import{s as to,o as oo,n as nt}from"../chunks/scheduler.6062bdaf.js";import{S as no,i as ao,g as i,s as n,r as h,A as ro,h as l,f as o,c as a,j as k,u as p,x as y,k as T,y as t,a as d,v as m,d as u,t as f,w as _}from"../chunks/index.4bca734e.js";import{T as Ot}from"../chunks/Tip.b9ac1f03.js";import{D as H}from"../chunks/Docstring.ed07512f.js";import{C as eo}from"../chunks/CodeBlock.fa01571c.js";import{E as Qt}from"../chunks/ExampleCodeBlock.823fb173.js";import{H as Pe}from"../chunks/Heading.723dceba.js";function so(N){let s,w="Example:",c,b,x;return b=new eo({props:{code:"aW1wb3J0JTIwdG9yY2glMEFpbXBvcnQlMjB0b3JjaC5ubiUyMGFzJTIwbm4lMEFmcm9tJTIwaHVnZ2luZ2ZhY2VfaHViJTIwaW1wb3J0JTIwUHlUb3JjaE1vZGVsSHViTWl4aW4lMEElMEElMEFjbGFzcyUyME15TW9kZWwobm4uTW9kdWxlJTJDJTIwUHlUb3JjaE1vZGVsSHViTWl4aW4pJTNBJTBBJTIwJTIwJTIwJTIwZGVmJTIwX19pbml0X18oc2VsZiklM0ElMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBzdXBlcigpLl9faW5pdF9fKCklMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBzZWxmLnBhcmFtJTIwJTNEJTIwbm4uUGFyYW1ldGVyKHRvcmNoLnJhbmQoMyUyQyUyMDQpKSUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMHNlbGYubGluZWFyJTIwJTNEJTIwbm4uTGluZWFyKDQlMkMlMjA1KSUwQSUwQSUyMCUyMCUyMCUyMGRlZiUyMGZvcndhcmQoc2VsZiUyQyUyMHgpJTNBJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwcmV0dXJuJTIwc2VsZi5saW5lYXIoeCUyMCUyQiUyMHNlbGYucGFyYW0pJTBBbW9kZWwlMjAlM0QlMjBNeU1vZGVsKCklMEElMEFtb2RlbC5zYXZlX3ByZXRyYWluZWQoJTIybXktYXdlc29tZS1tb2RlbCUyMiklMEElMEFtb2RlbC5wdXNoX3RvX2h1YiglMjJteS1hd2Vzb21lLW1vZGVsJTIyKSUwQSUwQW1vZGVsJTIwJTNEJTIwTXlNb2RlbC5mcm9tX3ByZXRyYWluZWQoJTIydXNlcm5hbWUlMkZteS1hd2Vzb21lLW1vZGVsJTIyKQ==",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>(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</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(<span class="hljs-number">3</span>, <span class="hljs-number">4</span>))
<span class="hljs-meta">... </span>        self.linear = nn.Linear(<span class="hljs-number">4</span>, <span class="hljs-number">5</span>)

<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(){s=i("p"),s.textContent=w,c=n(),h(b.$$.fragment)},l(g){s=l(g,"P",{"data-svelte-h":!0}),y(s)!=="svelte-11lpom8"&&(s.textContent=w),c=a(g),p(b.$$.fragment,g)},m(g,$){d(g,s,$),d(g,c,$),m(b,g,$),x=!0},p:nt,i(g){x||(u(b.$$.fragment,g),x=!0)},o(g){f(b.$$.fragment,g),x=!1},d(g){g&&(o(s),o(c)),_(b,g)}}}function io(N){let s,w;return s=new eo({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(){h(s.$$.fragment)},l(c){p(s.$$.fragment,c)},m(c,b){m(s,c,b),w=!0},p:nt,i(c){w||(u(s.$$.fragment,c),w=!0)},o(c){f(s.$$.fragment,c),w=!1},d(c){_(s,c)}}}function lo(N){let s,w=`Passing <code>token=True</code> is required when you want to use a private
model.`;return{c(){s=i("p"),s.innerHTML=w},l(c){s=l(c,"P",{"data-svelte-h":!0}),y(s)!=="svelte-1j5fb81"&&(s.innerHTML=w)},m(c,b){d(c,s,b)},p:nt,d(c){c&&o(s)}}}function co(N){let s,w="Raises the following error:",c,b,x=`<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=w,c=n(),b=i("ul"),b.innerHTML=x},l(g){s=l(g,"P",{"data-svelte-h":!0}),y(s)!=="svelte-1f6kjc7"&&(s.textContent=w),c=a(g),b=l(g,"UL",{"data-svelte-h":!0}),y(b)!=="svelte-qftit0"&&(b.innerHTML=x)},m(g,$){d(g,s,$),d(g,c,$),d(g,b,$)},p:nt,d(g){g&&(o(s),o(c),o(b))}}}function go(N){let s,w,c,b,x,g,$,Ze,X,Jt=`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,Y,Be,v,R,at,pe,Wt="A generic mixin to integrate ANY machine learning framework with the Hub.",rt,me,Lt=`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/v0.19.0.rc0/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.`,st,Z,q,it,ue,Nt=`Overwrite this method in subclass to define how to save your model.
Check out our <a href="../guides/integrations">integration guide</a> for instructions.`,lt,U,K,dt,fe,Et="Overwrite this method in subclass to define how to load your model from pretrained.",ct,_e,Pt=`Use <a href="/docs/huggingface_hub/v0.19.0.rc0/en/package_reference/file_download#huggingface_hub.hf_hub_download">hf_hub_download()</a> or <a href="/docs/huggingface_hub/v0.19.0.rc0/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.`,gt,be,At='Check out our <a href="../guides/integrations">integration guide</a> for more instructions.',ht,F,O,pt,ye,Zt="Download a model from the Huggingface Hub and instantiate it.",mt,E,Q,ut,ve,Ft="Upload model checkpoint to the Hub.",ft,Me,Bt=`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/v0.19.0.rc0/en/package_reference/hf_api#huggingface_hub.HfApi.upload_folder">upload_folder()</a> reference for more
details.`,_t,B,ee,bt,we,zt="Save weights in local directory.",ze,te,Ve,C,oe,yt,xe,Vt=`Implementation of <a href="/docs/huggingface_hub/v0.19.0.rc0/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,z,De,ne,Ge,I,ae,Mt,$e,Dt=`Implementation of <a href="/docs/huggingface_hub/v0.19.0.rc0/en/package_reference/mixins#huggingface_hub.ModelHubMixin">ModelHubMixin</a> to provide model Hub upload/download
capabilities to Keras models.`,wt,V,Se,J,re,xt,ke,Gt=`Instantiate a pretrained Keras model from a pre-trained model from the Hub.
The model is expected to be in <code>SavedModel</code> format.`,$t,D,Xe,W,se,kt,Te,St="Upload model checkpoint to the Hub.",Tt,je,Xt=`Use <code>allow_patterns</code> and <code>ignore_patterns</code> to precisely filter which files should be pushed to the hub. Use
<code>delete_patterns</code> to delete existing remote files in the same commit. See <a href="/docs/huggingface_hub/v0.19.0.rc0/en/package_reference/hf_api#huggingface_hub.HfApi.upload_folder">upload_folder()</a> reference for more
details.`,Ye,P,ie,jt,He,Yt=`Saves a Keras model to save_directory in SavedModel format. Use this if
you’re using the Functional or Sequential APIs.`,Re,le,qe,A,de,Ht,Ue,Rt="Load pretrained fastai model from the Hub or from a local directory.",Ke,j,ce,Ut,Ce,qt="Upload learner checkpoint files to the Hub.",Ct,Ie,Kt=`Use <em>allow_patterns</em> and <em>ignore_patterns</em> to precisely filter which files should be pushed to the hub. Use
<em>delete_patterns</em> to delete existing remote files in the same commit. See [<em>upload_folder</em>] reference for more
details.`,It,G,Oe,Ae,Qe;return x=new Pe({props:{title:"Mixins & serialization methods",local:"mixins--serialization-methods",headingTag:"h1"}}),$=new Pe({props:{title:"Mixins",local:"mixins",headingTag:"h2"}}),Y=new Pe({props:{title:"Generic",local:"huggingface_hub.ModelHubMixin",headingTag:"h3"}}),R=new H({props:{name:"class huggingface_hub.ModelHubMixin",anchor:"huggingface_hub.ModelHubMixin",parameters:[],source:"https://github.com/huggingface/huggingface_hub/blob/v0.19.0.rc0/src/huggingface_hub/hub_mixin.py#L21"}}),q=new H({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/v0.19.0.rc0/src/huggingface_hub/hub_mixin.py#L74"}}),K=new H({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:": 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.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/v0.19.0.rc0/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/v0.19.0.rc0/src/huggingface_hub/hub_mixin.py#L169"}}),O=new H({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:": bool = False"},{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
<a href="https://huggingface.co/docs/transformers/v4.35.0/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/v0.19.0.rc0/src/huggingface_hub/hub_mixin.py#L85"}}),Q=new H({props:{name:"push_to_hub",anchor:"huggingface_hub.ModelHubMixin.push_to_hub",parameters:[{name:"repo_id",val:": str"},{name:"config",val:": typing.Optional[dict] = None"},{name:"commit_message",val:": str = 'Push model using huggingface_hub.'"},{name:"private",val:": bool = False"},{name:"api_endpoint",val:": typing.Optional[str] = None"},{name:"token",val:": typing.Optional[str] = None"},{name:"branch",val:": typing.Optional[str] = None"},{name:"create_pr",val:": typing.Optional[bool] = None"},{name:"allow_patterns",val:": typing.Union[typing.List[str], str, NoneType] = None"},{name:"ignore_patterns",val:": typing.Union[typing.List[str], str, NoneType] = None"},{name:"delete_patterns",val:": typing.Union[typing.List[str], str, NoneType] = 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>, <em>optional</em>) &#x2014;
Configuration object to be saved alongside the model weights.`,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/v0.19.0.rc0/src/huggingface_hub/hub_mixin.py#L218",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The url of the commit of your model in the given repository.</p>
`}}),ee=new H({props:{name:"save_pretrained",anchor:"huggingface_hub.ModelHubMixin.save_pretrained",parameters:[{name:"save_directory",val:": typing.Union[str, pathlib.Path]"},{name:"config",val:": typing.Optional[dict] = None"},{name:"repo_id",val:": typing.Optional[str] = None"},{name:"push_to_hub",val:": bool = False"},{name:"**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>, <em>optional</em>) &#x2014;
Model configuration specified as a key/value dictionary.`,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/v0.19.0.rc0/en/package_reference/mixins#huggingface_hub.ModelHubMixin._from_pretrained">_from_pretrained()</a> method.`,name:"repo_id"}],source:"https://github.com/huggingface/huggingface_hub/blob/v0.19.0.rc0/src/huggingface_hub/hub_mixin.py#L30"}}),te=new Pe({props:{title:"PyTorch",local:"huggingface_hub.PyTorchModelHubMixin",headingTag:"h3"}}),oe=new H({props:{name:"class huggingface_hub.PyTorchModelHubMixin",anchor:"huggingface_hub.PyTorchModelHubMixin",parameters:[],source:"https://github.com/huggingface/huggingface_hub/blob/v0.19.0.rc0/src/huggingface_hub/hub_mixin.py#L290"}}),z=new Qt({props:{anchor:"huggingface_hub.PyTorchModelHubMixin.example",$$slots:{default:[so]},$$scope:{ctx:N}}}),ne=new Pe({props:{title:"Keras",local:"huggingface_hub.KerasModelHubMixin",headingTag:"h3"}}),ae=new H({props:{name:"class huggingface_hub.KerasModelHubMixin",anchor:"huggingface_hub.KerasModelHubMixin",parameters:[],source:"https://github.com/huggingface/huggingface_hub/blob/v0.19.0.rc0/src/huggingface_hub/keras_mixin.py#L398"}}),V=new Qt({props:{anchor:"huggingface_hub.KerasModelHubMixin.example",$$slots:{default:[io]},$$scope:{ctx:N}}}),re=new H({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/v4.35.0/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
the model).`,name:"local_files_only(bool,"},{anchor:"huggingface_hub.from_pretrained_keras.model_kwargs",description:`<strong>model_kwargs</strong> (<code>Dict</code>, <em>optional</em>) &#x2014;
model_kwargs will be passed to the model during initialization`,name:"model_kwargs"}],source:"https://github.com/huggingface/huggingface_hub/blob/v0.19.0.rc0/src/huggingface_hub/keras_mixin.py#L217"}}),D=new Ot({props:{$$slots:{default:[lo]},$$scope:{ctx:N}}}),se=new H({props:{name:"huggingface_hub.push_to_hub_keras",anchor:"huggingface_hub.push_to_hub_keras",parameters:[{name:"model",val:""},{name:"repo_id",val:": str"},{name:"config",val:": typing.Optional[dict] = None"},{name:"commit_message",val:": str = 'Push Keras model using huggingface_hub.'"},{name:"private",val:": bool = False"},{name:"api_endpoint",val:": typing.Optional[str] = None"},{name:"token",val:": typing.Optional[str] = None"},{name:"branch",val:": typing.Optional[str] = None"},{name:"create_pr",val:": typing.Optional[bool] = None"},{name:"allow_patterns",val:": typing.Union[typing.List[str], str, NoneType] = None"},{name:"ignore_patterns",val:": typing.Union[typing.List[str], str, NoneType] = None"},{name:"delete_patterns",val:": typing.Union[typing.List[str], str, NoneType] = None"},{name:"log_dir",val:": typing.Optional[str] = None"},{name:"include_optimizer",val:": bool = False"},{name:"tags",val:": typing.Union[list, str, NoneType] = None"},{name:"plot_model",val:": bool = True"},{name:"**model_save_kwargs",val:""}],parametersDescription:[{anchor:"huggingface_hub.push_to_hub_keras.model",description:`<strong>model</strong> (<code>Keras.Model</code>) &#x2014;
The <a href="%60https://www.tensorflow.org/api_docs/python/tf/keras/Model%60">Keras model</a> you&#x2019;d like to push to the
Hub. The model must be compiled and built.`,name:"model"},{anchor:"huggingface_hub.push_to_hub_keras.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.push_to_hub_keras.commit_message",description:`<strong>commit_message</strong> (<code>str</code>, <em>optional</em>, defaults to &#x201C;Add Keras model&#x201D;) &#x2014;
Message to commit while pushing.`,name:"commit_message"},{anchor:"huggingface_hub.push_to_hub_keras.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.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
not set, will use the token set when logging in with
<code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"token"},{anchor:"huggingface_hub.push_to_hub_keras.branch",description:`<strong>branch</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The git branch on which to push the model. This defaults to
the default branch as specified in your repository, which
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;
If provided, only files matching at least one pattern are pushed.`,name:"allow_patterns"},{anchor:"huggingface_hub.push_to_hub_keras.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.push_to_hub_keras.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"},{anchor:"huggingface_hub.push_to_hub_keras.log_dir",description:`<strong>log_dir</strong> (<code>str</code>, <em>optional</em>) &#x2014;
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
<a href="https://github.com/huggingface/hub-docs/blame/main/modelcard.md" rel="nofollow">here</a>.`,name:"tags"},{anchor:"huggingface_hub.push_to_hub_keras.plot_model",description:`<strong>plot_model</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Setting this to <code>True</code> will plot the model and put it in the model
card. Requires graphviz and pydot to be installed.`,name:"plot_model"},{anchor:"huggingface_hub.push_to_hub_keras.model_save_kwargs(dict,",description:`<strong>model_save_kwargs(<code>dict</code>,</strong> <em>optional</em>) &#x2014;
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/v0.19.0.rc0/src/huggingface_hub/keras_mixin.py#L276",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The url of the commit of your model in the given repository.</p>
`}}),ie=new H({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.Union[typing.Dict[str, typing.Any], NoneType] = 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;
Whether or not to include optimizer in serialization.`,name:"include_optimizer(bool,"},{anchor:"huggingface_hub.save_pretrained_keras.plot_model",description:`<strong>plot_model</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Setting this to <code>True</code> will plot the model and put it in the model
card. Requires graphviz and pydot to be installed.`,name:"plot_model"},{anchor:"huggingface_hub.save_pretrained_keras.tags",description:`<strong>tags</strong> (Union[<code>str</code>,<code>list</code>], <em>optional</em>) &#x2014;
List of tags that are related to model or string of a single tag. See example tags
<a href="https://github.com/huggingface/hub-docs/blame/main/modelcard.md" rel="nofollow">here</a>.`,name:"tags"},{anchor:"huggingface_hub.save_pretrained_keras.model_save_kwargs(dict,",description:`<strong>model_save_kwargs(<code>dict</code>,</strong> <em>optional</em>) &#x2014;
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/v0.19.0.rc0/src/huggingface_hub/keras_mixin.py#L132"}}),le=new Pe({props:{title:"Fastai",local:"huggingface_hub.from_pretrained_fastai",headingTag:"h3"}}),de=new H({props:{name:"huggingface_hub.from_pretrained_fastai",anchor:"huggingface_hub.from_pretrained_fastai",parameters:[{name:"repo_id",val:": str"},{name:"revision",val:": typing.Optional[str] = None"}],parametersDescription:[{anchor:"huggingface_hub.from_pretrained_fastai.repo_id",description:`<strong>repo_id</strong> (<code>str</code>) &#x2014;
The location where the pickled fastai.Learner is. It can be either of the two:<ul>
<li>Hosted on the Hugging Face Hub. E.g.: &#x2018;espejelomar/fatai-pet-breeds-classification&#x2019; or &#x2018;distilgpt2&#x2019;.
You can add a <code>revision</code> by appending <code>@</code> at the end of <code>repo_id</code>. E.g.: <code>dbmdz/bert-base-german-cased@main</code>.
Revision is the specific model version to use. Since we use a git-based system for storing models and other
artifacts on the Hugging Face Hub, it can be a branch name, a tag name, or a commit id.</li>
<li>Hosted locally. <code>repo_id</code> would be a directory containing the pickle and a pyproject.toml
indicating the fastai and fastcore versions used to build the <code>fastai.Learner</code>. E.g.: <code>./my_model_directory/</code>.</li>
</ul>`,name:"repo_id"},{anchor:"huggingface_hub.from_pretrained_fastai.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Revision at which the repo&#x2019;s files are downloaded. See documentation of <code>snapshot_download</code>.`,name:"revision"}],source:"https://github.com/huggingface/huggingface_hub/blob/v0.19.0.rc0/src/huggingface_hub/fastai_utils.py#L298",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The <code>fastai.Learner</code> model in the <code>repo_id</code> repo.</p>
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The git branch on which to push the model. This defaults to
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<p>The url of the commit of your model in the given repository.</p>
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