import{s as un,o as _n,n as Xe}from"../chunks/scheduler.6062bdaf.js";import{S as fn,i as bn,g as r,s as a,r as g,A as Mn,h as c,f as n,c as o,j as x,u as m,x as M,k as U,y as t,a as d,v as u,d as _,t as f,w as b}from"../chunks/index.4bca734e.js";import{T as mn}from"../chunks/Tip.b9ac1f03.js";import{D as $}from"../chunks/Docstring.81ba5e3c.js";import{C as St}from"../chunks/CodeBlock.fa01571c.js";import{E as Vt}from"../chunks/ExampleCodeBlock.30e7542f.js";import{H as Ve,E as yn}from"../chunks/EditOnGithub.74ab2baa.js";function wn(I){let l,j="Example:",p,h,v;return h=new St({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> ModelHubMixin

<span class="hljs-comment"># Inherit from ModelHubMixin</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">class</span> <span class="hljs-title class_">MyCustomModel</span>(
<span class="hljs-meta">... </span>        ModelHubMixin,
<span class="hljs-meta">... </span>        library_name=<span class="hljs-string">&quot;my-library&quot;</span>,
<span class="hljs-meta">... </span>        tags=[<span class="hljs-string">&quot;computer-vision&quot;</span>],
<span class="hljs-meta">... </span>        repo_url=<span class="hljs-string">&quot;https://github.com/huggingface/my-cool-library&quot;</span>,
<span class="hljs-meta">... </span>        paper_url=<span class="hljs-string">&quot;https://arxiv.org/abs/2304.12244&quot;</span>,
<span class="hljs-meta">... </span>        docs_url=<span class="hljs-string">&quot;https://huggingface.co/docs/my-cool-library&quot;</span>,
<span class="hljs-meta">... </span>        <span class="hljs-comment"># ^ optional metadata to generate model card</span>
<span class="hljs-meta">... </span>    ):
<span class="hljs-meta">... </span>    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">self, size: <span class="hljs-built_in">int</span> = <span class="hljs-number">512</span>, device: <span class="hljs-built_in">str</span> = <span class="hljs-string">&quot;cpu&quot;</span></span>):
<span class="hljs-meta">... </span>        <span class="hljs-comment"># define how to initialize your model</span>
<span class="hljs-meta">... </span>        <span class="hljs-built_in">super</span>().__init__()
<span class="hljs-meta">... </span>        ...
...
<span class="hljs-meta">... </span>    <span class="hljs-keyword">def</span> <span class="hljs-title function_">_save_pretrained</span>(<span class="hljs-params">self, save_directory: Path</span>) -&gt; <span class="hljs-literal">None</span>:
<span class="hljs-meta">... </span>        <span class="hljs-comment"># define how to serialize your model</span>
<span class="hljs-meta">... </span>        ...
...
<span class="hljs-meta">... </span>    @<span class="hljs-built_in">classmethod</span>
<span class="hljs-meta">... </span>    <span class="hljs-keyword">def</span> <span class="hljs-title function_">from_pretrained</span>(<span class="hljs-params">
<span class="hljs-meta">... </span>        cls: <span class="hljs-type">Type</span>[T],
<span class="hljs-meta">... </span>        pretrained_model_name_or_path: <span class="hljs-type">Union</span>[<span class="hljs-built_in">str</span>, Path],
<span class="hljs-meta">... </span>        *,
<span class="hljs-meta">... </span>        force_download: <span class="hljs-built_in">bool</span> = <span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>        resume_download: <span class="hljs-type">Optional</span>[<span class="hljs-built_in">bool</span>] = <span class="hljs-literal">None</span>,
<span class="hljs-meta">... </span>        proxies: <span class="hljs-type">Optional</span>[<span class="hljs-type">Dict</span>] = <span class="hljs-literal">None</span>,
<span class="hljs-meta">... </span>        token: <span class="hljs-type">Optional</span>[<span class="hljs-type">Union</span>[<span class="hljs-built_in">str</span>, <span class="hljs-built_in">bool</span>]] = <span class="hljs-literal">None</span>,
<span class="hljs-meta">... </span>        cache_dir: <span class="hljs-type">Optional</span>[<span class="hljs-type">Union</span>[<span class="hljs-built_in">str</span>, Path]] = <span class="hljs-literal">None</span>,
<span class="hljs-meta">... </span>        local_files_only: <span class="hljs-built_in">bool</span> = <span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>        revision: <span class="hljs-type">Optional</span>[<span class="hljs-built_in">str</span>] = <span class="hljs-literal">None</span>,
<span class="hljs-meta">... </span>        **model_kwargs,
<span class="hljs-meta">... </span>    </span>) -&gt; T:
<span class="hljs-meta">... </span>        <span class="hljs-comment"># define how to deserialize your model</span>
<span class="hljs-meta">... </span>        ...

<span class="hljs-meta">&gt;&gt;&gt; </span>model = MyCustomModel(size=<span class="hljs-number">256</span>, device=<span class="hljs-string">&quot;gpu&quot;</span>)

<span class="hljs-comment"># Save model weights to local directory</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model.save_pretrained(<span class="hljs-string">&quot;my-awesome-model&quot;</span>)

<span class="hljs-comment"># Push model weights to the Hub</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model.push_to_hub(<span class="hljs-string">&quot;my-awesome-model&quot;</span>)

<span class="hljs-comment"># Download and initialize weights from the Hub</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>reloaded_model = MyCustomModel.from_pretrained(<span class="hljs-string">&quot;username/my-awesome-model&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>reloaded_model.size
<span class="hljs-number">256</span>

<span class="hljs-comment"># Model card has been correctly populated</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> ModelCard
<span class="hljs-meta">&gt;&gt;&gt; </span>card = ModelCard.load(<span class="hljs-string">&quot;username/my-awesome-model&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>card.data.tags
[<span class="hljs-string">&quot;x-custom-tag&quot;</span>, <span class="hljs-string">&quot;pytorch_model_hub_mixin&quot;</span>, <span class="hljs-string">&quot;model_hub_mixin&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>card.data.library_name
<span class="hljs-string">&quot;my-library&quot;</span>`,wrap:!1}}),{c(){l=r("p"),l.textContent=j,p=a(),g(h.$$.fragment)},l(i){l=c(i,"P",{"data-svelte-h":!0}),M(l)!=="svelte-11lpom8"&&(l.textContent=j),p=o(i),m(h.$$.fragment,i)},m(i,T){d(i,l,T),d(i,p,T),u(h,i,T),v=!0},p:Xe,i(i){v||(_(h.$$.fragment,i),v=!0)},o(i){f(h.$$.fragment,i),v=!1},d(i){i&&(n(l),n(p)),b(h,i)}}}function jn(I){let l,j="Example:",p,h,v;return h=new St({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch.nn <span class="hljs-keyword">as</span> nn
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> PyTorchModelHubMixin

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">class</span> <span class="hljs-title class_">MyModel</span>(
<span class="hljs-meta">... </span>        nn.Module,
<span class="hljs-meta">... </span>        PyTorchModelHubMixin,
<span class="hljs-meta">... </span>        library_name=<span class="hljs-string">&quot;keras-nlp&quot;</span>,
<span class="hljs-meta">... </span>        repo_url=<span class="hljs-string">&quot;https://github.com/keras-team/keras-nlp&quot;</span>,
<span class="hljs-meta">... </span>        paper_url=<span class="hljs-string">&quot;https://arxiv.org/abs/2304.12244&quot;</span>,
<span class="hljs-meta">... </span>        docs_url=<span class="hljs-string">&quot;https://keras.io/keras_nlp/&quot;</span>,
<span class="hljs-meta">... </span>        <span class="hljs-comment"># ^ optional metadata to generate model card</span>
<span class="hljs-meta">... </span>    ):
<span class="hljs-meta">... </span>    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">self, hidden_size: <span class="hljs-built_in">int</span> = <span class="hljs-number">512</span>, vocab_size: <span class="hljs-built_in">int</span> = <span class="hljs-number">30000</span>, output_size: <span class="hljs-built_in">int</span> = <span class="hljs-number">4</span></span>):
<span class="hljs-meta">... </span>        <span class="hljs-built_in">super</span>().__init__()
<span class="hljs-meta">... </span>        self.param = nn.Parameter(torch.rand(hidden_size, vocab_size))
<span class="hljs-meta">... </span>        self.linear = nn.Linear(output_size, vocab_size)

<span class="hljs-meta">... </span>    <span class="hljs-keyword">def</span> <span class="hljs-title function_">forward</span>(<span class="hljs-params">self, x</span>):
<span class="hljs-meta">... </span>        <span class="hljs-keyword">return</span> self.linear(x + self.param)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MyModel(hidden_size=<span class="hljs-number">256</span>)

<span class="hljs-comment"># Save model weights to local directory</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model.save_pretrained(<span class="hljs-string">&quot;my-awesome-model&quot;</span>)

<span class="hljs-comment"># Push model weights to the Hub</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model.push_to_hub(<span class="hljs-string">&quot;my-awesome-model&quot;</span>)

<span class="hljs-comment"># Download and initialize weights from the Hub</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MyModel.from_pretrained(<span class="hljs-string">&quot;username/my-awesome-model&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.hidden_size
<span class="hljs-number">256</span>`,wrap:!1}}),{c(){l=r("p"),l.textContent=j,p=a(),g(h.$$.fragment)},l(i){l=c(i,"P",{"data-svelte-h":!0}),M(l)!=="svelte-11lpom8"&&(l.textContent=j),p=o(i),m(h.$$.fragment,i)},m(i,T){d(i,l,T),d(i,p,T),u(h,i,T),v=!0},p:Xe,i(i){v||(_(h.$$.fragment,i),v=!0)},o(i){f(h.$$.fragment,i),v=!1},d(i){i&&(n(l),n(p)),b(h,i)}}}function vn(I){let l,j;return l=new St({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> KerasModelHubMixin


<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">class</span> <span class="hljs-title class_">MyModel</span>(tf.keras.Model, KerasModelHubMixin):
<span class="hljs-meta">... </span>    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">self, **kwargs</span>):
<span class="hljs-meta">... </span>        <span class="hljs-built_in">super</span>().__init__()
<span class="hljs-meta">... </span>        self.config = kwargs.pop(<span class="hljs-string">&quot;config&quot;</span>, <span class="hljs-literal">None</span>)
<span class="hljs-meta">... </span>        self.dummy_inputs = ...
<span class="hljs-meta">... </span>        self.layer = ...

<span class="hljs-meta">... </span>    <span class="hljs-keyword">def</span> <span class="hljs-title function_">call</span>(<span class="hljs-params">self, *args</span>):
<span class="hljs-meta">... </span>        <span class="hljs-keyword">return</span> ...


<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initialize and compile the model as you normally would</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MyModel()
<span class="hljs-meta">&gt;&gt;&gt; </span>model.<span class="hljs-built_in">compile</span>(...)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Build the graph by training it or passing dummy inputs</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>_ = model(model.dummy_inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Save model weights to local directory</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model.save_pretrained(<span class="hljs-string">&quot;my-awesome-model&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Push model weights to the Hub</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model.push_to_hub(<span class="hljs-string">&quot;my-awesome-model&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download and initialize weights from the Hub</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MyModel.from_pretrained(<span class="hljs-string">&quot;username/super-cool-model&quot;</span>)`,wrap:!1}}),{c(){g(l.$$.fragment)},l(p){m(l.$$.fragment,p)},m(p,h){u(l,p,h),j=!0},p:Xe,i(p){j||(_(l.$$.fragment,p),j=!0)},o(p){f(l.$$.fragment,p),j=!1},d(p){b(l,p)}}}function Tn(I){let l,j=`Passing <code>token=True</code> is required when you want to use a private
model.`;return{c(){l=r("p"),l.innerHTML=j},l(p){l=c(p,"P",{"data-svelte-h":!0}),M(l)!=="svelte-1j5fb81"&&(l.innerHTML=j)},m(p,h){d(p,l,h)},p:Xe,d(p){p&&n(l)}}}function xn(I){let l,j="Raises the following error:",p,h,v=`<li><a href="https://docs.python.org/3/library/exceptions.html#ValueError" rel="nofollow"><em>ValueError</em></a>
if the user is not log on to the Hugging Face Hub.</li>`;return{c(){l=r("p"),l.textContent=j,p=a(),h=r("ul"),h.innerHTML=v},l(i){l=c(i,"P",{"data-svelte-h":!0}),M(l)!=="svelte-1f6kjc7"&&(l.textContent=j),p=o(i),h=c(i,"UL",{"data-svelte-h":!0}),M(h)!=="svelte-qftit0"&&(h.innerHTML=v)},m(i,T){d(i,l,T),d(i,p,T),d(i,h,T)},p:Xe,d(i){i&&(n(l),n(p),n(h))}}}function Un(I){let l,j,p,h,v,i,T,Fe,Q,Xt=`<code>huggingface_hub</code> 라이브러리는 객체에 함수들의 업로드 및 다운로드 기능을 손쉽게 제공하기 위해서, 부모 클래스로 사용될 수 있는 다양한 믹스인을 제공합니다.
ML 프레임워크를 Hub와 통합하는 방법은 <a href="../guides/integrations">통합 가이드</a>를 통해 배울 수 있습니다.`,Re,q,De,y,K,dt,fe,Ft="A generic mixin to integrate ANY machine learning framework with the Hub.",pt,be,Rt=`To integrate your framework, your model class must inherit from this class. Custom logic for saving/loading models
have to be overwritten in  <code>_from_pretrained</code> and <code>_save_pretrained</code>. <a href="/docs/huggingface_hub/main/ko/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin">PyTorchModelHubMixin</a> is a good example
of mixin integration with the Hub. Check out our <a href="../guides/integrations">integration guide</a> for more instructions.`,ht,Me,Dt=`When inheriting from <a href="/docs/huggingface_hub/main/ko/package_reference/mixins#huggingface_hub.ModelHubMixin">ModelHubMixin</a>, you can define class-level attributes. These attributes are not passed to
<code>__init__</code> but to the class definition itself. This is useful to define metadata about the library integrating
<a href="/docs/huggingface_hub/main/ko/package_reference/mixins#huggingface_hub.ModelHubMixin">ModelHubMixin</a>.`,gt,ye,Pt='For more details on how to integrate the mixin with your library, checkout the <a href="../guides/integrations">integration guide</a>.',mt,G,ut,L,O,_t,we,Yt=`Overwrite this method in subclass to define how to save your model.
Check out our <a href="../guides/integrations">integration guide</a> for instructions.`,ft,H,ee,bt,je,Qt="Overwrite this method in subclass to define how to load your model from pretrained.",Mt,ve,qt=`Use <code>hf_hub_download()</code> or <code>snapshot_download()</code> to download files from the Hub before loading them. Most
args taken as input can be directly passed to those 2 methods. If needed, you can add more arguments to this
method using “model_kwargs”. For example <code>PyTorchModelHubMixin._from_pretrained()</code> takes as input a <code>map_location</code>
parameter to set on which device the model should be loaded.`,yt,Te,Kt='Check out our <a href="../guides/integrations">integration guide</a> for more instructions.',wt,V,te,jt,xe,Ot="Download a model from the Huggingface Hub and instantiate it.",vt,B,ne,Tt,Ue,en="Upload model checkpoint to the Hub.",xt,Je,tn=`Use <code>allow_patterns</code> and <code>ignore_patterns</code> to precisely filter which files should be pushed to the hub. Use
<code>delete_patterns</code> to delete existing remote files in the same commit. See <a href="/docs/huggingface_hub/main/ko/package_reference/hf_api#huggingface_hub.HfApi.upload_folder">upload_folder()</a> reference for more
details.`,Ut,S,ae,Jt,ke,nn="Save weights in local directory.",Pe,oe,Ye,J,se,kt,$e,an=`Implementation of <a href="/docs/huggingface_hub/main/ko/package_reference/mixins#huggingface_hub.ModelHubMixin">ModelHubMixin</a> to provide model Hub upload/download capabilities to PyTorch models. The model
is set in evaluation mode by default using <code>model.eval()</code> (dropout modules are deactivated). To train the model,
you should first set it back in training mode with <code>model.train()</code>.`,$t,Ie,on='See <a href="/docs/huggingface_hub/main/ko/package_reference/mixins#huggingface_hub.ModelHubMixin">ModelHubMixin</a> for more details on how to use the mixin.',It,X,Qe,le,qe,C,ie,Ht,He,sn=`Implementation of <a href="/docs/huggingface_hub/main/ko/package_reference/mixins#huggingface_hub.ModelHubMixin">ModelHubMixin</a> to provide model Hub upload/download
capabilities to Keras models.`,Ct,F,Ke,A,re,At,Ce,ln=`Instantiate a pretrained Keras model from a pre-trained model from the Hub.
The model is expected to be in <code>SavedModel</code> format.`,Zt,R,Oe,Z,ce,Wt,Ae,rn="Upload model checkpoint to the Hub.",Bt,Ze,cn=`Use <code>allow_patterns</code> and <code>ignore_patterns</code> to precisely filter which files should be pushed to the hub. Use
<code>delete_patterns</code> to delete existing remote files in the same commit. See <a href="/docs/huggingface_hub/main/ko/package_reference/hf_api#huggingface_hub.HfApi.upload_folder">upload_folder()</a> reference for more
details.`,et,N,de,Nt,We,dn=`Saves a Keras model to save_directory in SavedModel format. Use this if
you’re using the Functional or Sequential APIs.`,tt,pe,nt,E,he,Et,Be,pn="Load pretrained fastai model from the Hub or from a local directory.",at,k,ge,zt,Ne,hn="Upload learner checkpoint files to the Hub.",Gt,Ee,gn=`Use <em>allow_patterns</em> and <em>ignore_patterns</em> to precisely filter which files should be pushed to the hub. Use
<em>delete_patterns</em> to delete existing remote files in the same commit. See [<em>upload_folder</em>] reference for more
details.`,Lt,D,ot,me,st,Se,lt;return v=new Ve({props:{title:"믹스인 & 직렬화 메소드",local:"mixins—serialization-methods",headingTag:"h1"}}),T=new Ve({props:{title:"믹스인",local:"mixins",headingTag:"h2"}}),q=new Ve({props:{title:"제네릭",local:"huggingface_hub.ModelHubMixin ][ huggingface_hub.ModelHubMixin",headingTag:"h3"}}),K=new $({props:{name:"class huggingface_hub.ModelHubMixin",anchor:"huggingface_hub.ModelHubMixin",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"huggingface_hub.ModelHubMixin.repo_url",description:`<strong>repo_url</strong> (<code>str</code>, <em>optional</em>) &#x2014;
URL of the library repository. Used to generate model card.`,name:"repo_url"},{anchor:"huggingface_hub.ModelHubMixin.paper_url",description:`<strong>paper_url</strong> (<code>str</code>, <em>optional</em>) &#x2014;
URL of the library paper. Used to generate model card.`,name:"paper_url"},{anchor:"huggingface_hub.ModelHubMixin.docs_url",description:`<strong>docs_url</strong> (<code>str</code>, <em>optional</em>) &#x2014;
URL of the library documentation. Used to generate model card.`,name:"docs_url"},{anchor:"huggingface_hub.ModelHubMixin.model_card_template",description:`<strong>model_card_template</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Template of the model card. Used to generate model card. Defaults to a generic template.`,name:"model_card_template"},{anchor:"huggingface_hub.ModelHubMixin.language",description:`<strong>language</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
Language supported by the library. Used to generate model card.`,name:"language"},{anchor:"huggingface_hub.ModelHubMixin.library_name",description:`<strong>library_name</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Name of the library integrating ModelHubMixin. Used to generate model card.`,name:"library_name"},{anchor:"huggingface_hub.ModelHubMixin.license",description:`<strong>license</strong> (<code>str</code>, <em>optional</em>) &#x2014;
License of the library integrating ModelHubMixin. Used to generate model card.
E.g: &#x201C;apache-2.0&#x201D;`,name:"license"},{anchor:"huggingface_hub.ModelHubMixin.license_name",description:`<strong>license_name</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Name of the library integrating ModelHubMixin. Used to generate model card.
Only used if <code>license</code> is set to <code>other</code>.
E.g: &#x201C;coqui-public-model-license&#x201D;.`,name:"license_name"},{anchor:"huggingface_hub.ModelHubMixin.license_link",description:`<strong>license_link</strong> (<code>str</code>, <em>optional</em>) &#x2014;
URL to the license of the library integrating ModelHubMixin. Used to generate model card.
Only used if <code>license</code> is set to <code>other</code> and <code>license_name</code> is set.
E.g: &#x201D;<a href="https://coqui.ai/cpml%22" rel="nofollow">https://coqui.ai/cpml&#x201D;</a>.`,name:"license_link"},{anchor:"huggingface_hub.ModelHubMixin.pipeline_tag",description:`<strong>pipeline_tag</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Tag of the pipeline. Used to generate model card. E.g. &#x201C;text-classification&#x201D;.`,name:"pipeline_tag"},{anchor:"huggingface_hub.ModelHubMixin.tags",description:`<strong>tags</strong> (<code>List[str]</code>, <em>optional</em>) &#x2014;
Tags to be added to the model card. Used to generate model card. E.g. [&#x201C;computer-vision&#x201D;]`,name:"tags"},{anchor:"huggingface_hub.ModelHubMixin.coders",description:`<strong>coders</strong> (<code>Dict[Type, Tuple[Callable, Callable]]</code>, <em>optional</em>) &#x2014;
Dictionary of custom types and their encoders/decoders. Used to encode/decode arguments that are not
jsonable by default. E.g dataclasses, argparse.Namespace, OmegaConf, etc.`,name:"coders"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/hub_mixin.py#L76"}}),G=new Vt({props:{anchor:"huggingface_hub.ModelHubMixin.example",$$slots:{default:[wn]},$$scope:{ctx:I}}}),O=new $({props:{name:"_save_pretrained",anchor:"huggingface_hub.ModelHubMixin._save_pretrained",parameters:[{name:"save_directory",val:": Path"}],parametersDescription:[{anchor:"huggingface_hub.ModelHubMixin._save_pretrained.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>Path</code>) &#x2014;
Path to directory in which the model weights and configuration will be saved.`,name:"save_directory"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/hub_mixin.py#L449"}}),ee=new $({props:{name:"_from_pretrained",anchor:"huggingface_hub.ModelHubMixin._from_pretrained",parameters:[{name:"model_id",val:": str"},{name:"revision",val:": typing.Optional[str]"},{name:"cache_dir",val:": typing.Union[str, pathlib.Path, NoneType]"},{name:"force_download",val:": bool"},{name:"proxies",val:": typing.Optional[typing.Dict]"},{name:"resume_download",val:": typing.Optional[bool]"},{name:"local_files_only",val:": bool"},{name:"token",val:": typing.Union[str, bool, NoneType]"},{name:"**model_kwargs",val:""}],parametersDescription:[{anchor:"huggingface_hub.ModelHubMixin._from_pretrained.model_id",description:`<strong>model_id</strong> (<code>str</code>) &#x2014;
ID of the model to load from the Huggingface Hub (e.g. <code>bigscience/bloom</code>).`,name:"model_id"},{anchor:"huggingface_hub.ModelHubMixin._from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Revision of the model on the Hub. Can be a branch name, a git tag or any commit id. Defaults to the
latest commit on <code>main</code> branch.`,name:"revision"},{anchor:"huggingface_hub.ModelHubMixin._from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to force (re-)downloading the model weights and configuration files from the Hub, overriding
the existing cache.`,name:"force_download"},{anchor:"huggingface_hub.ModelHubMixin._from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint (e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>).`,name:"proxies"},{anchor:"huggingface_hub.ModelHubMixin._from_pretrained.token",description:`<strong>token</strong> (<code>str</code> or <code>bool</code>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. By default, it will use the token
cached when running <code>huggingface-cli login</code>.`,name:"token"},{anchor:"huggingface_hub.ModelHubMixin._from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code>, <code>Path</code>, <em>optional</em>) &#x2014;
Path to the folder where cached files are stored.`,name:"cache_dir"},{anchor:"huggingface_hub.ModelHubMixin._from_pretrained.local_files_only",description:`<strong>local_files_only</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>True</code>, avoid downloading the file and return the path to the local cached file if it exists.`,name:"local_files_only"},{anchor:"huggingface_hub.ModelHubMixin._from_pretrained.model_kwargs",description:`<strong>model_kwargs</strong> &#x2014;
Additional keyword arguments passed along to the <a href="/docs/huggingface_hub/main/ko/package_reference/mixins#huggingface_hub.ModelHubMixin._from_pretrained">_from_pretrained()</a> method.`,name:"model_kwargs"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/hub_mixin.py#L585"}}),te=new $({props:{name:"from_pretrained",anchor:"huggingface_hub.ModelHubMixin.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:": typing.Union[str, pathlib.Path]"},{name:"force_download",val:": bool = False"},{name:"resume_download",val:": typing.Optional[bool] = None"},{name:"proxies",val:": typing.Optional[typing.Dict] = None"},{name:"token",val:": typing.Union[str, bool, NoneType] = None"},{name:"cache_dir",val:": typing.Union[str, pathlib.Path, NoneType] = None"},{name:"local_files_only",val:": bool = False"},{name:"revision",val:": typing.Optional[str] = None"},{name:"**model_kwargs",val:""}],parametersDescription:[{anchor:"huggingface_hub.ModelHubMixin.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code>, <code>Path</code>) &#x2014;<ul>
<li>Either the <code>model_id</code> (string) of a model hosted on the Hub, e.g. <code>bigscience/bloom</code>.</li>
<li>Or a path to a <code>directory</code> containing model weights saved using
<code>save_pretrained</code>, e.g., <code>../path/to/my_model_directory/</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"huggingface_hub.ModelHubMixin.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Revision of the model on the Hub. Can be a branch name, a git tag or any commit id.
Defaults to the latest commit on <code>main</code> branch.`,name:"revision"},{anchor:"huggingface_hub.ModelHubMixin.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to force (re-)downloading the model weights and configuration files from the Hub, overriding
the existing cache.`,name:"force_download"},{anchor:"huggingface_hub.ModelHubMixin.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on every request.`,name:"proxies"},{anchor:"huggingface_hub.ModelHubMixin.from_pretrained.token",description:`<strong>token</strong> (<code>str</code> or <code>bool</code>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. By default, it will use the token
cached when running <code>huggingface-cli login</code>.`,name:"token"},{anchor:"huggingface_hub.ModelHubMixin.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code>, <code>Path</code>, <em>optional</em>) &#x2014;
Path to the folder where cached files are stored.`,name:"cache_dir"},{anchor:"huggingface_hub.ModelHubMixin.from_pretrained.local_files_only",description:`<strong>local_files_only</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>True</code>, avoid downloading the file and return the path to the local cached file if it exists.`,name:"local_files_only"},{anchor:"huggingface_hub.ModelHubMixin.from_pretrained.model_kwargs",description:`<strong>model_kwargs</strong> (<code>Dict</code>, <em>optional</em>) &#x2014;
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The git branch on which to push the model. This defaults to <code>&quot;main&quot;</code>.`,name:"branch"},{anchor:"huggingface_hub.ModelHubMixin.push_to_hub.create_pr",description:`<strong>create_pr</strong> (<code>boolean</code>, <em>optional</em>) &#x2014;
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<p>The url of the commit of your model in the given repository.</p>
`}}),ae=new $({props:{name:"save_pretrained",anchor:"huggingface_hub.ModelHubMixin.save_pretrained",parameters:[{name:"save_directory",val:": typing.Union[str, pathlib.Path]"},{name:"config",val:": typing.Union[dict, huggingface_hub.hub_mixin.DataclassInstance, NoneType] = None"},{name:"repo_id",val:": typing.Optional[str] = None"},{name:"push_to_hub",val:": bool = False"},{name:"model_card_kwargs",val:": typing.Optional[typing.Dict[str, typing.Any]] = None"},{name:"**push_to_hub_kwargs",val:""}],parametersDescription:[{anchor:"huggingface_hub.ModelHubMixin.save_pretrained.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>Path</code>) &#x2014;
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<p>url of the commit on the Hub if <code>push_to_hub=True</code>, <code>None</code> otherwise.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>str</code> or <code>None</code></p>
`}}),oe=new Ve({props:{title:"PyTorch",local:"huggingface_hub.PyTorchModelHubMixin ][ huggingface_hub.PyTorchModelHubMixin",headingTag:"h3"}}),se=new $({props:{name:"class huggingface_hub.PyTorchModelHubMixin",anchor:"huggingface_hub.PyTorchModelHubMixin",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/hub_mixin.py#L715"}}),X=new Vt({props:{anchor:"huggingface_hub.PyTorchModelHubMixin.example",$$slots:{default:[jn]},$$scope:{ctx:I}}}),le=new Ve({props:{title:"Keras",local:"huggingface_hub.KerasModelHubMixin ][ huggingface_hub.KerasModelHubMixin",headingTag:"h3"}}),ie=new $({props:{name:"class huggingface_hub.KerasModelHubMixin",anchor:"huggingface_hub.KerasModelHubMixin",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/keras_mixin.py#L420"}}),F=new Vt({props:{anchor:"huggingface_hub.KerasModelHubMixin.example",$$slots:{default:[vn]},$$scope:{ctx:I}}}),re=new $({props:{name:"huggingface_hub.from_pretrained_keras",anchor:"huggingface_hub.from_pretrained_keras",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"huggingface_hub.from_pretrained_keras.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:<ul>
<li>A string, the <code>model id</code> of a pretrained model hosted inside a
model repo on huggingface.co. Valid model ids can be located
at the root-level, like <code>bert-base-uncased</code>, or namespaced
under a user or organization name, like
<code>dbmdz/bert-base-german-cased</code>.</li>
<li>You can add <code>revision</code> by appending <code>@</code> at the end of model_id
simply like this: <code>dbmdz/bert-base-german-cased@main</code> Revision
is the specific model version to use. It can be a branch name,
a tag name, or a commit id, since we use a git-based system
for storing models and other artifacts on huggingface.co, so
<code>revision</code> can be any identifier allowed by git.</li>
<li>A path to a <code>directory</code> containing model weights saved using
<code>save_pretrained</code>, e.g.,
<code>./my_model_directory/</code>.</li>
<li><code>None</code> if you are both providing the configuration and state
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<code>state_dict</code>).</li>
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Whether the repository created should be private.
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The API endpoint to use when pushing the model to the hub.`,name:"api_endpoint"},{anchor:"huggingface_hub.push_to_hub_keras.token",description:`<strong>token</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If
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The git branch on which to push the model. This defaults to
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defaults to <code>&quot;main&quot;</code>.`,name:"branch"},{anchor:"huggingface_hub.push_to_hub_keras.create_pr",description:`<strong>create_pr</strong> (<code>boolean</code>, <em>optional</em>) &#x2014;
Whether or not to create a Pull Request from <code>branch</code> with that commit.
Defaults to <code>False</code>.`,name:"create_pr"},{anchor:"huggingface_hub.push_to_hub_keras.config",description:`<strong>config</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Configuration object to be saved alongside the model weights.`,name:"config"},{anchor:"huggingface_hub.push_to_hub_keras.allow_patterns",description:`<strong>allow_patterns</strong> (<code>List[str]</code> or <code>str</code>, <em>optional</em>) &#x2014;
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TensorBoard logging directory to be pushed. The Hub automatically
hosts and displays a TensorBoard instance if log files are included
in the repository.`,name:"log_dir"},{anchor:"huggingface_hub.push_to_hub_keras.include_optimizer",description:`<strong>include_optimizer</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to include optimizer during serialization.`,name:"include_optimizer"},{anchor:"huggingface_hub.push_to_hub_keras.tags",description:`<strong>tags</strong> (Union[<code>list</code>, <code>str</code>], <em>optional</em>) &#x2014;
List of tags that are related to model or string of a single tag. See example tags
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Setting this to <code>True</code> will plot the model and put it in the model
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model_save_kwargs will be passed to
<a href="https://www.tensorflow.org/api_docs/python/tf/keras/models/save_model" rel="nofollow"><code>tf.keras.models.save_model()</code></a>.`,name:"model_save_kwargs(dict,"}],source:"https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/keras_mixin.py#L296",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The url of the commit of your model in the given repository.</p>
`}}),de=new $({props:{name:"huggingface_hub.save_pretrained_keras",anchor:"huggingface_hub.save_pretrained_keras",parameters:[{name:"model",val:""},{name:"save_directory",val:": typing.Union[str, pathlib.Path]"},{name:"config",val:": typing.Optional[typing.Dict[str, typing.Any]] = None"},{name:"include_optimizer",val:": bool = False"},{name:"plot_model",val:": bool = True"},{name:"tags",val:": typing.Union[list, str, NoneType] = None"},{name:"**model_save_kwargs",val:""}],parametersDescription:[{anchor:"huggingface_hub.save_pretrained_keras.model",description:`<strong>model</strong> (<code>Keras.Model</code>) &#x2014;
The <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow">Keras
model</a>
you&#x2019;d like to save. The model must be compiled and built.`,name:"model"},{anchor:"huggingface_hub.save_pretrained_keras.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>Path</code>) &#x2014;
Specify directory in which you want to save the Keras model.`,name:"save_directory"},{anchor:"huggingface_hub.save_pretrained_keras.config",description:`<strong>config</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Configuration object to be saved alongside the model weights.`,name:"config"},{anchor:"huggingface_hub.save_pretrained_keras.include_optimizer(bool,",description:`<strong>include_optimizer(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
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Setting this to <code>True</code> will plot the model and put it in the model
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List of tags that are related to model or string of a single tag. See example tags
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The location where the pickled fastai.Learner is. It can be either of the two:<ul>
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<li>Hosted locally. <code>repo_id</code> would be a directory containing the pickle and a pyproject.toml
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<p>The <code>fastai.Learner</code> model in the <code>repo_id</code> repo.</p>
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