import{s as xp,o as Hp,n as Ip}from"../chunks/scheduler.37c15a92.js";import{S as Np,i as Vp,g as a,s as p,r as y,A as Lp,h as m,f as l,c as r,j as Gp,u as d,x as f,k as B,y as Xp,a as n,v as _,t as c,b as tt,d as b,w as h,m as Rp,n as zp,p as et}from"../chunks/index.2bf4358c.js";import{T as Wp}from"../chunks/Tip.363c041f.js";import{Y as Yi}from"../chunks/Youtube.1e50a667.js";import{C as U}from"../chunks/CodeBlock.4e987730.js";import{C as Bp}from"../chunks/CourseFloatingBanner.9ff4c771.js";import{F as Yp}from"../chunks/FrameworkSwitchCourse.8d4d4ab6.js";import{H as ot,E as Ap}from"../chunks/getInferenceSnippets.39afd8fb.js";function Qp($){let i,o;return i=new Bp({props:{chapter:4,classNames:"absolute z-10 right-0 top-0",notebooks:[{label:"Google Colab",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/zh-CN/chapter4/section3_tf.ipynb"},{label:"Aws Studio",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/zh-CN/chapter4/section3_tf.ipynb"}]}}),{c(){y(i.$$.fragment)},l(s){d(i.$$.fragment,s)},m(s,M){_(i,s,M),o=!0},i(s){o||(b(i.$$.fragment,s),o=!0)},o(s){c(i.$$.fragment,s),o=!1},d(s){h(i,s)}}}function Sp($){let i,o;return i=new Bp({props:{chapter:4,classNames:"absolute z-10 right-0 top-0",notebooks:[{label:"Google Colab",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/zh-CN/chapter4/section3_pt.ipynb"},{label:"Aws Studio",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/zh-CN/chapter4/section3_pt.ipynb"}]}}),{c(){y(i.$$.fragment)},l(s){d(i.$$.fragment,s)},m(s,M){_(i,s,M),o=!0},i(s){o||(b(i.$$.fragment,s),o=!0)},o(s){c(i.$$.fragment,s),o=!1},d(s){h(i,s)}}}function Fp($){let i,o;return i=new Yi({props:{id:"pUh5cGmNV8Y"}}),{c(){y(i.$$.fragment)},l(s){d(i.$$.fragment,s)},m(s,M){_(i,s,M),o=!0},i(s){o||(b(i.$$.fragment,s),o=!0)},o(s){c(i.$$.fragment,s),o=!1},d(s){h(i,s)}}}function Ep($){let i,o;return i=new Yi({props:{id:"Zh0FfmVrKX0"}}),{c(){y(i.$$.fragment)},l(s){d(i.$$.fragment,s)},m(s,M){_(i,s,M),o=!0},i(s){o||(b(i.$$.fragment,s),o=!0)},o(s){c(i.$$.fragment,s),o=!1},d(s){h(i,s)}}}function Pp($){let i,o="如果您使用Keras來訓練您的模型，則將其上傳到Hub的最簡單方法是在調用 <strong>model.fit()</strong>  時傳遞<strong>PushToHubCallback</strong>：",s,M,T,u,w="然後，您應該在對<strong>model.fit()</strong>的調用中添加<strong>callbacks=[callback]</strong>。然後，每次將模型保存在命名空間的存儲庫中（此處為每個 epoch）時，回調都會將模型上傳到 Hub。該存儲庫的名稱將類似於您選擇的輸出目錄（此處為<strong>bert-finetuned-mrpc</strong>），但您可以選擇另一個名稱，名稱為<strong>hub_model_id = a_different_name</strong>。",C,j,v="要將您的模型上傳到您所屬的組織，只需將其傳遞給 <strong>hub_model_id = my_organization/my_repo_name</strong> 。",Z;return M=new U({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFB1c2hUb0h1YkNhbGxiYWNrJTBBJTBBY2FsbGJhY2slMjAlM0QlMjBQdXNoVG9IdWJDYWxsYmFjayglMEElMjAlMjAlMjAlMjAlMjJiZXJ0LWZpbmV0dW5lZC1tcnBjJTIyJTJDJTIwc2F2ZV9zdHJhdGVneSUzRCUyMmVwb2NoJTIyJTJDJTIwdG9rZW5pemVyJTNEdG9rZW5pemVyJTBBKQ==",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> PushToHubCallback

callback = PushToHubCallback(
    <span class="hljs-string">&quot;bert-finetuned-mrpc&quot;</span>, save_strategy=<span class="hljs-string">&quot;epoch&quot;</span>, tokenizer=tokenizer
)`,wrap:!1}}),{c(){i=a("p"),i.innerHTML=o,s=p(),y(M.$$.fragment),T=p(),u=a("p"),u.innerHTML=w,C=p(),j=a("p"),j.innerHTML=v},l(g){i=m(g,"P",{"data-svelte-h":!0}),f(i)!=="svelte-1kddlaa"&&(i.innerHTML=o),s=r(g),d(M.$$.fragment,g),T=r(g),u=m(g,"P",{"data-svelte-h":!0}),f(u)!=="svelte-bkkgh9"&&(u.innerHTML=w),C=r(g),j=m(g,"P",{"data-svelte-h":!0}),f(j)!=="svelte-4arcmo"&&(j.innerHTML=v)},m(g,J){n(g,i,J),n(g,s,J),_(M,g,J),n(g,T,J),n(g,u,J),n(g,C,J),n(g,j,J),Z=!0},i(g){Z||(b(M.$$.fragment,g),Z=!0)},o(g){c(M.$$.fragment,g),Z=!1},d(g){g&&(l(i),l(s),l(T),l(u),l(C),l(j)),h(M,g)}}}function qp($){let i,o="如果你玩過 <strong>Trainer</strong> 用於訓練模型的 API，將其上傳到 Hub 的最簡單方法是設置 <strong>push_to_hub=True</strong> 當你定義你的 <strong>TrainingArguments</strong> ：",s,M,T,u,w="你聲明 <strong>trainer.train()</strong> 的時候， 這 <strong>Trainer</strong> 然後每次將您的模型保存到您的命名空間中的存儲庫中時（這裡是每個時代），它將上傳到模型中心。該存儲庫將命名為您選擇的輸出目錄（此處 <strong>bert-finetuned-mrpc</strong> ) 但您可以選擇不同的名稱 <strong>hub_model_id = a_different_name</strong> 。",C,j,v="要將您的模型上傳到您所屬的組織，只需將其傳遞給 <strong>hub_model_id = my_organization/my_repo_name</strong> 。",Z,g,J="訓練結束後，你應該做最後的 <strong>trainer.push_to_hub()</strong> 上傳模型的最新版本。它還將生成包含所有相關元數據的模型卡，報告使用的超參數和評估結果！以下是您可能會在此類模型卡中找到的內容示例：",D,G,at='<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter4/model_card.png" alt="An example of an auto-generated model card." width="100%"/>',x;return M=new U({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRyYWluaW5nQXJndW1lbnRzJTBBJTBBdHJhaW5pbmdfYXJncyUyMCUzRCUyMFRyYWluaW5nQXJndW1lbnRzKCUwQSUyMCUyMCUyMCUyMCUyMmJlcnQtZmluZXR1bmVkLW1ycGMlMjIlMkMlMjBzYXZlX3N0cmF0ZWd5JTNEJTIyZXBvY2glMjIlMkMlMjBwdXNoX3RvX2h1YiUzRFRydWUlMEEp",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TrainingArguments

training_args = TrainingArguments(
    <span class="hljs-string">&quot;bert-finetuned-mrpc&quot;</span>, save_strategy=<span class="hljs-string">&quot;epoch&quot;</span>, push_to_hub=<span class="hljs-literal">True</span>
)`,wrap:!1}}),{c(){i=a("p"),i.innerHTML=o,s=p(),y(M.$$.fragment),T=p(),u=a("p"),u.innerHTML=w,C=p(),j=a("p"),j.innerHTML=v,Z=p(),g=a("p"),g.innerHTML=J,D=p(),G=a("div"),G.innerHTML=at,this.h()},l(k){i=m(k,"P",{"data-svelte-h":!0}),f(i)!=="svelte-1d8nzk8"&&(i.innerHTML=o),s=r(k),d(M.$$.fragment,k),T=r(k),u=m(k,"P",{"data-svelte-h":!0}),f(u)!=="svelte-zw37cm"&&(u.innerHTML=w),C=r(k),j=m(k,"P",{"data-svelte-h":!0}),f(j)!=="svelte-4arcmo"&&(j.innerHTML=v),Z=r(k),g=m(k,"P",{"data-svelte-h":!0}),f(g)!=="svelte-1ci5fik"&&(g.innerHTML=J),D=r(k),G=m(k,"DIV",{class:!0,"data-svelte-h":!0}),f(G)!=="svelte-1lhrimn"&&(G.innerHTML=at),this.h()},h(){B(G,"class","flex justify-center")},m(k,W){n(k,i,W),n(k,s,W),_(M,k,W),n(k,T,W),n(k,u,W),n(k,C,W),n(k,j,W),n(k,Z,W),n(k,g,W),n(k,D,W),n(k,G,W),x=!0},i(k){x||(b(M.$$.fragment,k),x=!0)},o(k){c(M.$$.fragment,k),x=!1},d(k){k&&(l(i),l(s),l(T),l(u),l(C),l(j),l(Z),l(g),l(D),l(G)),h(M,k)}}}function Dp($){let i,o;return i=new U({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRGQXV0b01vZGVsRm9yTWFza2VkTE0lMkMlMjBBdXRvVG9rZW5pemVyJTBBJTBBY2hlY2twb2ludCUyMCUzRCUyMCUyMmNhbWVtYmVydC1iYXNlJTIyJTBBJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvck1hc2tlZExNLmZyb21fcHJldHJhaW5lZChjaGVja3BvaW50KSUwQXRva2VuaXplciUyMCUzRCUyMEF1dG9Ub2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKGNoZWNrcG9pbnQp",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForMaskedLM, AutoTokenizer

checkpoint = <span class="hljs-string">&quot;camembert-base&quot;</span>

model = TFAutoModelForMaskedLM.from_pretrained(checkpoint)
tokenizer = AutoTokenizer.from_pretrained(checkpoint)`,wrap:!1}}),{c(){y(i.$$.fragment)},l(s){d(i.$$.fragment,s)},m(s,M){_(i,s,M),o=!0},i(s){o||(b(i.$$.fragment,s),o=!0)},o(s){c(i.$$.fragment,s),o=!1},d(s){h(i,s)}}}function Kp($){let i,o;return i=new U({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbEZvck1hc2tlZExNJTJDJTIwQXV0b1Rva2VuaXplciUwQSUwQWNoZWNrcG9pbnQlMjAlM0QlMjAlMjJjYW1lbWJlcnQtYmFzZSUyMiUwQSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yTWFza2VkTE0uZnJvbV9wcmV0cmFpbmVkKGNoZWNrcG9pbnQpJTBBdG9rZW5pemVyJTIwJTNEJTIwQXV0b1Rva2VuaXplci5mcm9tX3ByZXRyYWluZWQoY2hlY2twb2ludCk=",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForMaskedLM, AutoTokenizer

checkpoint = <span class="hljs-string">&quot;camembert-base&quot;</span>

model = AutoModelForMaskedLM.from_pretrained(checkpoint)
tokenizer = AutoTokenizer.from_pretrained(checkpoint)`,wrap:!1}}),{c(){y(i.$$.fragment)},l(s){d(i.$$.fragment,s)},m(s,M){_(i,s,M),o=!0},i(s){o||(b(i.$$.fragment,s),o=!0)},o(s){c(i.$$.fragment,s),o=!1},d(s){h(i,s)}}}function Op($){let i,o='<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter4/push_to_hub_dummy_model_tf.png" alt="Dummy model containing both the tokenizer and model files." width="80%"/>';return{c(){i=a("div"),i.innerHTML=o,this.h()},l(s){i=m(s,"DIV",{class:!0,"data-svelte-h":!0}),f(i)!=="svelte-5a3kk0"&&(i.innerHTML=o),this.h()},h(){B(i,"class","flex justify-center")},m(s,M){n(s,i,M)},d(s){s&&l(i)}}}function tr($){let i,o='<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter4/push_to_hub_dummy_model.png" alt="Dummy model containing both the tokenizer and model files." width="80%"/>';return{c(){i=a("div"),i.innerHTML=o,this.h()},l(s){i=m(s,"DIV",{class:!0,"data-svelte-h":!0}),f(i)!=="svelte-19u1vyv"&&(i.innerHTML=o),this.h()},h(){B(i,"class","flex justify-center")},m(s,M){n(s,i,M)},d(s){s&&l(i)}}}function er($){let i,o="✏️ <strong>試試看</strong>！獲取與檢查點關聯的模型和標記器，並使用該方法將它們上傳到您的命名空間中的存儲庫。在刪除之前，請仔細檢查該存儲庫是否正確顯示在您的頁面上。";return{c(){i=a("p"),i.innerHTML=o},l(s){i=m(s,"P",{"data-svelte-h":!0}),f(i)!=="svelte-173pvtw"&&(i.innerHTML=o)},m(s,M){n(s,i,M)},p:Ip,d(s){s&&l(i)}}}function lr($){let i,o;return i=new U({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TFAutoModelForMaskedLM, AutoTokenizer

checkpoint = <span class="hljs-string">&quot;camembert-base&quot;</span>

model = TFAutoModelForMaskedLM.from_pretrained(checkpoint)
tokenizer = AutoTokenizer.from_pretrained(checkpoint)

<span class="hljs-comment"># Do whatever with the model, train it, fine-tune it...</span>

model.save_pretrained(<span class="hljs-string">&quot;&lt;path_to_dummy_folder&gt;&quot;</span>)
tokenizer.save_pretrained(<span class="hljs-string">&quot;&lt;path_to_dummy_folder&gt;&quot;</span>)`,wrap:!1}}),{c(){y(i.$$.fragment)},l(s){d(i.$$.fragment,s)},m(s,M){_(i,s,M),o=!0},i(s){o||(b(i.$$.fragment,s),o=!0)},o(s){c(i.$$.fragment,s),o=!1},d(s){h(i,s)}}}function nr($){let i,o;return i=new U({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbEZvck1hc2tlZExNJTJDJTIwQXV0b1Rva2VuaXplciUwQSUwQWNoZWNrcG9pbnQlMjAlM0QlMjAlMjJjYW1lbWJlcnQtYmFzZSUyMiUwQSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yTWFza2VkTE0uZnJvbV9wcmV0cmFpbmVkKGNoZWNrcG9pbnQpJTBBdG9rZW5pemVyJTIwJTNEJTIwQXV0b1Rva2VuaXplci5mcm9tX3ByZXRyYWluZWQoY2hlY2twb2ludCklMEElMEElMjMlMjBEbyUyMHdoYXRldmVyJTIwd2l0aCUyMHRoZSUyMG1vZGVsJTJDJTIwdHJhaW4lMjBpdCUyQyUyMGZpbmUtdHVuZSUyMGl0Li4uJTBBJTBBbW9kZWwuc2F2ZV9wcmV0cmFpbmVkKCUyMiUzQ3BhdGhfdG9fZHVtbXlfZm9sZGVyJTNFJTIyKSUwQXRva2VuaXplci5zYXZlX3ByZXRyYWluZWQoJTIyJTNDcGF0aF90b19kdW1teV9mb2xkZXIlM0UlMjIp",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForMaskedLM, AutoTokenizer

checkpoint = <span class="hljs-string">&quot;camembert-base&quot;</span>

model = AutoModelForMaskedLM.from_pretrained(checkpoint)
tokenizer = AutoTokenizer.from_pretrained(checkpoint)

<span class="hljs-comment"># Do whatever with the model, train it, fine-tune it...</span>

model.save_pretrained(<span class="hljs-string">&quot;&lt;path_to_dummy_folder&gt;&quot;</span>)
tokenizer.save_pretrained(<span class="hljs-string">&quot;&lt;path_to_dummy_folder&gt;&quot;</span>)`,wrap:!1}}),{c(){y(i.$$.fragment)},l(s){d(i.$$.fragment,s)},m(s,M){_(i,s,M),o=!0},i(s){o||(b(i.$$.fragment,s),o=!0)},o(s){c(i.$$.fragment,s),o=!1},d(s){h(i,s)}}}function ir($){let i,o,s,M="如果您查看文件大小（例如， <strong>ls -lh</strong> )，您應該會看到模型狀態 dict 文件 (pytorch_model.bin) 是唯一的異常值，超過 400 MB。",T;return i=new U({props:{code:"Y29uZmlnLmpzb24lMjAlMjBSRUFETUUubWQlMjAlMjBzZW50ZW5jZXBpZWNlLmJwZS5tb2RlbCUyMCUyMHNwZWNpYWxfdG9rZW5zX21hcC5qc29uJTIwJTIwdGZfbW9kZWwuaDUlMjAlMjB0b2tlbml6ZXJfY29uZmlnLmpzb24lMjAlMjB0b2tlbml6ZXIuanNvbg==",highlighted:"config.json  README.md  sentencepiece.bpe.model  special_tokens_map.json  tf_model.h5  tokenizer_config.json  tokenizer.json",wrap:!1}}),{c(){y(i.$$.fragment),o=p(),s=a("p"),s.innerHTML=M},l(u){d(i.$$.fragment,u),o=r(u),s=m(u,"P",{"data-svelte-h":!0}),f(s)!=="svelte-1csho22"&&(s.innerHTML=M)},m(u,w){_(i,u,w),n(u,o,w),n(u,s,w),T=!0},i(u){T||(b(i.$$.fragment,u),T=!0)},o(u){c(i.$$.fragment,u),T=!1},d(u){u&&(l(o),l(s)),h(i,u)}}}function sr($){let i,o,s,M="If you look at the file sizes (for example, with <code>ls -lh</code>), you should see that the model state dict file (<em>pytorch_model.bin</em>) is the only outlier, at more than 400 MB.",T;return i=new U({props:{code:"Y29uZmlnLmpzb24lMjAlMjBweXRvcmNoX21vZGVsLmJpbiUyMCUyMFJFQURNRS5tZCUyMCUyMHNlbnRlbmNlcGllY2UuYnBlLm1vZGVsJTIwJTIwc3BlY2lhbF90b2tlbnNfbWFwLmpzb24lMjB0b2tlbml6ZXJfY29uZmlnLmpzb24lMjAlMjB0b2tlbml6ZXIuanNvbg==",highlighted:"config.json  pytorch_model.bin  README.md  sentencepiece.bpe.model  special_tokens_map.json tokenizer_config.json  tokenizer.json",wrap:!1}}),{c(){y(i.$$.fragment),o=p(),s=a("p"),s.innerHTML=M},l(u){d(i.$$.fragment,u),o=r(u),s=m(u,"P",{"data-svelte-h":!0}),f(s)!=="svelte-174jxm4"&&(s.innerHTML=M)},m(u,w){_(i,u,w),n(u,o,w),n(u,s,w),T=!0},i(u){T||(b(i.$$.fragment,u),T=!0)},o(u){c(i.$$.fragment,u),T=!1},d(u){u&&(l(o),l(s)),h(i,u)}}}function pr($){let i;return{c(){i=Rp("✏️ 從 web 界面創建存儲庫時，*.gitattributes* 文件會自動設置為將具有某些擴展名的文件，例如 *.bin* 和 *.h5* 視為大文件，git-lfs 會對其進行跟蹤您無需進行必要的設置。")},l(o){i=zp(o,"✏️ 從 web 界面創建存儲庫時，*.gitattributes* 文件會自動設置為將具有某些擴展名的文件，例如 *.bin* 和 *.h5* 視為大文件，git-lfs 會對其進行跟蹤您無需進行必要的設置。")},m(o,s){n(o,i,s)},d(o){o&&l(i)}}}function rr($){let i,o;return i=new U({props:{code:"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",highlighted:`On branch main
Your branch is up to <span class="hljs-built_in">date</span> with <span class="hljs-string">&#x27;origin/main&#x27;</span>.

Changes to be committed:
  (use <span class="hljs-string">&quot;git restore --staged &lt;file&gt;...&quot;</span> to unstage)
  modified:   .gitattributes
  	new file:   config.json
	new file:   sentencepiece.bpe.model
	new file:   special_tokens_map.json
	new file:   tf_model.h5
	new file:   tokenizer.json
	new file:   tokenizer_config.json`,wrap:!1}}),{c(){y(i.$$.fragment)},l(s){d(i.$$.fragment,s)},m(s,M){_(i,s,M),o=!0},i(s){o||(b(i.$$.fragment,s),o=!0)},o(s){c(i.$$.fragment,s),o=!1},d(s){h(i,s)}}}function or($){let i,o;return i=new U({props:{code:"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",highlighted:`On branch main
Your branch is up to <span class="hljs-built_in">date</span> with <span class="hljs-string">&#x27;origin/main&#x27;</span>.

Changes to be committed:
  (use <span class="hljs-string">&quot;git restore --staged &lt;file&gt;...&quot;</span> to unstage)
  modified:   .gitattributes
	new file:   config.json
	new file:   pytorch_model.bin
	new file:   sentencepiece.bpe.model
	new file:   special_tokens_map.json
	new file:   tokenizer.json
	new file:   tokenizer_config.json`,wrap:!1}}),{c(){y(i.$$.fragment)},l(s){d(i.$$.fragment,s)},m(s,M){_(i,s,M),o=!0},i(s){o||(b(i.$$.fragment,s),o=!0)},o(s){c(i.$$.fragment,s),o=!1},d(s){h(i,s)}}}function ar($){let i,o,s,M="我們可以看到所有文件都有 <strong>Git</strong> 作為處理程序，除了其中有 <strong>LFS</strong>的<em>t5_model.h5</em>。",T;return i=new U({props:{code:"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",highlighted:`On branch main
Objects to be pushed to origin/main:


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	config.json (Git: bc20ff2)
	sentencepiece.bpe.model (LFS: 988bc5a)
	special_tokens_map.json (Git: cb23931)
	tf_model.h5 (LFS: 86fce29)
	tokenizer.json (Git: 851ff3e)
	tokenizer_config.json (Git: f0f7783)

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`,wrap:!1}}),{c(){y(i.$$.fragment),o=p(),s=a("p"),s.innerHTML=M},l(u){d(i.$$.fragment,u),o=r(u),s=m(u,"P",{"data-svelte-h":!0}),f(s)!=="svelte-cg1to"&&(s.innerHTML=M)},m(u,w){_(i,u,w),n(u,o,w),n(u,s,w),T=!0},i(u){T||(b(i.$$.fragment,u),T=!0)},o(u){c(i.$$.fragment,u),T=!1},d(u){u&&(l(o),l(s)),h(i,u)}}}function mr($){let i,o,s,M="我們可以看到所有文件都有 <strong>Git</strong> 作為處理程序，除了其中有 <strong>LFS</strong>的<em>pytorch_model.bin</em> 和 <em>sentencepiece.bpe.model</em>。",T;return i=new U({props:{code:"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",highlighted:`On branch main
Objects to be pushed to origin/main:


Objects to be committed:

	config.json (Git: bc20ff2)
	pytorch_model.bin (LFS: 35686c2)
	sentencepiece.bpe.model (LFS: 988bc5a)
	special_tokens_map.json (Git: cb23931)
	tokenizer.json (Git: 851ff3e)
	tokenizer_config.json (Git: f0f7783)

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`,wrap:!1}}),{c(){y(i.$$.fragment),o=p(),s=a("p"),s.innerHTML=M},l(u){d(i.$$.fragment,u),o=r(u),s=m(u,"P",{"data-svelte-h":!0}),f(s)!=="svelte-1tchun8"&&(s.innerHTML=M)},m(u,w){_(i,u,w),n(u,o,w),n(u,s,w),T=!0},i(u){T||(b(i.$$.fragment,u),T=!0)},o(u){c(i.$$.fragment,u),T=!1},d(u){u&&(l(o),l(s)),h(i,u)}}}function fr($){let i,o;return i=new U({props:{code:"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",highlighted:`[main b08aab1] First model version
 6 files changed, 36 insertions(+)
 create mode 100644 config.json
 create mode 100644 sentencepiece.bpe.model
 create mode 100644 special_tokens_map.json
 create mode 100644 tf_model.h5
 create mode 100644 tokenizer.json
 create mode 100644 tokenizer_config.json`,wrap:!1}}),{c(){y(i.$$.fragment)},l(s){d(i.$$.fragment,s)},m(s,M){_(i,s,M),o=!0},i(s){o||(b(i.$$.fragment,s),o=!0)},o(s){c(i.$$.fragment,s),o=!1},d(s){h(i,s)}}}function ur($){let i,o;return i=new U({props:{code:"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",highlighted:`[main b08aab1] First model version
 7 files changed, 29027 insertions(+)
  6 files changed, 36 insertions(+)
 create mode 100644 config.json
 create mode 100644 pytorch_model.bin
 create mode 100644 sentencepiece.bpe.model
 create mode 100644 special_tokens_map.json
 create mode 100644 tokenizer.json
 create mode 100644 tokenizer_config.json`,wrap:!1}}),{c(){y(i.$$.fragment)},l(s){d(i.$$.fragment,s)},m(s,M){_(i,s,M),o=!0},i(s){o||(b(i.$$.fragment,s),o=!0)},o(s){c(i.$$.fragment,s),o=!1},d(s){h(i,s)}}}function Mr($){let i,o="如果我們在完成後查看模型存儲庫，我們可以看到所有最近添加的文件：",s,M,T='<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter4/full_model_tf.png" alt="The &#39;Files and versions&#39; tab now contains all the recently uploaded files." width="80%"/>',u,w,C="UI 允許您瀏覽模型文件和提交，並查看每個提交引入的差異：",j,v,Z='<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter4/diffstf.gif" alt="The diff introduced by the recent commit." width="80%"/>';return{c(){i=a("p"),i.textContent=o,s=p(),M=a("div"),M.innerHTML=T,u=p(),w=a("p"),w.textContent=C,j=p(),v=a("div"),v.innerHTML=Z,this.h()},l(g){i=m(g,"P",{"data-svelte-h":!0}),f(i)!=="svelte-xel6h2"&&(i.textContent=o),s=r(g),M=m(g,"DIV",{class:!0,"data-svelte-h":!0}),f(M)!=="svelte-tz4fi2"&&(M.innerHTML=T),u=r(g),w=m(g,"P",{"data-svelte-h":!0}),f(w)!=="svelte-4kssbx"&&(w.textContent=C),j=r(g),v=m(g,"DIV",{class:!0,"data-svelte-h":!0}),f(v)!=="svelte-2jd8bj"&&(v.innerHTML=Z),this.h()},h(){B(M,"class","flex justify-center"),B(v,"class","flex justify-center")},m(g,J){n(g,i,J),n(g,s,J),n(g,M,J),n(g,u,J),n(g,w,J),n(g,j,J),n(g,v,J)},d(g){g&&(l(i),l(s),l(M),l(u),l(w),l(j),l(v))}}}function cr($){let i,o="If we take a look at the model repository when this is finished, we can see all the recently added files:",s,M,T='<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter4/full_model.png" alt="The &#39;Files and versions&#39; tab now contains all the recently uploaded files." width="80%"/>',u,w,C="UI 允許您瀏覽模型文件和提交，並查看每個提交引入的差異：",j,v,Z='<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter4/diffs.gif" alt="The diff introduced by the recent commit." width="80%"/>';return{c(){i=a("p"),i.textContent=o,s=p(),M=a("div"),M.innerHTML=T,u=p(),w=a("p"),w.textContent=C,j=p(),v=a("div"),v.innerHTML=Z,this.h()},l(g){i=m(g,"P",{"data-svelte-h":!0}),f(i)!=="svelte-dr5wot"&&(i.textContent=o),s=r(g),M=m(g,"DIV",{class:!0,"data-svelte-h":!0}),f(M)!=="svelte-1v39mjb"&&(M.innerHTML=T),u=r(g),w=m(g,"P",{"data-svelte-h":!0}),f(w)!=="svelte-4kssbx"&&(w.textContent=C),j=r(g),v=m(g,"DIV",{class:!0,"data-svelte-h":!0}),f(v)!=="svelte-1g561m5"&&(v.innerHTML=Z),this.h()},h(){B(M,"class","flex justify-center"),B(v,"class","flex justify-center")},m(g,J){n(g,i,J),n(g,s,J),n(g,M,J),n(g,u,J),n(g,w,J),n(g,j,J),n(g,v,J)},d(g){g&&(l(i),l(s),l(M),l(u),l(w),l(j),l(v))}}}function br($){let i,o,s,M,T,u,w,C,j,v,Z,g,J="在下面的步驟中，我們將看看將預訓練模型分享到 🤗 Hub 的最簡單方法。有可用的工具和實用程序可以讓直接在 Hub 上共享和更新模型變得簡單，我們將在下面進行探討。",D,G,at,x,k="我們鼓勵所有訓練模型的用戶通過與社區共享來做出貢獻——共享模型，即使是在非常特定的數據集上進行訓練，也將幫助他人，節省他們的時間和計算資源，並提供對有用的訓練工件的訪問。反過來，您可以從其他人所做的工作中受益！",W,mt,Ai="創建新模型存儲庫的方法有以下三種：",kl,ft,Qi="<li>使用 push_to_hub API 接口</li> <li>使用 huggingface_hub Python 庫</li> <li>使用 web 界面</li>",Jl,ut,Si="創建存儲庫後，您可以通過 git 和 git-lfs 將文件上傳到其中。我們將在以下部分引導您創建模型存儲庫並將文件上傳到它們",Zl,Mt,Cl,H,I,gl,ct,Fi="將文件上傳到模型中心的最簡單方法是利用 <strong>push_to_hub</strong> API 接口。",Gl,bt,Ei='在繼續之前，您需要生成一個身份驗證令牌，以便 <strong>huggingface_hub</strong> API 知道您是誰以及您對哪些名稱空間具有寫入權限。確保你在一個環境中 <strong>transformers</strong> 已安裝（見<a href="/course/chapter0">Setup</a>）。如果您在筆記本中，可以使用以下功能登錄：',Wl,gt,Bl,yt,Pi="在終端中，您可以運行：",xl,dt,Hl,_t,qi='在這兩種情況下，系統都會提示您輸入用戶名和密碼，這與您用於登錄 Hub 的用戶名和密碼相同。如果您還沒有 Hub 配置文件，則應該創建一個<a href="https://huggingface.co/join" rel="nofollow">here</a>。',Il,ht,Di="好的！您現在已將身份驗證令牌存儲在緩存文件夾中。讓我們創建一些存儲庫！",Nl,N,V,yl,$t,Ki="在較低級別，可以通過模型、標記器和配置對象直接訪問模型中心 <strong>push_to_hub()</strong> 方法。此方法負責創建存儲庫並將模型和標記器文件直接推送到存儲庫。與我們將在下面看到的 API 不同，不需要手動處理。",Vl,wt,Oi="為了瞭解它是如何工作的，讓我們首先初始化一個模型和一個標記器：",Ll,L,X,dl,Tt,ts="你可以自由地用這些做任何你想做的事情——向標記器添加標記，訓練模型，微調它。一旦您對生成的模型、權重和標記器感到滿意，您就可以利用 <strong>push_to_hub()</strong> 方法直接在 <strong>model</strong> 中：",Xl,Ut,Rl,jt,es=`這將創建新的存儲庫 <strong>dummy-model</strong> 在您的個人資料中，並用您的模型文件填充它。
對標記器執行相同的操作，以便所有文件現在都可以在此存儲庫中使用：`,zl,vt,Yl,kt,ls="如果您屬於一個組織，只需指定 <strong>organization</strong> 上傳到該組織的命名空間的參數：",Al,Jt,Ql,Zt,ns="如果您希望使用特定的 Hugging Face 令牌，您可以自由地將其指定給 <strong>push_to_hub()</strong> 方法也是：",Sl,Ct,Fl,Gt,is='現在前往模型中心找到您新上傳的模型：<em><a href="https://huggingface.co/user-or-organization/dummy-model" rel="nofollow">https://huggingface.co/user-or-organization/dummy-model</a></em>。',El,Wt,ss="單擊“文件和版本”選項卡，您應該會在以下屏幕截圖中看到可見的文件：",Pl,_l,lt,ql,Bt,ps='如您所見， <strong>push_to_hub()</strong> 方法接受多個參數，從而可以上傳到特定的存儲庫或組織命名空間，或使用不同的 API 令牌。我們建議您查看直接在<a href="https://huggingface.co/transformers/model_sharing.html" rel="nofollow">🤗 Transformers documentation</a>瞭解什麼是可能的',Dl,xt,rs='這 <strong>push_to_hub()</strong> 方法由<a href="https://github.com/huggingface/huggingface_hub" rel="nofollow">huggingface_hub</a>Python 包，為 Hugging Face Hub 提供直接 API。它集成在 🤗 Transformers 和其他幾個機器學習庫中，例如<a href="https://github.com/allenai/allennlp" rel="nofollow">allenlp</a>.雖然我們在本章中專注於 🤗 Transformers 集成，但將其集成到您自己的代碼或庫中很簡單。',Kl,Ht,os="跳到最後一部分，瞭解如何將文件上傳到新創建的存儲庫！",Ol,It,tn,Nt,as=`這 <strong>huggingface_hub</strong> Python 庫是一個包，它為模型和數據集中心提供了一組工具。它為常見任務提供了簡單的方法和類，例如
獲取有關模型中心上存儲庫的信息並對其進行管理。它提供了在 git 之上工作的簡單 API 來管理這些存儲庫的內容並集成 Hub
在您的項目和庫中。`,en,Vt,ms="類似於使用 <strong>push_to_hub</strong> API，這將要求您將 API 令牌保存在緩存中。為此，您需要使用 <strong>login</strong> 來自 CLI 的命令，如上一節所述（同樣，確保在這些命令前面加上 <strong>!</strong> 字符（如果在 Google Colab 中運行）：",ln,Lt,nn,Xt,fs="這 <strong>huggingface_hub</strong> 包提供了幾種對我們有用的方法和類。首先，有幾種方法可以管理存儲庫的創建、刪除等：",sn,Rt,pn,zt,us="此外，它還提供了非常強大的 <strong>Repository</strong> 用於管理本地存儲庫的類。我們將在接下來的幾節中探討這些方法和該類，以瞭解如何利用它們。",rn,Yt,Ms="這 <strong>create_repo</strong> 方法可用於在模型中心上創建新存儲庫：",on,At,an,Qt,cs="這將創建存儲庫 <strong>dummy-model</strong> 在您的命名空間中。如果願意，您可以使用 <strong>organization</strong> 爭論：",mn,St,fn,Ft,bs=`這將創建 <strong>dummy-model</strong> 存儲庫中的 <strong>huggingface</strong> 命名空間，假設您屬於該組織。
其他可能有用的參數是：`,un,Et,gs="<li>private 以指定存儲庫是否應對其他人可見。</li> <li>token 如果您想用給定的令牌覆蓋存儲在緩存中的令牌。</li> <li>repo_type 如果你想創建一個或一個替代一個的而不是模型。接受的值和 datasetspace “dataset&quot;&quot;space”。</li>",Mn,Pt,ys="創建存儲庫後，我們應該向其中添加文件！跳到下一部分以查看可以處理此問題的三種方法。",cn,qt,bn,Dt,ds="Web 界面提供了直接在 Hub 中管理存儲庫的工具。使用該界面，您可以輕鬆創建存儲庫、添加文件（甚至是大文件！）、探索模型、可視化差異等等。",gn,Kt,_s='要創建新的存儲庫，請訪問<a href="https://huggingface.co/new" rel="nofollow">huggingface.co/new</a>：',yn,nt,hs='<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter4/new_model.png" alt="Page showcasing the model used for the creation of a new model repository." width="80%"/>',dn,Ot,$s="首先，指定存儲庫的所有者：這可以是您或您所屬的任何組織。如果您選擇一個組織，該模型將出現在該組織的頁面上，並且該組織的每個成員都可以為存儲庫做出貢獻。",_n,te,ws="接下來，輸入您的模型名稱。這也將是存儲庫的名稱。最後，您可以指定您的模型是公開的還是私有的。私人模特要求您擁有付費 Hugging Face 帳戶，並允許您將模特隱藏在公眾視野之外。",hn,ee,Ts="創建模型存儲庫後，您應該看到如下頁面：",$n,it,Us='<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter4/empty_model.png" alt="An empty model page after creating a new repository." width="80%"/>',wn,le,js="這是您的模型將被託管的地方。要開始填充它，您可以直接從 Web 界面添加 README 文件。",Tn,st,vs='<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter4/dummy_model.png" alt="The README file showing the Markdown capabilities." width="80%"/>',Un,ne,ks="README 文件在 Markdown 中 - 隨意使用它！本章的第三部分致力於構建模型卡。這些對於為您的模型帶來價值至關重要，因為它們是您告訴其他人它可以做什麼的地方。",jn,ie,Js="如果您查看“文件和版本”選項卡，您會發現那裡還沒有很多文件——只有自述文件你剛剛創建和.git 屬性跟蹤大文件的文件。",vn,pt,Zs='<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter4/files.png" alt="The &#39;Files and versions&#39; tab only shows the .gitattributes and README.md files." width="80%"/>',kn,se,Cs="接下來我們將看看如何添加一些新文件。",Jn,pe,Zn,re,Gs='Hugging Face Hub 上的文件管理系統基於用於常規文件的 git 和 git-lfs（代表<a href="https://git-lfs.github.com/" rel="nofollow">Git Large File Storage</a>) 對於較大的文件。',Cn,oe,Ws="在下一節中，我們將介紹將文件上傳到 Hub 的三種不同方式：通過 <strong>huggingface_hub</strong> 並通過 git 命令。",Gn,ae,Wn,me,Bs=`使用 <strong>upload_file</strong> 不需要在您的系統上安裝 git 和 git-lfs。它使用 HTTP POST 請求將文件直接推送到 🤗 Hub。這種方法的一個限制是它不能處理大於 5GB 的文件。
如果您的文件大於 5GB，請按照下面詳述的另外兩種方法進行操作。API 可以按如下方式使用：`,Bn,fe,xn,ue,xs=`這將上傳文件 <strong>config.json</strong> 可在 <strong>path_to_file</strong> 到存儲庫的根目錄 <strong>config.json</strong> , 到 <strong>dummy-model</strong> 存儲庫。
其他可能有用的參數是：`,Hn,Me,Hs="<li>token，如果要通過給定的令牌覆蓋緩存中存儲的令牌。</li> <li>repo_type, 如果你想要上傳一個 <code>dataset</code> 或一個 <code>space</code> 而不是模型。 接受的值為 <code>&quot;dataset&quot;</code> 和 <code>&quot;space&quot;</code>.</li>",In,ce,Nn,be,Is="以類似 git 的方式管理本地存儲庫。它抽象了 git 可能遇到的大部分痛點，以提供我們需要的所有功能。",Vn,ge,Ns='使用這個類需要安裝 git 和 git-lfs，所以確保你已經安裝了 git-lfs（參見<a href="https://git-lfs.github.com/" rel="nofollow">here</a>安裝說明）並在開始之前進行設置。',Ln,ye,Vs="為了開始使用我們剛剛創建的存儲庫，我們可以通過克隆遠程存儲庫將其初始化到本地文件夾開始：",Xn,de,Rn,_e,Ls="這創建了文件夾 <strong>path_to_dummy_folder</strong> 在我們的工作目錄中。該文件夾僅包含 <strong>.gitattributes</strong> 文件，因為這是通過實例化存儲庫時創建的唯一文件 <strong>create_repo</strong>。",zn,he,Xs="從現在開始，我們可以利用幾種傳統的 git 方法：",Yn,$e,An,we,Rs='另外！我們建議您查看 <strong>Repository</strong> 可用文件<a href="https://github.com/huggingface/huggingface_hub/tree/main/src/huggingface_hub#advanced-programmatic-repository-management" rel="nofollow">here</a>有關所有可用方法的概述。',Qn,Te,zs="目前，我們有一個模型和一個標記器，我們希望將其推送到模型中心。我們已經成功克隆了存儲庫，因此我們可以將文件保存在該存儲庫中。",Sn,Ue,Ys="我們首先通過拉取最新更改來確保我們的本地克隆是最新的：",Fn,je,En,ve,As="完成後，我們保存模型和標記器文件：",Pn,ke,qn,Je,Qs="這 <strong>path_to_dummy_folder</strong> 現在包含所有模型和標記器文件。我們遵循通常的 git 工作流程，將文件添加到暫存區，提交它們並將它們推送到模型中心：",Dn,Ze,Kn,Ce,Ss="恭喜！您剛剛將第一個文件推送到hub上。",On,Ge,ti,We,Fs="這是上傳文件的非常簡單的方法：我們將直接使用 git 和 git-lfs 來完成。大多數困難都被以前的方法抽象掉了，但是下面的方法有一些警告，所以我們將遵循一個更復雜的用例。",ei,Be,Es='使用這個類需要安裝 git 和 git-lfs，所以請確保你有<a href="https://git-lfs.github.com/" rel="nofollow">git-lfs</a>安裝（請參閱此處瞭解安裝說明）並在開始之前進行設置。',li,xe,Ps="首先從初始化 git-lfs 開始：",ni,He,ii,Ie,si,Ne,qs="完成後，第一步是克隆您的模型存儲庫：",pi,Ve,ri,Le,Ds="我的用戶名是 <strong>lysandre</strong> 我使用了模型名稱 <strong>dummy</strong> ，所以對我來說，命令最終如下所示：",oi,Xe,ai,Re,Ks="我現在有一個名為的文件夾假在我的工作目錄中。我能 <strong>cd</strong> 進入文件夾並查看內容：",mi,ze,fi,Ye,ui,Ae,Os="如果您剛剛使用 Hugging Face Hub 創建了您的存儲庫 <strong>create_repo</strong> 方法，這個文件夾應該只包含一個隱藏的 <strong>.gitattributes</strong> 文件。如果您按照上一節中的說明使用 Web 界面創建存儲庫，則該文件夾應包含一個自述文件文件旁邊的隱藏 <strong>.gitattributes</strong> 文件，如圖所示。",Mi,Qe,tp="添加一個常規大小的文件，例如配置文件、詞彙文件，或者基本上任何幾兆字節以下的文件，就像在任何基於 git 的系統中所做的一樣。但是，更大的文件必須通過 git-lfs 註冊才能將它們推送到Hugging Face。",ci,Se,ep="讓我們回到 Python 來生成我們想要提交到我們的虛擬存儲庫的模型和標記器：",bi,R,z,hl,Fe,lp="現在我們已經保存了一些模型和標記器工件，讓我們再看看假文件夾：",gi,Ee,yi,Y,A,$l,rt,di,Pe,np="我們現在可以繼續進行，就像我們通常使用傳統 Git 存儲庫一樣。我們可以使用以下命令將所有文件添加到 Git 的暫存環境中 <strong>git add</strong> 命令：",_i,qe,hi,De,ip="然後我們可以查看當前暫存的文件：",$i,Ke,wi,Q,S,wl,Oe,sp="同樣，我們可以確保 git-lfs 使用其跟蹤正確的文件 <strong>status</strong> 命令：",Ti,tl,Ui,F,E,Tl,el,pp="讓我們繼續最後的步驟，提交併推送到Hugging Face遠程倉庫：",ji,ll,vi,P,q,Ul,nl,rp="推送可能需要一些時間，具體取決於您的互聯網連接速度和文件大小：",ki,il,Ji,sl,Zi,jl,pl,Ci,vl,Gi;T=new Yp({props:{fw:$[0]}}),w=new ot({props:{title:"共享預訓練模型",local:"共享預訓練模型",headingTag:"h1"}});const op=[Sp,Qp],rl=[];function ap(t,e){return t[0]==="pt"?0:1}j=ap($),v=rl[j]=op[j]($),G=new Yi({props:{id:"9yY3RB_GSPM"}}),Mt=new ot({props:{title:"使用 push_to_hub API",local:"使用-pushtohub-api",headingTag:"h2"}});const mp=[Ep,Fp],ol=[];function fp(t,e){return t[0]==="pt"?0:1}H=fp($),I=ol[H]=mp[H]($),gt=new U({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMG5vdGVib29rX2xvZ2luJTBBJTBBbm90ZWJvb2tfbG9naW4oKQ==",highlighted:`<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> notebook_login

notebook_login()`,wrap:!1}}),dt=new U({props:{code:"aHVnZ2luZ2ZhY2UtY2xpJTIwbG9naW4=",highlighted:"huggingface-cli login",wrap:!1}});const up=[qp,Pp],al=[];function Mp(t,e){return t[0]==="pt"?0:1}N=Mp($),V=al[N]=up[N]($);const cp=[Kp,Dp],ml=[];function bp(t,e){return t[0]==="pt"?0:1}L=bp($),X=ml[L]=cp[L]($),Ut=new U({props:{code:"bW9kZWwucHVzaF90b19odWIoJTIyZHVtbXktbW9kZWwlMjIp",highlighted:'model.push_to_hub(<span class="hljs-string">&quot;dummy-model&quot;</span>)',wrap:!1}}),vt=new U({props:{code:"dG9rZW5pemVyLnB1c2hfdG9faHViKCUyMmR1bW15LW1vZGVsJTIyKQ==",highlighted:'tokenizer.push_to_hub(<span class="hljs-string">&quot;dummy-model&quot;</span>)',wrap:!1}}),Jt=new U({props:{code:"dG9rZW5pemVyLnB1c2hfdG9faHViKCUyMmR1bW15LW1vZGVsJTIyJTJDJTIwb3JnYW5pemF0aW9uJTNEJTIyaHVnZ2luZ2ZhY2UlMjIp",highlighted:'tokenizer.push_to_hub(<span class="hljs-string">&quot;dummy-model&quot;</span>, organization=<span class="hljs-string">&quot;huggingface&quot;</span>)',wrap:!1}}),Ct=new U({props:{code:"dG9rZW5pemVyLnB1c2hfdG9faHViKCUyMmR1bW15LW1vZGVsJTIyJTJDJTIwb3JnYW5pemF0aW9uJTNEJTIyaHVnZ2luZ2ZhY2UlMjIlMkMlMjB1c2VfYXV0aF90b2tlbiUzRCUyMiUzQ1RPS0VOJTNFJTIyKQ==",highlighted:'tokenizer.push_to_hub(<span class="hljs-string">&quot;dummy-model&quot;</span>, organization=<span class="hljs-string">&quot;huggingface&quot;</span>, use_auth_token=<span class="hljs-string">&quot;&lt;TOKEN&gt;&quot;</span>)',wrap:!1}});function gp(t,e){return t[0]==="pt"?tr:Op}let Wi=gp($),K=Wi($);lt=new Wp({props:{$$slots:{default:[er]},$$scope:{ctx:$}}}),It=new ot({props:{title:"使用 huggingface_hub python庫",local:"使用-huggingfacehub-python庫",headingTag:"h2"}}),Lt=new U({props:{code:"aHVnZ2luZ2ZhY2UtY2xpJTIwbG9naW4=",highlighted:"huggingface-cli login",wrap:!1}}),Rt=new U({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> (
    <span class="hljs-comment"># User management</span>
    login,
    logout,
    whoami,

    <span class="hljs-comment"># Repository creation and management</span>
    create_repo,
    delete_repo,
    update_repo_visibility,

    <span class="hljs-comment"># And some methods to retrieve/change information about the content</span>
    list_models,
    list_datasets,
    list_metrics,
    list_repo_files,
    upload_file,
    delete_file,
)`,wrap:!1}}),At=new U({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMGNyZWF0ZV9yZXBvJTBBJTBBY3JlYXRlX3JlcG8oJTIyZHVtbXktbW9kZWwlMjIp",highlighted:`<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> create_repo

create_repo(<span class="hljs-string">&quot;dummy-model&quot;</span>)`,wrap:!1}}),St=new U({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMGNyZWF0ZV9yZXBvJTBBJTBBY3JlYXRlX3JlcG8oJTIyZHVtbXktbW9kZWwlMjIlMkMlMjBvcmdhbml6YXRpb24lM0QlMjJodWdnaW5nZmFjZSUyMik=",highlighted:`<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> create_repo

create_repo(<span class="hljs-string">&quot;dummy-model&quot;</span>, organization=<span class="hljs-string">&quot;huggingface&quot;</span>)`,wrap:!1}}),qt=new ot({props:{title:"使用網絡界面",local:"使用網絡界面",headingTag:"h2"}}),pe=new ot({props:{title:"上傳模型文件",local:"上傳模型文件",headingTag:"h2"}}),ae=new ot({props:{title:"The upload_file approach",local:"the-uploadfile-approach",headingTag:"h3"}}),fe=new U({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMHVwbG9hZF9maWxlJTBBJTBBdXBsb2FkX2ZpbGUoJTBBJTIwJTIwJTIwJTIwJTIyJTNDcGF0aF90b19maWxlJTNFJTJGY29uZmlnLmpzb24lMjIlMkMlMEElMjAlMjAlMjAlMjBwYXRoX2luX3JlcG8lM0QlMjJjb25maWcuanNvbiUyMiUyQyUwQSUyMCUyMCUyMCUyMHJlcG9faWQlM0QlMjIlM0NuYW1lc3BhY2UlM0UlMkZkdW1teS1tb2RlbCUyMiUyQyUwQSk=",highlighted:`<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> upload_file

upload_file(
    <span class="hljs-string">&quot;&lt;path_to_file&gt;/config.json&quot;</span>,
    path_in_repo=<span class="hljs-string">&quot;config.json&quot;</span>,
    repo_id=<span class="hljs-string">&quot;&lt;namespace&gt;/dummy-model&quot;</span>,
)`,wrap:!1}}),ce=new ot({props:{title:"The Repository class",local:"the-repository-class",headingTag:"h3"}}),de=new U({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMFJlcG9zaXRvcnklMEElMEFyZXBvJTIwJTNEJTIwUmVwb3NpdG9yeSglMjIlM0NwYXRoX3RvX2R1bW15X2ZvbGRlciUzRSUyMiUyQyUyMGNsb25lX2Zyb20lM0QlMjIlM0NuYW1lc3BhY2UlM0UlMkZkdW1teS1tb2RlbCUyMik=",highlighted:`<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> Repository

repo = Repository(<span class="hljs-string">&quot;&lt;path_to_dummy_folder&gt;&quot;</span>, clone_from=<span class="hljs-string">&quot;&lt;namespace&gt;/dummy-model&quot;</span>)`,wrap:!1}}),$e=new U({props:{code:"cmVwby5naXRfcHVsbCgpJTBBcmVwby5naXRfYWRkKCklMEFyZXBvLmdpdF9jb21taXQoKSUwQXJlcG8uZ2l0X3B1c2goKSUwQXJlcG8uZ2l0X3RhZygp",highlighted:`repo.git_pull()
repo.git_add()
repo.git_commit()
repo.git_push()
repo.git_tag()`,wrap:!1}}),je=new U({props:{code:"cmVwby5naXRfcHVsbCgp",highlighted:"repo.git_pull()",wrap:!1}}),ke=new U({props:{code:"bW9kZWwuc2F2ZV9wcmV0cmFpbmVkKCUyMiUzQ3BhdGhfdG9fZHVtbXlfZm9sZGVyJTNFJTIyKSUwQXRva2VuaXplci5zYXZlX3ByZXRyYWluZWQoJTIyJTNDcGF0aF90b19kdW1teV9mb2xkZXIlM0UlMjIp",highlighted:`model.save_pretrained(<span class="hljs-string">&quot;&lt;path_to_dummy_folder&gt;&quot;</span>)
tokenizer.save_pretrained(<span class="hljs-string">&quot;&lt;path_to_dummy_folder&gt;&quot;</span>)`,wrap:!1}}),Ze=new U({props:{code:"cmVwby5naXRfYWRkKCklMEFyZXBvLmdpdF9jb21taXQoJTIyQWRkJTIwbW9kZWwlMjBhbmQlMjB0b2tlbml6ZXIlMjBmaWxlcyUyMiklMEFyZXBvLmdpdF9wdXNoKCk=",highlighted:`repo.git_add()
repo.git_commit(<span class="hljs-string">&quot;Add model and tokenizer files&quot;</span>)
repo.git_push()`,wrap:!1}}),Ge=new ot({props:{title:"The git-based approach",local:"the-git-based-approach",headingTag:"h3"}}),He=new U({props:{code:"Z2l0JTIwbGZzJTIwaW5zdGFsbA==",highlighted:"git lfs install",wrap:!1}}),Ie=new U({props:{code:"VXBkYXRlZCUyMGdpdCUyMGhvb2tzLiUwQUdpdCUyMExGUyUyMGluaXRpYWxpemVkLg==",highlighted:`Updated git hooks.
Git LFS initialized.`,wrap:!1}}),Ve=new U({props:{code:"Z2l0JTIwY2xvbmUlMjBodHRwcyUzQSUyRiUyRmh1Z2dpbmdmYWNlLmNvJTJGJTNDbmFtZXNwYWNlJTNFJTJGJTNDeW91ci1tb2RlbC1pZCUzRQ==",highlighted:'git <span class="hljs-built_in">clone</span> https://huggingface.co/&lt;namespace&gt;/&lt;your-model-id&gt;',wrap:!1}}),Xe=new U({props:{code:"Z2l0JTIwY2xvbmUlMjBodHRwcyUzQSUyRiUyRmh1Z2dpbmdmYWNlLmNvJTJGbHlzYW5kcmUlMkZkdW1teQ==",highlighted:'git clone https:<span class="hljs-regexp">//</span>huggingface.co<span class="hljs-regexp">/lysandre/</span>dummy',wrap:!1}}),ze=new U({props:{code:"Y2QlMjBkdW1teSUyMCUyNiUyNiUyMGxz",highlighted:'<span class="hljs-built_in">cd</span> dummy &amp;&amp; <span class="hljs-built_in">ls</span>',wrap:!1}}),Ye=new U({props:{code:"UkVBRE1FLm1k",highlighted:"README.md",wrap:!1}});const yp=[nr,lr],fl=[];function dp(t,e){return t[0]==="pt"?0:1}R=dp($),z=fl[R]=yp[R]($),Ee=new U({props:{code:"bHM=",highlighted:'<span class="hljs-built_in">ls</span>',wrap:!1}});const _p=[sr,ir],ul=[];function hp(t,e){return t[0]==="pt"?0:1}Y=hp($),A=ul[Y]=_p[Y]($),rt=new Wp({props:{$$slots:{default:[pr]},$$scope:{ctx:$}}}),qe=new U({props:{code:"Z2l0JTIwYWRkJTIwLg==",highlighted:"git add .",wrap:!1}}),Ke=new U({props:{code:"Z2l0JTIwc3RhdHVz",highlighted:"git status",wrap:!1}});const $p=[or,rr],Ml=[];function wp(t,e){return t[0]==="pt"?0:1}Q=wp($),S=Ml[Q]=$p[Q]($),tl=new U({props:{code:"Z2l0JTIwbGZzJTIwc3RhdHVz",highlighted:"git lfs status",wrap:!1}});const Tp=[mr,ar],cl=[];function Up(t,e){return t[0]==="pt"?0:1}F=Up($),E=cl[F]=Tp[F]($),ll=new U({props:{code:"Z2l0JTIwY29tbWl0JTIwLW0lMjAlMjJGaXJzdCUyMG1vZGVsJTIwdmVyc2lvbiUyMg==",highlighted:'git commit -m <span class="hljs-string">&quot;First model version&quot;</span>',wrap:!1}});const jp=[ur,fr],bl=[];function vp(t,e){return t[0]==="pt"?0:1}P=vp($),q=bl[P]=jp[P]($),il=new U({props:{code:"Z2l0JTIwcHVzaA==",highlighted:"git push",wrap:!1}}),sl=new U({props:{code:"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",highlighted:`Uploading LFS objects: 100% (1/1), 433 MB | 1.3 MB/s, <span class="hljs-keyword">done</span>.
Enumerating objects: 11, <span class="hljs-keyword">done</span>.
Counting objects: 100% (11/11), <span class="hljs-keyword">done</span>.
Delta compression using up to 12 threads
Compressing objects: 100% (9/9), <span class="hljs-keyword">done</span>.
Writing objects: 100% (9/9), 288.27 KiB | 6.27 MiB/s, <span class="hljs-keyword">done</span>.
Total 9 (delta 1), reused 0 (delta 0), pack-reused 0
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