import{s as ws,f as _s,o as bs,n as tt}from"../chunks/scheduler.889ebb13.js";import{S as Ms,i as ys,g as r,s as i,u as w,A as Ts,h as o,f as t,c as p,j as x,v as _,y as h,k as g,o as k,a as l,w as b,d as M,t as y,x as T}from"../chunks/index.8980e686.js";import{T as ks}from"../chunks/Tip.7191df11.js";import{Y as Cs}from"../chunks/Youtube.532e53e0.js";import{I as le}from"../chunks/IconCopyLink.357761cd.js";import{C as U}from"../chunks/CodeBlock.25fc3430.js";import{F as vs,M as mt}from"../chunks/Markdown.31057263.js";function xs(W){let n,m='커뮤니티에 모델을 공유하려면, <a href="https://huggingface.co/join" rel="nofollow">huggingface.co</a>에 계정이 필요합니다. 기존 조직에 가입하거나 새로 만들 수도 있습니다.';return{c(){n=r("p"),n.innerHTML=m},l(a){n=o(a,"P",{"data-svelte-h":!0}),h(n)!=="svelte-1x1qpix"&&(n.innerHTML=m)},m(a,u){l(a,n,u)},p:tt,d(a){a&&t(n)}}}function Hs(W){let n,m="체크포인트를 TensorFlow에서 PyTorch로 변환하려면 <code>from_tf=True</code>를 지정하세요:",a,u,$;return u=new U({props:{code:"cHRfbW9kZWwlMjAlM0QlMjBEaXN0aWxCZXJ0Rm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIycGF0aCUyRnRvJTJGYXdlc29tZS1uYW1lLXlvdS1waWNrZWQlMjIlMkMlMjBmcm9tX3RmJTNEVHJ1ZSklMEFwdF9tb2RlbC5zYXZlX3ByZXRyYWluZWQoJTIycGF0aCUyRnRvJTJGYXdlc29tZS1uYW1lLXlvdS1waWNrZWQlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>pt_model = DistilBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;path/to/awesome-name-you-picked&quot;</span>, from_tf=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pt_model.save_pretrained(<span class="hljs-string">&quot;path/to/awesome-name-you-picked&quot;</span>)`}}),{c(){n=r("p"),n.innerHTML=m,a=i(),w(u.$$.fragment)},l(f){n=o(f,"P",{"data-svelte-h":!0}),h(n)!=="svelte-ulhn3k"&&(n.innerHTML=m),a=p(f),_(u.$$.fragment,f)},m(f,C){l(f,n,C),l(f,a,C),b(u,f,C),$=!0},p:tt,i(f){$||(M(u.$$.fragment,f),$=!0)},o(f){y(u.$$.fragment,f),$=!1},d(f){f&&(t(n),t(a)),T(u,f)}}}function Js(W){let n,m;return n=new mt({props:{$$slots:{default:[Hs]},$$scope:{ctx:W}}}),{c(){w(n.$$.fragment)},l(a){_(n.$$.fragment,a)},m(a,u){b(n,a,u),m=!0},p(a,u){const $={};u&2&&($.$$scope={dirty:u,ctx:a}),n.$set($)},i(a){m||(M(n.$$.fragment,a),m=!0)},o(a){y(n.$$.fragment,a),m=!1},d(a){T(n,a)}}}function js(W){let n,m="체크포인트를 PyTorch에서 TensorFlow로 변환하려면 <code>from_pt=True</code>를 지정하세요:",a,u,$,f,C="그런 다음 새로운 체크포인트와 함께 새로운 TensorFlow 모델을 저장할 수 있습니다:",H,j,F;return u=new U({props:{code:"dGZfbW9kZWwlMjAlM0QlMjBURkRpc3RpbEJlcnRGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZCglMjJwYXRoJTJGdG8lMkZhd2Vzb21lLW5hbWUteW91LXBpY2tlZCUyMiUyQyUyMGZyb21fcHQlM0RUcnVlKQ==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model = TFDistilBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;path/to/awesome-name-you-picked&quot;</span>, from_pt=<span class="hljs-literal">True</span>)'}}),j=new U({props:{code:"dGZfbW9kZWwuc2F2ZV9wcmV0cmFpbmVkKCUyMnBhdGglMkZ0byUyRmF3ZXNvbWUtbmFtZS15b3UtcGlja2VkJTIyKQ==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model.save_pretrained(<span class="hljs-string">&quot;path/to/awesome-name-you-picked&quot;</span>)'}}),{c(){n=r("p"),n.innerHTML=m,a=i(),w(u.$$.fragment),$=i(),f=r("p"),f.textContent=C,H=i(),w(j.$$.fragment)},l(d){n=o(d,"P",{"data-svelte-h":!0}),h(n)!=="svelte-sn4jxc"&&(n.innerHTML=m),a=p(d),_(u.$$.fragment,d),$=p(d),f=o(d,"P",{"data-svelte-h":!0}),h(f)!=="svelte-eampn5"&&(f.textContent=C),H=p(d),_(j.$$.fragment,d)},m(d,J){l(d,n,J),l(d,a,J),b(u,d,J),l(d,$,J),l(d,f,J),l(d,H,J),b(j,d,J),F=!0},p:tt,i(d){F||(M(u.$$.fragment,d),M(j.$$.fragment,d),F=!0)},o(d){y(u.$$.fragment,d),y(j.$$.fragment,d),F=!1},d(d){d&&(t(n),t(a),t($),t(f),t(H)),T(u,d),T(j,d)}}}function Ws(W){let n,m;return n=new mt({props:{$$slots:{default:[js]},$$scope:{ctx:W}}}),{c(){w(n.$$.fragment)},l(a){_(n.$$.fragment,a)},m(a,u){b(n,a,u),m=!0},p(a,u){const $={};u&2&&($.$$scope={dirty:u,ctx:a}),n.$set($)},i(a){m||(M(n.$$.fragment,a),m=!0)},o(a){y(n.$$.fragment,a),m=!1},d(a){T(n,a)}}}function Zs(W){let n,m="Flax에서 모델을 사용하는 경우, PyTorch에서 Flax로 체크포인트를 변환할 수도 있습니다:",a,u,$;return u=new U({props:{code:"ZmxheF9tb2RlbCUyMCUzRCUyMEZsYXhEaXN0aWxCZXJ0Rm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIycGF0aCUyRnRvJTJGYXdlc29tZS1uYW1lLXlvdS1waWNrZWQlMjIlMkMlMjBmcm9tX3B0JTNEVHJ1ZSUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>flax_model = FlaxDistilBertForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;path/to/awesome-name-you-picked&quot;</span>, from_pt=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span>)`}}),{c(){n=r("p"),n.textContent=m,a=i(),w(u.$$.fragment)},l(f){n=o(f,"P",{"data-svelte-h":!0}),h(n)!=="svelte-qsi1v0"&&(n.textContent=m),a=p(f),_(u.$$.fragment,f)},m(f,C){l(f,n,C),l(f,a,C),b(u,f,C),$=!0},p:tt,i(f){$||(M(u.$$.fragment,f),$=!0)},o(f){y(u.$$.fragment,f),$=!1},d(f){f&&(t(n),t(a)),T(u,f)}}}function Ls(W){let n,m;return n=new mt({props:{$$slots:{default:[Zs]},$$scope:{ctx:W}}}),{c(){w(n.$$.fragment)},l(a){_(n.$$.fragment,a)},m(a,u){b(n,a,u),m=!0},p(a,u){const $={};u&2&&($.$$scope={dirty:u,ctx:a}),n.$set($)},i(a){m||(M(n.$$.fragment,a),m=!0)},o(a){y(n.$$.fragment,a),m=!1},d(a){T(n,a)}}}function Fs(W){let n,m,a,u='모델을 허브에 공유하는 것은 추가 매개변수나 콜백을 추가하는 것만큼 간단합니다. <a href="training">미세 조정 튜토리얼</a>에서 <code>TrainingArguments</code> 클래스는 하이퍼파라미터와 추가 훈련 옵션을 지정하는 곳이라는 것을 기억하세요. 이러한 훈련 옵션 중 하나는 모델을 허브로 직접 푸시하는 기능을 포함합니다. <code>TrainingArguments</code>에서 <code>push_to_hub=True</code>를 설정하세요:',$,f,C,H,j="평소와 같이 훈련 인수를 <code>Trainer</code>에 전달합니다:",F,d,J,L,v="모델을 미세 조정한 후, <code>Trainer</code>에서 <code>push_to_hub()</code>를 호출하여 훈련된 모델을 허브로 푸시하세요. 🤗 Transformers는 훈련 하이퍼파라미터, 훈련 결과 및 프레임워크 버전을 모델 카드에 자동으로 추가합니다!",X,P,se;return n=new Cs({props:{id:"Z1-XMy-GNLQ"}}),f=new U({props:{code:"dHJhaW5pbmdfYXJncyUyMCUzRCUyMFRyYWluaW5nQXJndW1lbnRzKG91dHB1dF9kaXIlM0QlMjJteS1hd2Vzb21lLW1vZGVsJTIyJTJDJTIwcHVzaF90b19odWIlM0RUcnVlKQ==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>training_args = TrainingArguments(output_dir=<span class="hljs-string">&quot;my-awesome-model&quot;</span>, push_to_hub=<span class="hljs-literal">True</span>)'}}),d=new U({props:{code:"dHJhaW5lciUyMCUzRCUyMFRyYWluZXIoJTBBJTIwJTIwJTIwJTIwbW9kZWwlM0Rtb2RlbCUyQyUwQSUyMCUyMCUyMCUyMGFyZ3MlM0R0cmFpbmluZ19hcmdzJTJDJTBBJTIwJTIwJTIwJTIwdHJhaW5fZGF0YXNldCUzRHNtYWxsX3RyYWluX2RhdGFzZXQlMkMlMEElMjAlMjAlMjAlMjBldmFsX2RhdGFzZXQlM0RzbWFsbF9ldmFsX2RhdGFzZXQlMkMlMEElMjAlMjAlMjAlMjBjb21wdXRlX21ldHJpY3MlM0Rjb21wdXRlX21ldHJpY3MlMkMlMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>trainer = Trainer(
<span class="hljs-meta">... </span>    model=model,
<span class="hljs-meta">... </span>    args=training_args,
<span class="hljs-meta">... </span>    train_dataset=small_train_dataset,
<span class="hljs-meta">... </span>    eval_dataset=small_eval_dataset,
<span class="hljs-meta">... </span>    compute_metrics=compute_metrics,
<span class="hljs-meta">... </span>)`}}),P=new U({props:{code:"dHJhaW5lci5wdXNoX3RvX2h1Yigp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>trainer.push_to_hub()'}}),{c(){w(n.$$.fragment),m=i(),a=r("p"),a.innerHTML=u,$=i(),w(f.$$.fragment),C=i(),H=r("p"),H.innerHTML=j,F=i(),w(d.$$.fragment),J=i(),L=r("p"),L.innerHTML=v,X=i(),w(P.$$.fragment)},l(c){_(n.$$.fragment,c),m=p(c),a=o(c,"P",{"data-svelte-h":!0}),h(a)!=="svelte-1ivzhup"&&(a.innerHTML=u),$=p(c),_(f.$$.fragment,c),C=p(c),H=o(c,"P",{"data-svelte-h":!0}),h(H)!=="svelte-wl6tpp"&&(H.innerHTML=j),F=p(c),_(d.$$.fragment,c),J=p(c),L=o(c,"P",{"data-svelte-h":!0}),h(L)!=="svelte-147nj48"&&(L.innerHTML=v),X=p(c),_(P.$$.fragment,c)},m(c,Z){b(n,c,Z),l(c,m,Z),l(c,a,Z),l(c,$,Z),b(f,c,Z),l(c,C,Z),l(c,H,Z),l(c,F,Z),b(d,c,Z),l(c,J,Z),l(c,L,Z),l(c,X,Z),b(P,c,Z),se=!0},p:tt,i(c){se||(M(n.$$.fragment,c),M(f.$$.fragment,c),M(d.$$.fragment,c),M(P.$$.fragment,c),se=!0)},o(c){y(n.$$.fragment,c),y(f.$$.fragment,c),y(d.$$.fragment,c),y(P.$$.fragment,c),se=!1},d(c){c&&(t(m),t(a),t($),t(C),t(H),t(F),t(J),t(L),t(X)),T(n,c),T(f,c),T(d,c),T(P,c)}}}function Xs(W){let n,m;return n=new mt({props:{$$slots:{default:[Fs]},$$scope:{ctx:W}}}),{c(){w(n.$$.fragment)},l(a){_(n.$$.fragment,a)},m(a,u){b(n,a,u),m=!0},p(a,u){const $={};u&2&&($.$$scope={dirty:u,ctx:a}),n.$set($)},i(a){m||(M(n.$$.fragment,a),m=!0)},o(a){y(n.$$.fragment,a),m=!1},d(a){T(n,a)}}}function Us(W){let n,m="<code>PushToHubCallback</code>을 사용하여 모델을 허브에 공유하려면, <code>PushToHubCallback</code>에 다음 인수를 정의하세요:",a,u,$="<li>출력된 모델의 파일 경로</li> <li>토크나이저</li> <li><code>{Hub 사용자 이름}/{모델 이름}</code> 형식의 <code>hub_model_id</code></li>",f,C,H,j,F='<a href="https://keras.io/api/models/model_training_apis/" rel="nofollow"><code>fit</code></a>에 콜백을 추가하면, 🤗 Transformers가 훈련된 모델을 허브로 푸시합니다:',d,J,L;return C=new U({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFB1c2hUb0h1YkNhbGxiYWNrJTBBJTBBcHVzaF90b19odWJfY2FsbGJhY2slMjAlM0QlMjBQdXNoVG9IdWJDYWxsYmFjayglMEElMjAlMjAlMjAlMjBvdXRwdXRfZGlyJTNEJTIyLiUyRnlvdXJfbW9kZWxfc2F2ZV9wYXRoJTIyJTJDJTIwdG9rZW5pemVyJTNEdG9rZW5pemVyJTJDJTIwaHViX21vZGVsX2lkJTNEJTIyeW91ci11c2VybmFtZSUyRm15LWF3ZXNvbWUtbW9kZWwlMjIlMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> PushToHubCallback

<span class="hljs-meta">&gt;&gt;&gt; </span>push_to_hub_callback = PushToHubCallback(
<span class="hljs-meta">... </span>    output_dir=<span class="hljs-string">&quot;./your_model_save_path&quot;</span>, tokenizer=tokenizer, hub_model_id=<span class="hljs-string">&quot;your-username/my-awesome-model&quot;</span>
<span class="hljs-meta">... </span>)`}}),J=new U({props:{code:"bW9kZWwuZml0KHRmX3RyYWluX2RhdGFzZXQlMkMlMjB2YWxpZGF0aW9uX2RhdGElM0R0Zl92YWxpZGF0aW9uX2RhdGFzZXQlMkMlMjBlcG9jaHMlM0QzJTJDJTIwY2FsbGJhY2tzJTNEcHVzaF90b19odWJfY2FsbGJhY2sp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=<span class="hljs-number">3</span>, callbacks=push_to_hub_callback)'}}),{c(){n=r("p"),n.innerHTML=m,a=i(),u=r("ul"),u.innerHTML=$,f=i(),w(C.$$.fragment),H=i(),j=r("p"),j.innerHTML=F,d=i(),w(J.$$.fragment)},l(v){n=o(v,"P",{"data-svelte-h":!0}),h(n)!=="svelte-1m85w97"&&(n.innerHTML=m),a=p(v),u=o(v,"UL",{"data-svelte-h":!0}),h(u)!=="svelte-omd9uv"&&(u.innerHTML=$),f=p(v),_(C.$$.fragment,v),H=p(v),j=o(v,"P",{"data-svelte-h":!0}),h(j)!=="svelte-1u6bst"&&(j.innerHTML=F),d=p(v),_(J.$$.fragment,v)},m(v,X){l(v,n,X),l(v,a,X),l(v,u,X),l(v,f,X),b(C,v,X),l(v,H,X),l(v,j,X),l(v,d,X),b(J,v,X),L=!0},p:tt,i(v){L||(M(C.$$.fragment,v),M(J.$$.fragment,v),L=!0)},o(v){y(C.$$.fragment,v),y(J.$$.fragment,v),L=!1},d(v){v&&(t(n),t(a),t(u),t(f),t(H),t(j),t(d)),T(C,v),T(J,v)}}}function Ps(W){let n,m;return n=new mt({props:{$$slots:{default:[Us]},$$scope:{ctx:W}}}),{c(){w(n.$$.fragment)},l(a){_(n.$$.fragment,a)},m(a,u){b(n,a,u),m=!0},p(a,u){const $={};u&2&&($.$$scope={dirty:u,ctx:a}),n.$set($)},i(a){m||(M(n.$$.fragment,a),m=!0)},o(a){y(n.$$.fragment,a),m=!1},d(a){T(n,a)}}}function Gs(W){let n,m,a,u,$,f,C,H,j,F,d="모델 공유하기",J,L,v="지난 두 튜토리얼에서 분산 설정을 위해 PyTorch, Keras 및 🤗 Accelerate를 사용하여 모델을 미세 조정하는 방법을 보았습니다. 다음 단계는 모델을 커뮤니티와 공유하는 것입니다! Hugging Face는 인공지능의 민주화를 위해 모두에게 지식과 자원을 공개적으로 공유해야 한다고 믿습니다. 다른 사람들이 시간과 자원을 절약할 수 있도록 커뮤니티에 모델을 공유하는 것을 고려해 보세요.",X,P,se='이 튜토리얼에서 <a href="https://huggingface.co/models" rel="nofollow">Model Hub</a>에서 훈련되거나 미세 조정 모델을 공유하는 두 가지 방법에 대해 알아봅시다:',c,Z,Ml="<li>API를 통해 파일을 Hub에 푸시합니다.</li> <li>웹사이트를 통해 파일을 Hub로 끌어다 놓습니다.</li>",ht,G,yl,ct,V,$t,R,z,lt,ne,$l,Be,Tl="저장소 특징",dt,ae,kl="모델 허브의 각 저장소는 일반적인 GitHub 저장소처럼 작동합니다. 저장소는 버전 관리, 커밋 기록, 차이점 시각화 기능을 제공합니다.",gt,re,Cl='모델 허브에 내장된 버전 관리는 git 및 <a href="https://git-lfs.github.com/" rel="nofollow">git-lfs</a>를 기반으로 합니다. 즉, 하나의 모델을 하나의 저장소로 취급하여 접근 제어 및 확장성이 향상됩니다. 버전 제어는 커밋 해시, 태그 또는 브랜치로 모델의 특정 버전을 고정하는 방법인 <em>revision</em>을 허용합니다.',vt,oe,xl="따라서 <code>revision</code> 매개변수를 사용하여 특정 모델 버전을 가져올 수 있습니다:",wt,ie,_t,pe,Hl="또한 저장소에서 파일을 쉽게 편집할 수 있으며, 커밋 기록과 차이를 볼 수 있습니다:",bt,ue,Jl='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/vis_diff.png" alt="vis_diff"/>',Mt,E,B,st,fe,dl,qe,jl="설정",yt,me,Wl="모델을 허브에 공유하기 전에 Hugging Face 자격 증명이 필요합니다. 터미널에 액세스할 수 있는 경우, 🤗 Transformers가 설치된 가상 환경에서 다음 명령을 실행합니다. 그러면 Hugging Face 캐시 폴더(기본적으로 <code>~/.cache/</code>)에 액세스 토큰을 저장합니다:",Tt,he,kt,ce,Zl='Jupyter 또는 Colaboratory와 같은 노트북을 사용 중인 경우, <a href="https://huggingface.co/docs/hub/adding-a-library" rel="nofollow"><code>huggingface_hub</code></a> 라이브러리가 설치되었는지 확인하세요. 이 라이브러리를 사용하면 API로 허브와 상호 작용할 수 있습니다.',Ct,$e,xt,de,Ll='그런 다음 <code>notebook_login</code>로 허브에 로그인하고, <a href="https://huggingface.co/settings/token" rel="nofollow">여기</a> 링크에서 로그인할 토큰을 생성합니다:',Ht,ge,Jt,I,q,nt,ve,gl,Qe,Fl="프레임워크 간 모델 변환하기",jt,we,Xl="다른 프레임워크로 작업하는 사용자가 모델을 사용할 수 있도록 하려면, PyTorch 및 TensorFlow 체크포인트를 모두 사용하여 모델을 변환하고 업로드하는 것이 좋습니다. 이 단계를 건너뛰어도 사용자는 다른 프레임워크에서 모델을 가져올 수 있지만, 🤗 Transformers가 체크포인트를 즉석에서 변환해야 하므로 속도가 느려질 수 있습니다.",Wt,_e,Ul='체크포인트를 다른 프레임워크로 변환하는 것은 쉽습니다. PyTorch 및 TensorFlow가 설치되어 있는지 확인한 다음(설치 지침은 <a href="installation">여기</a> 참조) 다른 프레임워크에서 작업에 대한 특정 모델을 찾습니다.',Zt,Q,Lt,Y,D,at,be,vl,De,Pl="훈련 중 모델 푸시하기",Ft,K,Xt,A,O,rt,Me,wl,Ke,Gl="<code>push_to_hub</code> 함수 사용하기",Ut,ye,Rl="모델에서 직접 <code>push_to_hub</code>를 호출하여 허브에 업로드할 수도 있습니다.",Pt,Te,El="<code>push_to_hub</code>에 모델 이름을 지정하세요:",Gt,ke,Rt,Ce,Il="이렇게 하면 사용자 이름 아래에 모델 이름 <code>my-awesome-model</code>로 저장소가 생성됩니다. 이제 사용자는 <code>from_pretrained</code> 함수를 사용하여 모델을 가져올 수 있습니다:",Et,xe,It,He,Yl="조직에 속하고 모델을 조직 이름으로 대신 푸시하려면 <code>repo_id</code>에 추가하세요:",Yt,Je,At,je,Al="<code>push_to_hub</code> 함수는 모델 저장소에 다른 파일을 추가하는 데에도 사용할 수 있습니다. 예를 들어 모델 저장소에 토크나이저를 추가할 수 있습니다:",St,We,Nt,Ze,Sl="또는 미세 조정된 PyTorch 모델의 TensorFlow 버전을 추가할 수도 있습니다:",Vt,Le,zt,Fe,Nl="이제 Hugging Face 프로필로 이동하면, 새로 생성한 모델 저장소가 표시됩니다. <strong>Files</strong> 탭을 클릭하면 저장소에 업로드한 모든 파일이 표시됩니다.",Bt,Xe,Vl='저장소에 파일을 만들고 업로드하는 방법에 대한 자세한 내용은 허브 설명서 <a href="https://huggingface.co/docs/hub/how-to-upstream" rel="nofollow">여기</a>를 참조하세요.',qt,S,ee,ot,Ue,_l,Oe,zl="웹 인터페이스로 업로드하기",Qt,Pe,Bl='코드 없는 접근 방식을 선호하는 사용자는 허브의 웹 인터페이스를 통해 모델을 업로드할 수 있습니다. <a href="https://huggingface.co/new" rel="nofollow">huggingface.co/new</a>를 방문하여 새로운 저장소를 생성하세요:',Dt,Ge,ql='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/new_model_repo.png" alt="new_model_repo"/>',Kt,Re,Ql="여기서 모델에 대한 몇 가지 정보를 추가하세요:",Ot,Ee,Dl="<li>저장소의 <strong>소유자</strong>를 선택합니다. 이는 사용자 또는 사용자가 속한 조직일 수 있습니다.</li> <li>저장소 이름이 될 모델의 이름을 선택합니다.</li> <li>모델이 공개인지 비공개인지 선택합니다.</li> <li>모델의 라이센스 사용을 지정합니다.</li>",el,Ie,Kl="이제 <strong>Files</strong> 탭을 클릭하고 <strong>Add file</strong> 버튼을 클릭하여 새로운 파일을 저장소에 업로드합니다. 그런 다음 업로드할 파일을 끌어다 놓고 커밋 메시지를 추가하세요.",tl,Ye,Ol='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/upload_file.png" alt="upload_file"/>',ll,N,te,it,Ae,bl,et,es="모델 카드 추가하기",sl,Se,ts="사용자가 모델의 기능, 제한, 잠재적 편향 및 윤리적 고려 사항을 이해할 수 있도록 저장소에 모델 카드를 추가하세요. 모델 카드는 <code>README.md</code> 파일에 정의되어 있습니다. 다음 방법으로 모델 카드를 추가할 수 있습니다:",nl,Ne,ls="<li><code>README.md</code> 파일을 수동으로 생성하여 업로드합니다.</li> <li>모델 저장소에서 <strong>Edit model card</strong> 버튼을 클릭합니다.</li>",al,Ve,ss='모델 카드에 포함할 정보 유형에 대한 좋은 예는 DistilBert <a href="https://huggingface.co/distilbert-base-uncased" rel="nofollow">모델 카드</a>를 참조하세요. 모델의 탄소 발자국이나 위젯 예시 등 <code>README.md</code> 파일에서 제어할 수 있는 다른 옵션에 대한 자세한 내용은 <a href="https://huggingface.co/docs/hub/models-cards" rel="nofollow">여기</a> 문서를 참조하세요.',rl,pt,ol;return H=new le({}),V=new ks({props:{$$slots:{default:[xs]},$$scope:{ctx:W}}}),ne=new le({}),ie=new U({props:{code:"bW9kZWwlMjAlM0QlMjBBdXRvTW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUwQSUyMCUyMCUyMCUyMCUyMmp1bGllbi1jJTJGRXNwZXJCRVJUby1zbWFsbCUyMiUyQyUyMHJldmlzaW9uJTNEJTIydjIuMC4xJTIyJTIwJTIwJTIzJTIwdGFnJTIwbmFtZSUyQyUyMG9yJTIwYnJhbmNoJTIwbmFtZSUyQyUyMG9yJTIwY29tbWl0JTIwaGFzaCUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;julien-c/EsperBERTo-small&quot;</span>, revision=<span class="hljs-string">&quot;v2.0.1&quot;</span>  <span class="hljs-comment"># tag name, or branch name, or commit hash</span>
<span class="hljs-meta">... </span>)`}}),fe=new le({}),he=new U({props:{code:"aHVnZ2luZ2ZhY2UtY2xpJTIwbG9naW4=",highlighted:"huggingface-cli login"}}),$e=new U({props:{code:"cGlwJTIwaW5zdGFsbCUyMGh1Z2dpbmdmYWNlX2h1Yg==",highlighted:"pip install huggingface_hub"}}),ge=new U({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMG5vdGVib29rX2xvZ2luJTBBJTBBbm90ZWJvb2tfbG9naW4oKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> notebook_login

<span class="hljs-meta">&gt;&gt;&gt; </span>notebook_login()`}}),ve=new le({}),Q=new vs({props:{pytorch:!0,tensorflow:!0,jax:!0,$$slots:{jax:[Ls],tensorflow:[Ws],pytorch:[Js]},$$scope:{ctx:W}}}),be=new le({}),K=new vs({props:{pytorch:!0,tensorflow:!0,jax:!1,$$slots:{tensorflow:[Ps],pytorch:[Xs]},$$scope:{ctx:W}}}),Me=new le({}),ke=new U({props:{code:"cHRfbW9kZWwucHVzaF90b19odWIoJTIybXktYXdlc29tZS1tb2RlbCUyMik=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>pt_model.push_to_hub(<span class="hljs-string">&quot;my-awesome-model&quot;</span>)'}}),xe=new U({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbCUwQSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsLmZyb21fcHJldHJhaW5lZCglMjJ5b3VyX3VzZXJuYW1lJTJGbXktYXdlc29tZS1tb2RlbCUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(<span class="hljs-string">&quot;your_username/my-awesome-model&quot;</span>)`}}),Je=new U({props:{code:"cHRfbW9kZWwucHVzaF90b19odWIoJTIybXktYXdlc29tZS1vcmclMkZteS1hd2Vzb21lLW1vZGVsJTIyKQ==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>pt_model.push_to_hub(<span class="hljs-string">&quot;my-awesome-org/my-awesome-model&quot;</span>)'}}),We=new U({props:{code:"dG9rZW5pemVyLnB1c2hfdG9faHViKCUyMm15LWF3ZXNvbWUtbW9kZWwlMjIp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.push_to_hub(<span class="hljs-string">&quot;my-awesome-model&quot;</span>)'}}),Le=new U({props:{code:"dGZfbW9kZWwucHVzaF90b19odWIoJTIybXktYXdlc29tZS1tb2RlbCUyMik=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>tf_model.push_to_hub(<span class="hljs-string">&quot;my-awesome-model&quot;</span>)'}}),Ue=new le({}),Ae=new 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