optimum-intel/pr_1503/en/_app/immutable/nodes/2.b2bb7ea5.js

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The users can easily apply static, dynamic and aware-training quantization approaches while giving an expected accuracy criteria. It also supports different weight pruning techniques enabling the creation of pruned model giving a predefined sparsity target.',$,f,H='<a href="https://docs.openvino.ai" rel="nofollow">OpenVINO</a> is an open-source toolkit that enables high performance inference capabilities for Intel CPUs, GPUs, and special DL inference accelerators (<a href="https://docs.openvino.ai/2024/about-openvino/compatibility-and-support/supported-devices.html" rel="nofollow">see</a> the full list of supported devices). It is supplied with a set of tools to optimize your models with compression techniques such as quantization, pruning and knowledge distillation. Optimum Intel provides a simple interface to optimize your Transformers and Diffusers models, convert them to the OpenVINO Intermediate Representation (IR) format and run inference using OpenVINO Runtime.',P,h,k='<a href="https://intel.github.io/intel-extension-for-pytorch/#introduction" rel="nofollow">Intel Extension for PyTorch</a> (IPEX) is an open-source library which provides optimizations for both eager mode and graph mode, however, compared to eager mode, graph mode in PyTorch* normally yields better performance from optimization techniques, such as operation fusion.',T,s,E='<div class="w-full flex flex-col space-x-4 md:grid md:grid-cols-3 md:gap-x-5"><a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="neural_compressor/optimization"><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">Neural Compressor</div> <p class="text-gray-700">Learn how to apply compression techniques such as quantization, pruning and knowledge distillation to speed up inference.</p></a> <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="openvino/export"><div class="w-full text-center bg-gradient-to-br from-purple-400 to-purple-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">OpenVINO</div> <p class="text-gray-700">Learn how to run inference with OpenVINO Runtime and to apply quantization to further speed up inference.</p></a> <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="ipex/inference"><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">IPEX</div> <p class="text-gray-700">Learn how to optimize your model with IPEX.</p></a></div>',z,w,L;return d=new Q({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),u=new W({props:{title:"🤗 Optimum Intel",local:"-optimum-intel",headingTag:"h1"}}),{c(){o=l("meta"),b=a(),g=l("p"),x=a(),N(d.$$.fragment),y=a(),N(u.$$.fragment),I=a(),m=l("p"),m.textContent=C,_=a(),c=l("p"),c.innerHTML=M,$=a(),f=l("p"),f.innerHTML=H,P=a(),h=l("p"),h.innerHTML=k,T=a(),s=l("div"),s.innerHTML=E,z=a(),w=l("p"),this.h()},l(e){const t=J("svelte-u9bgzb",document.head);o=p(t,"META",{name:!0,content:!0}),t.forEach(n),b=r(e),g=p(e,"P",{}),V(g).forEach(n),x=r(e),R(d.$$.fragment,e),y=r(e),R(u.$$.fragment,e),I=r(e),m=p(e,"P",{"data-svelte-h":!0}),v(m)!=="svelte-1y6q76e"&&(m.textContent=C),_=r(e),c=p(e,"P",{"data-svelte-h":!0}),v(c)!=="svelte-1xbmto8"&&(c.innerHTML=M),$=r(e),f=p(e,"P",{"data-svelte-h":!0}),v(f)!=="svelte-6499sz"&&(f.innerHTML=H),P=r(e),h=p(e,"P",{"data-svelte-h":!0}),v(h)!=="svelte-ieerj6"&&(h.innerHTML=k),T=r(e),s=p(e,"DIV",{class:!0,"data-svelte-h":!0}),v(s)!=="svelte-3up1bp"&&(s.innerHTML=E),z=r(e),w=p(e,"P",{}),V(w).forEach(n),this.h()},h(){O(o,"name","hf:doc:metadata"),O(o,"content",Z),O(s,"class","mt-10")},m(e,t){K(document.head,o),i(e,b,t),i(e,g,t),i(e,x,t),D(d,e,t),i(e,y,t),D(u,e,t),i(e,I,t),i(e,m,t),i(e,_,t),i(e,c,t),i(e,$,t),i(e,f,t),i(e,P,t),i(e,h,t),i(e,T,t),i(e,s,t),i(e,z,t),i(e,w,t),L=!0},p:A,i(e){L||(S(d.$$.fragment,e),S(u.$$.fragment,e),L=!0)},o(e){U(d.$$.fragment,e),U(u.$$.fragment,e),L=!1},d(e){e&&(n(b),n(g),n(x),n(y),n(I),n(m),n(_),n(c),n($),n(f),n(P),n(h),n(T),n(s),n(z),n(w)),n(o),X(d,e),X(u,e)}}}const Z='{"title":"🤗 Optimum Intel","local":"-optimum-intel","sections":[],"depth":1}';function ee(q){return G(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class oe extends B{constructor(o){super(),F(this,o,ee,Y,j,{})}}export{oe as component};