import{s as Ie,f as ce,n as xe,o as Ge}from"../chunks/scheduler.37c15a92.js";import{S as Ze,i as $e,g as i,s as o,r as g,A as ve,h as r,f as l,c as n,j as Q,u as y,x as M,k as s,y as Be,a,v as f,d as h,t as J,w as b}from"../chunks/index.2bf4358c.js";import{C as pe}from"../chunks/CodeBlock.4e987730.js";import{D as He}from"../chunks/DocNotebookDropdown.efc1fb7c.js";import{H as me,E as Ce}from"../chunks/getInferenceSnippets.1837c472.js";function We(ue){let u,S,Y,E,d,N,w,X,T,Me="為了讓你的生活更輕鬆, Gradio 直接與 Hugging Face Hub 和 Hugging Face Spaces 集成。你可以僅使用 <em>一行代碼</em> 從中心和空間加載演示。",F,U,_,j,ge='首先, 從 Hugging Face 通過 Hub 提供的數千個模型中選擇一個, 如 <a href="/course/chapter4/2">第四章</a> 中所述。',z,I,ye='使用特殊的 <code>Interface.load()</code> 方法, 你可以傳遞 <code>&quot;model/&quot;</code> (或者, 等效的, <code>&quot;huggingface/&quot;</code>) 後面是模型名稱。例如, 這裡是為大型語言模型 <a href="https://huggingface.co/EleutherAI/gpt-j-6B" rel="nofollow">GPT-J</a>構建演示的代碼, 添加幾個示例輸入:',A,x,q,G,fe="上述代碼將生成以下界面:",L,c,he,P,Z,Je='以這種方式加載模型使用 Hugging Face 的 <a href="https://huggingface.co/inference-api" rel="nofollow">Inference API</a>,而不是將模型加載到內存中。這對於像 GPT-J 或 T0pp這樣需要大量 RAM 的大型模型是理想的。',D,$,K,v,be="要從hugs Face Hub加載任何空間並在本地重新創建它, 你可以將 <code>spaces/</code> 傳遞給 <code>Interface</code>, 再加上空間的名稱。",O,B,de="還記得第 1 節中刪除圖像背景的演示嗎? 讓我們從 Hugging Face Spaces 加載它:",ee,H,te,p,we,le,C,Te="從Hub或Spaces加載演示的一個很酷的地方是, 你可以通過覆蓋任何參數來自定義它們。在這裡, 我們添加一個標題並讓它與網絡攝像頭一起使用:",ae,W,se,m,Ue,oe,k,je="現在我們已經探索了幾種將Gradio與hugs Face Hub集成的方法, 讓我們來看看 <code>Interface</code> 類的一些高級功能。這就是下一節的主題！",ne,R,ie,V,re;return d=new me({props:{title:"與 Hugging Face Hub 整合",local:"與-hugging-face-hub-整合",headingTag:"h1"}}),w=new He({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Google Colab",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/chapter9/section5.ipynb"},{label:"Aws Studio",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/chapter9/section5.ipynb"}]}}),U=new me({props:{title:"從 Hugging Face Hub 加載模型",local:"從-hugging-face-hub-加載模型",headingTag:"h3"}}),x=new pe({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> gradio <span class="hljs-keyword">as</span> gr

title = <span class="hljs-string">&quot;GPT-J-6B&quot;</span>
description = <span class="hljs-string">&quot;Gradio Demo for GPT-J 6B, a transformer model trained using Ben Wang&#x27;s Mesh Transformer JAX. &#x27;GPT-J&#x27; refers to the class of model, while &#x27;6B&#x27; represents the number of trainable parameters. To use it, simply add your text, or click one of the examples to load them. Read more at the links below.&quot;</span>
article = <span class="hljs-string">&quot;&lt;p style=&#x27;text-align: center&#x27;&gt;&lt;a href=&#x27;https://github.com/kingoflolz/mesh-transformer-jax&#x27; target=&#x27;_blank&#x27;&gt;GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model&lt;/a&gt;&lt;/p&gt;&quot;</span>
examples = [
    [<span class="hljs-string">&quot;The tower is 324 metres (1,063 ft) tall,&quot;</span>],
    [<span class="hljs-string">&quot;The Moon&#x27;s orbit around Earth has&quot;</span>],
    [<span class="hljs-string">&quot;The smooth Borealis basin in the Northern Hemisphere covers 40%&quot;</span>],
]
gr.Interface.load(
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    inputs=gr.Textbox(lines=<span class="hljs-number">5</span>, label=<span class="hljs-string">&quot;Input Text&quot;</span>),
    title=title,
    description=description,
    article=article,
    examples=examples,
    enable_queue=<span class="hljs-literal">True</span>,
).launch()`,wrap:!1}}),$=new me({props:{title:"從 Hugging Face Spaces 空間加載",local:"從-hugging-face-spaces-空間加載",headingTag:"h3"}}),H=new pe({props:{code:"Z3IuSW50ZXJmYWNlLmxvYWQoJTIyc3BhY2VzJTJGYWJpZGxhYnMlMkZyZW1vdmUtYmclMjIpLmxhdW5jaCgp",highlighted:'gr.Interface.load(<span class="hljs-string">&quot;spaces/abidlabs/remove-bg&quot;</span>).launch()',wrap:!1}}),W=new pe({props:{code:"Z3IuSW50ZXJmYWNlLmxvYWQoJTBBJTIwJTIwJTIwJTIwJTIyc3BhY2VzJTJGYWJpZGxhYnMlMkZyZW1vdmUtYmclMjIlMkMlMjBpbnB1dHMlM0QlMjJ3ZWJjYW0lMjIlMkMlMjB0aXRsZSUzRCUyMlJlbW92ZSUyMHlvdXIlMjB3ZWJjYW0lMjBiYWNrZ3JvdW5kISUyMiUwQSkubGF1bmNoKCk=",highlighted:`gr.Interface.load(
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