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<h1 class="relative group"><a id="optimum" 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"><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
	</span></h1>

<p>🤗 Optimum is an extension of 🤗 Transformers that provides a set of performance optimization tools to train and run models on targeted hardware with maximum efficiency.</p>
<p>The AI ecosystem evolves quickly, and more and more specialized hardware along with their own optimizations are emerging every day.
As such, 🤗 Optimum enables developers to efficiently use any of these platforms with the same ease inherent to 🤗 Transformers.</p>
<p>🤗 Optimum is distributed as a collection of packages - check out the links below for an in-depth look at each one.</p>
<div class="mt-10"><div class="w-full flex flex-col space-y-4 md:space-y-0 md:grid md:grid-cols-3 md:gap-y-4 md:gap-x-5"><a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./graphcore/index"><div class="w-full text-center bg-gradient-to-br from-blue-400 to-blue-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Optimum Graphcore</div>
      <p class="text-gray-700">Train transformers on <a href="https://www.graphcore.ai/products/ipu">Graphcore IPUs</a>, a completely new kind of massively parallel processor to accelerate machine intelligence.</p></a>
    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./habana/index"><div class="w-full text-center bg-gradient-to-br from-indigo-400 to-indigo-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Optimum Habana</div>
      <p class="text-gray-700">Maximize training throughput and efficiency with <a href="https://docs.habana.ai/en/latest/Gaudi_Overview/Gaudi_Architecture.html">Habana&#39;s Gaudi processor</a>.</p></a>
    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./intel/index"><div class="w-full text-center bg-gradient-to-br from-pink-400 to-pink-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Optimum Intel</div>
      <p class="text-gray-700">Use Intel&#39;s <a href="https://www.intel.com/content/www/us/en/developer/tools/oneapi/neural-compressor.html">Neural Compressor</a> and <a href="https://docs.openvino.ai/latest/index.html">OpenVINO</a> frameworks to accelerate transformer inference.</p></a>
    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./onnxruntime/overview"><div class="w-full text-center bg-gradient-to-br from-yellow-400 to-yellow-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">ONNX Runtime</div>
      <p class="text-gray-700">Apply quantization and graph optimization to accelerate transformer training and inference with <a href="https://onnxruntime.ai/">ONNX Runtime</a></p></a>
    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./torch_fx/overview"><div class="w-full text-center bg-gradient-to-br from-green-400 to-green-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Torch FX</div>
      <p class="text-gray-700">Create and compose custom graph transformations to optimize PyTorch transformer models with <a href="https://pytorch.org/docs/stable/fx.html#">Torch FX</a></p></a></div></div>


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