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- Intel optimizes the most widely adopted and innovative AI software
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  tools, frameworks, and libraries for Intel® architecture. Whether
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  you are computing locally or deploying AI applications on a massive
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  scale, your organization can achieve peak performance with AI
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  Intel’s engineering collaboration with Hugging Face offers state-of-the-art hardware and software acceleration to train, fine-tune and predict with Transformers.
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  </p>
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- <p>
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- Useful Resources:
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- </p>
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  <ul>
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- <li class="ml-6"><a href="https://huggingface.co/hardware/intel" class="underline" data-ga-category="intel-org" data-ga-action="clicked partner page" data-ga-label="partner page">- Intel AI + Hugging Face partner page</a></li>
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- <li class="ml-6"><a href="https://github.com/IntelAI" class="underline" data-ga-category="intel-org" data-ga-action="clicked intel ai github" data-ga-label="intel ai github">- Intel AI GitHub</a></li>
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- <li class="ml-6"><a href="https://www.intel.com/content/www/us/en/developer/partner/hugging-face.html" class="underline" data-ga-category="intel-org" data-ga-action="clicked intel partner page" data-ga-label="intel partner page">- Developer Resources from Intel and Hugging Face</a></li>
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  </ul>
 
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  </div>
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  <div class="lg:col-span-3">
 
 
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  <p class="mb-2">
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- To get started with Intel® hardware and software optimizations, download and install the Optimum-Intel®
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- and Intel® Extension for Transformers libraries with the following commands:
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  </p>
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- <pre><code>
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- $ python -m pip install "optimum-intel[extras]"@git+https://github.com/huggingface/optimum-intel.git
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- $ python -m pip install intel-extension-for-transformers
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- </code></pre>
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- <p>
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- <i>For additional information on these two libraries including installation, features, and usage, see the two links below.</i>
 
 
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  </p>
 
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  <p class="mb-2">
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- Next, find your desired model (and dataset) by searching in the search box at the top-left of Hugging Face’s website.
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- Add “intel” to your search to narrow your search to Intel®-pretrained models.
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  </p>
 
 
 
 
 
 
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  <p class="mb-2">
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  On the model’s page (called a “Model Card”) you will find description and usage information, an embedded
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  inferencing demo, and the associated dataset. In the upper-right of your screen, click “Use in Transformers”
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  for helpful code hints on how to import the model to your own workspace with an established Hugging Face pipeline and tokenizer.
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  </p>
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- <p>
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- Library Source and Documentation:
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- </p>
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- <ul>
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- <li class="ml-6"><a href="https://github.com/huggingface/optimum-intel" class="underline" data-ga-category="intel-org" data-ga-action="clicked optimum intel" data-ga-label="optimum intel">- 🤗 Optimum-Intel® library</a></li>
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- <li class="ml-6"><a href="https://github.com/intel/intel-extension-for-transformers" class="underline" data-ga-category="intel-org" data-ga-action="clicked intel extension for transformers" data-ga-label="intel extension for transformers">- Intel® Extension for Transformers</a></li>
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- </ul>
 
 
 
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  </div>
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  </div>
 
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  </a>
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  <div class="lg:col-span-3">
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  <p class="mb-2">
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+ Intel optimizes widely adopted and innovative AI software
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  tools, frameworks, and libraries for Intel® architecture. Whether
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  you are computing locally or deploying AI applications on a massive
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  scale, your organization can achieve peak performance with AI
 
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  <p class="mb-2">
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  Intel’s engineering collaboration with Hugging Face offers state-of-the-art hardware and software acceleration to train, fine-tune and predict with Transformers.
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  </p>
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+ <h3>Useful Resources:</h3>
 
 
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  <ul>
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+ <li class="ml-6"><a href="https://huggingface.co/hardware/intel" class="underline" data-ga-category="intel-org" data-ga-action="clicked partner page" data-ga-label="partner page">Intel AI + Hugging Face partner page</a></li>
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+ <li class="ml-6"><a href="https://github.com/IntelAI" class="underline" data-ga-category="intel-org" data-ga-action="clicked intel ai github" data-ga-label="intel ai github">Intel AI GitHub</a></li>
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+ <li class="ml-6"><a href="https://www.intel.com/content/www/us/en/developer/partner/hugging-face.html" class="underline" data-ga-category="intel-org" data-ga-action="clicked intel partner page" data-ga-label="intel partner page">Developer Resources from Intel and Hugging Face</a></li>
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  </ul>
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+ <p>&nbsp;</p>
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  </div>
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  <div class="lg:col-span-3">
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+ <h1>First Steps</h1>
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+ <h3>Intel acceleration libraries</h3>
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  <p class="mb-2">
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+ To get started with Intel hardware and software optimizations, download and install the Optimum Intel
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+ and Intel® Extension for Transformers libraries. The source, installation, and usage for each library can be found here:
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  </p>
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+ <ul>
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+ <li class="ml-6"><a href="https://github.com/huggingface/optimum-intel" class="underline" data-ga-category="intel-org" data-ga-action="clicked optimum intel" data-ga-label="optimum intel">🤗 Optimum Intel library</a></li>
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+ <li class="ml-6"><a href="https://github.com/intel/intel-extension-for-transformers" class="underline" data-ga-category="intel-org" data-ga-action="clicked intel extension for transformers" data-ga-label="intel extension for transformers">Intel® Extension for Transformers</a></li>
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+ </ul>
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+ <p class="mb-2">
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+ The Optimum Intel library provides primarily hardware acceleration, while the Intel® Extension
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+ for Transformers is focused more on software accleration. Both should be present to achieve ideal
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+ performance and productivity gains in transfer learning and fine-tuning with Hugging Face.
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  </p>
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+ <h3>Find your model</h3>
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  <p class="mb-2">
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+ Next, find your desired model (and dataset) by using the search box at the top-left of Hugging Face’s website.
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+ Add “intel” to your search to narrow your search to models pretrained by Intel.
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  </p>
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+ <img
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+ alt=""
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+ src="https://huggingface.co/spaces/Intel/README/resolve/main/hf-model_search.png"
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+ style="margin:auto;transform:scale(0.8);"
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+ />
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+ <h3>Demo, dataset, and quick-start commands</h3>
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  <p class="mb-2">
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  On the model’s page (called a “Model Card”) you will find description and usage information, an embedded
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  inferencing demo, and the associated dataset. In the upper-right of your screen, click “Use in Transformers”
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  for helpful code hints on how to import the model to your own workspace with an established Hugging Face pipeline and tokenizer.
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  </p>
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+ <img
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+ alt=""
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+ src="https://huggingface.co/spaces/Intel/README/resolve/main/hf-use_transformers.png"
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+ style="margin:auto;transform:scale(0.8);"
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+ />
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+ <img
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+ alt=""
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+ src="https://huggingface.co/spaces/Intel/README/resolve/main/hf-quickstart.png"
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+ style="margin:auto;transform:scale(0.8);"
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+ />
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  </div>
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  </div>