kernels/pr_622/en/_app/immutable/nodes/21.1c19db2b.js

import{s as ee,n as le,o as te}from"../chunks/scheduler.f3b1e791.js";import{S as ne,i as se,e as a,s,c as X,h as ie,a as o,d as t,b as i,f as K,g as $,j as m,k as S,l as ae,m as n,n as _,t as z,o as N,p as V}from"../chunks/index.023a9934.js";import{C as oe}from"../chunks/CodeBlock.0716e5f2.js";import{H as re,E as ce}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.28cf3f3c.js";function me(R){let r,J,y,x,d,U,u,P=`Use <code>kernel-builder skills add</code> to install the skills for AI coding assistants like Claude, Codex, and OpenCode.
Supported skills include:`,C,p,Y="<li><code>cuda-kernels</code> (default)</li> <li><code>rocm-kernels</code></li> <li><code>xpu-kernels</code></li> <li><code>cpu-kernels</code></li>",j,M,Q='Skill files are downloaded from the <code>huggingface/kernels</code> directory in this <a href="https://github.com/huggingface/kernels/tree/main/kernel-builder/skills" rel="nofollow">repository</a>.',W,f,q="Skills instruct agents how to deal with hardware-specific optimizations, integrate with libraries like diffusers and transformers, and benchmark kernel performance in consistent ways.",v,c,A="<p><strong>When are CPU kernels actually helpful?</strong> Two main cases:</p> <ul><li><strong>Better performance on Intel Xeon</strong> — custom AVX2/AVX512 kernels (and AMX via brgemm for quantized GEMM) outperform generic PyTorch ops for element-wise and quantized workloads, especially in CPU-only or latency-sensitive serving.</li> <li><strong>Enabling functionality that otherwise can’t run</strong> — some kernels are a hard requirement, e.g. <code>megablocks</code> MoE on CPU, where without the kernel you simply cannot run MXFP4.</li></ul>",I,k,F='Example CPU kernels built with this skill (available on the Hub under <a href="https://huggingface.co/kernels-community" rel="nofollow"><code>kernels-community</code></a>):',B,b,D='<li><a href="https://huggingface.co/kernels-community/megablocks" rel="nofollow"><code>kernels-community/megablocks</code></a> — MoE kernels with a CPU backend that enable running MXFP4 MoE models on CPU.</li> <li><a href="https://huggingface.co/kernels-community/quantization-gptq" rel="nofollow"><code>kernels-community/quantization-gptq</code></a> — INT4 quantized GEMM using AVX512.</li> <li><a href="https://huggingface.co/kernels-community/rmsnorm" rel="nofollow"><code>kernels-community/rmsnorm</code></a> — RMSNorm with AVX2/AVX512 element-wise paths.</li>',E,h,O="Examples:",G,w,L,T,Z,g,H;return d=new re({props:{title:"kernel-builder skills add",local:"kernel-builder-skills-add",headingTag:"h3"}}),w=new oe({props:{code:"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",highlighted:`&lt;CopyLLMTxtMenu containerStyle=<span class="hljs-string">&quot;float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;&quot;</span>&gt;&lt;/CopyLLMTxtMenu&gt;

<span class="hljs-comment"># install for Claude in the current project</span>
kernel-builder skills add --claude

<span class="hljs-comment"># install ROCm kernels skill for Codex</span>
kernel-builder skills add --skill rocm-kernels --codex

<span class="hljs-comment"># install globally for Codex</span>
kernel-builder skills add --codex --global

<span class="hljs-comment"># install for multiple assistants</span>
kernel-builder skills add --claude --codex --opencode

<span class="hljs-comment"># install to a custom destination and overwrite if already present</span>
kernel-builder skills add --dest ~/my-skills --force`,lang:"bash",wrap:!1}}),T=new ce({props:{source:"https://github.com/huggingface/kernels/blob/main/docs/source/cli-skills.md"}}),{c(){r=a("meta"),J=s(),y=a("p"),x=s(),X(d.$$.fragment),U=s(),u=a("p"),u.innerHTML=P,C=s(),p=a("ul"),p.innerHTML=Y,j=s(),M=a("p"),M.innerHTML=Q,W=s(),f=a("p"),f.textContent=q,v=s(),c=a("blockquote"),c.innerHTML=A,I=s(),k=a("p"),k.innerHTML=F,B=s(),b=a("ul"),b.innerHTML=D,E=s(),h=a("p"),h.textContent=O,G=s(),X(w.$$.fragment),L=s(),X(T.$$.fragment),Z=s(),g=a("p"),this.h()},l(e){const l=ie("svelte-u9bgzb",document.head);r=o(l,"META",{name:!0,content:!0}),l.forEach(t),J=i(e),y=o(e,"P",{}),K(y).forEach(t),x=i(e),$(d.$$.fragment,e),U=i(e),u=o(e,"P",{"data-svelte-h":!0}),m(u)!=="svelte-n2lu6b"&&(u.innerHTML=P),C=i(e),p=o(e,"UL",{"data-svelte-h":!0}),m(p)!=="svelte-uzg8fh"&&(p.innerHTML=Y),j=i(e),M=o(e,"P",{"data-svelte-h":!0}),m(M)!=="svelte-y8wod8"&&(M.innerHTML=Q),W=i(e),f=o(e,"P",{"data-svelte-h":!0}),m(f)!=="svelte-sfhhkf"&&(f.textContent=q),v=i(e),c=o(e,"BLOCKQUOTE",{class:!0,"data-svelte-h":!0}),m(c)!=="svelte-1y9lxuv"&&(c.innerHTML=A),I=i(e),k=o(e,"P",{"data-svelte-h":!0}),m(k)!=="svelte-xvut5g"&&(k.innerHTML=F),B=i(e),b=o(e,"UL",{"data-svelte-h":!0}),m(b)!=="svelte-19jhox4"&&(b.innerHTML=D),E=i(e),h=o(e,"P",{"data-svelte-h":!0}),m(h)!=="svelte-kvfsh7"&&(h.textContent=O),G=i(e),$(w.$$.fragment,e),L=i(e),$(T.$$.fragment,e),Z=i(e),g=o(e,"P",{}),K(g).forEach(t),this.h()},h(){S(r,"name","hf:doc:metadata"),S(r,"content",de),S(c,"class","tip")},m(e,l){ae(document.head,r),n(e,J,l),n(e,y,l),n(e,x,l),_(d,e,l),n(e,U,l),n(e,u,l),n(e,C,l),n(e,p,l),n(e,j,l),n(e,M,l),n(e,W,l),n(e,f,l),n(e,v,l),n(e,c,l),n(e,I,l),n(e,k,l),n(e,B,l),n(e,b,l),n(e,E,l),n(e,h,l),n(e,G,l),_(w,e,l),n(e,L,l),_(T,e,l),n(e,Z,l),n(e,g,l),H=!0},p:le,i(e){H||(z(d.$$.fragment,e),z(w.$$.fragment,e),z(T.$$.fragment,e),H=!0)},o(e){N(d.$$.fragment,e),N(w.$$.fragment,e),N(T.$$.fragment,e),H=!1},d(e){e&&(t(J),t(y),t(x),t(U),t(u),t(C),t(p),t(j),t(M),t(W),t(f),t(v),t(c),t(I),t(k),t(B),t(b),t(E),t(h),t(G),t(L),t(Z),t(g)),t(r),V(d,e),V(w,e),V(T,e)}}}const de='{"title":"kernel-builder skills add","local":"kernel-builder-skills-add","sections":[],"depth":3}';function ue(R){return te(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class be extends ne{constructor(r){super(),se(this,r,ue,me,ee,{})}}export{be as component};