Buckets:
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| <link rel="modulepreload" href="/docs/transformers/main/ja/_app/immutable/chunks/EditOnGithub.922df6ba.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{"title":"Instantiating a big model","local":"instantiating-a-big-model","sections":[{"title":"Sharded checkpoints","local":"sharded-checkpoints","sections":[],"depth":2},{"title":"Low memory loading","local":"low-memory-loading","sections":[],"depth":2}],"depth":1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <h1 class="relative group"><a id="instantiating-a-big-model" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#instantiating-a-big-model"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Instantiating a big model</span></h1> <p data-svelte-h="svelte-1ohy6fz">非常に大規模な事前学習済みモデルを使用する場合、RAMの使用量を最小限に抑えることは課題の1つです。通常のPyTorchのワークフローは次のとおりです：</p> <ol data-svelte-h="svelte-63ajfg"><li>ランダムな重みを持つモデルを作成します。</li> <li>事前学習済みの重みをロードします。</li> <li>これらの事前学習済みの重みをランダムなモデルに配置します。</li></ol> <p data-svelte-h="svelte-vzt1dk">ステップ1と2の両方がメモリにモデルの完全なバージョンを必要とし、ほとんどの場合は問題ありませんが、モデルのサイズが数ギガバイトになると、これらの2つのコピーをRAMから排除することができなくなる可能性があります。さらに悪いことに、分散トレーニングを実行するために<code>torch.distributed</code>を使用している場合、各プロセスは事前学習済みモデルをロードし、これらの2つのコピーをRAMに保存します。</p> <div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"><p data-svelte-h="svelte-qbck2l">ランダムに作成されたモデルは、メモリ内に「空の」テンソルで初期化されます。これらのランダムな値は、メモリの特定のチャンクにあったものを使用します（したがって、ランダムな値はその時点でのメモリチャンク内の値です）。モデル/パラメータの種類に適した分布（たとえば、正規分布）に従うランダムな初期化は、ステップ3で初期化されていない重みに対して、できるだけ高速に実行されます！</p></div> <p data-svelte-h="svelte-6plqs6">このガイドでは、Transformersがこの問題に対処するために提供するソリューションを探ります。なお、これは現在も開発が進行中の分野であり、将来、ここで説明されているAPIがわずかに変更される可能性があることに注意してください。</p> <h2 class="relative group"><a id="sharded-checkpoints" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#sharded-checkpoints"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Sharded checkpoints</span></h2> <p data-svelte-h="svelte-z76oid">バージョン4.18.0から、10GBを超えるサイズのモデルチェックポイントは自動的に複数の小さな部分に分割されます。<code>model.save_pretrained(save_dir)</code>を実行する際に1つの単一のチェックポイントを持つ代わりに、いくつかの部分的なチェックポイント（それぞれのサイズが<10GB）と、パラメータ名をそれらが格納されているファイルにマップするインデックスが生成されます。</p> <p data-svelte-h="svelte-1sj6luw"><code>max_shard_size</code>パラメータでシャーディング前の最大サイズを制御できるため、例として通常サイズのモデルと小さなシャードサイズを使用します。従来のBERTモデルを使用してみましょう。</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModel | |
| model = AutoModel.from_pretrained(<span class="hljs-string">"google-bert/bert-base-cased"</span>)<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1gob20u">もし<a href="/docs/transformers/main/ja/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>を使用して保存する場合、新しいフォルダが2つのファイルを含む形で作成されます: モデルの設定情報とその重み情報です。</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> os | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> tempfile | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">with</span> tempfile.TemporaryDirectory() <span class="hljs-keyword">as</span> tmp_dir: | |
| <span class="hljs-meta">... </span> model.save_pretrained(tmp_dir) | |
| <span class="hljs-meta">... </span> <span class="hljs-built_in">print</span>(<span class="hljs-built_in">sorted</span>(os.listdir(tmp_dir))) | |
| [<span class="hljs-string">'config.json'</span>, <span class="hljs-string">'pytorch_model.bin'</span>]<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-9p05eq">最大シャードサイズを200MBに設定します：</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">with</span> tempfile.TemporaryDirectory() <span class="hljs-keyword">as</span> tmp_dir: | |
| <span class="hljs-meta">... </span> model.save_pretrained(tmp_dir, max_shard_size=<span class="hljs-string">"200MB"</span>) | |
| <span class="hljs-meta">... </span> <span class="hljs-built_in">print</span>(<span class="hljs-built_in">sorted</span>(os.listdir(tmp_dir))) | |
| [<span class="hljs-string">'config.json'</span>, <span class="hljs-string">'pytorch_model-00001-of-00003.bin'</span>, <span class="hljs-string">'pytorch_model-00002-of-00003.bin'</span>, <span class="hljs-string">'pytorch_model-00003-of-00003.bin'</span>, <span class="hljs-string">'pytorch_model.bin.index.json'</span>]<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-66fjk2">モデルの設定の上に、3つの異なる重みファイルと、<code>index.json</code>ファイルが見られます。これは私たちのインデックスです。 | |
| このようなチェックポイントは、<a href="/docs/transformers/main/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a>メソッドを使用して完全に再ロードできます：</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">with</span> tempfile.TemporaryDirectory() <span class="hljs-keyword">as</span> tmp_dir: | |
| <span class="hljs-meta">... </span> model.save_pretrained(tmp_dir, max_shard_size=<span class="hljs-string">"200MB"</span>) | |
| <span class="hljs-meta">... </span> new_model = AutoModel.from_pretrained(tmp_dir)<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-3gy4l0">主要な利点は、大規模なモデルの場合、上記のワークフローのステップ2において、各チェックポイントのシャードが前のシャードの後にロードされ、RAMのメモリ使用量をモデルのサイズと最大のシャードのサイズを合わせたものに制限できることです。</p> <p data-svelte-h="svelte-6bnz20">内部では、インデックスファイルが使用され、どのキーがチェックポイントに存在し、対応する重みがどこに格納されているかを判断します。このインデックスは通常のJSONファイルのように読み込むことができ、辞書として取得できます。</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> json | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">with</span> tempfile.TemporaryDirectory() <span class="hljs-keyword">as</span> tmp_dir: | |
| <span class="hljs-meta">... </span> model.save_pretrained(tmp_dir, max_shard_size=<span class="hljs-string">"200MB"</span>) | |
| <span class="hljs-meta">... </span> <span class="hljs-keyword">with</span> <span class="hljs-built_in">open</span>(os.path.join(tmp_dir, <span class="hljs-string">"pytorch_model.bin.index.json"</span>), <span class="hljs-string">"r"</span>) <span class="hljs-keyword">as</span> f: | |
| <span class="hljs-meta">... </span> index = json.load(f) | |
| <span class="hljs-meta">>>> </span><span class="hljs-built_in">print</span>(index.keys()) | |
| dict_keys([<span class="hljs-string">'metadata'</span>, <span class="hljs-string">'weight_map'</span>])<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1u7xh92">メタデータには現時点ではモデルの総サイズのみが含まれています。 | |
| 将来的には他の情報を追加する予定です：</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>index[<span class="hljs-string">"metadata"</span>] | |
| {<span class="hljs-string">'total_size'</span>: <span class="hljs-number">433245184</span>}<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-pi5r2w">重みマップはこのインデックスの主要な部分であり、各パラメータ名（通常はPyTorchモデルの<code>state_dict</code>で見つかるもの）をその格納されているファイルにマップします：</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span>index[<span class="hljs-string">"weight_map"</span>] | |
| {<span class="hljs-string">'embeddings.LayerNorm.bias'</span>: <span class="hljs-string">'pytorch_model-00001-of-00003.bin'</span>, | |
| <span class="hljs-string">'embeddings.LayerNorm.weight'</span>: <span class="hljs-string">'pytorch_model-00001-of-00003.bin'</span>, | |
| ...<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-19esqp9">直接モデル内で<a href="/docs/transformers/main/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a>を使用せずに、 | |
| シャーディングされたチェックポイントをロードしたい場合（フルチェックポイントの場合に<code>model.load_state_dict()</code>を使用するように行う方法）、<a href="/docs/transformers/main/ja/main_classes/model#transformers.modeling_utils.load_sharded_checkpoint">load_sharded_checkpoint()</a>を使用する必要があります：</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers.modeling_utils <span class="hljs-keyword">import</span> load_sharded_checkpoint | |
| <span class="hljs-meta">>>> </span><span class="hljs-keyword">with</span> tempfile.TemporaryDirectory() <span class="hljs-keyword">as</span> tmp_dir: | |
| <span class="hljs-meta">... </span> model.save_pretrained(tmp_dir, max_shard_size=<span class="hljs-string">"200MB"</span>) | |
| <span class="hljs-meta">... </span> load_sharded_checkpoint(model, tmp_dir)<!-- HTML_TAG_END --></pre></div> <h2 class="relative group"><a id="low-memory-loading" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#low-memory-loading"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Low memory loading</span></h2> <p data-svelte-h="svelte-1bp1ma3">シャードされたチェックポイントは、上記のワークフローのステップ2におけるメモリ使用量を削減しますが、 | |
| 低メモリの環境でそのモデルを使用するために、Accelerateライブラリに基づいた当社のツールを活用することをお勧めします。</p> <p data-svelte-h="svelte-1hzfpqz">詳細については、以下のガイドをご覧ください：<a href="./main_classes/model#large-model-loading">Accelerateを使用した大規模モデルの読み込み</a></p> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/transformers/blob/main/docs/source/ja/big_models.md" target="_blank"><span data-svelte-h="svelte-1kd6by1"><</span> <span data-svelte-h="svelte-x0xyl0">></span> <span data-svelte-h="svelte-1dajgef"><span class="underline ml-1.5">Update</span> on GitHub</span></a> <p></p> | |
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