import{s as oa,o as na,n as Ae}from"../chunks/scheduler.6062bdaf.js";import{S as sa,i as la,g as i,s as n,r as m,A as ra,h as d,f as a,c as s,j as x,u as g,x as M,k as J,y as t,a as p,v as u,d as f,t as _,w as b}from"../chunks/index.4bca734e.js";import{T as aa}from"../chunks/Tip.b9ac1f03.js";import{D as k}from"../chunks/Docstring.ed07512f.js";import{C as Wt}from"../chunks/CodeBlock.fa01571c.js";import{E as Bt}from"../chunks/ExampleCodeBlock.823fb173.js";import{H as ze}from"../chunks/Heading.723dceba.js";function ia($){let o,y="Example:",c,h,T;return h=new Wt({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> dataclasses <span class="hljs-keyword">import</span> dataclass
<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"># Define your model configuration (optional)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>@dataclass
<span class="hljs-meta">... </span><span class="hljs-keyword">class</span> <span class="hljs-title class_">Config</span>:
<span class="hljs-meta">... </span>    foo: <span class="hljs-built_in">int</span> = <span class="hljs-number">512</span>
<span class="hljs-meta">... </span>    bar: <span class="hljs-built_in">str</span> = <span class="hljs-string">&quot;cpu&quot;</span>

<span class="hljs-comment"># Inherit from ModelHubMixin (and optionally from your framework&#x27;s model class)</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-title class_ inherited__">ModelHubMixin</span>):
<span class="hljs-meta">... </span>    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">self, config: Config</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(config=Config(foo=<span class="hljs-number">256</span>, bar=<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.config
Config(foo=<span class="hljs-number">256</span>, bar=<span class="hljs-string">&quot;gpu&quot;</span>)`,wrap:!1}}),{c(){o=i("p"),o.textContent=y,c=n(),m(h.$$.fragment)},l(r){o=d(r,"P",{"data-svelte-h":!0}),M(o)!=="svelte-11lpom8"&&(o.textContent=y),c=s(r),g(h.$$.fragment,r)},m(r,j){p(r,o,j),p(r,c,j),u(h,r,j),T=!0},p:Ae,i(r){T||(f(h.$$.fragment,r),T=!0)},o(r){_(h.$$.fragment,r),T=!1},d(r){r&&(a(o),a(c)),b(h,r)}}}function da($){let o,y="Example:",c,h,T;return h=new Wt({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> dataclasses <span class="hljs-keyword">import</span> dataclass
<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>@dataclass
<span class="hljs-meta">... </span><span class="hljs-keyword">class</span> <span class="hljs-title class_">Config</span>:
<span class="hljs-meta">... </span>    hidden_size: <span class="hljs-built_in">int</span> = <span class="hljs-number">512</span>
<span class="hljs-meta">... </span>    vocab_size: <span class="hljs-built_in">int</span> = <span class="hljs-number">30000</span>
<span class="hljs-meta">... </span>    output_size: <span class="hljs-built_in">int</span> = <span class="hljs-number">4</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">class</span> <span class="hljs-title class_">MyModel</span>(nn.Module, PyTorchModelHubMixin):
<span class="hljs-meta">... </span>    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">self, config: Config</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(config.hidden_size, config.vocab_size))
<span class="hljs-meta">... </span>        self.linear = nn.Linear(config.output_size, config.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()

<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>)`,wrap:!1}}),{c(){o=i("p"),o.textContent=y,c=n(),m(h.$$.fragment)},l(r){o=d(r,"P",{"data-svelte-h":!0}),M(o)!=="svelte-11lpom8"&&(o.textContent=y),c=s(r),g(h.$$.fragment,r)},m(r,j){p(r,o,j),p(r,c,j),u(h,r,j),T=!0},p:Ae,i(r){T||(f(h.$$.fragment,r),T=!0)},o(r){_(h.$$.fragment,r),T=!1},d(r){r&&(a(o),a(c)),b(h,r)}}}function ca($){let o,y;return o=new Wt({props:{code:"aW1wb3J0JTIwdGVuc29yZmxvdyUyMGFzJTIwdGYlMEFmcm9tJTIwaHVnZ2luZ2ZhY2VfaHViJTIwaW1wb3J0JTIwS2VyYXNNb2RlbEh1Yk1peGluJTBBJTBBJTBBY2xhc3MlMjBNeU1vZGVsKHRmLmtlcmFzLk1vZGVsJTJDJTIwS2VyYXNNb2RlbEh1Yk1peGluKSUzQSUwQSUyMCUyMCUyMCUyMGRlZiUyMF9faW5pdF9fKHNlbGYlMkMlMjAqKmt3YXJncyklM0ElMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBzdXBlcigpLl9faW5pdF9fKCklMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBzZWxmLmNvbmZpZyUyMCUzRCUyMGt3YXJncy5wb3AoJTIyY29uZmlnJTIyJTJDJTIwTm9uZSklMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBzZWxmLmR1bW15X2lucHV0cyUyMCUzRCUyMC4uLiUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMHNlbGYubGF5ZXIlMjAlM0QlMjAuLi4lMEElMEElMjAlMjAlMjAlMjBkZWYlMjBjYWxsKHNlbGYlMkMlMjAqYXJncyklM0ElMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjByZXR1cm4lMjAuLi4lMEElMEElMEElMjMlMjBJbml0aWFsaXplJTIwYW5kJTIwY29tcGlsZSUyMHRoZSUyMG1vZGVsJTIwYXMlMjB5b3UlMjBub3JtYWxseSUyMHdvdWxkJTBBbW9kZWwlMjAlM0QlMjBNeU1vZGVsKCklMEFtb2RlbC5jb21waWxlKC4uLiklMEElMjMlMjBCdWlsZCUyMHRoZSUyMGdyYXBoJTIwYnklMjB0cmFpbmluZyUyMGl0JTIwb3IlMjBwYXNzaW5nJTIwZHVtbXklMjBpbnB1dHMlMEFfJTIwJTNEJTIwbW9kZWwobW9kZWwuZHVtbXlfaW5wdXRzKSUwQSUyMyUyMFNhdmUlMjBtb2RlbCUyMHdlaWdodHMlMjB0byUyMGxvY2FsJTIwZGlyZWN0b3J5JTBBbW9kZWwuc2F2ZV9wcmV0cmFpbmVkKCUyMm15LWF3ZXNvbWUtbW9kZWwlMjIpJTBBJTIzJTIwUHVzaCUyMG1vZGVsJTIwd2VpZ2h0cyUyMHRvJTIwdGhlJTIwSHViJTBBbW9kZWwucHVzaF90b19odWIoJTIybXktYXdlc29tZS1tb2RlbCUyMiklMEElMjMlMjBEb3dubG9hZCUyMGFuZCUyMGluaXRpYWxpemUlMjB3ZWlnaHRzJTIwZnJvbSUyMHRoZSUyMEh1YiUwQW1vZGVsJTIwJTNEJTIwTXlNb2RlbC5mcm9tX3ByZXRyYWluZWQoJTIydXNlcm5hbWUlMkZzdXBlci1jb29sLW1vZGVsJTIyKQ==",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(o.$$.fragment)},l(c){g(o.$$.fragment,c)},m(c,h){u(o,c,h),y=!0},p:Ae,i(c){y||(f(o.$$.fragment,c),y=!0)},o(c){_(o.$$.fragment,c),y=!1},d(c){b(o,c)}}}function pa($){let o,y=`Passing <code>token=True</code> is required when you want to use a private
model.`;return{c(){o=i("p"),o.innerHTML=y},l(c){o=d(c,"P",{"data-svelte-h":!0}),M(o)!=="svelte-1j5fb81"&&(o.innerHTML=y)},m(c,h){p(c,o,h)},p:Ae,d(c){c&&a(o)}}}function ha($){let o,y="Raises the following error:",c,h,T=`<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(){o=i("p"),o.textContent=y,c=n(),h=i("ul"),h.innerHTML=T},l(r){o=d(r,"P",{"data-svelte-h":!0}),M(o)!=="svelte-1f6kjc7"&&(o.textContent=y),c=s(r),h=d(r,"UL",{"data-svelte-h":!0}),M(h)!=="svelte-qftit0"&&(h.innerHTML=T)},m(r,j){p(r,o,j),p(r,c,j),p(r,h,j)},p:Ae,d(r){r&&(a(o),a(c),a(h))}}}function ma($){let o,y,c,h,T,r,j,Ve,D,Et=`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.`,Fe,Q,Se,w,q,st,ue,Nt="A generic mixin to integrate ANY machine learning framework with the Hub.",lt,fe,zt=`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/v0.21.4/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.`,rt,A,it,V,K,dt,_e,Gt=`Overwrite this method in subclass to define how to save your model.
Check out our <a href="../guides/integrations">integration guide</a> for instructions.`,ct,I,O,pt,be,At="Overwrite this method in subclass to define how to load your model from pretrained.",ht,Me,Vt=`Use <a href="/docs/huggingface_hub/v0.21.4/en/package_reference/file_download#huggingface_hub.hf_hub_download">hf_hub_download()</a> or <a href="/docs/huggingface_hub/v0.21.4/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.`,mt,we,Ft='Check out our <a href="../guides/integrations">integration guide</a> for more instructions.',gt,F,ee,ut,ye,St="Download a model from the Huggingface Hub and instantiate it.",ft,E,te,_t,ve,Xt="Upload model checkpoint to the Hub.",bt,Te,Lt=`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/v0.21.4/en/package_reference/hf_api#huggingface_hub.HfApi.upload_folder">upload_folder()</a> reference for more
details.`,Mt,S,ae,wt,je,Rt="Save weights in local directory.",Xe,oe,Le,C,ne,yt,xe,Pt=`Implementation of <a href="/docs/huggingface_hub/v0.21.4/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>.`,vt,X,Re,se,Pe,H,le,Tt,Je,Yt=`Implementation of <a href="/docs/huggingface_hub/v0.21.4/en/package_reference/mixins#huggingface_hub.ModelHubMixin">ModelHubMixin</a> to provide model Hub upload/download
capabilities to Keras models.`,jt,L,Ye,Z,re,xt,Ue,Dt=`Instantiate a pretrained Keras model from a pre-trained model from the Hub.
The model is expected to be in <code>SavedModel</code> format.`,Jt,R,De,B,ie,Ut,ke,Qt="Upload model checkpoint to the Hub.",kt,$e,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/v0.21.4/en/package_reference/hf_api#huggingface_hub.HfApi.upload_folder">upload_folder()</a> reference for more
details.`,Qe,N,de,$t,Ie,Kt=`Saves a Keras model to save_directory in SavedModel format. Use this if
you’re using the Functional or Sequential APIs.`,qe,ce,Ke,z,pe,It,Ce,Ot="Load pretrained fastai model from the Hub or from a local directory.",Oe,U,he,Ct,He,ea="Upload learner checkpoint files to the Hub.",Ht,Ze,ta=`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.`,Zt,P,et,Ge,tt;return T=new ze({props:{title:"Mixins & serialization methods",local:"mixins--serialization-methods",headingTag:"h1"}}),j=new ze({props:{title:"Mixins",local:"mixins",headingTag:"h2"}}),Q=new ze({props:{title:"Generic",local:"huggingface_hub.ModelHubMixin",headingTag:"h3"}}),q=new k({props:{name:"class huggingface_hub.ModelHubMixin",anchor:"huggingface_hub.ModelHubMixin",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/huggingface_hub/blob/v0.21.4/src/huggingface_hub/hub_mixin.py#L40"}}),A=new Bt({props:{anchor:"huggingface_hub.ModelHubMixin.example",$$slots:{default:[ia]},$$scope:{ctx:$}}}),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/v0.21.4/src/huggingface_hub/hub_mixin.py#L171"}}),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/v0.21.4/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/v0.21.4/src/huggingface_hub/hub_mixin.py#L296"}}),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/v4.38.2/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/v0.21.4/src/huggingface_hub/hub_mixin.py#L182"}}),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;
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.ModelHubMixin.push_to_hub.allow_patterns",description:`<strong>allow_patterns</strong> (<code>List[str]</code> or <code>str</code>, <em>optional</em>) &#x2014;
If provided, only files matching at least one pattern are pushed.`,name:"allow_patterns"},{anchor:"huggingface_hub.ModelHubMixin.push_to_hub.ignore_patterns",description:`<strong>ignore_patterns</strong> (<code>List[str]</code> or <code>str</code>, <em>optional</em>) &#x2014;
If provided, files matching any of the patterns are not pushed.`,name:"ignore_patterns"},{anchor:"huggingface_hub.ModelHubMixin.push_to_hub.delete_patterns",description:`<strong>delete_patterns</strong> (<code>List[str]</code> or <code>str</code>, <em>optional</em>) &#x2014;
If provided, remote files matching any of the patterns will be deleted from the repo.`,name:"delete_patterns"}],source:"https://github.com/huggingface/huggingface_hub/blob/v0.21.4/src/huggingface_hub/hub_mixin.py#L345",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The url of the commit of your model in the given repository.</p>
`}}),ae=new k({props:{name:"save_pretrained",anchor:"huggingface_hub.ModelHubMixin.save_pretrained",parameters:[{name:"save_directory",val:": Union"},{name:"config",val:": Union = None"},{name:"repo_id",val:": Optional = None"},{name:"push_to_hub",val:": bool = False"},{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;
Path to directory in which the model weights and configuration will be saved.`,name:"save_directory"},{anchor:"huggingface_hub.ModelHubMixin.save_pretrained.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.save_pretrained.push_to_hub",description:`<strong>push_to_hub</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to push your model to the Huggingface Hub after saving it.`,name:"push_to_hub"},{anchor:"huggingface_hub.ModelHubMixin.save_pretrained.repo_id",description:`<strong>repo_id</strong> (<code>str</code>, <em>optional</em>) &#x2014;
ID of your repository on the Hub. Used only if <code>push_to_hub=True</code>. Will default to the folder name if
not provided.
kwargs &#x2014;
Additional key word arguments passed along to the <a href="/docs/huggingface_hub/v0.21.4/en/package_reference/mixins#huggingface_hub.ModelHubMixin.push_to_hub">push_to_hub()</a> method.`,name:"repo_id"}],source:"https://github.com/huggingface/huggingface_hub/blob/v0.21.4/src/huggingface_hub/hub_mixin.py#L122"}}),oe=new ze({props:{title:"PyTorch",local:"huggingface_hub.PyTorchModelHubMixin",headingTag:"h3"}}),ne=new k({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/v0.21.4/src/huggingface_hub/hub_mixin.py#L422"}}),X=new Bt({props:{anchor:"huggingface_hub.PyTorchModelHubMixin.example",$$slots:{default:[da]},$$scope:{ctx:$}}}),se=new ze({props:{title:"Keras",local:"huggingface_hub.KerasModelHubMixin",headingTag:"h3"}}),le=new k({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/v0.21.4/src/huggingface_hub/keras_mixin.py#L397"}}),L=new Bt({props:{anchor:"huggingface_hub.KerasModelHubMixin.example",$$slots:{default:[ca]},$$scope:{ctx:$}}}),re=new k({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
<a href="https://huggingface.co/docs/transformers/v4.38.2/en/main_classes/model#transformers.PreTrainedModel.save_pretrained" rel="nofollow">save_pretrained</a>, e.g.,
<code>./my_model_directory/</code>.</li>
<li><code>None</code> if you are both providing the configuration and state
dictionary (resp. with keyword arguments <code>config</code> and
<code>state_dict</code>).</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"huggingface_hub.from_pretrained_keras.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to force the (re-)download of the model weights and
configuration files, overriding the cached versions if they exist.`,name:"force_download"},{anchor:"huggingface_hub.from_pretrained_keras.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.from_pretrained_keras.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 each request.`,name:"proxies"},{anchor:"huggingface_hub.from_pretrained_keras.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. If
<code>True</code>, will use the token generated when running <code>transformers-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"token"},{anchor:"huggingface_hub.from_pretrained_keras.cache_dir",description:`<strong>cache_dir</strong> (<code>Union[str, os.PathLike]</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model
configuration should be cached if the standard cache should not be
used.`,name:"cache_dir"},{anchor:"huggingface_hub.from_pretrained_keras.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to only look at local files (i.e., do not try to download
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Whether the repository created should be private.`,name:"private"},{anchor:"huggingface_hub.push_to_hub_keras.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.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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Whether or not to create a Pull Request from <code>branch</code> with that commit.
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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
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<p>The url of the commit of your model in the given repository.</p>
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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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