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<h1 class="relative group"><a id="optimum-graphcore" 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="#optimum-graphcore"><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>Optimum Graphcore
	</span></h1>

<p>🤗 Optimum Graphcore is the interface between the 🤗 Transformers library and <a href="https://www.graphcore.ai/products/ipu" rel="nofollow">Graphcore IPUs</a>. It provides a set of tools enabling model parallelization and loading on IPUs, training and fine-tuning on all the tasks already supported by Transformers while being compatible with the Hugging Face Hub and every model available on it out of the box.</p>
<h2 class="relative group"><a id="what-is-an-intelligence-processing-unit-ipu" 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="#what-is-an-intelligence-processing-unit-ipu"><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>What is an Intelligence Processing Unit (IPU)?
	</span></h2>

<p>Quote from the Hugging Face <a href="https://huggingface.co/blog/graphcore#what-is-an-intelligence-processing-unit" rel="nofollow">blog post</a>:</p>
<blockquote><p>IPUs are the processors that power Graphcore’s IPU-POD datacenter compute systems. This new type of processor is designed to support the very specific computational requirements of AI and machine learning. Characteristics such as fine-grained parallelism, low precision arithmetic, and the ability to handle sparsity have been built into our silicon.</p></blockquote>
<blockquote><p>Instead of adopting a SIMD/SIMT architecture like GPUs, Graphcore’s IPU uses a massively parallel, MIMD architecture, with ultra-high bandwidth memory placed adjacent to the processor cores, right on the silicon die.</p></blockquote>
<blockquote><p>This design delivers high performance and new levels of efficiency, whether running today’s most popular models, such as BERT and EfficientNet, or exploring next-generation AI applications.</p></blockquote>
<h2 class="relative group"><a id="supported-models" 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="#supported-models"><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>Supported Models
	</span></h2>

<p>The following model architectures and tasks are currently supported by 🤗 Optimum Graphcore:</p>
<table><thead><tr><th></th>
<th>Pre-Training</th>
<th>Masked LM</th>
<th>Causal LM</th>
<th>Seq2Seq LM (Summarization, Translation, etc)</th>
<th>Sequence Classification</th>
<th>Token Classification</th>
<th>Question Answering</th>
<th>Multiple Choice</th>
<th>Image Classification</th></tr></thead>
<tbody><tr><td>BART</td>
<td>✅</td>
<td></td>
<td>❌</td>
<td>✅</td>
<td>✅</td>
<td></td>
<td>❌</td>
<td></td>
<td></td></tr>
<tr><td>BERT</td>
<td>✅</td>
<td>✅</td>
<td>❌</td>
<td></td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td></td></tr>
<tr><td>ConvNeXt</td>
<td>✅</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>✅</td></tr>
<tr><td>DeBERTa</td>
<td>❌</td>
<td>❌</td>
<td></td>
<td></td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td></td>
<td></td></tr>
<tr><td>DistilBERT</td>
<td>❌</td>
<td>✅</td>
<td></td>
<td></td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td></td></tr>
<tr><td>GPT-2</td>
<td>✅</td>
<td></td>
<td>✅</td>
<td></td>
<td>✅</td>
<td>✅</td>
<td></td>
<td></td>
<td></td></tr>
<tr><td>HuBERT</td>
<td>❌</td>
<td></td>
<td></td>
<td></td>
<td>✅</td>
<td></td>
<td></td>
<td></td>
<td></td></tr>
<tr><td>LXMERT</td>
<td>❌</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>✅</td>
<td></td>
<td></td></tr>
<tr><td>RoBERTa</td>
<td>✅</td>
<td>✅</td>
<td>❌</td>
<td></td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
<td></td></tr>
<tr><td>T5</td>
<td>✅</td>
<td></td>
<td></td>
<td>✅</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td></tr>
<tr><td>ViT</td>
<td>❌</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td>✅</td></tr>
<tr><td>Wav2Vec2</td>
<td>✅</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td></tr></tbody></table>


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