}),Ze=new C({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer
<span class="hljs-keyword">from</span> peft <span class="hljs-keyword">import</span> PeftConfig

model_id = <span class="hljs-string">&quot;facebook/opt-350m&quot;</span>
adapter_model_id = <span class="hljs-string">&quot;ybelkada/opt-350m-lora&quot;</span>
tokenizer = AutoTokenizer.from_pretrained(model_id)
text = <span class="hljs-string">&quot;Hello&quot;</span>
inputs = tokenizer(text, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

model = AutoModelForCausalLM.from_pretrained(model_id)
peft_config = PeftConfig.from_pretrained(adapter_model_id)

<span class="hljs-comment"># to initiate with random weights</span>
peft_config.init_lora_weights = <span class="hljs-literal">False</span>

model.add_adapter(peft_config)
model.enable_adapters()
output = model.generate(**inputs)`}}),Ue=new C({props:{code:"bW9kZWwuZGlzYWJsZV9hZGFwdGVycygpJTBBb3V0cHV0JTIwJTNEJTIwbW9kZWwuZ2VuZXJhdGUoKippbnB1dHMp",highlighted:`model.disable_adapters()
output = model.generate(**inputs)`}}),Ce=new pe({}),re=new _s({props:{$$slots:{default:[Us]},$$scope:{ctx:Ae}}}),je=new C({props:{code:"ZnJvbSUyMHBlZnQlMjBpbXBvcnQlMjBMb3JhQ29uZmlnJTBBJTBBcGVmdF9jb25maWclMjAlM0QlMjBMb3JhQ29uZmlnKCUwQSUyMCUyMCUyMCUyMGxvcmFfYWxwaGElM0QxNiUyQyUwQSUyMCUyMCUyMCUyMGxvcmFfZHJvcG91dCUzRDAuMSUyQyUwQSUyMCUyMCUyMCUyMHIlM0Q2NCUyQyUwQSUyMCUyMCUyMCUyMGJpYXMlM0QlMjJub25lJTIyJTJDJTBBJTIwJTIwJTIwJTIwdGFza190eXBlJTNEJTIyQ0FVU0FMX0xNJTIyJTJDJTBBKQ==",highlighted:`<span class="hljs-keyword">from</span> peft <span class="hljs-keyword">import</span> LoraConfig

peft_config = LoraConfig(
    lora_alpha=<span class="hljs-number">16</span>,
    lora_dropout=<span class="hljs-number">0.1</span>,
    r=<span class="hljs-number">64</span>,
    bias=<span class="hljs-string">&quot;none&quot;</span>,
    task_type=<span class="hljs-string">&quot;CAUSAL_LM&quot;</span>,
)`}}),Re=new C({props:{code:"bW9kZWwuYWRkX2FkYXB0ZXIocGVmdF9jb25maWcp",highlighted:"model.add_adapter(peft_config)"}}),Ve=new C({props:{code:"dHJhaW5lciUyMCUzRCUyMFRyYWluZXIobW9kZWwlM0Rtb2RlbCUyQyUyMC4uLiklMEF0cmFpbmVyLnRyYWluKCk=",highlighted:`trainer = Trainer(model=model, ...)
trainer.train()`}}),Ge=new C({props:{code:"bW9kZWwuc2F2ZV9wcmV0cmFpbmVkKHNhdmVfZGlyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yQ2F1c2FsTE0uZnJvbV9wcmV0cmFpbmVkKHNhdmVfZGlyKQ==",highlighted:`model.save_pretrained(save_dir)
model = AutoModelForCausalLM.from_pretrained(save_dir)`}}),{c(){c=o("meta"),w=n(),E=o("h1"),T=o("a"),_=o("span"),u(J.$$.fragment),W=n(),$=o("span"),F=f("\u{1F917} PEFT\uB85C \uC5B4\uB311\uD130 \uAC00\uC838\uC624\uAE30"),j=n(),u(k.$$.fragment),U=n(),R=o("p"),g=o("a"),Ne=f("Parameter-Efficient Fine Tuning (PEFT)"),Ht=f(" \uBC29\uBC95\uC740 \uC0AC\uC804\uD6C8\uB828\uB41C \uBAA8\uB378\uC758 \uB9E4\uAC1C\uBCC0\uC218\uB97C \uBBF8\uC138 \uC870\uC815 \uC911 \uACE0\uC815\uC2DC\uD0A4\uACE0, \uADF8 \uC704\uC5D0 \uD6C8\uB828\uD560 \uC218 \uC788\uB294 \uB9E4\uC6B0 \uC801\uC740 \uC218\uC758 \uB9E4\uAC1C\uBCC0\uC218(\uC5B4\uB311\uD130)\uB97C \uCD94\uAC00\uD569\uB2C8\uB2E4. \uC5B4\uB311\uD130\uB294 \uC791\uC5C5\uBCC4 \uC815\uBCF4\uB97C \uD559\uC2B5\uD558\uB3C4\uB85D \uD6C8\uB828\uB429\uB2C8\uB2E4. \uC774 \uC811\uADFC \uBC29\uC2DD\uC740 \uC644\uC804\uD788 \uBBF8\uC138 \uC870\uC815\uB41C \uBAA8\uB378\uC5D0 \uD544\uC801\uD558\uB294 \uACB0\uACFC\uB97C \uC0DD\uC131\uD558\uBA74\uC11C, \uBA54\uBAA8\uB9AC 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