import{s as la,o as ra,n as Ve}from"../chunks/scheduler.6062bdaf.js";import{S as ia,i as da,g as i,s as n,r as m,A as ca,h as d,f as a,c as o,j as x,u as g,x as M,k as J,y as t,a as p,v as u,d as _,t as f,w as b}from"../chunks/index.4bca734e.js";import{T as sa}from"../chunks/Tip.b9ac1f03.js";import{D as k}from"../chunks/Docstring.ed07512f.js";import{C as Et}from"../chunks/CodeBlock.fa01571c.js";import{E as Zt}from"../chunks/ExampleCodeBlock.823fb173.js";import{H as ze}from"../chunks/Heading.723dceba.js";function pa(I){let s,j="Example:",c,h,v;return h=new Et({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;x-custom-tag&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>        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-built_in">bool</span> = <span class="hljs-literal">False</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._hub_mixin_config
{<span class="hljs-string">&quot;size&quot;</span>: <span class="hljs-number">256</span>, <span class="hljs-string">&quot;device&quot;</span>: <span class="hljs-string">&quot;gpu&quot;</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(){s=i("p"),s.textContent=j,c=n(),m(h.$$.fragment)},l(r){s=d(r,"P",{"data-svelte-h":!0}),M(s)!=="svelte-11lpom8"&&(s.textContent=j),c=o(r),g(h.$$.fragment,r)},m(r,T){p(r,s,T),p(r,c,T),u(h,r,T),v=!0},p:Ve,i(r){v||(_(h.$$.fragment,r),v=!0)},o(r){f(h.$$.fragment,r),v=!1},d(r){r&&(a(s),a(c)),b(h,r)}}}function ha(I){let s,j="Example:",c,h,v;return h=new Et({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>        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(){s=i("p"),s.textContent=j,c=n(),m(h.$$.fragment)},l(r){s=d(r,"P",{"data-svelte-h":!0}),M(s)!=="svelte-11lpom8"&&(s.textContent=j),c=o(r),g(h.$$.fragment,r)},m(r,T){p(r,s,T),p(r,c,T),u(h,r,T),v=!0},p:Ve,i(r){v||(_(h.$$.fragment,r),v=!0)},o(r){f(h.$$.fragment,r),v=!1},d(r){r&&(a(s),a(c)),b(h,r)}}}function ma(I){let s,j;return s=new Et({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(){m(s.$$.fragment)},l(c){g(s.$$.fragment,c)},m(c,h){u(s,c,h),j=!0},p:Ve,i(c){j||(_(s.$$.fragment,c),j=!0)},o(c){f(s.$$.fragment,c),j=!1},d(c){b(s,c)}}}function ga(I){let s,j=`Passing <code>token=True</code> is required when you want to use a private
model.`;return{c(){s=i("p"),s.innerHTML=j},l(c){s=d(c,"P",{"data-svelte-h":!0}),M(s)!=="svelte-1j5fb81"&&(s.innerHTML=j)},m(c,h){p(c,s,h)},p:Ve,d(c){c&&a(s)}}}function ua(I){let s,j="Raises the following error:",c,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(){s=i("p"),s.textContent=j,c=n(),h=i("ul"),h.innerHTML=v},l(r){s=d(r,"P",{"data-svelte-h":!0}),M(s)!=="svelte-1f6kjc7"&&(s.textContent=j),c=o(r),h=d(r,"UL",{"data-svelte-h":!0}),M(h)!=="svelte-qftit0"&&(h.innerHTML=v)},m(r,T){p(r,s,T),p(r,c,T),p(r,h,T)},p:Ve,d(r){r&&(a(s),a(c),a(h))}}}function _a(I){let s,j,c,h,v,r,T,Fe,P,Nt=`The <code>huggingface_hub</code> library offers a range of mixins that can be used as a parent class for your objects, in order to
provide simple uploading and downloading functions. Check out our <a href="../guides/integrations">integration guide</a> to learn
how to integrate any ML framework with the Hub.`,Le,Q,Se,w,D,lt,ue,zt="A generic mixin to integrate ANY machine learning framework with the Hub.",rt,_e,Gt=`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/v0.22.1/en/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.`,it,fe,Vt=`When inheriting from <a href="/docs/huggingface_hub/v0.22.1/en/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/v0.22.1/en/package_reference/mixins#huggingface_hub.ModelHubMixin">ModelHubMixin</a>.`,dt,G,ct,V,K,pt,be,Ft=`Overwrite this method in subclass to define how to save your model.
Check out our <a href="../guides/integrations">integration guide</a> for instructions.`,ht,$,O,mt,Me,Lt="Overwrite this method in subclass to define how to load your model from pretrained.",gt,we,St=`Use <a href="/docs/huggingface_hub/v0.22.1/en/package_reference/file_download#huggingface_hub.hf_hub_download">hf_hub_download()</a> or <a href="/docs/huggingface_hub/v0.22.1/en/package_reference/file_download#huggingface_hub.snapshot_download">snapshot_download()</a> 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.`,ut,ye,Xt='Check out our <a href="../guides/integrations">integration guide</a> for more instructions.',_t,F,ee,ft,je,Yt="Download a model from the Huggingface Hub and instantiate it.",bt,Z,te,Mt,ve,Rt="Upload model checkpoint to the Hub.",wt,Te,qt=`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/v0.22.1/en/package_reference/hf_api#huggingface_hub.HfApi.upload_folder">upload_folder()</a> reference for more
details.`,yt,L,ae,jt,xe,Pt="Save weights in local directory.",Xe,ne,Ye,H,oe,vt,Je,Qt=`Implementation of <a href="/docs/huggingface_hub/v0.22.1/en/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>.`,Tt,S,Re,se,qe,C,le,xt,Ue,Dt=`Implementation of <a href="/docs/huggingface_hub/v0.22.1/en/package_reference/mixins#huggingface_hub.ModelHubMixin">ModelHubMixin</a> to provide model Hub upload/download
capabilities to Keras models.`,Jt,X,Pe,A,re,Ut,ke,Kt=`Instantiate a pretrained Keras model from a pre-trained model from the Hub.
The model is expected to be in <code>SavedModel</code> format.`,kt,Y,Qe,W,ie,It,Ie,Ot="Upload model checkpoint to the Hub.",$t,$e,ea=`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/v0.22.1/en/package_reference/hf_api#huggingface_hub.HfApi.upload_folder">upload_folder()</a> reference for more
details.`,De,E,de,Ht,He,ta=`Saves a Keras model to save_directory in SavedModel format. Use this if
you’re using the Functional or Sequential APIs.`,Ke,ce,Oe,N,pe,Ct,Ce,aa="Load pretrained fastai model from the Hub or from a local directory.",et,U,he,At,Ae,na="Upload learner checkpoint files to the Hub.",Wt,We,oa=`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.`,Bt,R,tt,Ge,at;return v=new ze({props:{title:"Mixins & serialization methods",local:"mixins--serialization-methods",headingTag:"h1"}}),T=new ze({props:{title:"Mixins",local:"mixins",headingTag:"h2"}}),Q=new ze({props:{title:"Generic",local:"huggingface_hub.ModelHubMixin",headingTag:"h3"}}),D=new k({props:{name:"class huggingface_hub.ModelHubMixin",anchor:"huggingface_hub.ModelHubMixin",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{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.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.`,name:"tags"},{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.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"}],source:"https://github.com/huggingface/huggingface_hub/blob/v0.22.1/src/huggingface_hub/hub_mixin.py#L62"}}),G=new Zt({props:{anchor:"huggingface_hub.ModelHubMixin.example",$$slots:{default:[pa]},$$scope:{ctx:I}}}),K=new k({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/v0.22.1/src/huggingface_hub/hub_mixin.py#L299"}}),O=new k({props:{name:"_from_pretrained",anchor:"huggingface_hub.ModelHubMixin._from_pretrained",parameters:[{name:"model_id",val:": str"},{name:"revision",val:": Optional"},{name:"cache_dir",val:": Union"},{name:"force_download",val:": bool"},{name:"proxies",val:": Optional"},{name:"resume_download",val:": bool"},{name:"local_files_only",val:": bool"},{name:"token",val:": Union"},{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.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to delete incompletely received files. Will attempt to resume the download if such a file exists.`,name:"resume_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.
model_kwargs &#x2014;
Additional keyword arguments passed along to the <a href="/docs/huggingface_hub/v0.22.1/en/package_reference/mixins#huggingface_hub.ModelHubMixin._from_pretrained">_from_pretrained()</a> method.`,name:"local_files_only"}],source:"https://github.com/huggingface/huggingface_hub/blob/v0.22.1/src/huggingface_hub/hub_mixin.py#L439"}}),ee=new k({props:{name:"from_pretrained",anchor:"huggingface_hub.ModelHubMixin.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:": Union"},{name:"force_download",val:": bool = False"},{name:"resume_download",val:": bool = False"},{name:"proxies",val:": Optional = None"},{name:"token",val:": Union = None"},{name:"cache_dir",val:": Union = None"},{name:"local_files_only",val:": bool = False"},{name:"revision",val:": Optional = 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
<a href="https://huggingface.co/docs/transformers/v4.39.1/en/main_classes/model#transformers.PreTrainedModel.save_pretrained" rel="nofollow">save_pretrained</a>, 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.resume_download",description:`<strong>resume_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to delete incompletely received files. Will attempt to resume the download if such a file exists.`,name:"resume_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;
Additional kwargs to pass to the model during initialization.`,name:"model_kwargs"}],source:"https://github.com/huggingface/huggingface_hub/blob/v0.22.1/src/huggingface_hub/hub_mixin.py#L310"}}),te=new k({props:{name:"push_to_hub",anchor:"huggingface_hub.ModelHubMixin.push_to_hub",parameters:[{name:"repo_id",val:": str"},{name:"config",val:": Union = None"},{name:"commit_message",val:": str = 'Push model using huggingface_hub.'"},{name:"private",val:": bool = False"},{name:"token",val:": Optional = None"},{name:"branch",val:": Optional = None"},{name:"create_pr",val:": Optional = None"},{name:"allow_patterns",val:": Union = None"},{name:"ignore_patterns",val:": Union = None"},{name:"delete_patterns",val:": Union = None"},{name:"api_endpoint",val:": Optional = None"}],parametersDescription:[{anchor:"huggingface_hub.ModelHubMixin.push_to_hub.repo_id",description:`<strong>repo_id</strong> (<code>str</code>) &#x2014;
ID of the repository to push to (example: <code>&quot;username/my-model&quot;</code>).`,name:"repo_id"},{anchor:"huggingface_hub.ModelHubMixin.push_to_hub.config",description:`<strong>config</strong> (<code>dict</code> or <code>DataclassInstance</code>, <em>optional</em>) &#x2014;
Model configuration specified as a key/value dictionary or a dataclass instance.`,name:"config"},{anchor:"huggingface_hub.ModelHubMixin.push_to_hub.commit_message",description:`<strong>commit_message</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Message to commit while pushing.`,name:"commit_message"},{anchor:"huggingface_hub.ModelHubMixin.push_to_hub.private",description:`<strong>private</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether the repository created should be private.`,name:"private"},{anchor:"huggingface_hub.ModelHubMixin.push_to_hub.api_endpoint",description:`<strong>api_endpoint</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The API endpoint to use when pushing the model to the hub.`,name:"api_endpoint"},{anchor:"huggingface_hub.ModelHubMixin.push_to_hub.token",description:`<strong>token</strong> (<code>str</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.push_to_hub.branch",description:`<strong>branch</strong> (<code>str</code>, <em>optional</em>) &#x2014;
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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kwargs &#x2014;
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at the root-level, like <code>bert-base-uncased</code>, or namespaced
under a user or organization name, like
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<li>You can add <code>revision</code> by appending <code>@</code> at the end of model_id
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is the specific model version to use. It can be a branch name,
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<li>A path to a <code>directory</code> containing model weights saved using
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<li><code>None</code> if you are both providing the configuration and state
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The git branch on which to push the model. This defaults to
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TensorBoard logging directory to be pushed. The Hub automatically
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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
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<p>The url of the commit of your model in the given repository.</p>
`}}),de=new k({props:{name:"huggingface_hub.save_pretrained_keras",anchor:"huggingface_hub.save_pretrained_keras",parameters:[{name:"model",val:""},{name:"save_directory",val:": Union"},{name:"config",val:": Optional = None"},{name:"include_optimizer",val:": bool = False"},{name:"plot_model",val:": bool = True"},{name:"tags",val:": Union = None"},{name:"**model_save_kwargs",val:""}],parametersDescription:[{anchor:"huggingface_hub.save_pretrained_keras.model",description:`<strong>model</strong> (<code>Keras.Model</code>) &#x2014;
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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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<p>The url of the commit of your model in the given repository.</p>
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