diff --git a/404.html b/404.html
index ca2c4325..72c7b796 100644
--- a/404.html
+++ b/404.html
@@ -13,21 +13,20 @@
         <meta name="viewport" content="width=device-width, initial-scale=1">
         <meta name="theme-color" content="#ffffff">
 
-        <link rel="icon" href="favicon.svg">
-        <link rel="shortcut icon" href="favicon.png">
-        <link rel="stylesheet" href="css/variables.css">
-        <link rel="stylesheet" href="css/general.css">
-        <link rel="stylesheet" href="css/chrome.css">
-        <link rel="stylesheet" href="css/print.css" media="print">
+        <link rel="icon" href="favicon-de23e50b.svg">
+        <link rel="shortcut icon" href="favicon-8114d1fc.png">
+        <link rel="stylesheet" href="css/variables-8adf115d.css">
+        <link rel="stylesheet" href="css/general-2459343d.css">
+        <link rel="stylesheet" href="css/chrome-9dfbd86b.css">
+        <link rel="stylesheet" href="css/print-9e4910d8.css" media="print">
 
         <!-- Fonts -->
-        <link rel="stylesheet" href="FontAwesome/css/font-awesome.css">
-        <link rel="stylesheet" href="fonts/fonts.css">
+        <link rel="stylesheet" href="fonts/fonts-9644e21d.css">
 
         <!-- Highlight.js Stylesheets -->
-        <link rel="stylesheet" id="highlight-css" href="highlight.css">
-        <link rel="stylesheet" id="tomorrow-night-css" href="tomorrow-night.css">
-        <link rel="stylesheet" id="ayu-highlight-css" href="ayu-highlight.css">
+        <link rel="stylesheet" id="mdbook-highlight-css" href="highlight-493f70e1.css">
+        <link rel="stylesheet" id="mdbook-tomorrow-night-css" href="tomorrow-night-4c0ae647.css">
+        <link rel="stylesheet" id="mdbook-ayu-highlight-css" href="ayu-highlight-3fdfc3ac.css">
 
         <!-- Custom theme stylesheets -->
 
@@ -37,10 +36,10 @@
             const path_to_root = "";
             const default_light_theme = "light";
             const default_dark_theme = "navy";
-            window.path_to_searchindex_js = "searchindex.js";
+            window.path_to_searchindex_js = "searchindex-e61e839e.js";
         </script>
         <!-- Start loading toc.js asap -->
-        <script src="toc.js"></script>
+        <script src="toc-6a9a54e6.js"></script>
     </head>
     <body>
     <div id="mdbook-help-container">
@@ -54,7 +53,7 @@
             </div>
         </div>
     </div>
-    <div id="body-container">
+    <div id="mdbook-body-container">
         <!-- Work around some values being stored in localStorage wrapped in quotes -->
         <script>
             try {
@@ -83,12 +82,12 @@
             html.classList.add("js");
         </script>
 
-        <input type="checkbox" id="sidebar-toggle-anchor" class="hidden">
+        <input type="checkbox" id="mdbook-sidebar-toggle-anchor" class="hidden">
 
         <!-- Hide / unhide sidebar before it is displayed -->
         <script>
             let sidebar = null;
-            const sidebar_toggle = document.getElementById("sidebar-toggle-anchor");
+            const sidebar_toggle = document.getElementById("mdbook-sidebar-toggle-anchor");
             if (document.body.clientWidth >= 1080) {
                 try { sidebar = localStorage.getItem('mdbook-sidebar'); } catch(e) { }
                 sidebar = sidebar || 'visible';
@@ -103,39 +102,39 @@
             }
         </script>
 
-        <nav id="sidebar" class="sidebar" aria-label="Table of contents">
+        <nav id="mdbook-sidebar" class="sidebar" aria-label="Table of contents">
             <!-- populated by js -->
             <mdbook-sidebar-scrollbox class="sidebar-scrollbox"></mdbook-sidebar-scrollbox>
             <noscript>
                 <iframe class="sidebar-iframe-outer" src="toc.html"></iframe>
             </noscript>
-            <div id="sidebar-resize-handle" class="sidebar-resize-handle">
+            <div id="mdbook-sidebar-resize-handle" class="sidebar-resize-handle">
                 <div class="sidebar-resize-indicator"></div>
             </div>
         </nav>
 
-        <div id="page-wrapper" class="page-wrapper">
+        <div id="mdbook-page-wrapper" class="page-wrapper">
 
             <div class="page">
-                <div id="menu-bar-hover-placeholder"></div>
-                <div id="menu-bar" class="menu-bar sticky">
+                <div id="mdbook-menu-bar-hover-placeholder"></div>
+                <div id="mdbook-menu-bar" class="menu-bar sticky">
                     <div class="left-buttons">
-                        <label id="sidebar-toggle" class="icon-button" for="sidebar-toggle-anchor" title="Toggle Table of Contents" aria-label="Toggle Table of Contents" aria-controls="sidebar">
-                            <i class="fa fa-bars"></i>
+                        <label id="mdbook-sidebar-toggle" class="icon-button" for="mdbook-sidebar-toggle-anchor" title="Toggle Table of Contents" aria-label="Toggle Table of Contents" aria-controls="mdbook-sidebar">
+                            <span class=fa-svg><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc. --><path d="M0 96C0 78.3 14.3 64 32 64H416c17.7 0 32 14.3 32 32s-14.3 32-32 32H32C14.3 128 0 113.7 0 96zM0 256c0-17.7 14.3-32 32-32H416c17.7 0 32 14.3 32 32s-14.3 32-32 32H32c-17.7 0-32-14.3-32-32zM448 416c0 17.7-14.3 32-32 32H32c-17.7 0-32-14.3-32-32s14.3-32 32-32H416c17.7 0 32 14.3 32 32z"/></svg></span>
                         </label>
-                        <button id="theme-toggle" class="icon-button" type="button" title="Change theme" aria-label="Change theme" aria-haspopup="true" aria-expanded="false" aria-controls="theme-list">
-                            <i class="fa fa-paint-brush"></i>
+                        <button id="mdbook-theme-toggle" class="icon-button" type="button" title="Change theme" aria-label="Change theme" aria-haspopup="true" aria-expanded="false" aria-controls="mdbook-theme-list">
+                            <span class=fa-svg><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc. --><path d="M371.3 367.1c27.3-3.9 51.9-19.4 67.2-42.9L600.2 74.1c12.6-19.5 9.4-45.3-7.6-61.2S549.7-4.4 531.1 9.6L294.4 187.2c-24 18-38.2 46.1-38.4 76.1L371.3 367.1zm-19.6 25.4l-116-104.4C175.9 290.3 128 339.6 128 400c0 3.9 .2 7.8 .6 11.6c1.8 17.5-10.2 36.4-27.8 36.4H96c-17.7 0-32 14.3-32 32s14.3 32 32 32H240c61.9 0 112-50.1 112-112c0-2.5-.1-5-.2-7.5z"/></svg></span>
                         </button>
-                        <ul id="theme-list" class="theme-popup" aria-label="Themes" role="menu">
-                            <li role="none"><button role="menuitem" class="theme" id="default_theme">Auto</button></li>
-                            <li role="none"><button role="menuitem" class="theme" id="light">Light</button></li>
-                            <li role="none"><button role="menuitem" class="theme" id="rust">Rust</button></li>
-                            <li role="none"><button role="menuitem" class="theme" id="coal">Coal</button></li>
-                            <li role="none"><button role="menuitem" class="theme" id="navy">Navy</button></li>
-                            <li role="none"><button role="menuitem" class="theme" id="ayu">Ayu</button></li>
+                        <ul id="mdbook-theme-list" class="theme-popup" aria-label="Themes" role="menu">
+                            <li role="none"><button role="menuitem" class="theme" id="mdbook-theme-default_theme">Auto</button></li>
+                            <li role="none"><button role="menuitem" class="theme" id="mdbook-theme-light">Light</button></li>
+                            <li role="none"><button role="menuitem" class="theme" id="mdbook-theme-rust">Rust</button></li>
+                            <li role="none"><button role="menuitem" class="theme" id="mdbook-theme-coal">Coal</button></li>
+                            <li role="none"><button role="menuitem" class="theme" id="mdbook-theme-navy">Navy</button></li>
+                            <li role="none"><button role="menuitem" class="theme" id="mdbook-theme-ayu">Ayu</button></li>
                         </ul>
-                        <button id="search-toggle" class="icon-button" type="button" title="Search (`/`)" aria-label="Toggle Searchbar" aria-expanded="false" aria-keyshortcuts="/ s" aria-controls="searchbar">
-                            <i class="fa fa-search"></i>
+                        <button id="mdbook-search-toggle" class="icon-button" type="button" title="Search (`/`)" aria-label="Toggle Searchbar" aria-expanded="false" aria-keyshortcuts="/ s" aria-controls="mdbook-searchbar">
+                            <span class=fa-svg><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc. --><path d="M416 208c0 45.9-14.9 88.3-40 122.7L502.6 457.4c12.5 12.5 12.5 32.8 0 45.3s-32.8 12.5-45.3 0L330.7 376c-34.4 25.2-76.8 40-122.7 40C93.1 416 0 322.9 0 208S93.1 0 208 0S416 93.1 416 208zM208 352c79.5 0 144-64.5 144-144s-64.5-144-144-144S64 128.5 64 208s64.5 144 144 144z"/></svg></span>
                         </button>
                     </div>
 
@@ -143,41 +142,41 @@
 
                     <div class="right-buttons">
                         <a href="print.html" title="Print this book" aria-label="Print this book">
-                            <i id="print-button" class="fa fa-print"></i>
+                            <span class=fa-svg id="print-button"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc. --><path d="M128 0C92.7 0 64 28.7 64 64v96h64V64H354.7L384 93.3V160h64V93.3c0-17-6.7-33.3-18.7-45.3L400 18.7C388 6.7 371.7 0 354.7 0H128zM384 352v32 64H128V384 368 352H384zm64 32h32c17.7 0 32-14.3 32-32V256c0-35.3-28.7-64-64-64H64c-35.3 0-64 28.7-64 64v96c0 17.7 14.3 32 32 32H64v64c0 35.3 28.7 64 64 64H384c35.3 0 64-28.7 64-64V384zm-16-88c-13.3 0-24-10.7-24-24s10.7-24 24-24s24 10.7 24 24s-10.7 24-24 24z"/></svg></span>
                         </a>
                         <a href="https://github.com/kvcache-ai/ktransformers" title="Git repository" aria-label="Git repository">
-                            <i id="git-repository-button" class="fa fa-github"></i>
+                            <span class=fa-svg><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc. --><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg></span>
                         </a>
 
                     </div>
                 </div>
 
-                <div id="search-wrapper" class="hidden">
-                    <form id="searchbar-outer" class="searchbar-outer">
+                <div id="mdbook-search-wrapper" class="hidden">
+                    <form id="mdbook-searchbar-outer" class="searchbar-outer">
                         <div class="search-wrapper">
-                            <input type="search" id="searchbar" name="searchbar" placeholder="Search this book ..." aria-controls="searchresults-outer" aria-describedby="searchresults-header">
+                            <input type="search" id="mdbook-searchbar" name="searchbar" placeholder="Search this book ..." aria-controls="mdbook-searchresults-outer" aria-describedby="searchresults-header">
                             <div class="spinner-wrapper">
-                                <i class="fa fa-spinner fa-spin"></i>
+                                <span class=fa-svg id="fa-spin"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc. --><path d="M304 48c0-26.5-21.5-48-48-48s-48 21.5-48 48s21.5 48 48 48s48-21.5 48-48zm0 416c0-26.5-21.5-48-48-48s-48 21.5-48 48s21.5 48 48 48s48-21.5 48-48zM48 304c26.5 0 48-21.5 48-48s-21.5-48-48-48s-48 21.5-48 48s21.5 48 48 48zm464-48c0-26.5-21.5-48-48-48s-48 21.5-48 48s21.5 48 48 48s48-21.5 48-48zM142.9 437c18.7-18.7 18.7-49.1 0-67.9s-49.1-18.7-67.9 0s-18.7 49.1 0 67.9s49.1 18.7 67.9 0zm0-294.2c18.7-18.7 18.7-49.1 0-67.9S93.7 56.2 75 75s-18.7 49.1 0 67.9s49.1 18.7 67.9 0zM369.1 437c18.7 18.7 49.1 18.7 67.9 0s18.7-49.1 0-67.9s-49.1-18.7-67.9 0s-18.7 49.1 0 67.9z"/></svg></span>
                             </div>
                         </div>
                     </form>
-                    <div id="searchresults-outer" class="searchresults-outer hidden">
-                        <div id="searchresults-header" class="searchresults-header"></div>
-                        <ul id="searchresults">
+                    <div id="mdbook-searchresults-outer" class="searchresults-outer hidden">
+                        <div id="mdbook-searchresults-header" class="searchresults-header"></div>
+                        <ul id="mdbook-searchresults">
                         </ul>
                     </div>
                 </div>
 
                 <!-- Apply ARIA attributes after the sidebar and the sidebar toggle button are added to the DOM -->
                 <script>
-                    document.getElementById('sidebar-toggle').setAttribute('aria-expanded', sidebar === 'visible');
-                    document.getElementById('sidebar').setAttribute('aria-hidden', sidebar !== 'visible');
-                    Array.from(document.querySelectorAll('#sidebar a')).forEach(function(link) {
+                    document.getElementById('mdbook-sidebar-toggle').setAttribute('aria-expanded', sidebar === 'visible');
+                    document.getElementById('mdbook-sidebar').setAttribute('aria-hidden', sidebar !== 'visible');
+                    Array.from(document.querySelectorAll('#mdbook-sidebar a')).forEach(function(link) {
                         link.setAttribute('tabIndex', sidebar === 'visible' ? 0 : -1);
                     });
                 </script>
 
-                <div id="content" class="content">
+                <div id="mdbook-content" class="content">
                     <main>
                         <h1 id="document-not-found-404"><a class="header" href="#document-not-found-404">Document not found (404)</a></h1>
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@@ -199,6 +198,11 @@
 
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+        <template id=fa-eye><span class=fa-svg><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc. --><path d="M288 32c-80.8 0-145.5 36.8-192.6 80.6C48.6 156 17.3 208 2.5 243.7c-3.3 7.9-3.3 16.7 0 24.6C17.3 304 48.6 356 95.4 399.4C142.5 443.2 207.2 480 288 480s145.5-36.8 192.6-80.6c46.8-43.5 78.1-95.4 93-131.1c3.3-7.9 3.3-16.7 0-24.6c-14.9-35.7-46.2-87.7-93-131.1C433.5 68.8 368.8 32 288 32zM432 256c0 79.5-64.5 144-144 144s-144-64.5-144-144s64.5-144 144-144s144 64.5 144 144zM288 192c0 35.3-28.7 64-64 64c-11.5 0-22.3-3-31.6-8.4c-.2 2.8-.4 5.5-.4 8.4c0 53 43 96 96 96s96-43 96-96s-43-96-96-96c-2.8 0-5.6 .1-8.4 .4c5.3 9.3 8.4 20.1 8.4 31.6z"/></svg></span></template>
+        <template id=fa-eye-slash><span class=fa-svg><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 640 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc. --><path d="M38.8 5.1C28.4-3.1 13.3-1.2 5.1 9.2S-1.2 34.7 9.2 42.9l592 464c10.4 8.2 25.5 6.3 33.7-4.1s6.3-25.5-4.1-33.7L525.6 386.7c39.6-40.6 66.4-86.1 79.9-118.4c3.3-7.9 3.3-16.7 0-24.6c-14.9-35.7-46.2-87.7-93-131.1C465.5 68.8 400.8 32 320 32c-68.2 0-125 26.3-169.3 60.8L38.8 5.1zM223.1 149.5C248.6 126.2 282.7 112 320 112c79.5 0 144 64.5 144 144c0 24.9-6.3 48.3-17.4 68.7L408 294.5c5.2-11.8 8-24.8 8-38.5c0-53-43-96-96-96c-2.8 0-5.6 .1-8.4 .4c5.3 9.3 8.4 20.1 8.4 31.6c0 10.2-2.4 19.8-6.6 28.3l-90.3-70.8zm223.1 298L373 389.9c-16.4 6.5-34.3 10.1-53 10.1c-79.5 0-144-64.5-144-144c0-6.9 .5-13.6 1.4-20.2L83.1 161.5C60.3 191.2 44 220.8 34.5 243.7c-3.3 7.9-3.3 16.7 0 24.6c14.9 35.7 46.2 87.7 93 131.1C174.5 443.2 239.2 480 320 480c47.8 0 89.9-12.9 126.2-32.5z"/></svg></span></template>
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+        <template id=fa-play><span class=fa-svg><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 384 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc. --><path d="M73 39c-14.8-9.1-33.4-9.4-48.5-.9S0 62.6 0 80V432c0 17.4 9.4 33.4 24.5 41.9s33.7 8.1 48.5-.9L361 297c14.3-8.7 23-24.2 23-41s-8.7-32.2-23-41L73 39z"/></svg></span></template>
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diff --git a/FontAwesome/css/font-awesome.css b/FontAwesome/css/font-awesome.css
deleted file mode 100644
index 540440ce..00000000
--- a/FontAwesome/css/font-awesome.css
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- />
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- />
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- />
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- />
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- />
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- />
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- />
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- />
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- />
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- />
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- />
-    <glyph glyph-name="lessequal" unicode="&#xf500;" horiz-adv-x="1792" 
- />
-  </font>
-</defs></svg>
diff --git a/FontAwesome/fonts/fontawesome-webfont.ttf b/FontAwesome/fonts/fontawesome-webfont.ttf
deleted file mode 100644
index 35acda2f..00000000
Binary files a/FontAwesome/fonts/fontawesome-webfont.ttf and /dev/null differ
diff --git a/FontAwesome/fonts/fontawesome-webfont.woff b/FontAwesome/fonts/fontawesome-webfont.woff
deleted file mode 100644
index 400014a4..00000000
Binary files a/FontAwesome/fonts/fontawesome-webfont.woff and /dev/null differ
diff --git a/FontAwesome/fonts/fontawesome-webfont.woff2 b/FontAwesome/fonts/fontawesome-webfont.woff2
deleted file mode 100644
index 4d13fc60..00000000
Binary files a/FontAwesome/fonts/fontawesome-webfont.woff2 and /dev/null differ
diff --git a/ace.js b/ace-2a3cd908.js
similarity index 100%
rename from ace.js
rename to ace-2a3cd908.js
diff --git a/ayu-highlight.css b/ayu-highlight-3fdfc3ac.css
similarity index 97%
rename from ayu-highlight.css
rename to ayu-highlight-3fdfc3ac.css
index 32c94322..dba94b09 100644
--- a/ayu-highlight.css
+++ b/ayu-highlight-3fdfc3ac.css
@@ -13,7 +13,6 @@ Original by Dempfi (https://github.com/dempfi/ayu)
 .hljs-comment,
 .hljs-quote {
   color: #5c6773;
-  font-style: italic;
 }
 
 .hljs-variable,
diff --git a/book.js b/book-a0b12cfe.js
similarity index 92%
rename from book.js
rename to book-a0b12cfe.js
index de8219fd..62d7c4cc 100644
--- a/book.js
+++ b/book-a0b12cfe.js
@@ -85,7 +85,6 @@ function playground_text(playground, hidden = true) {
         const re = /extern\s+crate\s+([a-zA-Z_0-9]+)\s*;/g;
         const snippet_crates = [];
         let item;
-        // eslint-disable-next-line no-cond-assign
         while (item = re.exec(txt)) {
             snippet_crates.push(item[1]);
         }
@@ -97,6 +96,7 @@ function playground_text(playground, hidden = true) {
 
         if (all_available) {
             play_button.classList.remove('hidden');
+            play_button.hidden = false;
         } else {
             play_button.classList.add('hidden');
         }
@@ -207,26 +207,25 @@ function playground_text(playground, hidden = true) {
 
         const buttons = document.createElement('div');
         buttons.className = 'buttons';
-        buttons.innerHTML = '<button class="fa fa-eye" title="Show hidden lines" \
+        buttons.innerHTML = '<button title="Show hidden lines" \
 aria-label="Show hidden lines"></button>';
+        buttons.firstChild.innerHTML = document.getElementById('fa-eye').innerHTML;
 
         // add expand button
         const pre_block = block.parentNode;
         pre_block.insertBefore(buttons, pre_block.firstChild);
 
-        pre_block.querySelector('.buttons').addEventListener('click', function(e) {
-            if (e.target.classList.contains('fa-eye')) {
-                e.target.classList.remove('fa-eye');
-                e.target.classList.add('fa-eye-slash');
-                e.target.title = 'Hide lines';
-                e.target.setAttribute('aria-label', e.target.title);
+        buttons.firstChild.addEventListener('click', function(e) {
+            if (this.title === 'Show hidden lines') {
+                this.innerHTML = document.getElementById('fa-eye-slash').innerHTML;
+                this.title = 'Hide lines';
+                this.setAttribute('aria-label', e.target.title);
 
                 block.classList.remove('hide-boring');
-            } else if (e.target.classList.contains('fa-eye-slash')) {
-                e.target.classList.remove('fa-eye-slash');
-                e.target.classList.add('fa-eye');
-                e.target.title = 'Show hidden lines';
-                e.target.setAttribute('aria-label', e.target.title);
+            } else if (this.title === 'Hide lines') {
+                this.innerHTML = document.getElementById('fa-eye').innerHTML;
+                this.title = 'Show hidden lines';
+                this.setAttribute('aria-label', e.target.title);
 
                 block.classList.add('hide-boring');
             }
@@ -266,10 +265,11 @@ aria-label="Show hidden lines"></button>';
         }
 
         const runCodeButton = document.createElement('button');
-        runCodeButton.className = 'fa fa-play play-button';
+        runCodeButton.className = 'play-button';
         runCodeButton.hidden = true;
         runCodeButton.title = 'Run this code';
         runCodeButton.setAttribute('aria-label', runCodeButton.title);
+        runCodeButton.innerHTML = document.getElementById('fa-play').innerHTML;
 
         buttons.insertBefore(runCodeButton, buttons.firstChild);
         runCodeButton.addEventListener('click', () => {
@@ -289,9 +289,11 @@ aria-label="Show hidden lines"></button>';
         const code_block = pre_block.querySelector('code');
         if (window.ace && code_block.classList.contains('editable')) {
             const undoChangesButton = document.createElement('button');
-            undoChangesButton.className = 'fa fa-history reset-button';
+            undoChangesButton.className = 'reset-button';
             undoChangesButton.title = 'Undo changes';
             undoChangesButton.setAttribute('aria-label', undoChangesButton.title);
+            undoChangesButton.innerHTML +=
+                document.getElementById('fa-clock-rotate-left').innerHTML;
 
             buttons.insertBefore(undoChangesButton, buttons.firstChild);
 
@@ -306,23 +308,23 @@ aria-label="Show hidden lines"></button>';
 
 (function themes() {
     const html = document.querySelector('html');
-    const themeToggleButton = document.getElementById('theme-toggle');
-    const themePopup = document.getElementById('theme-list');
+    const themeToggleButton = document.getElementById('mdbook-theme-toggle');
+    const themePopup = document.getElementById('mdbook-theme-list');
     const themeColorMetaTag = document.querySelector('meta[name="theme-color"]');
     const themeIds = [];
     themePopup.querySelectorAll('button.theme').forEach(function(el) {
         themeIds.push(el.id);
     });
     const stylesheets = {
-        ayuHighlight: document.querySelector('#ayu-highlight-css'),
-        tomorrowNight: document.querySelector('#tomorrow-night-css'),
-        highlight: document.querySelector('#highlight-css'),
+        ayuHighlight: document.querySelector('#mdbook-ayu-highlight-css'),
+        tomorrowNight: document.querySelector('#mdbook-tomorrow-night-css'),
+        highlight: document.querySelector('#mdbook-highlight-css'),
     };
 
     function showThemes() {
         themePopup.style.display = 'block';
         themeToggleButton.setAttribute('aria-expanded', true);
-        themePopup.querySelector('button#' + get_theme()).focus();
+        themePopup.querySelector('button#mdbook-theme-' + get_theme()).focus();
     }
 
     function updateThemeSelected() {
@@ -330,10 +332,10 @@ aria-label="Show hidden lines"></button>';
             el.classList.remove('theme-selected');
         });
         const selected = get_saved_theme() ?? 'default_theme';
-        let element = themePopup.querySelector('button#' + selected);
+        let element = themePopup.querySelector('button#mdbook-theme-' + selected);
         if (element === null) {
             // Fall back in case there is no "Default" item.
-            element = themePopup.querySelector('button#' + get_theme());
+            element = themePopup.querySelector('button#mdbook-theme-' + get_theme());
         }
         element.classList.add('theme-selected');
     }
@@ -348,7 +350,7 @@ aria-label="Show hidden lines"></button>';
         let theme = null;
         try {
             theme = localStorage.getItem('mdbook-theme');
-        } catch (e) {
+        } catch {
             // ignore error.
         }
         return theme;
@@ -360,7 +362,7 @@ aria-label="Show hidden lines"></button>';
 
     function get_theme() {
         const theme = get_saved_theme();
-        if (theme === null || theme === undefined || !themeIds.includes(theme)) {
+        if (theme === null || theme === undefined || !themeIds.includes('mdbook-theme-' + theme)) {
             if (typeof default_dark_theme === 'undefined') {
                 // A customized index.hbs might not define this, so fall back to
                 // old behavior of determining the default on page load.
@@ -409,7 +411,7 @@ aria-label="Show hidden lines"></button>';
         if (store) {
             try {
                 localStorage.setItem('mdbook-theme', theme);
-            } catch (e) {
+            } catch {
                 // ignore error.
             }
         }
@@ -445,6 +447,8 @@ aria-label="Show hidden lines"></button>';
         } else {
             return;
         }
+        theme = theme.replace(/^mdbook-theme-/, '');
+
         if (theme === 'default_theme' || theme === null) {
             delete_saved_theme();
             set_theme(get_theme(), false);
@@ -515,11 +519,11 @@ aria-label="Show hidden lines"></button>';
 })();
 
 (function sidebar() {
-    const sidebar = document.getElementById('sidebar');
-    const sidebarLinks = document.querySelectorAll('#sidebar a');
-    const sidebarToggleButton = document.getElementById('sidebar-toggle');
-    const sidebarResizeHandle = document.getElementById('sidebar-resize-handle');
-    const sidebarCheckbox = document.getElementById('sidebar-toggle-anchor');
+    const sidebar = document.getElementById('mdbook-sidebar');
+    const sidebarLinks = document.querySelectorAll('#mdbook-sidebar a');
+    const sidebarToggleButton = document.getElementById('mdbook-sidebar-toggle');
+    const sidebarResizeHandle = document.getElementById('mdbook-sidebar-resize-handle');
+    const sidebarCheckbox = document.getElementById('mdbook-sidebar-toggle-anchor');
     let firstContact = null;
 
 
@@ -555,7 +559,7 @@ aria-label="Show hidden lines"></button>';
         sidebar.setAttribute('aria-hidden', false);
         try {
             localStorage.setItem('mdbook-sidebar', 'visible');
-        } catch (e) {
+        } catch {
             // Ignore error.
         }
     }
@@ -569,7 +573,7 @@ aria-label="Show hidden lines"></button>';
         sidebar.setAttribute('aria-hidden', true);
         try {
             localStorage.setItem('mdbook-sidebar', 'hidden');
-        } catch (e) {
+        } catch {
             // Ignore error.
         }
     }
@@ -780,7 +784,7 @@ aria-label="Show hidden lines"></button>';
 })();
 
 (function controllMenu() {
-    const menu = document.getElementById('menu-bar');
+    const menu = document.getElementById('mdbook-menu-bar');
 
     (function controllPosition() {
         let scrollTop = document.scrollingElement.scrollTop;
diff --git a/clipboard.min.js b/clipboard-1626706a.min.js
similarity index 100%
rename from clipboard.min.js
rename to clipboard-1626706a.min.js
diff --git a/css/chrome.css b/css/chrome-9dfbd86b.css
similarity index 83%
rename from css/chrome.css
rename to css/chrome-9dfbd86b.css
index ce7fe048..072a9b5d 100644
--- a/css/chrome.css
+++ b/css/chrome-9dfbd86b.css
@@ -3,7 +3,7 @@
 html {
     scrollbar-color: var(--scrollbar) var(--bg);
 }
-#searchresults a,
+#mdbook-searchresults a,
 .content a:link,
 a:visited,
 a > .hljs {
@@ -11,10 +11,10 @@ a > .hljs {
 }
 
 /*
-    body-container is necessary because mobile browsers don't seem to like
+    mdbook-body-container is necessary because mobile browsers don't seem to like
     overflow-x on the body tag when there is a <meta name="viewport"> tag.
 */
-#body-container {
+#mdbook-body-container {
     /*
         This is used when the sidebar pushes the body content off the side of
         the screen on small screens. Without it, dragging on mobile Safari
@@ -25,12 +25,12 @@ a > .hljs {
 
 /* Menu Bar */
 
-#menu-bar,
-#menu-bar-hover-placeholder {
+#mdbook-menu-bar,
+#mdbook-menu-bar-hover-placeholder {
     z-index: 101;
     margin: auto calc(0px - var(--page-padding));
 }
-#menu-bar {
+#mdbook-menu-bar {
     position: relative;
     display: flex;
     flex-wrap: wrap;
@@ -39,24 +39,24 @@ a > .hljs {
     border-block-end-width: 1px;
     border-block-end-style: solid;
 }
-#menu-bar.sticky,
-#menu-bar-hover-placeholder:hover + #menu-bar,
-#menu-bar:hover,
-html.sidebar-visible #menu-bar {
+#mdbook-menu-bar.sticky,
+#mdbook-menu-bar-hover-placeholder:hover + #mdbook-menu-bar,
+#mdbook-menu-bar:hover,
+html.sidebar-visible #mdbook-menu-bar {
     position: -webkit-sticky;
     position: sticky;
     top: 0 !important;
 }
-#menu-bar-hover-placeholder {
+#mdbook-menu-bar-hover-placeholder {
     position: sticky;
     position: -webkit-sticky;
     top: 0;
     height: var(--menu-bar-height);
 }
-#menu-bar.bordered {
+#mdbook-menu-bar.bordered {
     border-block-end-color: var(--table-border-color);
 }
-#menu-bar i, #menu-bar .icon-button {
+#mdbook-menu-bar .fa-svg, #mdbook-menu-bar .icon-button {
     position: relative;
     padding: 0 8px;
     z-index: 10;
@@ -65,7 +65,7 @@ html.sidebar-visible #menu-bar {
     transition: color 0.5s;
 }
 @media only screen and (max-width: 420px) {
-    #menu-bar i, #menu-bar .icon-button {
+    #mdbook-menu-bar .fa-svg, #mdbook-menu-bar .icon-button {
         padding: 0 5px;
     }
 }
@@ -76,7 +76,7 @@ html.sidebar-visible #menu-bar {
     padding: 0;
     color: inherit;
 }
-.icon-button i {
+.icon-button .fa-svg {
     margin: 0;
 }
 
@@ -118,14 +118,14 @@ html:not(.js) .left-buttons button {
 .mobile-nav-chapters,
 .mobile-nav-chapters:visited,
 .menu-bar .icon-button,
-.menu-bar a i {
+.menu-bar a .fa-svg {
     color: var(--icons);
 }
 
-.menu-bar i:hover,
+.menu-bar .fa-svg:hover,
 .menu-bar .icon-button:hover,
 .nav-chapters:hover,
-.mobile-nav-chapters i:hover {
+.mobile-nav-chapters .fa-svg:hover {
     color: var(--icons-hover);
 }
 
@@ -186,10 +186,6 @@ html:not(.js) .left-buttons button {
     left: var(--page-padding);
 }
 
-/* Use the correct buttons for RTL layouts*/
-[dir=rtl] .previous i.fa-angle-left:before {content:"\f105";}
-[dir=rtl] .next i.fa-angle-right:before { content:"\f104"; }
-
 @media only screen and (max-width: 1080px) {
     .nav-wide-wrapper { display: none; }
     .nav-wrapper { display: block; }
@@ -197,8 +193,8 @@ html:not(.js) .left-buttons button {
 
 /* sidebar-visible */
 @media only screen and (max-width: 1380px) {
-    #sidebar-toggle-anchor:checked ~ .page-wrapper .nav-wide-wrapper { display: none; }
-    #sidebar-toggle-anchor:checked ~ .page-wrapper .nav-wrapper { display: block; }
+    #mdbook-sidebar-toggle-anchor:checked ~ .page-wrapper .nav-wide-wrapper { display: none; }
+    #mdbook-sidebar-toggle-anchor:checked ~ .page-wrapper .nav-wrapper { display: block; }
 }
 
 /* Inline code */
@@ -244,13 +240,10 @@ pre > .buttons :hover {
     border-color: var(--icons-hover);
     background-color: var(--theme-hover);
 }
-pre > .buttons i {
-    margin-inline-start: 8px;
-}
 pre > .buttons button {
     cursor: inherit;
     margin: 0px 5px;
-    padding: 4px 4px 3px 5px;
+    padding: 2px 3px 0px 4px;
     font-size: 23px;
 
     border-style: solid;
@@ -314,7 +307,7 @@ pre > .result {
 
 /* Search */
 
-#searchresults a {
+#mdbook-searchresults a {
     text-decoration: none;
 }
 
@@ -344,13 +337,13 @@ mark.fade-out {
     max-width: var(--content-max-width);
 }
 
-#searchbar-outer.searching #searchbar {
+#mdbook-searchbar-outer.searching #mdbook-searchbar {
     padding-right: 30px;
 }
-#searchbar-outer .spinner-wrapper {
+#mdbook-searchbar-outer .spinner-wrapper {
     display: none;
 }
-#searchbar-outer.searching .spinner-wrapper {
+#mdbook-searchbar-outer.searching .spinner-wrapper {
     display: block;
 }
 
@@ -369,7 +362,21 @@ mark.fade-out {
     background-color: var(--bg);
 }
 
-#searchbar {
+#fa-spin {
+    animation: rotating 2s linear infinite;
+    display: inline-block;
+}
+
+@keyframes rotating {
+    from {
+        transform: rotate(0deg);
+    }
+    to {
+        transform: rotate(360deg);
+    }
+}
+
+#mdbook-searchbar {
     width: 100%;
     margin-block-start: var(--searchbar-margin-block-start);
     margin-block-end: 0;
@@ -382,8 +389,8 @@ mark.fade-out {
     background-color: var(--searchbar-bg);
     color: var(--searchbar-fg);
 }
-#searchbar:focus,
-#searchbar.active {
+#mdbook-searchbar:focus,
+#mdbook-searchbar.active {
     box-shadow: 0 0 3px var(--searchbar-shadow-color);
 }
 
@@ -404,19 +411,19 @@ mark.fade-out {
     border-block-end: 1px dashed var(--searchresults-border-color);
 }
 
-ul#searchresults {
+ul#mdbook-searchresults {
     list-style: none;
     padding-inline-start: 20px;
 }
-ul#searchresults li {
+ul#mdbook-searchresults li {
     margin: 10px 0px;
     padding: 2px;
     border-radius: 2px;
 }
-ul#searchresults li.focus {
+ul#mdbook-searchresults li.focus {
     background-color: var(--searchresults-li-bg);
 }
-ul#searchresults span.teaser {
+ul#mdbook-searchresults span.teaser {
     display: block;
     clear: both;
     margin-block-start: 5px;
@@ -425,7 +432,7 @@ ul#searchresults span.teaser {
     margin-inline-end: 0;
     font-size: 0.8em;
 }
-ul#searchresults span.teaser em {
+ul#mdbook-searchresults span.teaser em {
     font-weight: bold;
     font-style: normal;
 }
@@ -502,9 +509,9 @@ html:not(.sidebar-resizing) .sidebar {
     height: 16px;
     color: var(--icons);
     margin-inline-start: var(--sidebar-resize-indicator-space);
-	display: flex;
-	align-items: center;
-	justify-content: flex-start;
+    display: flex;
+    align-items: center;
+    justify-content: flex-start;
 }
 .sidebar-resize-handle .sidebar-resize-indicator::before {
     content: "";
@@ -527,11 +534,16 @@ html:not(.sidebar-resizing) .sidebar {
     cursor: col-resize;
     width: calc(var(--sidebar-resize-indicator-width) - var(--sidebar-resize-indicator-space));
 }
+
+html:not(.js) .sidebar-resize-handle {
+    display: none;
+}
+
 /* sidebar-hidden */
-#sidebar-toggle-anchor:not(:checked) ~ .sidebar {
+#mdbook-sidebar-toggle-anchor:not(:checked) ~ .sidebar {
     transform: translateX(calc(0px - var(--sidebar-width) - var(--sidebar-resize-indicator-width)));
 }
-[dir=rtl] #sidebar-toggle-anchor:not(:checked) ~ .sidebar {
+[dir=rtl] #mdbook-sidebar-toggle-anchor:not(:checked) ~ .sidebar {
     transform: translateX(calc(var(--sidebar-width) + var(--sidebar-resize-indicator-width)));
 }
 .sidebar::-webkit-scrollbar {
@@ -542,18 +554,18 @@ html:not(.sidebar-resizing) .sidebar {
 }
 
 /* sidebar-visible */
-#sidebar-toggle-anchor:checked ~ .page-wrapper {
+#mdbook-sidebar-toggle-anchor:checked ~ .page-wrapper {
     transform: translateX(calc(var(--sidebar-width) + var(--sidebar-resize-indicator-width)));
 }
-[dir=rtl] #sidebar-toggle-anchor:checked ~ .page-wrapper {
+[dir=rtl] #mdbook-sidebar-toggle-anchor:checked ~ .page-wrapper {
     transform: translateX(calc(0px - var(--sidebar-width) - var(--sidebar-resize-indicator-width)));
 }
 @media only screen and (min-width: 620px) {
-    #sidebar-toggle-anchor:checked ~ .page-wrapper {
+    #mdbook-sidebar-toggle-anchor:checked ~ .page-wrapper {
         transform: none;
         margin-inline-start: calc(var(--sidebar-width) + var(--sidebar-resize-indicator-width));
     }
-    [dir=rtl] #sidebar-toggle-anchor:checked ~ .page-wrapper {
+    [dir=rtl] #mdbook-sidebar-toggle-anchor:checked ~ .page-wrapper {
         transform: none;
     }
 }
@@ -564,17 +576,18 @@ html:not(.sidebar-resizing) .sidebar {
     line-height: 2.2em;
 }
 
-.chapter ol {
-    width: 100%;
-}
-
 .chapter li {
-    display: flex;
     color: var(--sidebar-non-existant);
 }
+
+/* This is a span wrapping the chapter link and the fold chevron. */
+.chapter-link-wrapper {
+    /* Used to position the chevron to the right, allowing the text to wrap before it. */
+    display: flex;
+}
+
 .chapter li a {
-    display: block;
-    padding: 0;
+    /* Remove underlines. */
     text-decoration: none;
     color: var(--sidebar-fg);
 }
@@ -587,21 +600,22 @@ html:not(.sidebar-resizing) .sidebar {
     color: var(--sidebar-active);
 }
 
-.chapter li > a.toggle {
+/* This is the toggle chevron. */
+.chapter-fold-toggle {
     cursor: pointer;
-    display: block;
+    /* Positions the chevron to the side. */
     margin-inline-start: auto;
     padding: 0 10px;
     user-select: none;
     opacity: 0.68;
 }
 
-.chapter li > a.toggle div {
+.chapter-fold-toggle div {
     transition: transform 0.5s;
 }
 
 /* collapse the section */
-.chapter li:not(.expanded) + li > ol {
+.chapter li:not(.expanded) > ol {
     display: none;
 }
 
@@ -610,10 +624,26 @@ html:not(.sidebar-resizing) .sidebar {
     margin-block-start: 0.6em;
 }
 
-.chapter li.expanded > a.toggle div {
+/* When expanded, rotate the chevron to point down. */
+.chapter li.expanded > span > .chapter-fold-toggle div {
     transform: rotate(90deg);
 }
 
+.chapter a.current-header {
+    color: var(--sidebar-active);
+}
+
+.on-this-page {
+    margin-left: 22px;
+    border-inline-start: 4px solid var(--sidebar-header-border-color);
+    padding-left: 8px;
+}
+
+.on-this-page > ol {
+    padding-left: 0;
+}
+
+/* Horizontal line in chapter list. */
 .spacer {
     width: 100%;
     height: 3px;
@@ -623,6 +653,7 @@ html:not(.sidebar-resizing) .sidebar {
     background-color: var(--sidebar-spacer);
 }
 
+/* On touch devices, add more vertical spacing to make it easier to tap links. */
 @media (-moz-touch-enabled: 1), (pointer: coarse) {
     .chapter li a { padding: 5px 0; }
     .spacer { margin: 10px 0; }
diff --git a/css/general.css b/css/general-2459343d.css
similarity index 65%
rename from css/general.css
rename to css/general-2459343d.css
index 9121c6f5..df8a3efe 100644
--- a/css/general.css
+++ b/css/general-2459343d.css
@@ -61,7 +61,8 @@ h2:target::before,
 h3:target::before,
 h4:target::before,
 h5:target::before,
-h6:target::before {
+h6:target::before,
+dt:target::before {
     display: inline-block;
     content: "»";
     margin-inline-start: -30px;
@@ -80,7 +81,7 @@ h6:target::before {
 .page {
     outline: 0;
     padding: 0 var(--page-padding);
-    margin-block-start: calc(0px - var(--menu-bar-height)); /* Compensate for the #menu-bar-hover-placeholder */
+    margin-block-start: calc(0px - var(--menu-bar-height)); /* Compensate for the #mdbook-menu-bar-hover-placeholder */
 }
 .page-wrapper {
     box-sizing: border-box;
@@ -155,6 +156,8 @@ blockquote {
     border-block-end: .1em solid var(--quote-border);
 }
 
+/* TODO: Remove .warning in a future version of mdbook, it is replaced by
+blockquote tags. */
 .warning {
     margin: 20px;
     padding: 0 20px;
@@ -278,3 +281,128 @@ sup {
 .result-no-output {
     font-style: italic;
 }
+
+.fa-svg svg {
+    width: 1em;
+    height: 1em;
+    fill: currentColor;
+    margin-bottom: -0.1em;
+}
+
+dt {
+    font-weight: bold;
+    margin-top: 0.5em;
+    margin-bottom: 0.1em;
+}
+
+/* This uses a CSS counter to add numbers to definitions, but only if there is
+ more than one definition. */
+dl, dt {
+    counter-reset: dd-counter;
+}
+
+/* When there is more than one definition, increment the counter. The first
+selector selects the first definition, and the second one selects definitions
+2 and beyond.*/
+dd:has(+ dd), dd + dd {
+    counter-increment: dd-counter;
+    /* Use flex display to help with positioning the numbers when there is a p
+    tag inside the definition. */
+    display: flex;
+    align-items: flex-start;
+}
+
+/* Shows the counter for definitions. The first selector selects the first
+definition, and the second one selections definitions 2 and beyond.*/
+dd:has(+ dd)::before, dd + dd::before  {
+    content: counter(dd-counter) ". ";
+    font-weight: 600;
+    display: inline-block;
+    margin-right: 0.5em;
+}
+
+dd > p {
+    /* For loose definitions that have a p tag inside, don't add a bunch of
+    space before the definition. */
+    margin-top: 0;
+}
+
+/* Remove some excess space from the bottom. */
+.blockquote-tag p:last-child {
+    margin-bottom: 2px;
+}
+
+.blockquote-tag {
+    /* Add some padding to make the vertical bar a little taller than the text.*/
+    padding: 2px 0px 2px 20px;
+    /* Add a solid color bar on the left side. */
+    border-inline-start-style: solid;
+    border-inline-start-width: 4px;
+    /* Disable the background color from normal blockquotes . */
+    background-color: inherit;
+    /* Disable border blocks from blockquotes. */
+    border-block-start: none;
+    border-block-end: none;
+}
+
+.blockquote-tag-title svg {
+    fill: currentColor;
+    /* Add space between the icon and the title. */
+    margin-right: 8px;
+}
+
+.blockquote-tag-note {
+    border-inline-start-color: var(--blockquote-note-color);
+}
+
+.blockquote-tag-tip {
+    border-inline-start-color: var(--blockquote-tip-color);
+}
+
+.blockquote-tag-important {
+    border-inline-start-color: var(--blockquote-important-color);
+}
+
+.blockquote-tag-warning {
+    border-inline-start-color: var(--blockquote-warning-color);
+}
+
+.blockquote-tag-caution {
+    border-inline-start-color: var(--blockquote-caution-color);
+}
+
+.blockquote-tag-note .blockquote-tag-title {
+    color: var(--blockquote-note-color);
+}
+
+.blockquote-tag-tip .blockquote-tag-title {
+    color: var(--blockquote-tip-color);
+}
+
+.blockquote-tag-important .blockquote-tag-title {
+    color: var(--blockquote-important-color);
+}
+
+.blockquote-tag-warning .blockquote-tag-title {
+    color: var(--blockquote-warning-color);
+}
+
+.blockquote-tag-caution .blockquote-tag-title {
+    color: var(--blockquote-caution-color);
+}
+
+.blockquote-tag-title {
+    /* Slightly increase the weight for more emphasis. */
+    font-weight: 600;
+    /* Vertically center the icon with the text. */
+    display: flex;
+    align-items: center;
+    /* Remove default large margins for a more compact display. */
+    margin: 2px 0 8px 0;
+}
+
+.blockquote-tag-title .fa-svg {
+    fill: currentColor;
+    /* Add some space between the icon and the text. */
+    margin-right: 8px;
+}
diff --git a/css/print.css b/css/print-9e4910d8.css
similarity index 87%
rename from css/print.css
rename to css/print-9e4910d8.css
index 80ec3a54..2004384d 100644
--- a/css/print.css
+++ b/css/print-9e4910d8.css
@@ -1,18 +1,18 @@
 
-#sidebar,
-#menu-bar,
+#mdbook-sidebar,
+#mdbook-menu-bar,
 .nav-chapters,
 .mobile-nav-chapters {
     display: none;
 }
 
-#page-wrapper.page-wrapper {
+#mdbook-page-wrapper.page-wrapper {
     transform: none !important;
     margin-inline-start: 0px;
     overflow-y: initial;
 }
 
-#content {
+#mdbook-content {
     max-width: none;
     margin: 0;
     padding: 0;
diff --git a/css/variables.css b/css/variables-8adf115d.css
similarity index 84%
rename from css/variables.css
rename to css/variables-8adf115d.css
index 3854b3a7..af5023bc 100644
--- a/css/variables.css
+++ b/css/variables-8adf115d.css
@@ -67,6 +67,14 @@
     --footnote-highlight: #2668a6;
 
     --overlay-bg: rgba(33, 40, 48, 0.4);
+
+    --blockquote-note-color: #74b9ff;
+    --blockquote-tip-color: #09ca09;
+    --blockquote-important-color: #d3abff;
+    --blockquote-warning-color: #f0b72f;
+    --blockquote-caution-color: #f21424;
+
+    --sidebar-header-border-color: #c18639;
 }
 
 .coal {
@@ -120,6 +128,14 @@
     --footnote-highlight: #4079ae;
 
     --overlay-bg: rgba(33, 40, 48, 0.4);
+
+    --blockquote-note-color: #4493f8;
+    --blockquote-tip-color: #08ae08;
+    --blockquote-important-color: #ab7df8;
+    --blockquote-warning-color: #d29922;
+    --blockquote-caution-color: #d91b29;
+
+    --sidebar-header-border-color: #3473ad;
 }
 
 .light, html:not(.js) {
@@ -173,6 +189,14 @@
     --footnote-highlight: #7e7eff;
 
     --overlay-bg: rgba(200, 200, 205, 0.4);
+
+    --blockquote-note-color: #0969da;
+    --blockquote-tip-color: #008000;
+    --blockquote-important-color: #8250df;
+    --blockquote-warning-color: #9a6700;
+    --blockquote-caution-color: #b52731;
+
+    --sidebar-header-border-color: #6e6edb;
 }
 
 .navy {
@@ -226,6 +250,14 @@
     --footnote-highlight: #4079ae;
 
     --overlay-bg: rgba(33, 40, 48, 0.4);
+
+    --blockquote-note-color: #4493f8;
+    --blockquote-tip-color: #09ca09;
+    --blockquote-important-color: #ab7df8;
+    --blockquote-warning-color: #d29922;
+    --blockquote-caution-color: #f21424;
+
+    --sidebar-header-border-color: #2f6ab5;
 }
 
 .rust {
@@ -277,6 +309,14 @@
     --footnote-highlight: #d3a17a;
 
     --overlay-bg: rgba(150, 150, 150, 0.25);
+
+    --blockquote-note-color: #023b95;
+    --blockquote-tip-color: #007700;
+    --blockquote-important-color: #8250df;
+    --blockquote-warning-color: #603700;
+    --blockquote-caution-color: #aa1721;
+
+    --sidebar-header-border-color: #8c391f;
 }
 
 @media (prefers-color-scheme: dark) {
@@ -327,5 +367,17 @@
         --copy-button-filter: invert(26%) sepia(8%) saturate(575%) hue-rotate(169deg) brightness(87%) contrast(82%);
         /* Same as `--sidebar-active` */
         --copy-button-filter-hover: invert(36%) sepia(70%) saturate(503%) hue-rotate(167deg) brightness(98%) contrast(89%);
+
+        --footnote-highlight: #4079ae;
+
+        --overlay-bg: rgba(33, 40, 48, 0.4);
+
+        --blockquote-note-color: #4493f8;
+        --blockquote-tip-color: #08ae08;
+        --blockquote-important-color: #ab7df8;
+        --blockquote-warning-color: #d29922;
+        --blockquote-caution-color: #d91b29;
+
+        --sidebar-header-border-color: #3473ad;
     }
 }
diff --git a/editor.js b/editor-16ca416c.js
similarity index 100%
rename from editor.js
rename to editor-16ca416c.js
diff --git a/elasticlunr.min.js b/elasticlunr-ef4e11c1.min.js
similarity index 100%
rename from elasticlunr.min.js
rename to elasticlunr-ef4e11c1.min.js
diff --git a/en/FAQ.html b/en/FAQ.html
index e0acde6c..e8d2d72c 100644
--- a/en/FAQ.html
+++ b/en/FAQ.html
@@ -12,21 +12,20 @@
         <meta name="viewport" content="width=device-width, initial-scale=1">
         <meta name="theme-color" content="#ffffff">
 
-        <link rel="icon" href="../favicon.svg">
-        <link rel="shortcut icon" href="../favicon.png">
-        <link rel="stylesheet" href="../css/variables.css">
-        <link rel="stylesheet" href="../css/general.css">
-        <link rel="stylesheet" href="../css/chrome.css">
-        <link rel="stylesheet" href="../css/print.css" media="print">
+        <link rel="icon" href="../favicon-de23e50b.svg">
+        <link rel="shortcut icon" href="../favicon-8114d1fc.png">
+        <link rel="stylesheet" href="../css/variables-8adf115d.css">
+        <link rel="stylesheet" href="../css/general-2459343d.css">
+        <link rel="stylesheet" href="../css/chrome-9dfbd86b.css">
+        <link rel="stylesheet" href="../css/print-9e4910d8.css" media="print">
 
         <!-- Fonts -->
-        <link rel="stylesheet" href="../FontAwesome/css/font-awesome.css">
-        <link rel="stylesheet" href="../fonts/fonts.css">
+        <link rel="stylesheet" href="../fonts/fonts-9644e21d.css">
 
         <!-- Highlight.js Stylesheets -->
-        <link rel="stylesheet" id="highlight-css" href="../highlight.css">
-        <link rel="stylesheet" id="tomorrow-night-css" href="../tomorrow-night.css">
-        <link rel="stylesheet" id="ayu-highlight-css" href="../ayu-highlight.css">
+        <link rel="stylesheet" id="mdbook-highlight-css" href="../highlight-493f70e1.css">
+        <link rel="stylesheet" id="mdbook-tomorrow-night-css" href="../tomorrow-night-4c0ae647.css">
+        <link rel="stylesheet" id="mdbook-ayu-highlight-css" href="../ayu-highlight-3fdfc3ac.css">
 
         <!-- Custom theme stylesheets -->
 
@@ -36,10 +35,10 @@
             const path_to_root = "../";
             const default_light_theme = "light";
             const default_dark_theme = "navy";
-            window.path_to_searchindex_js = "../searchindex.js";
+            window.path_to_searchindex_js = "../searchindex-e61e839e.js";
         </script>
         <!-- Start loading toc.js asap -->
-        <script src="../toc.js"></script>
+        <script src="../toc-6a9a54e6.js"></script>
     </head>
     <body>
     <div id="mdbook-help-container">
@@ -53,7 +52,7 @@
             </div>
         </div>
     </div>
-    <div id="body-container">
+    <div id="mdbook-body-container">
         <!-- Work around some values being stored in localStorage wrapped in quotes -->
         <script>
             try {
@@ -82,12 +81,12 @@
             html.classList.add("js");
         </script>
 
-        <input type="checkbox" id="sidebar-toggle-anchor" class="hidden">
+        <input type="checkbox" id="mdbook-sidebar-toggle-anchor" class="hidden">
 
         <!-- Hide / unhide sidebar before it is displayed -->
         <script>
             let sidebar = null;
-            const sidebar_toggle = document.getElementById("sidebar-toggle-anchor");
+            const sidebar_toggle = document.getElementById("mdbook-sidebar-toggle-anchor");
             if (document.body.clientWidth >= 1080) {
                 try { sidebar = localStorage.getItem('mdbook-sidebar'); } catch(e) { }
                 sidebar = sidebar || 'visible';
@@ -102,39 +101,39 @@
             }
         </script>
 
-        <nav id="sidebar" class="sidebar" aria-label="Table of contents">
+        <nav id="mdbook-sidebar" class="sidebar" aria-label="Table of contents">
             <!-- populated by js -->
             <mdbook-sidebar-scrollbox class="sidebar-scrollbox"></mdbook-sidebar-scrollbox>
             <noscript>
                 <iframe class="sidebar-iframe-outer" src="../toc.html"></iframe>
             </noscript>
-            <div id="sidebar-resize-handle" class="sidebar-resize-handle">
+            <div id="mdbook-sidebar-resize-handle" class="sidebar-resize-handle">
                 <div class="sidebar-resize-indicator"></div>
             </div>
         </nav>
 
-        <div id="page-wrapper" class="page-wrapper">
+        <div id="mdbook-page-wrapper" class="page-wrapper">
 
             <div class="page">
-                <div id="menu-bar-hover-placeholder"></div>
-                <div id="menu-bar" class="menu-bar sticky">
+                <div id="mdbook-menu-bar-hover-placeholder"></div>
+                <div id="mdbook-menu-bar" class="menu-bar sticky">
                     <div class="left-buttons">
-                        <label id="sidebar-toggle" class="icon-button" for="sidebar-toggle-anchor" title="Toggle Table of Contents" aria-label="Toggle Table of Contents" aria-controls="sidebar">
-                            <i class="fa fa-bars"></i>
+                        <label id="mdbook-sidebar-toggle" class="icon-button" for="mdbook-sidebar-toggle-anchor" title="Toggle Table of Contents" aria-label="Toggle Table of Contents" aria-controls="mdbook-sidebar">
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                         <ul>
 <li><a href="#introduction">Introduction</a></li>
@@ -206,18 +205,24 @@
 <p>Recent open-source LLMs—from DeepSeek-V3/R1 to Qwen-MoE and Kimi-K2—have surged in performance and scale. Yet due to <strong>compute and memory constraints</strong>, it is difficult for typical researchers to fine-tune trillion-parameter-class models. We therefore integrate <strong>KTransformers</strong> with <strong>LLaMA-Factory</strong> so that, with <strong>2–4 RTX 4090 GPUs</strong> and sufficient CPU memory, one can fine-tune ultra-large Mixture-of-Experts (MoE) models such as DeepSeek-671B.</p>
 <p>This architecture bridges resource gaps, enabling <strong>local fine-tuning of ultra-large models</strong>, while also supporting <strong>efficient scenario customization</strong> at 14B/30B scales. We validate on stylized dialogue, Westernized translation tone, and medical Q&amp;A, achieving rapid adaptation within hours.</p>
 <p>Architecturally, LLaMA-Factory orchestrates data/config/training, LoRA insertion, and inference; KTransformers is a pluggable, high-performance operator backend that takes over Attention and MoE under the same training code, enabling <strong>GPU+CPU heterogeneity</strong> to accelerate training and reduce GPU memory.</p>
-<p><img src="../../assets/image-20251011010558909.png" alt="image-20251011010558909" /></p>
+<p><img src="../../assets/image-20251011010558909.png" alt="image-20251011010558909"></p>
 <p>We evaluated LoRA fine-tuning with HuggingFace default, Unsloth, and KTransformers backends (same settings and data). <strong>KTransformers</strong> is currently the only solution feasible on <strong>2–4×24GB 4090s</strong> for <strong>671B-scale MoE</strong>, and also shows higher throughput and lower GPU memory for 14B MoEs.</p>
-<div class="table-wrapper"><table><thead><tr><th>Under LoRA (BF16) + <a href="https://github.com/mindsRiverPonder/LLM-practice">NekoQA-10K stylized dialogue</a></th><th>HuggingFace Backend</th><th>Unsloth Backend</th><th>KTransformers Backend</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>Under LoRA (BF16) + <a href="https://github.com/mindsRiverPonder/LLM-practice">NekoQA-10K stylized dialogue</a></th><th>HuggingFace Backend</th><th>Unsloth Backend</th><th>KTransformers Backend</th></tr>
+</thead>
+<tbody>
 <tr><td>[14B-DeepSeekV2-Lite] LoRA fine-tuning throughput</td><td>303.58 token/s</td><td>455.37 token/s</td><td>530.38 token/s</td></tr>
 <tr><td>[14B-DeepSeekV2-Lite] GPU memory</td><td>32.12 GB</td><td>9.64 GB</td><td>6.08 GB</td></tr>
-<tr><td>[671B-DeepSeekV3] LoRA fine-tuning throughput</td><td><font color='red'>Too Huge to run</font></td><td><font color='red'>NOT SUPPORT</font></td><td>40.35 token/s</td></tr>
-<tr><td>[671B-DeepSeekV3] GPU memory (sum across GPUs)</td><td>theoretical 1400 GB †</td><td><font color='red'>NOT SUPPORT</font></td><td>70 GB †</td></tr>
-</tbody></table>
+<tr><td>[671B-DeepSeekV3] LoRA fine-tuning throughput</td><td><font color="red">Too Huge to run</font></td><td><font color="red">NOT SUPPORT</font></td><td>40.35 token/s</td></tr>
+<tr><td>[671B-DeepSeekV3] GPU memory (sum across GPUs)</td><td>theoretical 1400 GB †</td><td><font color="red">NOT SUPPORT</font></td><td>70 GB †</td></tr>
+</tbody>
+</table>
 </div>
 <p>† The <strong>1400 GB</strong> is the <strong>theoretical</strong> FP16 full-resident footprint (not runnable). <strong>70 GB</strong> is the <strong>measured peak</strong> with KT (Attention on GPU + layered MoE offload).</p>
 <p>From the table above, it can be seen that for the 14B model, the KTransformers backend achieves approximately 75% higher throughput than the default HuggingFace solution, while using only about one-fifth of the GPU memory. For the 671B model, both HuggingFace and Unsloth fail to run on a single 4090 GPU, whereas KTransformers is able to perform LoRA fine-tuning at 40 tokens/s, keeping the GPU memory usage within 70 GB.</p>
-<p><img src="../../assets/image-compare_model.png" alt="按照模型划分的对比图_02" /></p>
+<p><img src="../../assets/image-compare_model.png" alt="按照模型划分的对比图_02"></p>
 <h2 id="overall-view-of-the-kt-fine-tuning-framework"><a class="header" href="#overall-view-of-the-kt-fine-tuning-framework">Overall View of the KT Fine-Tuning Framework</a></h2>
 <p>We detail how KTransformers takes over core operators in LLaMA-Factory’s fine-tuning framework to optimize Attention and MoE.</p>
 <p>DeepSeek-V3/V2 MoE models comprise a small-parameter dense Attention part and a large-parameter sparse MoE part. For illustration, consider layer 2 of DeepSeek-V2-Lite-Chat (from which each layer includes both Attention and MoE). Attention compute and KV cache mainly reside on the GPU; the heavyweight MoE part is primarily executed on the CPU. We first cover <strong>Attention replacement and inheritance</strong>, then <strong>MoE encapsulation and backend interfacing</strong>, and finally <strong>multi-GPU placement</strong>.</p>
@@ -227,9 +232,9 @@
 <li><strong>Inheritance:</strong> <code>KTransformersLinearLora</code> retains KT’s high-performance paths (<code>prefill_linear</code>/<code>generate_linear</code>) while accepting LoRA parameters (<code>lora_A/lora_B</code>).</li>
 <li><strong>Replacement:</strong> During preparation, we replace original <code>KTransformersLinear</code> layers (Q/K/V/O) with <code>KTransformersLinearLora</code>, preserving KT optimizations while enabling LoRA trainability.</li>
 </ul>
-<p><img src="../../assets/image-20251016182810716.png" alt="image-20251016182810716" /></p>
+<p><img src="../../assets/image-20251016182810716.png" alt="image-20251016182810716"></p>
 <p>After replacement, LoRA is inserted at Q/K/V/O linear transforms (left), and <code>KTransformersLinearLora</code> contains both KT fast paths and LoRA matrices (right).</p>
-<p><img src="../../assets/image-20251016182920722.png" alt="image-20251016182920722" /></p>
+<p><img src="../../assets/image-20251016182920722.png" alt="image-20251016182920722"></p>
 <h3 id="moe-operator-encapsulation--backward"><a class="header" href="#moe-operator-encapsulation--backward">MoE (operator encapsulation + backward)</a></h3>
 <h4 id="encapsulation"><a class="header" href="#encapsulation">Encapsulation</a></h4>
 <p>Given large parameters and sparse compute, we encapsulate the expert computation as a <strong>differentiable black-box operator</strong>—transparent upstream, replaceable downstream.</p>
@@ -237,7 +242,7 @@
 <li><strong>Upstream (PyTorch graph):</strong> we register a custom Autograd Function so the MoE layer appears as <strong>a single node</strong>. In the left figure (red box), only <code>KSFTExpertsCPU</code> is visible; on the right, the unencapsulated graph expands routing, dispatch, and FFN experts. Encapsulation makes the MoE layer behave like a standard <code>nn.Module</code> with gradients.</li>
 <li><strong>Downstream (backend):</strong> inside the Autograd Function, pybind11 calls C++ extensions for forward/backward. Multiple <strong>pluggable backends</strong> exist (AMX BF16/INT8; <strong>llamafile</strong>). The backend can be switched via YAML (e.g., <code>"backend": "AMXBF16"</code> vs. <code>"llamafile"</code>).</li>
 </ul>
-<p><img src="../../assets/image-20250801174623919.png" alt="image-20250801174623919" /></p>
+<p><img src="../../assets/image-20250801174623919.png" alt="image-20250801174623919"></p>
 <h4 id="backward-cpu"><a class="header" href="#backward-cpu">Backward (CPU)</a></h4>
 <p>MoE backward frequently needs the transposed weights $W^\top$. To avoid repeated runtime transposes, we <strong>precompute/cache</strong> $W^\top$ at load time (blue box). We also <strong>cache necessary intermediate activations</strong> (e.g., expert projections, red box) to reuse in backward and reduce recomputation. We provide backward implementations for <strong>llamafile</strong> and <strong>AMX (INT8/BF16)</strong>, with NUMA-aware optimizations.</p>
 <img src="../../assets/image-20251016182942726.png" alt="image-20251016182942726" style="zoom:33%;" />
@@ -256,31 +261,50 @@
 <h3 id="results"><a class="header" href="#results">Results</a></h3>
 <h4 id="stylized-dialogue-catgirl-tone"><a class="header" href="#stylized-dialogue-catgirl-tone">Stylized Dialogue (CatGirl tone)</a></h4>
 <p>Dataset: <a href="https://zhuanlan.zhihu.com/p/1934983798233231689">NekoQA-10K</a>. The fine-tuned model consistently exhibits the target style (red boxes) versus neutral/rational base (blue). This shows <strong>KT-LoRA injects style features</strong> into the generation distribution with low GPU cost.</p>
-<p><img src="../../assets/image-20251016175848143.png" alt="image-20251016175848143" /></p>
+<p><img src="../../assets/image-20251016175848143.png" alt="image-20251016175848143"></p>
 <h4 id="translational-style-benchmark-generative"><a class="header" href="#translational-style-benchmark-generative">Translational-Style benchmark (generative)</a></h4>
 <p>Dataset: <a href="https://github.com/Benson114/Translational-Style-ChatLLM">Translational-Style-ChatLLM</a>. Metrics: BLEU-1/2/3/4, ROUGE-1/2/L.</p>
-<div class="table-wrapper"><table><thead><tr><th>Translational-Style dataset</th><th>BLEU-1</th><th>BLEU-2</th><th>BLEU-3</th><th>BLEU-4</th><th>ROUGE-1</th><th>ROUGE-2</th><th>ROUGE-L</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>Translational-Style dataset</th><th>BLEU-1</th><th>BLEU-2</th><th>BLEU-3</th><th>BLEU-4</th><th>ROUGE-1</th><th>ROUGE-2</th><th>ROUGE-L</th></tr>
+</thead>
+<tbody>
 <tr><td>V2-Lite (no LoRA)</td><td>20.66</td><td>8.33</td><td>4.54</td><td>2.89</td><td>22.71</td><td>4.52</td><td>19.19</td></tr>
 <tr><td><strong>KT-LoRA fine-tuned V2-Lite</strong></td><td><strong>35.41</strong></td><td><strong>22.44</strong></td><td><strong>15.42</strong></td><td><strong>11.18</strong></td><td><strong>42.03</strong></td><td><strong>18.38</strong></td><td><strong>33.10</strong></td></tr>
 <tr><td>V3 base (no LoRA)</td><td>8.49</td><td>3.34</td><td>1.62</td><td>0.96</td><td>15.91</td><td>2.55</td><td>10.07</td></tr>
 <tr><td><strong>KT-LoRA fine-tuned V3</strong></td><td><strong>37.02</strong></td><td><strong>23.70</strong></td><td><strong>16.21</strong></td><td><strong>11.49</strong></td><td><strong>43.43</strong></td><td><strong>18.96</strong></td><td><strong>34.54</strong></td></tr>
-</tbody></table>
+</tbody>
+</table>
 </div>
 <p>As shown by the test results in the tables above, under a unified workflow and placement strategy, <strong>both model scales exhibit consistent gains after fine-tuning</strong>, supporting the usability and effectiveness of the “KT backend + LoRA fine-tuning” combination for generative style control. At the same time, this indicates that KT’s heterogeneous placement and operator optimizations can stably support small-sample adaptation in the style domain.</p>
 <h4 id="medical-vertical-benchmark-afrimed-saqmcq"><a class="header" href="#medical-vertical-benchmark-afrimed-saqmcq">Medical Vertical Benchmark (AfriMed-SAQ/MCQ)</a></h4>
 <p>The dataset adopts <a href="https://aclanthology.org/2025.acl-long.96/">AfriMed-QA</a> (ACL 2025), a domain-specific dataset for the medical field in Africa with strong scenario customization characteristics, comprising two formats—multiple-choice questions (MCQ) and short-answer questions (SAQ)—which in this case serve as the evaluation for vertical-domain fine-tuning. In terms of evaluation criteria, BLEU/ROUGE are used for SAQ, and Accuracy is used for MCQ.</p>
-<div class="table-wrapper"><table><thead><tr><th>AfriMed-QA (SAQ)</th><th>BLEU-1</th><th>BLEU-2</th><th>BLEU-3</th><th>BLEU-4</th><th>ROUGE-1</th><th>ROUGE-2</th><th>ROUGE-L</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>AfriMed-QA (SAQ)</th><th>BLEU-1</th><th>BLEU-2</th><th>BLEU-3</th><th>BLEU-4</th><th>ROUGE-1</th><th>ROUGE-2</th><th>ROUGE-L</th></tr>
+</thead>
+<tbody>
 <tr><td>V2-Lite (no LoRA)</td><td>13.58</td><td>11.12</td><td>9.10</td><td>7.23</td><td>22.48</td><td>7.81</td><td>11.73</td></tr>
 <tr><td><strong>KT-LoRA fine-tuned V2-Lite</strong></td><td><strong>35.90</strong></td><td><strong>27.63</strong></td><td><strong>22.99</strong></td><td><strong>19.15</strong></td><td><strong>35.25</strong></td><td><strong>17.50</strong></td><td><strong>28.44</strong></td></tr>
 <tr><td>V3 base (no LoRA)</td><td>12.75</td><td>10.27</td><td>8.05</td><td>5.99</td><td>20.33</td><td>5.65</td><td>10.11</td></tr>
 <tr><td><strong>KT-LoRA fine-tuned V3</strong></td><td><strong>42.42</strong></td><td><strong>34.12</strong></td><td><strong>28.95</strong></td><td><strong>24.54</strong></td><td><strong>41.97</strong></td><td><strong>22.37</strong></td><td><strong>33.28</strong></td></tr>
-</tbody></table>
-</div><div class="table-wrapper"><table><thead><tr><th>AfriMed-QA (MCQ)</th><th>Accuracy</th></tr></thead><tbody>
+</tbody>
+</table>
+</div>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>AfriMed-QA (MCQ)</th><th>Accuracy</th></tr>
+</thead>
+<tbody>
 <tr><td>V2-Lite (no LoRA)</td><td>0.0645</td></tr>
 <tr><td><strong>KT-LoRA fine-tuned V2-Lite</strong></td><td><strong>0.4812</strong></td></tr>
 <tr><td>V3 base (no LoRA)</td><td>0.5833</td></tr>
 <tr><td><strong>KT-LoRA fine-tuned V3</strong></td><td><strong>0.7930</strong></td></tr>
-</tbody></table>
+</tbody>
+</table>
 </div>
 <p>As shown in the tables above, (1) DeepSeek-V3 (671B) after KT-LoRA fine-tuning achieves clearly higher performance than the fine-tuned DeepSeek-V2-Lite (14B) on both MCQ and SAQ, and it also surpasses the V3 base model. Within our small-scale setting, this preliminarily indicates that KT-LoRA fine-tuning of ultra-large-parameter models has practical significance in vertical domains.</p>
 <p>(2) Across both SAQ/MCQ sub-tasks, KT-LoRA delivers consistent gains, indicating that—with KT’s heterogeneous placement and backend operator support—LoRA fine-tuning can effectively inject the key knowledge points of vertical domains such as medicine into the model.</p>
@@ -293,10 +317,16 @@
 <p><code>step_time</code>：time per optimization step (tensor movement + Attention + MoE + others).</p>
 <p><code>tokens_per_step = GAS × qlen</code>；<code>token/s = tokens_per_step / step_time</code>。 We use <code>GAS=16</code>, <code>qlen=512</code> → <code>tokens_per_step=8192</code>.</p>
 <p><strong>Measured</strong></p>
-<div class="table-wrapper"><table><thead><tr><th>Model</th><th>step_time (s)</th><th>tokens/step</th><th>token/s</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>Model</th><th>step_time (s)</th><th>tokens/step</th><th>token/s</th></tr>
+</thead>
+<tbody>
 <tr><td>DeepSeek-V3-671B</td><td>203</td><td>8192</td><td><strong>40.35</strong></td></tr>
 <tr><td>DeepSeek-V2-Lite-14B</td><td>36</td><td>8192</td><td><strong>227.6</strong></td></tr>
-</tbody></table>
+</tbody>
+</table>
 </div>
 <h4 id="moe-compute-deepseek-v3-671b"><a class="header" href="#moe-compute-deepseek-v3-671b">MoE Compute (DeepSeek-V3-671B)</a></h4>
 <p><strong>Theory</strong></p>
@@ -317,9 +347,15 @@ $$</li>
 <p><strong>Measured (MoE TFLOPS on CPU)</strong></p>
 <p>If the <strong>MoE-only</strong> time per step is <code>t_moe</code> (seconds), $\text{TFLOPS} = \text{FLOPs}_{\text{per-step}} / \text{step_per_second}.$</p>
 <p>Use MoE-phase time, not full <code>step_time</code>, to get MoE throughput.</p>
-<div class="table-wrapper"><table><thead><tr><th>TFLOPS</th><th>Forward</th><th>Backward</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>TFLOPS</th><th>Forward</th><th>Backward</th></tr>
+</thead>
+<tbody>
 <tr><td>Average</td><td>17.55</td><td>18.41</td></tr>
-</tbody></table>
+</tbody>
+</table>
 </div>
 <h3 id="memory-footprint"><a class="header" href="#memory-footprint">Memory Footprint</a></h3>
 <ul>
@@ -335,11 +371,11 @@ $$</li>
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                         <ul>
 <li><a href="#introduction">Introduction</a>
@@ -209,45 +208,71 @@
 <p>From <strong>DeepSeek-V3/R1</strong> to <strong>Qwen3-MoE</strong> and <strong>Kimi-K2</strong>, each wave of open-sourced large models brings leaps in performance and scale. However, many researchers and developers are constrained by expensive GPUs and models with tens or even hundreds of billions of parameters, making it <strong>hard to fine-tune very large models under limited resources</strong>. To bridge this gap, we propose a practical approach: combining <strong>KTransformers</strong> with <strong>LLaMA-Factory</strong>. With just <strong>2–4 RTX 4090s</strong> and a high-memory CPU, you can fine-tune ultra-large MoE models like DeepSeek-671B.</p>
 <p>Our goal is to give resource-constrained researchers a <strong>local path to explore fine-tuning ultra-large models</strong>, and also a fast way to customize smaller models (e.g., 14B/30B) for specific scenarios. We validate the setup using <strong>stylized dialogue</strong>, <strong>Westernized translation tone</strong>, and <strong>medical Q&amp;A</strong> as representative tasks, showing that <strong>personalized adaptation can be achieved within hours</strong>.</p>
 <p>As shown below, LLaMA-Factory is the unified orchestration/configuration layer for the whole fine-tuning workflow—handling data, training scheduling, LoRA injection, and inference interfaces. <strong>KTransformers</strong> acts as a pluggable high-performance backend that takes over core operators like Attention/MoE under the same training configs, enabling efficient <strong>GPU+CPU heterogeneous cooperation</strong>.</p>
-<p><img src="../../assets/image-20251011010558909.png" alt="image-20251011010558909" /></p>
+<p><img src="../../assets/image-20251011010558909.png" alt="image-20251011010558909"></p>
 <p>Within LLaMA-Factory, we compared LoRA fine-tuning with <strong>HuggingFace</strong>, <strong>Unsloth</strong>, and <strong>KTransformers</strong> backends. KTransformers is the <strong>only workable 4090-class solution</strong> for ultra-large MoE models (e.g., 671B) and also delivers higher throughput and lower GPU memory on smaller MoE models (e.g., DeepSeek-14B).</p>
-<div class="table-wrapper"><table><thead><tr><th>Under LoRA (BF16) + <a href="https://github.com/mindsRiverPonder/LLM-practice">NekoQA-10K stylized dialogue</a></th><th>HuggingFace Backend</th><th>Unsloth Backend</th><th>KTransformers Backend</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>Under LoRA (BF16) + <a href="https://github.com/mindsRiverPonder/LLM-practice">NekoQA-10K stylized dialogue</a></th><th>HuggingFace Backend</th><th>Unsloth Backend</th><th>KTransformers Backend</th></tr>
+</thead>
+<tbody>
 <tr><td>[14B-DeepSeekV2-Lite] LoRA fine-tuning throughput</td><td>303.58 token/s</td><td>455.37 token/s</td><td>530.38 token/s</td></tr>
 <tr><td>[14B-DeepSeekV2-Lite] GPU memory</td><td>32.12 GB</td><td>9.64 GB</td><td>6.08 GB</td></tr>
-<tr><td>[671B-DeepSeekV3] LoRA fine-tuning throughput</td><td><font color='red'>Too Huge to run</font></td><td><font color='red'>NOT SUPPORT</font></td><td>40.35 token/s</td></tr>
-<tr><td>[671B-DeepSeekV3] GPU memory (sum across GPUs)</td><td>theoretical 1400 GB †</td><td><font color='red'>NOT SUPPORT</font></td><td>70 GB †</td></tr>
-</tbody></table>
+<tr><td>[671B-DeepSeekV3] LoRA fine-tuning throughput</td><td><font color="red">Too Huge to run</font></td><td><font color="red">NOT SUPPORT</font></td><td>40.35 token/s</td></tr>
+<tr><td>[671B-DeepSeekV3] GPU memory (sum across GPUs)</td><td>theoretical 1400 GB †</td><td><font color="red">NOT SUPPORT</font></td><td>70 GB †</td></tr>
+</tbody>
+</table>
 </div>
 <p>† <strong>1400 GB</strong> is a <strong>theoretical</strong> FP16 full-parameter resident footprint (not runnable). <strong>70 GB</strong> is the <strong>measured peak</strong> with KT strategy (Attention on GPU + layered MoE offload).</p>
-<p><img src="../../assets/image-compare_model.png" alt="按照模型划分的对比图_02" /></p>
+<p><img src="../../assets/image-compare_model.png" alt="按照模型划分的对比图_02"></p>
 <h3 id="fine-tuning-results-examples"><a class="header" href="#fine-tuning-results-examples">Fine-Tuning Results (Examples)</a></h3>
 <h4 id="stylized-dialogue-catgirl-tone"><a class="header" href="#stylized-dialogue-catgirl-tone">Stylized Dialogue (CatGirl tone)</a></h4>
 <p>Dataset: <a href="https://zhuanlan.zhihu.com/p/1934983798233231689">NekoQA-10K</a>. Goal: improve style consistency and recognizability.</p>
 <p>The figure compares responses from the base vs. fine-tuned models. The fine-tuned model maintains the target tone and address terms more consistently (red boxes), validating the effectiveness of <strong>style-transfer fine-tuning</strong>.</p>
-<p><img src="../../assets/image-20251016175046882.png" alt="image-20251016175046882" /></p>
+<p><img src="../../assets/image-20251016175046882.png" alt="image-20251016175046882"></p>
 <h4 id="benchmarks"><a class="header" href="#benchmarks">Benchmarks</a></h4>
 <p>We use:</p>
 <p>(1) <a href="https://github.com/Benson114/Translational-Style-ChatLLM">Translational-Style-ChatLLM</a>, which asks for an exaggerated, Westernized translation tone—clear, stylized customization.</p>
 <p>(2) <a href="https://aclanthology.org/2025.acl-long.96/">AfriMed-QA</a> (ACL 2025), a medical dataset for African contexts with strong domain specificity, including multiple-choice and short-answer sub-tasks—well-suited for vertical fine-tuning evaluation.</p>
 <p>The tables show metrics before vs. after LoRA fine-tuning. We observe <strong>large improvements</strong> across metrics, verifying fine-tuning effectiveness:</p>
-<div class="table-wrapper"><table><thead><tr><th>Translational-Style dataset</th><th>BLEU-1</th><th>BLEU-2</th><th>BLEU-3</th><th>BLEU-4</th><th>ROUGE-1</th><th>ROUGE-2</th><th>ROUGE-L</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>Translational-Style dataset</th><th>BLEU-1</th><th>BLEU-2</th><th>BLEU-3</th><th>BLEU-4</th><th>ROUGE-1</th><th>ROUGE-2</th><th>ROUGE-L</th></tr>
+</thead>
+<tbody>
 <tr><td>V2-Lite (no LoRA)</td><td>20.66</td><td>8.33</td><td>4.54</td><td>2.89</td><td>22.71</td><td>4.52</td><td>19.19</td></tr>
 <tr><td><strong>KT-LoRA fine-tuned V2-Lite</strong></td><td><strong>35.41</strong></td><td><strong>22.44</strong></td><td><strong>15.42</strong></td><td><strong>11.18</strong></td><td><strong>42.03</strong></td><td><strong>18.38</strong></td><td><strong>33.10</strong></td></tr>
 <tr><td>V3 base (no LoRA)</td><td>8.49</td><td>3.34</td><td>1.62</td><td>0.96</td><td>15.91</td><td>2.55</td><td>10.07</td></tr>
 <tr><td><strong>KT-LoRA fine-tuned V3</strong></td><td><strong>37.02</strong></td><td><strong>23.70</strong></td><td><strong>16.21</strong></td><td><strong>11.49</strong></td><td><strong>43.43</strong></td><td><strong>18.96</strong></td><td><strong>34.54</strong></td></tr>
-</tbody></table>
-</div><div class="table-wrapper"><table><thead><tr><th>AfriMed-QA (short answer)</th><th>BLEU-1</th><th>BLEU-2</th><th>BLEU-3</th><th>BLEU-4</th><th>ROUGE-1</th><th>ROUGE-2</th><th>ROUGE-L</th></tr></thead><tbody>
+</tbody>
+</table>
+</div>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>AfriMed-QA (short answer)</th><th>BLEU-1</th><th>BLEU-2</th><th>BLEU-3</th><th>BLEU-4</th><th>ROUGE-1</th><th>ROUGE-2</th><th>ROUGE-L</th></tr>
+</thead>
+<tbody>
 <tr><td>V2-Lite (no LoRA)</td><td>13.58</td><td>11.12</td><td>9.10</td><td>7.23</td><td>22.48</td><td>7.81</td><td>11.73</td></tr>
 <tr><td><strong>KT-LoRA fine-tuned V2-Lite</strong></td><td><strong>35.90</strong></td><td><strong>27.63</strong></td><td><strong>22.99</strong></td><td><strong>19.15</strong></td><td><strong>35.25</strong></td><td><strong>17.50</strong></td><td><strong>28.44</strong></td></tr>
 <tr><td>V3 base (no LoRA)</td><td>12.75</td><td>10.27</td><td>8.05</td><td>5.99</td><td>20.33</td><td>5.65</td><td>10.11</td></tr>
 <tr><td><strong>KT-LoRA fine-tuned V3</strong></td><td><strong>42.42</strong></td><td><strong>34.12</strong></td><td><strong>28.95</strong></td><td><strong>24.54</strong></td><td><strong>41.97</strong></td><td><strong>22.37</strong></td><td><strong>33.28</strong></td></tr>
-</tbody></table>
-</div><div class="table-wrapper"><table><thead><tr><th>AfriMed-QA (multiple choice)</th><th>Accuracy</th></tr></thead><tbody>
+</tbody>
+</table>
+</div>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>AfriMed-QA (multiple choice)</th><th>Accuracy</th></tr>
+</thead>
+<tbody>
 <tr><td>V2-Lite (no LoRA)</td><td>0.0645</td></tr>
 <tr><td><strong>KT-LoRA fine-tuned V2-Lite</strong></td><td><strong>0.4812</strong></td></tr>
 <tr><td>V3 base (no LoRA)</td><td>0.5833</td></tr>
 <tr><td><strong>KT-LoRA fine-tuned V3</strong></td><td><strong>0.7930</strong></td></tr>
-</tbody></table>
+</tbody>
+</table>
 </div>
 <p>Even for ultra-large MoE models, <strong>KTransformers-backed fine-tuning</strong> achieves strong task performance quickly.</p>
 <h2 id="quick-to-start"><a class="header" href="#quick-to-start">Quick to Start</a></h2>
@@ -337,12 +362,18 @@ cpu_infer: 32
 chunk_size: 8192
 </code></pre>
 <p><code>kt_optimize_rule</code> controls <strong>placement strategy</strong>. See also <a href="https://github.com/kvcache-ai/ktransformers/tree/main/ktransformers/optimize/optimize_rules">ktransformers/optimize_rules</a>. Naming hints (<code>*</code> = wildcard):</p>
-<div class="table-wrapper"><table><thead><tr><th>Pattern</th><th>Meaning</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>Pattern</th><th>Meaning</th></tr>
+</thead>
+<tbody>
 <tr><td>DeepSeek-V2-Lite-Chat-* / DeepSeek-V3-Chat-*</td><td>Target model variants</td></tr>
 <tr><td><em>-sft-</em></td><td>Strategy for fine-tuning; others are for inference</td></tr>
 <tr><td><em>-amx-</em></td><td>Use AMX on CPU; otherwise use <strong>llamafile</strong></td></tr>
 <tr><td><em>-multi-gpu-X</em></td><td>Model parallel on X GPUs (X omitted → default 2 GPUs)</td></tr>
-</tbody></table>
+</tbody>
+</table>
 </div>
 <p>Example: <code>DeepSeek-V3-Chat-sft-amx-multi-gpu.yaml</code> = V3-Chat fine-tuning with AMX and 2-GPU model parallel.</p>
 <p>We recommend <strong>AMX acceleration</strong> where available (<code>lscpu | grep amx</code>). AMX supports BF16/INT8. Example:</p>
@@ -359,7 +390,7 @@ chunk_size: 8192
       backend: "AMXInt8" # or "AMXBF16" or "llamafile" (default)
 </code></pre>
 <p>Outputs go to <code>output_dir</code> in safetensors format plus adapter metadata for later loading.</p>
-<p><img src="../../assets/image-20251016171537997.png" alt="image-20251016171537997" /></p>
+<p><img src="../../assets/image-20251016171537997.png" alt="image-20251016171537997"></p>
 <h3 id="core-feature-2-chat-with-the-fine-tuned-model-base--lora-adapter"><a class="header" href="#core-feature-2-chat-with-the-fine-tuned-model-base--lora-adapter">Core Feature 2: Chat with the fine-tuned model (base + LoRA adapter)</a></h3>
 <p>Run the command: <code>llamafactory-cli chat examples/inference/deepseek3_lora_sft_kt.yaml</code>.</p>
 <p>Use the safetensors adapter trained with KT for inference.</p>
@@ -376,7 +407,7 @@ chunk_size: 8192
 </code></pre>
 <p>We also support <strong>GGUF</strong> adapters: for safetensors, set the <strong>directory</strong>; for GGUF, set the <strong>file path</strong> in <code>adapter_name_or_path</code>.</p>
 <p>During loading, LLaMA-Factory maps layer names to KT’s naming. You’ll see logs like <code>Loaded adapter weight: XXX -&gt; XXX</code>:</p>
-<p><img src="../../assets/image-20251016171526210.png" alt="image-20251016171526210" /></p>
+<p><img src="../../assets/image-20251016171526210.png" alt="image-20251016171526210"></p>
 <h3 id="core-feature-3-batch-inference--metrics-base--lora-adapter"><a class="header" href="#core-feature-3-batch-inference--metrics-base--lora-adapter">Core Feature 3: Batch inference + metrics (base + LoRA adapter)</a></h3>
 <p>Run the command: <code>API_PORT=8000 llamafactory-cli api examples/inference/deepseek3_lora_sft_kt.yaml</code>.
 Invoke the KT fine-tuned adapter to provide the API; the usage logic of other APIs is consistent with the native LLaMA-Factory approach.</p>
@@ -418,11 +449,11 @@ chunk_size: 8192
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                         <h1 id="tutorial-inject-operator-step-by-step"><a class="header" href="#tutorial-inject-operator-step-by-step">Tutorial: Inject Operator Step by Step</a></h1>
 <blockquote>
@@ -232,19 +231,26 @@
 <p>Fortunately, knowing the structure of a model is very simple. Open the file list on the <a href="https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite-Chat/tree/main">deepseek-ai/DeepSeek-V2-Lite</a> homepage, and you can see the following files:</p>
 <p align="center">
   <picture>
-    <img alt="Inject-Struction" src="../../assets/model_structure_guild.png" width=60%>
+    <img alt="Inject-Struction" src="../../assets/model_structure_guild.png" width="60%">
   </picture>
 </p>
+
 <p>From the <code>.saftensors</code> file, we can see the name of each layer’s weights, corresponding to the match.name attribute in the injection rules.
 From the <code>modeling_deepseek.py</code> file, we can see the specific implementation of each module class, with the class name corresponding to the match.class attribute in the injection rules.</p>
 <p>The structure of the DeepSeekV2 model from the <code>.saftensors</code> and <code>modeling_deepseek.py</code> files is as follows:</p>
 <p align="center">
   <picture>
-    <img alt="Inject-Struction" src="../../assets/deepseekv2_structure.png" width=60%>
+    <img alt="Inject-Struction" src="../../assets/deepseekv2_structure.png" width="60%">
   </picture>
 </p>
+
 <p>Supported operators and their corresponding classes are as follows:</p>
-<div class="table-wrapper"><table><thead><tr><th>match</th><th>replace</th><th>backends</th><th>descriptions</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>match</th><th>replace</th><th>backends</th><th>descriptions</th></tr>
+</thead>
+<tbody>
 <tr><td>Linear</td><td>KTransformersLinear</td><td>KLinearMarlin</td><td>Marlin as backend</td></tr>
 <tr><td></td><td></td><td>KLinearTorch</td><td>pytorch as backend</td></tr>
 <tr><td></td><td></td><td>KLinearCPUInfer</td><td>llamafile as backend</td></tr>
@@ -259,7 +265,8 @@ From the <code>modeling_deepseek.py</code> file, we can see the specific impleme
 <tr><td></td><td>KDeepseekV2Model</td><td>KDeepseekV2Model</td><td>Model for DeepseekV2</td></tr>
 <tr><td>RoPE</td><td>RotaryEmbedding</td><td>RotaryEmbedding</td><td>RoPE module</td></tr>
 <tr><td></td><td>YarnRotaryEmbedding</td><td>YarnRotaryEmbedding</td><td>RoPE module</td></tr>
-</tbody></table>
+</tbody>
+</table>
 </div>
 <p>Then we start step-by-step injection of custom modules, our targets are:</p>
 <ul>
@@ -338,9 +345,10 @@ From the <code>modeling_deepseek.py</code> file, we can see the specific impleme
 DeepseekV2-Chat got 60 layers, if we got 2 GPUs, we can allocate 30 layers to each GPU. Complete multi GPU rule examples <a href="https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/optimize/optimize_rules/DeepSeek-V2-Chat-multi-gpu.yaml">here</a>.</p>
 <p align="center">
   <picture>
-    <img alt="Inject-Struction" src="../../assets/multi_gpu.png" width=60%>
+    <img alt="Inject-Struction" src="../../assets/multi_gpu.png" width="60%">
   </picture>
 </p>
+
 <p>First of all, for multi-GPU, we have to inject an new operator <code>KDeepseekV2Model</code>. And set division of the layers to different GPUs. For our case, we have to set the <code>transfer_map</code> in the <code>KDeepseekV2Model</code> operatoras as follows:</p>
 <pre><code class="language-yaml">- match:
     name: "^model$"
@@ -484,11 +492,11 @@ DeepseekV2-Chat got 60 layers, if we got 2 GPUs, we can allocate 30 layers to ea
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@@ -498,16 +506,21 @@ DeepseekV2-Chat got 60 layers, if we got 2 GPUs, we can allocate 30 layers to ea
 
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@@ -515,19 +528,19 @@ DeepseekV2-Chat got 60 layers, if we got 2 GPUs, we can allocate 30 layers to ea
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index 387e544c..dc295722 100644
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@@ -12,21 +12,20 @@
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                         <h1 id="kt-kernel"><a class="header" href="#kt-kernel">KT-Kernel</a></h1>
 <p>High-performance kernel operations for KTransformers, featuring CPU-optimized MoE inference with AMX, AVX, KML and blis (amd library) support.</p>
@@ -219,8 +218,8 @@
 </li>
 <li><a href="#weight-quantization">Weight Quantization</a>
 <ul>
-<li><a href="#cpu-weights-for-cold-experts-on-cpu">CPU Weights (for "cold" experts on CPU)</a></li>
-<li><a href="#gpu-weights-for-hot-experts-on-gpu">GPU Weights (for "hot" experts on GPU)</a></li>
+<li><a href="#cpu-weights-for-cold-experts-on-cpu">CPU Weights (for “cold” experts on CPU)</a></li>
+<li><a href="#gpu-weights-for-hot-experts-on-gpu">GPU Weights (for “hot” experts on GPU)</a></li>
 </ul>
 </li>
 <li><a href="#before-commit">Before Commit!</a></li>
@@ -290,7 +289,7 @@ export CPUINFER_ENABLE_AMX=OFF       # Options: ON, OFF
 <pre><code class="language-bash">python -c "from kt_kernel import KTMoEWrapper; print('✓ kt-kernel installed successfully')"
 </code></pre>
 <h2 id="integration-with-sglang"><a class="header" href="#integration-with-sglang">Integration with SGLang</a></h2>
-<p>KT-Kernel can be used standalone via <a href="#direct-python-api-usage">Direct Python API</a> or integrated with SGLang for production deployment. This section describes SGLang integration to enable CPU-GPU heterogeneous inference, where "hot" experts run on GPU and "cold" experts run on CPU for optimal resource utilization.</p>
+<p>KT-Kernel can be used standalone via <a href="#direct-python-api-usage">Direct Python API</a> or integrated with SGLang for production deployment. This section describes SGLang integration to enable CPU-GPU heterogeneous inference, where “hot” experts run on GPU and “cold” experts run on CPU for optimal resource utilization.</p>
 <h3 id="installation-steps"><a class="header" href="#installation-steps">Installation Steps</a></h3>
 <h4 id="1-install-sglang"><a class="header" href="#1-install-sglang">1. Install SGLang</a></h4>
 <pre><code class="language-bash">git clone https://github.com/sgl-project/sglang.git
@@ -395,14 +394,20 @@ hf download Qwen/Qwen3-30B-A3B --local-dir /mnt/data/models/Qwen3-30B-A3B
   --kt-max-deferred-experts-per-token 2
 </code></pre>
 <h3 id="kt-kernel-parameters"><a class="header" href="#kt-kernel-parameters">KT-Kernel Parameters</a></h3>
-<div class="table-wrapper"><table><thead><tr><th>Parameter</th><th>Description</th><th>Example Value</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>Parameter</th><th>Description</th><th>Example Value</th></tr>
+</thead>
+<tbody>
 <tr><td><code>--kt-method</code></td><td>CPU inference backend method</td><td><code>AMXINT4</code>, <code>AMXINT8</code>, or <code>LLAMAFILE</code> (preview)</td></tr>
 <tr><td><code>--kt-weight-path</code></td><td>Path to quantized CPU weights</td><td><code>/path/to/cpu-weights</code></td></tr>
 <tr><td><code>--kt-cpuinfer</code></td><td>Number of CPU inference threads</td><td><code>64</code> (adjust based on CPU cores)</td></tr>
 <tr><td><code>--kt-threadpool-count</code></td><td>Number of thread pools for parallel execution</td><td><code>2</code> (typically 1-4)</td></tr>
 <tr><td><code>--kt-num-gpu-experts</code></td><td>Number of experts to keep on GPU</td><td><code>32</code> (remaining experts go to CPU)</td></tr>
 <tr><td><code>--kt-max-deferred-experts-per-token</code></td><td>Number of experts per token to defer for pipelined execution</td><td><code>2</code> (0 to disable, 1-2 recommended)</td></tr>
-</tbody></table>
+</tbody>
+</table>
 </div>
 <p><strong>Parameter Guidelines:</strong></p>
 <ul>
@@ -524,13 +529,19 @@ KTMoEWrapper.clear_buffer_cache()
 </ul>
 <h4 id="2-set-build-configuration"><a class="header" href="#2-set-build-configuration">2. Set Build Configuration</a></h4>
 <p><strong>Core Options:</strong></p>
-<div class="table-wrapper"><table><thead><tr><th>Variable</th><th>Options</th><th>Description</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>Variable</th><th>Options</th><th>Description</th></tr>
+</thead>
+<tbody>
 <tr><td><code>CPUINFER_CPU_INSTRUCT</code></td><td><code>NATIVE</code>, <code>AVX512</code>, <code>AVX2</code>, <code>FANCY</code></td><td>CPU instruction set to use</td></tr>
 <tr><td><code>CPUINFER_ENABLE_AMX</code></td><td><code>ON</code>, <code>OFF</code></td><td>Enable Intel AMX support</td></tr>
 <tr><td><code>CPUINFER_BUILD_TYPE</code></td><td><code>Release</code>, <code>Debug</code>, <code>RelWithDebInfo</code></td><td>Build type (default: <code>Release</code>)</td></tr>
 <tr><td><code>CPUINFER_PARALLEL</code></td><td>Number</td><td>Parallel build jobs (default: auto-detect)</td></tr>
 <tr><td><code>CPUINFER_VERBOSE</code></td><td><code>0</code>, <code>1</code></td><td>Verbose build output (default: <code>0</code>)</td></tr>
-</tbody></table>
+</tbody>
+</table>
 </div>
 <p><strong>Instruction Set Details:</strong></p>
 <ul>
@@ -582,7 +593,7 @@ sudo make install
 </code></pre>
 <h2 id="weight-quantization"><a class="header" href="#weight-quantization">Weight Quantization</a></h2>
 <p>KT-Kernel provides weight quantization tools for CPU-GPU hybrid inference (e.g., integrating with SGLang). Both tools work together to enable heterogeneous expert placement across CPUs and GPUs.</p>
-<h3 id="cpu-weights-for-cold-experts-on-cpu"><a class="header" href="#cpu-weights-for-cold-experts-on-cpu">CPU Weights (for "cold" experts on CPU)</a></h3>
+<h3 id="cpu-weights-for-cold-experts-on-cpu"><a class="header" href="#cpu-weights-for-cold-experts-on-cpu">CPU Weights (for “cold” experts on CPU)</a></h3>
 <p>Quantize weights to INT4/INT8 format optimized for AMX inference:</p>
 <pre><code class="language-bash">python scripts/convert_cpu_weights.py \
   --input-path /path/to/model \
@@ -591,7 +602,7 @@ sudo make install
   --quant-method int4
 </code></pre>
 <p><strong>Supported formats:</strong> FP8, FP16, BF16 → INT4/INT8</p>
-<h3 id="gpu-weights-for-hot-experts-on-gpu"><a class="header" href="#gpu-weights-for-hot-experts-on-gpu">GPU Weights (for "hot" experts on GPU)</a></h3>
+<h3 id="gpu-weights-for-hot-experts-on-gpu"><a class="header" href="#gpu-weights-for-hot-experts-on-gpu">GPU Weights (for “hot” experts on GPU)</a></h3>
 <p>Apply GPTQ quantization to model weights:</p>
 <pre><code class="language-bash"># Install additional dependencies first
 pip install accelerate transformers llmcompressor datasets
@@ -603,7 +614,7 @@ python scripts/convert_gpu_weights.py \
   --quant_type W4A16
 </code></pre>
 <p><strong>Supported types:</strong> W4A16 (GPTQ4), W8A16 (GPTQ8)</p>
-<hr />
+<hr>
 <p>For detailed documentation, advanced options, and low-memory mode, see <a href="scripts/README.html">scripts/README.md</a>.</p>
 <h2 id="before-commit"><a class="header" href="#before-commit">Before Commit!</a></h2>
 <p>your msg should match: Conventional Commits (https://www.conventionalcommits.org/) <br>and format your code before commit:</p>
@@ -624,11 +635,11 @@ rm -rf build
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+                                <span class=fa-svg><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 320 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc. --><path d="M41.4 233.4c-12.5 12.5-12.5 32.8 0 45.3l160 160c12.5 12.5 32.8 12.5 45.3 0s12.5-32.8 0-45.3L109.3 256 246.6 118.6c12.5-12.5 12.5-32.8 0-45.3s-32.8-12.5-45.3 0l-160 160z"/></svg></span>
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+                                <span class=fa-svg><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 320 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc. --><path d="M278.6 233.4c12.5 12.5 12.5 32.8 0 45.3l-160 160c-12.5 12.5-32.8 12.5-45.3 0s-12.5-32.8 0-45.3L210.7 256 73.4 118.6c-12.5-12.5-12.5-32.8 0-45.3s32.8-12.5 45.3 0l160 160z"/></svg></span>
                             </a>
 
                         <div style="clear: both"></div>
@@ -638,16 +649,21 @@ rm -rf build
 
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+                        <span class=fa-svg><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 320 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc. --><path d="M41.4 233.4c-12.5 12.5-12.5 32.8 0 45.3l160 160c12.5 12.5 32.8 12.5 45.3 0s12.5-32.8 0-45.3L109.3 256 246.6 118.6c12.5-12.5 12.5-32.8 0-45.3s-32.8-12.5-45.3 0l-160 160z"/></svg></span>
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                     </a>
             </nav>
 
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+        <template id=fa-eye><span class=fa-svg><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc. --><path d="M288 32c-80.8 0-145.5 36.8-192.6 80.6C48.6 156 17.3 208 2.5 243.7c-3.3 7.9-3.3 16.7 0 24.6C17.3 304 48.6 356 95.4 399.4C142.5 443.2 207.2 480 288 480s145.5-36.8 192.6-80.6c46.8-43.5 78.1-95.4 93-131.1c3.3-7.9 3.3-16.7 0-24.6c-14.9-35.7-46.2-87.7-93-131.1C433.5 68.8 368.8 32 288 32zM432 256c0 79.5-64.5 144-144 144s-144-64.5-144-144s64.5-144 144-144s144 64.5 144 144zM288 192c0 35.3-28.7 64-64 64c-11.5 0-22.3-3-31.6-8.4c-.2 2.8-.4 5.5-.4 8.4c0 53 43 96 96 96s96-43 96-96s-43-96-96-96c-2.8 0-5.6 .1-8.4 .4c5.3 9.3 8.4 20.1 8.4 31.6z"/></svg></span></template>
+        <template id=fa-eye-slash><span class=fa-svg><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 640 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc. --><path d="M38.8 5.1C28.4-3.1 13.3-1.2 5.1 9.2S-1.2 34.7 9.2 42.9l592 464c10.4 8.2 25.5 6.3 33.7-4.1s6.3-25.5-4.1-33.7L525.6 386.7c39.6-40.6 66.4-86.1 79.9-118.4c3.3-7.9 3.3-16.7 0-24.6c-14.9-35.7-46.2-87.7-93-131.1C465.5 68.8 400.8 32 320 32c-68.2 0-125 26.3-169.3 60.8L38.8 5.1zM223.1 149.5C248.6 126.2 282.7 112 320 112c79.5 0 144 64.5 144 144c0 24.9-6.3 48.3-17.4 68.7L408 294.5c5.2-11.8 8-24.8 8-38.5c0-53-43-96-96-96c-2.8 0-5.6 .1-8.4 .4c5.3 9.3 8.4 20.1 8.4 31.6c0 10.2-2.4 19.8-6.6 28.3l-90.3-70.8zm223.1 298L373 389.9c-16.4 6.5-34.3 10.1-53 10.1c-79.5 0-144-64.5-144-144c0-6.9 .5-13.6 1.4-20.2L83.1 161.5C60.3 191.2 44 220.8 34.5 243.7c-3.3 7.9-3.3 16.7 0 24.6c14.9 35.7 46.2 87.7 93 131.1C174.5 443.2 239.2 480 320 480c47.8 0 89.9-12.9 126.2-32.5z"/></svg></span></template>
+        <template id=fa-copy><span class=fa-svg><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc. --><path d="M502.6 70.63l-61.25-61.25C435.4 3.371 427.2 0 418.7 0H255.1c-35.35 0-64 28.66-64 64l.0195 256C192 355.4 220.7 384 256 384h192c35.2 0 64-28.8 64-64V93.25C512 84.77 508.6 76.63 502.6 70.63zM464 320c0 8.836-7.164 16-16 16H255.1c-8.838 0-16-7.164-16-16L239.1 64.13c0-8.836 7.164-16 16-16h128L384 96c0 17.67 14.33 32 32 32h47.1V320zM272 448c0 8.836-7.164 16-16 16H63.1c-8.838 0-16-7.164-16-16L47.98 192.1c0-8.836 7.164-16 16-16H160V128H63.99c-35.35 0-64 28.65-64 64l.0098 256C.002 483.3 28.66 512 64 512h192c35.2 0 64-28.8 64-64v-32h-47.1L272 448z"/></svg></span></template>
+        <template id=fa-play><span class=fa-svg><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 384 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc. --><path d="M73 39c-14.8-9.1-33.4-9.4-48.5-.9S0 62.6 0 80V432c0 17.4 9.4 33.4 24.5 41.9s33.7 8.1 48.5-.9L361 297c14.3-8.7 23-24.2 23-41s-8.7-32.2-23-41L73 39z"/></svg></span></template>
+        <template id=fa-clock-rotate-left><span class=fa-svg><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc. --><path d="M75 75L41 41C25.9 25.9 0 36.6 0 57.9V168c0 13.3 10.7 24 24 24H134.1c21.4 0 32.1-25.9 17-41l-30.8-30.8C155 85.5 203 64 256 64c106 0 192 86 192 192s-86 192-192 192c-40.8 0-78.6-12.7-109.7-34.4c-14.5-10.1-34.4-6.6-44.6 7.9s-6.6 34.4 7.9 44.6C151.2 495 201.7 512 256 512c141.4 0 256-114.6 256-256S397.4 0 256 0C185.3 0 121.3 28.7 75 75zm181 53c-13.3 0-24 10.7-24 24V256c0 6.4 2.5 12.5 7 17l72 72c9.4 9.4 24.6 9.4 33.9 0s9.4-24.6 0-33.9l-65-65V152c0-13.3-10.7-24-24-24z"/></svg></span></template>
 
 
 
@@ -655,19 +671,19 @@ rm -rf build
             window.playground_copyable = true;
         </script>
 
-        <script src="../../ace.js"></script>
-        <script src="../../mode-rust.js"></script>
-        <script src="../../editor.js"></script>
-        <script src="../../theme-dawn.js"></script>
-        <script src="../../theme-tomorrow_night.js"></script>
+        <script src="../../ace-2a3cd908.js"></script>
+        <script src="../../mode-rust-2c9d5c9a.js"></script>
+        <script src="../../editor-16ca416c.js"></script>
+        <script src="../../theme-dawn-4493f9c8.js"></script>
+        <script src="../../theme-tomorrow_night-9dbe62a9.js"></script>
 
-        <script src="../../elasticlunr.min.js"></script>
-        <script src="../../mark.min.js"></script>
-        <script src="../../searcher.js"></script>
+        <script src="../../elasticlunr-ef4e11c1.min.js"></script>
+        <script src="../../mark-09e88c2c.min.js"></script>
+        <script src="../../searcher-c2a407aa.js"></script>
 
-        <script src="../../clipboard.min.js"></script>
-        <script src="../../highlight.js"></script>
-        <script src="../../book.js"></script>
+        <script src="../../clipboard-1626706a.min.js"></script>
+        <script src="../../highlight-abc7f01d.js"></script>
+        <script src="../../book-a0b12cfe.js"></script>
 
         <!-- Custom JS scripts -->
 
diff --git a/favicon.png b/favicon-8114d1fc.png
similarity index 100%
rename from favicon.png
rename to favicon-8114d1fc.png
diff --git a/favicon.svg b/favicon-de23e50b.svg
similarity index 100%
rename from favicon.svg
rename to favicon-de23e50b.svg
diff --git a/fonts/fonts.css b/fonts/fonts-9644e21d.css
similarity index 74%
rename from fonts/fonts.css
rename to fonts/fonts-9644e21d.css
index 698e1e19..ce32fdc8 100644
--- a/fonts/fonts.css
+++ b/fonts/fonts-9644e21d.css
@@ -7,7 +7,7 @@
   font-style: normal;
   font-weight: 300;
   src: local('Open Sans Light'), local('OpenSans-Light'),
-       url('../fonts/open-sans-v17-all-charsets-300.woff2') format('woff2');
+       url('../fonts/open-sans-v17-all-charsets-300-7736aa35.woff2') format('woff2');
 }
 
 /* open-sans-300italic - latin_vietnamese_latin-ext_greek-ext_greek_cyrillic-ext_cyrillic */
@@ -16,7 +16,7 @@
   font-style: italic;
   font-weight: 300;
   src: local('Open Sans Light Italic'), local('OpenSans-LightItalic'),
-       url('../fonts/open-sans-v17-all-charsets-300italic.woff2') format('woff2');
+       url('../fonts/open-sans-v17-all-charsets-300italic-2c7b95c0.woff2') format('woff2');
 }
 
 /* open-sans-regular - latin_vietnamese_latin-ext_greek-ext_greek_cyrillic-ext_cyrillic */
@@ -25,7 +25,7 @@
   font-style: normal;
   font-weight: 400;
   src: local('Open Sans Regular'), local('OpenSans-Regular'),
-       url('../fonts/open-sans-v17-all-charsets-regular.woff2') format('woff2');
+       url('../fonts/open-sans-v17-all-charsets-regular-2e3b1d34.woff2') format('woff2');
 }
 
 /* open-sans-italic - latin_vietnamese_latin-ext_greek-ext_greek_cyrillic-ext_cyrillic */
@@ -34,7 +34,7 @@
   font-style: italic;
   font-weight: 400;
   src: local('Open Sans Italic'), local('OpenSans-Italic'),
-       url('../fonts/open-sans-v17-all-charsets-italic.woff2') format('woff2');
+       url('../fonts/open-sans-v17-all-charsets-italic-6c9463f7.woff2') format('woff2');
 }
 
 /* open-sans-600 - latin_vietnamese_latin-ext_greek-ext_greek_cyrillic-ext_cyrillic */
@@ -43,7 +43,7 @@
   font-style: normal;
   font-weight: 600;
   src: local('Open Sans SemiBold'), local('OpenSans-SemiBold'),
-       url('../fonts/open-sans-v17-all-charsets-600.woff2') format('woff2');
+       url('../fonts/open-sans-v17-all-charsets-600-486c6759.woff2') format('woff2');
 }
 
 /* open-sans-600italic - latin_vietnamese_latin-ext_greek-ext_greek_cyrillic-ext_cyrillic */
@@ -52,7 +52,7 @@
   font-style: italic;
   font-weight: 600;
   src: local('Open Sans SemiBold Italic'), local('OpenSans-SemiBoldItalic'),
-       url('../fonts/open-sans-v17-all-charsets-600italic.woff2') format('woff2');
+       url('../fonts/open-sans-v17-all-charsets-600italic-1a3e8659.woff2') format('woff2');
 }
 
 /* open-sans-700 - latin_vietnamese_latin-ext_greek-ext_greek_cyrillic-ext_cyrillic */
@@ -61,7 +61,7 @@
   font-style: normal;
   font-weight: 700;
   src: local('Open Sans Bold'), local('OpenSans-Bold'),
-       url('../fonts/open-sans-v17-all-charsets-700.woff2') format('woff2');
+       url('../fonts/open-sans-v17-all-charsets-700-c22fe8c7.woff2') format('woff2');
 }
 
 /* open-sans-700italic - latin_vietnamese_latin-ext_greek-ext_greek_cyrillic-ext_cyrillic */
@@ -70,7 +70,7 @@
   font-style: italic;
   font-weight: 700;
   src: local('Open Sans Bold Italic'), local('OpenSans-BoldItalic'),
-       url('../fonts/open-sans-v17-all-charsets-700italic.woff2') format('woff2');
+       url('../fonts/open-sans-v17-all-charsets-700italic-238ae959.woff2') format('woff2');
 }
 
 /* open-sans-800 - latin_vietnamese_latin-ext_greek-ext_greek_cyrillic-ext_cyrillic */
@@ -79,7 +79,7 @@
   font-style: normal;
   font-weight: 800;
   src: local('Open Sans ExtraBold'), local('OpenSans-ExtraBold'),
-       url('../fonts/open-sans-v17-all-charsets-800.woff2') format('woff2');
+       url('../fonts/open-sans-v17-all-charsets-800-3d2c812a.woff2') format('woff2');
 }
 
 /* open-sans-800italic - latin_vietnamese_latin-ext_greek-ext_greek_cyrillic-ext_cyrillic */
@@ -88,7 +88,7 @@
   font-style: italic;
   font-weight: 800;
   src: local('Open Sans ExtraBold Italic'), local('OpenSans-ExtraBoldItalic'),
-       url('../fonts/open-sans-v17-all-charsets-800italic.woff2') format('woff2');
+       url('../fonts/open-sans-v17-all-charsets-800italic-ba1521ec.woff2') format('woff2');
 }
 
 /* source-code-pro-500 - latin_vietnamese_latin-ext_greek_cyrillic-ext_cyrillic */
@@ -96,5 +96,5 @@
   font-family: 'Source Code Pro';
   font-style: normal;
   font-weight: 500;
-  src: url('../fonts/source-code-pro-v11-all-charsets-500.woff2') format('woff2');
+  src: url('../fonts/source-code-pro-v11-all-charsets-500-2bdd9410.woff2') format('woff2');
 }
diff --git a/fonts/open-sans-v17-all-charsets-300.woff2 b/fonts/open-sans-v17-all-charsets-300-7736aa35.woff2
similarity index 100%
rename from fonts/open-sans-v17-all-charsets-300.woff2
rename to fonts/open-sans-v17-all-charsets-300-7736aa35.woff2
diff --git a/fonts/open-sans-v17-all-charsets-300italic.woff2 b/fonts/open-sans-v17-all-charsets-300italic-2c7b95c0.woff2
similarity index 100%
rename from fonts/open-sans-v17-all-charsets-300italic.woff2
rename to fonts/open-sans-v17-all-charsets-300italic-2c7b95c0.woff2
diff --git a/fonts/open-sans-v17-all-charsets-600.woff2 b/fonts/open-sans-v17-all-charsets-600-486c6759.woff2
similarity index 100%
rename from fonts/open-sans-v17-all-charsets-600.woff2
rename to fonts/open-sans-v17-all-charsets-600-486c6759.woff2
diff --git a/fonts/open-sans-v17-all-charsets-600italic.woff2 b/fonts/open-sans-v17-all-charsets-600italic-1a3e8659.woff2
similarity index 100%
rename from fonts/open-sans-v17-all-charsets-600italic.woff2
rename to fonts/open-sans-v17-all-charsets-600italic-1a3e8659.woff2
diff --git a/fonts/open-sans-v17-all-charsets-700.woff2 b/fonts/open-sans-v17-all-charsets-700-c22fe8c7.woff2
similarity index 100%
rename from fonts/open-sans-v17-all-charsets-700.woff2
rename to fonts/open-sans-v17-all-charsets-700-c22fe8c7.woff2
diff --git a/fonts/open-sans-v17-all-charsets-700italic.woff2 b/fonts/open-sans-v17-all-charsets-700italic-238ae959.woff2
similarity index 100%
rename from fonts/open-sans-v17-all-charsets-700italic.woff2
rename to fonts/open-sans-v17-all-charsets-700italic-238ae959.woff2
diff --git a/fonts/open-sans-v17-all-charsets-800.woff2 b/fonts/open-sans-v17-all-charsets-800-3d2c812a.woff2
similarity index 100%
rename from fonts/open-sans-v17-all-charsets-800.woff2
rename to fonts/open-sans-v17-all-charsets-800-3d2c812a.woff2
diff --git a/fonts/open-sans-v17-all-charsets-800italic.woff2 b/fonts/open-sans-v17-all-charsets-800italic-ba1521ec.woff2
similarity index 100%
rename from fonts/open-sans-v17-all-charsets-800italic.woff2
rename to fonts/open-sans-v17-all-charsets-800italic-ba1521ec.woff2
diff --git a/fonts/open-sans-v17-all-charsets-italic.woff2 b/fonts/open-sans-v17-all-charsets-italic-6c9463f7.woff2
similarity index 100%
rename from fonts/open-sans-v17-all-charsets-italic.woff2
rename to fonts/open-sans-v17-all-charsets-italic-6c9463f7.woff2
diff --git a/fonts/open-sans-v17-all-charsets-regular.woff2 b/fonts/open-sans-v17-all-charsets-regular-2e3b1d34.woff2
similarity index 100%
rename from fonts/open-sans-v17-all-charsets-regular.woff2
rename to fonts/open-sans-v17-all-charsets-regular-2e3b1d34.woff2
diff --git a/fonts/source-code-pro-v11-all-charsets-500.woff2 b/fonts/source-code-pro-v11-all-charsets-500-2bdd9410.woff2
similarity index 100%
rename from fonts/source-code-pro-v11-all-charsets-500.woff2
rename to fonts/source-code-pro-v11-all-charsets-500-2bdd9410.woff2
diff --git a/highlight.css b/highlight-493f70e1.css
similarity index 100%
rename from highlight.css
rename to highlight-493f70e1.css
diff --git a/highlight.js b/highlight-abc7f01d.js
similarity index 100%
rename from highlight.js
rename to highlight-abc7f01d.js
diff --git a/index.html b/index.html
index 4f24c055..09996131 100644
--- a/index.html
+++ b/index.html
@@ -12,21 +12,20 @@
         <meta name="viewport" content="width=device-width, initial-scale=1">
         <meta name="theme-color" content="#ffffff">
 
-        <link rel="icon" href="favicon.svg">
-        <link rel="shortcut icon" href="favicon.png">
-        <link rel="stylesheet" href="css/variables.css">
-        <link rel="stylesheet" href="css/general.css">
-        <link rel="stylesheet" href="css/chrome.css">
-        <link rel="stylesheet" href="css/print.css" media="print">
+        <link rel="icon" href="favicon-de23e50b.svg">
+        <link rel="shortcut icon" href="favicon-8114d1fc.png">
+        <link rel="stylesheet" href="css/variables-8adf115d.css">
+        <link rel="stylesheet" href="css/general-2459343d.css">
+        <link rel="stylesheet" href="css/chrome-9dfbd86b.css">
+        <link rel="stylesheet" href="css/print-9e4910d8.css" media="print">
 
         <!-- Fonts -->
-        <link rel="stylesheet" href="FontAwesome/css/font-awesome.css">
-        <link rel="stylesheet" href="fonts/fonts.css">
+        <link rel="stylesheet" href="fonts/fonts-9644e21d.css">
 
         <!-- Highlight.js Stylesheets -->
-        <link rel="stylesheet" id="highlight-css" href="highlight.css">
-        <link rel="stylesheet" id="tomorrow-night-css" href="tomorrow-night.css">
-        <link rel="stylesheet" id="ayu-highlight-css" href="ayu-highlight.css">
+        <link rel="stylesheet" id="mdbook-highlight-css" href="highlight-493f70e1.css">
+        <link rel="stylesheet" id="mdbook-tomorrow-night-css" href="tomorrow-night-4c0ae647.css">
+        <link rel="stylesheet" id="mdbook-ayu-highlight-css" href="ayu-highlight-3fdfc3ac.css">
 
         <!-- Custom theme stylesheets -->
 
@@ -36,10 +35,10 @@
             const path_to_root = "";
             const default_light_theme = "light";
             const default_dark_theme = "navy";
-            window.path_to_searchindex_js = "searchindex.js";
+            window.path_to_searchindex_js = "searchindex-e61e839e.js";
         </script>
         <!-- Start loading toc.js asap -->
-        <script src="toc.js"></script>
+        <script src="toc-6a9a54e6.js"></script>
     </head>
     <body>
     <div id="mdbook-help-container">
@@ -53,7 +52,7 @@
             </div>
         </div>
     </div>
-    <div id="body-container">
+    <div id="mdbook-body-container">
         <!-- Work around some values being stored in localStorage wrapped in quotes -->
         <script>
             try {
@@ -82,12 +81,12 @@
             html.classList.add("js");
         </script>
 
-        <input type="checkbox" id="sidebar-toggle-anchor" class="hidden">
+        <input type="checkbox" id="mdbook-sidebar-toggle-anchor" class="hidden">
 
         <!-- Hide / unhide sidebar before it is displayed -->
         <script>
             let sidebar = null;
-            const sidebar_toggle = document.getElementById("sidebar-toggle-anchor");
+            const sidebar_toggle = document.getElementById("mdbook-sidebar-toggle-anchor");
             if (document.body.clientWidth >= 1080) {
                 try { sidebar = localStorage.getItem('mdbook-sidebar'); } catch(e) { }
                 sidebar = sidebar || 'visible';
@@ -102,39 +101,39 @@
             }
         </script>
 
-        <nav id="sidebar" class="sidebar" aria-label="Table of contents">
+        <nav id="mdbook-sidebar" class="sidebar" aria-label="Table of contents">
             <!-- populated by js -->
             <mdbook-sidebar-scrollbox class="sidebar-scrollbox"></mdbook-sidebar-scrollbox>
             <noscript>
                 <iframe class="sidebar-iframe-outer" src="toc.html"></iframe>
             </noscript>
-            <div id="sidebar-resize-handle" class="sidebar-resize-handle">
+            <div id="mdbook-sidebar-resize-handle" class="sidebar-resize-handle">
                 <div class="sidebar-resize-indicator"></div>
             </div>
         </nav>
 
-        <div id="page-wrapper" class="page-wrapper">
+        <div id="mdbook-page-wrapper" class="page-wrapper">
 
             <div class="page">
-                <div id="menu-bar-hover-placeholder"></div>
-                <div id="menu-bar" class="menu-bar sticky">
+                <div id="mdbook-menu-bar-hover-placeholder"></div>
+                <div id="mdbook-menu-bar" class="menu-bar sticky">
                     <div class="left-buttons">
-                        <label id="sidebar-toggle" class="icon-button" for="sidebar-toggle-anchor" title="Toggle Table of Contents" aria-label="Toggle Table of Contents" aria-controls="sidebar">
-                            <i class="fa fa-bars"></i>
+                        <label id="mdbook-sidebar-toggle" class="icon-button" for="mdbook-sidebar-toggle-anchor" title="Toggle Table of Contents" aria-label="Toggle Table of Contents" aria-controls="mdbook-sidebar">
+                            <span class=fa-svg><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc. --><path d="M0 96C0 78.3 14.3 64 32 64H416c17.7 0 32 14.3 32 32s-14.3 32-32 32H32C14.3 128 0 113.7 0 96zM0 256c0-17.7 14.3-32 32-32H416c17.7 0 32 14.3 32 32s-14.3 32-32 32H32c-17.7 0-32-14.3-32-32zM448 416c0 17.7-14.3 32-32 32H32c-17.7 0-32-14.3-32-32s14.3-32 32-32H416c17.7 0 32 14.3 32 32z"/></svg></span>
                         </label>
-                        <button id="theme-toggle" class="icon-button" type="button" title="Change theme" aria-label="Change theme" aria-haspopup="true" aria-expanded="false" aria-controls="theme-list">
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+                        <button id="mdbook-theme-toggle" class="icon-button" type="button" title="Change theme" aria-label="Change theme" aria-haspopup="true" aria-expanded="false" aria-controls="mdbook-theme-list">
+                            <span class=fa-svg><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc. --><path d="M371.3 367.1c27.3-3.9 51.9-19.4 67.2-42.9L600.2 74.1c12.6-19.5 9.4-45.3-7.6-61.2S549.7-4.4 531.1 9.6L294.4 187.2c-24 18-38.2 46.1-38.4 76.1L371.3 367.1zm-19.6 25.4l-116-104.4C175.9 290.3 128 339.6 128 400c0 3.9 .2 7.8 .6 11.6c1.8 17.5-10.2 36.4-27.8 36.4H96c-17.7 0-32 14.3-32 32s14.3 32 32 32H240c61.9 0 112-50.1 112-112c0-2.5-.1-5-.2-7.5z"/></svg></span>
                         </button>
-                        <ul id="theme-list" class="theme-popup" aria-label="Themes" role="menu">
-                            <li role="none"><button role="menuitem" class="theme" id="default_theme">Auto</button></li>
-                            <li role="none"><button role="menuitem" class="theme" id="light">Light</button></li>
-                            <li role="none"><button role="menuitem" class="theme" id="rust">Rust</button></li>
-                            <li role="none"><button role="menuitem" class="theme" id="coal">Coal</button></li>
-                            <li role="none"><button role="menuitem" class="theme" id="navy">Navy</button></li>
-                            <li role="none"><button role="menuitem" class="theme" id="ayu">Ayu</button></li>
+                        <ul id="mdbook-theme-list" class="theme-popup" aria-label="Themes" role="menu">
+                            <li role="none"><button role="menuitem" class="theme" id="mdbook-theme-default_theme">Auto</button></li>
+                            <li role="none"><button role="menuitem" class="theme" id="mdbook-theme-light">Light</button></li>
+                            <li role="none"><button role="menuitem" class="theme" id="mdbook-theme-rust">Rust</button></li>
+                            <li role="none"><button role="menuitem" class="theme" id="mdbook-theme-coal">Coal</button></li>
+                            <li role="none"><button role="menuitem" class="theme" id="mdbook-theme-navy">Navy</button></li>
+                            <li role="none"><button role="menuitem" class="theme" id="mdbook-theme-ayu">Ayu</button></li>
                         </ul>
-                        <button id="search-toggle" class="icon-button" type="button" title="Search (`/`)" aria-label="Toggle Searchbar" aria-expanded="false" aria-keyshortcuts="/ s" aria-controls="searchbar">
-                            <i class="fa fa-search"></i>
+                        <button id="mdbook-search-toggle" class="icon-button" type="button" title="Search (`/`)" aria-label="Toggle Searchbar" aria-expanded="false" aria-keyshortcuts="/ s" aria-controls="mdbook-searchbar">
+                            <span class=fa-svg><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc. --><path d="M416 208c0 45.9-14.9 88.3-40 122.7L502.6 457.4c12.5 12.5 12.5 32.8 0 45.3s-32.8 12.5-45.3 0L330.7 376c-34.4 25.2-76.8 40-122.7 40C93.1 416 0 322.9 0 208S93.1 0 208 0S416 93.1 416 208zM208 352c79.5 0 144-64.5 144-144s-64.5-144-144-144S64 128.5 64 208s64.5 144 144 144z"/></svg></span>
                         </button>
                     </div>
 
@@ -142,54 +141,59 @@
 
                     <div class="right-buttons">
                         <a href="print.html" title="Print this book" aria-label="Print this book">
-                            <i id="print-button" class="fa fa-print"></i>
+                            <span class=fa-svg id="print-button"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc. --><path d="M128 0C92.7 0 64 28.7 64 64v96h64V64H354.7L384 93.3V160h64V93.3c0-17-6.7-33.3-18.7-45.3L400 18.7C388 6.7 371.7 0 354.7 0H128zM384 352v32 64H128V384 368 352H384zm64 32h32c17.7 0 32-14.3 32-32V256c0-35.3-28.7-64-64-64H64c-35.3 0-64 28.7-64 64v96c0 17.7 14.3 32 32 32H64v64c0 35.3 28.7 64 64 64H384c35.3 0 64-28.7 64-64V384zm-16-88c-13.3 0-24-10.7-24-24s10.7-24 24-24s24 10.7 24 24s-10.7 24-24 24z"/></svg></span>
                         </a>
                         <a href="https://github.com/kvcache-ai/ktransformers" title="Git repository" aria-label="Git repository">
-                            <i id="git-repository-button" class="fa fa-github"></i>
+                            <span class=fa-svg><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 496 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc. --><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg></span>
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                         <a href="https://github.com/kvcache-ai/ktransformers/edit/main/doc/README.md" title="Suggest an edit" aria-label="Suggest an edit" rel="edit">
-                            <i id="git-edit-button" class="fa fa-edit"></i>
+                            <span class=fa-svg id="git-edit-button"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc. --><path d="M421.7 220.3l-11.3 11.3-22.6 22.6-205 205c-6.6 6.6-14.8 11.5-23.8 14.1L30.8 511c-8.4 2.5-17.5 .2-23.7-6.1S-1.5 489.7 1 481.2L38.7 353.1c2.6-9 7.5-17.2 14.1-23.8l205-205 22.6-22.6 11.3-11.3 33.9 33.9 62.1 62.1 33.9 33.9zM96 353.9l-9.3 9.3c-.9 .9-1.6 2.1-2 3.4l-25.3 86 86-25.3c1.3-.4 2.5-1.1 3.4-2l9.3-9.3H112c-8.8 0-16-7.2-16-16V353.9zM453.3 19.3l39.4 39.4c25 25 25 65.5 0 90.5l-14.5 14.5-22.6 22.6-11.3 11.3-33.9-33.9-62.1-62.1L314.3 67.7l11.3-11.3 22.6-22.6 14.5-14.5c25-25 65.5-25 90.5 0z"/></svg></span>
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-                <div id="search-wrapper" class="hidden">
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+                <div id="mdbook-search-wrapper" class="hidden">
+                    <form id="mdbook-searchbar-outer" class="searchbar-outer">
                         <div class="search-wrapper">
-                            <input type="search" id="searchbar" name="searchbar" placeholder="Search this book ..." aria-controls="searchresults-outer" aria-describedby="searchresults-header">
+                            <input type="search" id="mdbook-searchbar" name="searchbar" placeholder="Search this book ..." aria-controls="mdbook-searchresults-outer" aria-describedby="searchresults-header">
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-                                <i class="fa fa-spinner fa-spin"></i>
+                                <span class=fa-svg id="fa-spin"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc. --><path d="M304 48c0-26.5-21.5-48-48-48s-48 21.5-48 48s21.5 48 48 48s48-21.5 48-48zm0 416c0-26.5-21.5-48-48-48s-48 21.5-48 48s21.5 48 48 48s48-21.5 48-48zM48 304c26.5 0 48-21.5 48-48s-21.5-48-48-48s-48 21.5-48 48s21.5 48 48 48zm464-48c0-26.5-21.5-48-48-48s-48 21.5-48 48s21.5 48 48 48s48-21.5 48-48zM142.9 437c18.7-18.7 18.7-49.1 0-67.9s-49.1-18.7-67.9 0s-18.7 49.1 0 67.9s49.1 18.7 67.9 0zm0-294.2c18.7-18.7 18.7-49.1 0-67.9S93.7 56.2 75 75s-18.7 49.1 0 67.9s49.1 18.7 67.9 0zM369.1 437c18.7 18.7 49.1 18.7 67.9 0s18.7-49.1 0-67.9s-49.1-18.7-67.9 0s-18.7 49.1 0 67.9z"/></svg></span>
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                     </form>
-                    <div id="searchresults-outer" class="searchresults-outer hidden">
-                        <div id="searchresults-header" class="searchresults-header"></div>
-                        <ul id="searchresults">
+                    <div id="mdbook-searchresults-outer" class="searchresults-outer hidden">
+                        <div id="mdbook-searchresults-header" class="searchresults-header"></div>
+                        <ul id="mdbook-searchresults">
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                 <!-- Apply ARIA attributes after the sidebar and the sidebar toggle button are added to the DOM -->
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-                    Array.from(document.querySelectorAll('#sidebar a')).forEach(function(link) {
+                    document.getElementById('mdbook-sidebar-toggle').setAttribute('aria-expanded', sidebar === 'visible');
+                    document.getElementById('mdbook-sidebar').setAttribute('aria-hidden', sidebar !== 'visible');
+                    Array.from(document.querySelectorAll('#mdbook-sidebar a')).forEach(function(link) {
                         link.setAttribute('tabIndex', sidebar === 'visible' ? 0 : -1);
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                 </script>
 
-                <div id="content" class="content">
+                <div id="mdbook-content" class="content">
                     <main>
                         <div align="center">
-  <p align="center">
+  
+<p align="center">
 <picture>
-    <img alt="KTransformers" src="https://github.com/user-attachments/assets/d5a2492f-a415-4456-af99-4ab102f13f8b" width=50%>
+    <img alt="KTransformers" src="https://github.com/user-attachments/assets/d5a2492f-a415-4456-af99-4ab102f13f8b" width="50%">
 </picture>
 </p>
-  <h3 id="a-flexible-framework-for-experiencing-cutting-edge-llm-inferencefine-tune-optimizations"><a class="header" href="#a-flexible-framework-for-experiencing-cutting-edge-llm-inferencefine-tune-optimizations">A Flexible Framework for Experiencing Cutting-edge LLM Inference/Fine-tune Optimizations</a></h3>
+
+  
+<h3>A Flexible Framework for Experiencing Cutting-edge LLM Inference/Fine-tune Optimizations</h3>
+
   <strong><a href="#-overview">🎯 Overview</a> | <a href="#-kt-kernel---high-performance-inference-kernels">🚀 kt-kernel</a> | <a href="#-kt-sft---fine-tuning-framework">🎓 kt-sft</a> | <a href="#-citation">🔥 Citation</a> | <a href="https://github.com/kvcache-ai/ktransformers/issues/1582">🚀 Roadmap(2025Q4)</a>  </strong>
 </div>
+
 <h2 id="-overview"><a class="header" href="#-overview">🎯 Overview</a></h2>
 <p>KTransformers is a research project focused on efficient inference and fine-tuning of large language models through CPU-GPU heterogeneous computing. The project has evolved into <strong>two core modules</strong>: <a href="./kt-kernel/">kt-kernel</a> and <a href="./kt-sft/">kt-sft</a>.</p>
 <h2 id="-updates"><a class="header" href="#-updates">🔥 Updates</a></h2>
@@ -212,13 +216,13 @@
 <li><strong>Feb 25, 2025</strong>: Support <a href="./doc/en/fp8_kernel.html">FP8 GPU kernel</a> for DeepSeek-V3 and R1; <a href="./doc/en/DeepseekR1_V3_tutorial.html#v022-longer-context">Longer Context</a>.</li>
 <li><strong>Feb 15, 2025</strong>: Longer Context (from 4K to 8K for 24GB VRAM) &amp; Slightly Faster Speed （+15%, up to 16 Tokens/s), update <a href="./doc/en/DeepseekR1_V3_tutorial.html">docs</a> and <a href="https://kvcache-ai.github.io/ktransformers/">online books</a>.</li>
 <li><strong>Feb 10, 2025</strong>: Support Deepseek-R1 and V3 on single (24GB VRAM)/multi gpu and 382G DRAM, up to 3~28x speedup. For detailed show case and reproduction tutorial, see <a href="./doc/en/DeepseekR1_V3_tutorial.html">here</a>.</li>
-<li><strong>Aug 28, 2024</strong>: Decrease DeepseekV2's required VRAM from 21G to 11G.</li>
+<li><strong>Aug 28, 2024</strong>: Decrease DeepseekV2’s required VRAM from 21G to 11G.</li>
 <li><strong>Aug 15, 2024</strong>: Update detailed <a href="doc/en/injection_tutorial.html">tutorial</a> for injection and multi-GPU.</li>
 <li><strong>Aug 14, 2024</strong>: Support llamfile as linear backend.</li>
 <li><strong>Aug 12, 2024</strong>: Support multiple GPU; Support new model: mixtral 8*7B  and 8*22B; Support q2k, q3k, q5k dequant on gpu.</li>
 <li><strong>Aug 9, 2024</strong>: Support windows native.</li>
 </ul>
-<hr />
+<hr>
 <h2 id="-core-modules"><a class="header" href="#-core-modules">📦 Core Modules</a></h2>
 <h3 id="-kt-kernel---high-performance-inference-kernels"><a class="header" href="#-kt-kernel---high-performance-inference-kernels">🚀 <a href="./kt-kernel/">kt-kernel</a> - High-Performance Inference Kernels</a></h3>
 <p>CPU-optimized kernel operations for heterogeneous LLM inference.</p>
@@ -241,15 +245,21 @@ pip install .
 <li>Heterogeneous expert placement (hot experts on GPU, cold experts on CPU)</li>
 </ul>
 <p><strong>Performance Examples:</strong></p>
-<div class="table-wrapper"><table><thead><tr><th>Model</th><th>Hardware Configuration</th><th>Total Throughput</th><th>Output Throughput</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>Model</th><th>Hardware Configuration</th><th>Total Throughput</th><th>Output Throughput</th></tr>
+</thead>
+<tbody>
 <tr><td>DeepSeek-R1-0528 (FP8)</td><td>8×L20 GPU + Xeon Gold 6454S</td><td>227.85 tokens/s</td><td>87.58 tokens/s (8-way concurrency)</td></tr>
-</tbody></table>
+</tbody>
+</table>
 </div>
 <p>👉 <strong><a href="./kt-kernel/README.html">Full Documentation →</a></strong></p>
-<hr />
+<hr>
 <h3 id="-kt-sft---fine-tuning-framework"><a class="header" href="#-kt-sft---fine-tuning-framework">🎓 <a href="./kt-sft/">kt-sft</a> - Fine-Tuning Framework</a></h3>
 <p>KTransformers × LLaMA-Factory integration for ultra-large MoE model fine-tuning.</p>
-<p><img src="./doc/assets/image-20251011010558909.png" alt="image-20251011010558909" /></p>
+<p><img src="./doc/assets/image-20251011010558909.png" alt="image-20251011010558909"></p>
 <p><strong>Key Features:</strong></p>
 <ul>
 <li><strong>Resource Efficient</strong>: Fine-tune 671B DeepSeek-V3 with just <strong>70GB GPU memory</strong> + 1.3TB RAM</li>
@@ -258,10 +268,16 @@ pip install .
 <li><strong>Production Ready</strong>: Chat, batch inference, and metrics evaluation</li>
 </ul>
 <p><strong>Performance Examples:</strong></p>
-<div class="table-wrapper"><table><thead><tr><th>Model</th><th>Configuration</th><th>Throughput</th><th>GPU Memory</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>Model</th><th>Configuration</th><th>Throughput</th><th>GPU Memory</th></tr>
+</thead>
+<tbody>
 <tr><td>DeepSeek-V3 (671B)</td><td>LoRA + AMX</td><td>~40 tokens/s</td><td>70GB (multi-GPU)</td></tr>
 <tr><td>DeepSeek-V2-Lite (14B)</td><td>LoRA + AMX</td><td>~530 tokens/s</td><td>6GB</td></tr>
-</tbody></table>
+</tbody>
+</table>
 </div>
 <p><strong>Quick Start:</strong></p>
 <pre><code class="language-bash">cd kt-sft
@@ -269,7 +285,7 @@ pip install .
 USE_KT=1 llamafactory-cli train examples/train_lora/deepseek3_lora_sft_kt.yaml
 </code></pre>
 <p>👉 <strong><a href="./kt-sft/README.html">Full Documentation →</a></strong></p>
-<hr />
+<hr>
 <h2 id="-citation"><a class="header" href="#-citation">🔥 Citation</a></h2>
 <p>If you use KTransformers in your research, please cite our paper:</p>
 <pre><code class="language-bibtex">@inproceedings{10.1145/3731569.3764843,
@@ -306,7 +322,7 @@ USE_KT=1 llamafactory-cli train examples/train_lora/deepseek3_lora_sft_kt.yaml
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@@ -330,19 +351,19 @@ USE_KT=1 llamafactory-cli train examples/train_lora/deepseek3_lora_sft_kt.yaml
             window.playground_copyable = true;
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diff --git a/mark.min.js b/mark-09e88c2c.min.js
similarity index 100%
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rename from mode-rust.js
rename to mode-rust-2c9d5c9a.js
diff --git a/print.html b/print.html
index b037e280..60c76cbc 100644
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+++ b/print.html
@@ -13,21 +13,20 @@
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+        <link rel="stylesheet" id="mdbook-highlight-css" href="highlight-493f70e1.css">
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@@ -37,10 +36,10 @@
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             const default_dark_theme = "navy";
-            window.path_to_searchindex_js = "searchindex.js";
+            window.path_to_searchindex_js = "searchindex-e61e839e.js";
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+        <script src="toc-6a9a54e6.js"></script>
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@@ -54,7 +53,7 @@
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-    <div id="body-container">
+    <div id="mdbook-body-container">
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@@ -83,12 +82,12 @@
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-                <div id="content" class="content">
+                <div id="mdbook-content" class="content">
                     <main>
-                        <div align="center">
-  <p align="center">
+                        <h1 id="introduction-2"><a href="#introduction-2" class="header">Introduction</a></h1>
+<div align="center">
+  
+<p align="center">
 <picture>
-    <img alt="KTransformers" src="https://github.com/user-attachments/assets/d5a2492f-a415-4456-af99-4ab102f13f8b" width=50%>
+    <img alt="KTransformers" src="https://github.com/user-attachments/assets/d5a2492f-a415-4456-af99-4ab102f13f8b" width="50%">
 </picture>
 </p>
-  <h3 id="a-flexible-framework-for-experiencing-cutting-edge-llm-inferencefine-tune-optimizations"><a class="header" href="#a-flexible-framework-for-experiencing-cutting-edge-llm-inferencefine-tune-optimizations">A Flexible Framework for Experiencing Cutting-edge LLM Inference/Fine-tune Optimizations</a></h3>
-  <strong><a href="index.html#-overview">🎯 Overview</a> | <a href="index.html#-kt-kernel---high-performance-inference-kernels">🚀 kt-kernel</a> | <a href="index.html#-kt-sft---fine-tuning-framework">🎓 kt-sft</a> | <a href="index.html#-citation">🔥 Citation</a> | <a href="https://github.com/kvcache-ai/ktransformers/issues/1582">🚀 Roadmap(2025Q4)</a>  </strong>
+
+  
+<h3>A Flexible Framework for Experiencing Cutting-edge LLM Inference/Fine-tune Optimizations</h3>
+
+  <strong><a href="#-overview">🎯 Overview</a> | <a href="#-kt-kernel---high-performance-inference-kernels">🚀 kt-kernel</a> | <a href="#-kt-sft---fine-tuning-framework">🎓 kt-sft</a> | <a href="#-citation">🔥 Citation</a> | <a href="https://github.com/kvcache-ai/ktransformers/issues/1582">🚀 Roadmap(2025Q4)</a>  </strong>
 </div>
+
 <h2 id="-overview"><a class="header" href="#-overview">🎯 Overview</a></h2>
-<p>KTransformers is a research project focused on efficient inference and fine-tuning of large language models through CPU-GPU heterogeneous computing. The project has evolved into <strong>two core modules</strong>: <a href="./kt-kernel/">kt-kernel</a> and <a href="./kt-sft/">kt-sft</a>.</p>
+<p>KTransformers is a research project focused on efficient inference and fine-tuning of large language models through CPU-GPU heterogeneous computing. The project has evolved into <strong>two core modules</strong>: <a href="kt-kernel">kt-kernel</a> and <a href="kt-sft">kt-sft</a>.</p>
 <h2 id="-updates"><a class="header" href="#-updates">🔥 Updates</a></h2>
 <ul>
-<li><strong>Nov 6, 2025</strong>: Support Kimi-K2-Thinking inference (<a href="./doc/en/Kimi-K2-Thinking.html">Tutorial</a>) and fine-tune (<a href="./doc/en/SFT_Installation_Guide_KimiK2.html">Tutorial</a>)</li>
-<li><strong>Nov 4, 2025</strong>: KTransformers Fine-Tuning × LLaMA-Factory Integration. (<a href="./doc/en/KTransformers-Fine-Tuning_User-Guide.html">Tutorial</a>)</li>
-<li><strong>Oct 27, 2025</strong>: Support Ascend NPU. (<a href="./doc/zh/DeepseekR1_V3_tutorial_zh_for_Ascend_NPU.html">Tutorial</a>)</li>
+<li><strong>Nov 6, 2025</strong>: Support Kimi-K2-Thinking inference (<a href="doc/en/Kimi-K2-Thinking.html">Tutorial</a>) and fine-tune (<a href="doc/en/SFT_Installation_Guide_KimiK2.html">Tutorial</a>)</li>
+<li><strong>Nov 4, 2025</strong>: KTransformers Fine-Tuning × LLaMA-Factory Integration. (<a href="doc/en/KTransformers-Fine-Tuning_User-Guide.html">Tutorial</a>)</li>
+<li><strong>Oct 27, 2025</strong>: Support Ascend NPU. (<a href="doc/zh/DeepseekR1_V3_tutorial_zh_for_Ascend_NPU.html">Tutorial</a>)</li>
 <li><strong>Oct 10, 2025</strong>: Integrating into SGLang. (<a href="https://github.com/sgl-project/sglang/issues/11425">Roadmap</a>, <a href="https://lmsys.org/blog/2025-10-22-KTransformers/">Blog</a>)</li>
-<li><strong>Sept 11, 2025</strong>: Support Qwen3-Next. (<a href="./doc/en/Qwen3-Next.html">Tutorial</a>)</li>
-<li><strong>Sept 05, 2025</strong>: Support Kimi-K2-0905. (<a href="./doc/en/Kimi-K2.html">Tutorial</a>)</li>
-<li><strong>July 26, 2025</strong>: Support SmallThinker and GLM4-MoE. (<a href="./doc/en/SmallThinker_and_Glm4moe.html">Tutorial</a>)</li>
-<li><strong>July 11, 2025</strong>: Support Kimi-K2. (<a href="./doc/en/Kimi-K2.html">Tutorial</a>)</li>
-<li><strong>June 30, 2025</strong>: Support 3-layer (GPU-CPU-Disk) <a href="./doc/en/prefix_cache.html">prefix cache</a> reuse.</li>
-<li><strong>May 14, 2025</strong>: Support Intel Arc GPU (<a href="./doc/en/xpu.html">Tutorial</a>).</li>
-<li><strong>Apr 29, 2025</strong>: Support AMX-Int8、 AMX-BF16 and Qwen3MoE (<a href="./doc/en/AMX.html">Tutorial</a>)</li>
-<li><strong>Apr 9, 2025</strong>: Experimental support for LLaMA 4 models (<a href="./doc/en/llama4.html">Tutorial</a>).</li>
-<li><strong>Apr 2, 2025</strong>: Support Multi-concurrency. (<a href="./doc/en/balance-serve.html">Tutorial</a>).</li>
-<li><strong>Mar 15, 2025</strong>: Support ROCm on AMD GPU (<a href="./doc/en/ROCm.html">Tutorial</a>).</li>
-<li><strong>Mar 5, 2025</strong>: Support unsloth 1.58/2.51 bits weights and <a href="./doc/en/fp8_kernel.html">IQ1_S/FP8 hybrid</a> weights. Support 139K <a href="./doc/en/DeepseekR1_V3_tutorial.html#v022--v023-longer-context--fp8-kernel">Longer Context</a> for DeepSeek-V3 and R1 in 24GB VRAM.</li>
-<li><strong>Feb 25, 2025</strong>: Support <a href="./doc/en/fp8_kernel.html">FP8 GPU kernel</a> for DeepSeek-V3 and R1; <a href="./doc/en/DeepseekR1_V3_tutorial.html#v022-longer-context">Longer Context</a>.</li>
-<li><strong>Feb 15, 2025</strong>: Longer Context (from 4K to 8K for 24GB VRAM) &amp; Slightly Faster Speed （+15%, up to 16 Tokens/s), update <a href="./doc/en/DeepseekR1_V3_tutorial.html">docs</a> and <a href="https://kvcache-ai.github.io/ktransformers/">online books</a>.</li>
-<li><strong>Feb 10, 2025</strong>: Support Deepseek-R1 and V3 on single (24GB VRAM)/multi gpu and 382G DRAM, up to 3~28x speedup. For detailed show case and reproduction tutorial, see <a href="./doc/en/DeepseekR1_V3_tutorial.html">here</a>.</li>
-<li><strong>Aug 28, 2024</strong>: Decrease DeepseekV2's required VRAM from 21G to 11G.</li>
+<li><strong>Sept 11, 2025</strong>: Support Qwen3-Next. (<a href="doc/en/Qwen3-Next.html">Tutorial</a>)</li>
+<li><strong>Sept 05, 2025</strong>: Support Kimi-K2-0905. (<a href="doc/en/Kimi-K2.html">Tutorial</a>)</li>
+<li><strong>July 26, 2025</strong>: Support SmallThinker and GLM4-MoE. (<a href="doc/en/SmallThinker_and_Glm4moe.html">Tutorial</a>)</li>
+<li><strong>July 11, 2025</strong>: Support Kimi-K2. (<a href="doc/en/Kimi-K2.html">Tutorial</a>)</li>
+<li><strong>June 30, 2025</strong>: Support 3-layer (GPU-CPU-Disk) <a href="doc/en/prefix_cache.html">prefix cache</a> reuse.</li>
+<li><strong>May 14, 2025</strong>: Support Intel Arc GPU (<a href="doc/en/xpu.html">Tutorial</a>).</li>
+<li><strong>Apr 29, 2025</strong>: Support AMX-Int8、 AMX-BF16 and Qwen3MoE (<a href="doc/en/AMX.html">Tutorial</a>)</li>
+<li><strong>Apr 9, 2025</strong>: Experimental support for LLaMA 4 models (<a href="doc/en/llama4.html">Tutorial</a>).</li>
+<li><strong>Apr 2, 2025</strong>: Support Multi-concurrency. (<a href="doc/en/balance-serve.html">Tutorial</a>).</li>
+<li><strong>Mar 15, 2025</strong>: Support ROCm on AMD GPU (<a href="doc/en/ROCm.html">Tutorial</a>).</li>
+<li><strong>Mar 5, 2025</strong>: Support unsloth 1.58/2.51 bits weights and <a href="doc/en/fp8_kernel.html">IQ1_S/FP8 hybrid</a> weights. Support 139K <a href="doc/en/DeepseekR1_V3_tutorial.html#v022--v023-longer-context--fp8-kernel">Longer Context</a> for DeepSeek-V3 and R1 in 24GB VRAM.</li>
+<li><strong>Feb 25, 2025</strong>: Support <a href="doc/en/fp8_kernel.html">FP8 GPU kernel</a> for DeepSeek-V3 and R1; <a href="doc/en/DeepseekR1_V3_tutorial.html#v022-longer-context">Longer Context</a>.</li>
+<li><strong>Feb 15, 2025</strong>: Longer Context (from 4K to 8K for 24GB VRAM) &amp; Slightly Faster Speed （+15%, up to 16 Tokens/s), update <a href="doc/en/DeepseekR1_V3_tutorial.html">docs</a> and <a href="https://kvcache-ai.github.io/ktransformers/">online books</a>.</li>
+<li><strong>Feb 10, 2025</strong>: Support Deepseek-R1 and V3 on single (24GB VRAM)/multi gpu and 382G DRAM, up to 3~28x speedup. For detailed show case and reproduction tutorial, see <a href="doc/en/DeepseekR1_V3_tutorial.html">here</a>.</li>
+<li><strong>Aug 28, 2024</strong>: Decrease DeepseekV2’s required VRAM from 21G to 11G.</li>
 <li><strong>Aug 15, 2024</strong>: Update detailed <a href="doc/en/injection_tutorial.html">tutorial</a> for injection and multi-GPU.</li>
 <li><strong>Aug 14, 2024</strong>: Support llamfile as linear backend.</li>
 <li><strong>Aug 12, 2024</strong>: Support multiple GPU; Support new model: mixtral 8*7B  and 8*22B; Support q2k, q3k, q5k dequant on gpu.</li>
 <li><strong>Aug 9, 2024</strong>: Support windows native.</li>
 </ul>
-<hr />
+<hr>
 <h2 id="-core-modules"><a class="header" href="#-core-modules">📦 Core Modules</a></h2>
-<h3 id="-kt-kernel---high-performance-inference-kernels"><a class="header" href="#-kt-kernel---high-performance-inference-kernels">🚀 <a href="./kt-kernel/">kt-kernel</a> - High-Performance Inference Kernels</a></h3>
+<h3 id="-kt-kernel---high-performance-inference-kernels"><a class="header" href="#-kt-kernel---high-performance-inference-kernels">🚀 <a href="kt-kernel">kt-kernel</a> - High-Performance Inference Kernels</a></h3>
 <p>CPU-optimized kernel operations for heterogeneous LLM inference.</p>
 <img width="1049" height="593" alt="image" src="https://github.com/user-attachments/assets/68f423da-3f55-4025-bdc9-9ceaa554f00b" />
 <p><strong>Key Features:</strong></p>
@@ -239,15 +244,21 @@ pip install .
 <li>Heterogeneous expert placement (hot experts on GPU, cold experts on CPU)</li>
 </ul>
 <p><strong>Performance Examples:</strong></p>
-<div class="table-wrapper"><table><thead><tr><th>Model</th><th>Hardware Configuration</th><th>Total Throughput</th><th>Output Throughput</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>Model</th><th>Hardware Configuration</th><th>Total Throughput</th><th>Output Throughput</th></tr>
+</thead>
+<tbody>
 <tr><td>DeepSeek-R1-0528 (FP8)</td><td>8×L20 GPU + Xeon Gold 6454S</td><td>227.85 tokens/s</td><td>87.58 tokens/s (8-way concurrency)</td></tr>
-</tbody></table>
+</tbody>
+</table>
 </div>
-<p>👉 <strong><a href="./kt-kernel/README.html">Full Documentation →</a></strong></p>
-<hr />
-<h3 id="-kt-sft---fine-tuning-framework"><a class="header" href="#-kt-sft---fine-tuning-framework">🎓 <a href="./kt-sft/">kt-sft</a> - Fine-Tuning Framework</a></h3>
+<p>👉 <strong><a href="kt-kernel/README.html">Full Documentation →</a></strong></p>
+<hr>
+<h3 id="-kt-sft---fine-tuning-framework"><a class="header" href="#-kt-sft---fine-tuning-framework">🎓 <a href="kt-sft">kt-sft</a> - Fine-Tuning Framework</a></h3>
 <p>KTransformers × LLaMA-Factory integration for ultra-large MoE model fine-tuning.</p>
-<p><img src="./doc/assets/image-20251011010558909.png" alt="image-20251011010558909" /></p>
+<p><img src="doc/assets/image-20251011010558909.png" alt="image-20251011010558909"></p>
 <p><strong>Key Features:</strong></p>
 <ul>
 <li><strong>Resource Efficient</strong>: Fine-tune 671B DeepSeek-V3 with just <strong>70GB GPU memory</strong> + 1.3TB RAM</li>
@@ -256,18 +267,24 @@ pip install .
 <li><strong>Production Ready</strong>: Chat, batch inference, and metrics evaluation</li>
 </ul>
 <p><strong>Performance Examples:</strong></p>
-<div class="table-wrapper"><table><thead><tr><th>Model</th><th>Configuration</th><th>Throughput</th><th>GPU Memory</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>Model</th><th>Configuration</th><th>Throughput</th><th>GPU Memory</th></tr>
+</thead>
+<tbody>
 <tr><td>DeepSeek-V3 (671B)</td><td>LoRA + AMX</td><td>~40 tokens/s</td><td>70GB (multi-GPU)</td></tr>
 <tr><td>DeepSeek-V2-Lite (14B)</td><td>LoRA + AMX</td><td>~530 tokens/s</td><td>6GB</td></tr>
-</tbody></table>
+</tbody>
+</table>
 </div>
 <p><strong>Quick Start:</strong></p>
 <pre><code class="language-bash">cd kt-sft
 # Install environment following kt-sft/README.md
 USE_KT=1 llamafactory-cli train examples/train_lora/deepseek3_lora_sft_kt.yaml
 </code></pre>
-<p>👉 <strong><a href="./kt-sft/README.html">Full Documentation →</a></strong></p>
-<hr />
+<p>👉 <strong><a href="kt-sft/README.html">Full Documentation →</a></strong></p>
+<hr>
 <h2 id="-citation"><a class="header" href="#-citation">🔥 Citation</a></h2>
 <p>If you use KTransformers in your research, please cite our paper:</p>
 <pre><code class="language-bibtex">@inproceedings{10.1145/3731569.3764843,
@@ -288,58 +305,59 @@ USE_KT=1 llamafactory-cli train examples/train_lora/deepseek3_lora_sft_kt.yaml
 <h2 id="-community--support"><a class="header" href="#-community--support">💬 Community &amp; Support</a></h2>
 <ul>
 <li><strong>GitHub Issues</strong>: <a href="https://github.com/kvcache-ai/ktransformers/issues">Report bugs or request features</a></li>
-<li><strong>WeChat Group</strong>: See <a href="./archive/WeChatGroup.png">archive/WeChatGroup.png</a></li>
+<li><strong>WeChat Group</strong>: See <a href="archive/WeChatGroup.png">archive/WeChatGroup.png</a></li>
 </ul>
 <h2 id="-kt-original-code"><a class="header" href="#-kt-original-code">📦 KT original Code</a></h2>
-<p>The original integrated KTransformers framework has been archived to the <a href="./archive/"><code>archive/</code></a> directory for reference. The project now focuses on the two core modules above for better modularity and maintainability.</p>
+<p>The original integrated KTransformers framework has been archived to the <a href="archive"><code>archive/</code></a> directory for reference. The project now focuses on the two core modules above for better modularity and maintainability.</p>
 <p>For the original documentation with full quick-start guides and examples, see:</p>
 <ul>
-<li><a href="./archive/README.html">archive/README.md</a> (English)</li>
-<li><a href="./archive/README_ZH.html">archive/README_ZH.md</a> (中文)</li>
+<li><a href="archive/README.html">archive/README.md</a> (English)</li>
+<li><a href="archive/README_ZH.html">archive/README_ZH.md</a> (中文)</li>
 </ul>
-<div style="break-before: page; page-break-before: always;"></div><h1 id="kt-kernel"><a class="header" href="#kt-kernel">KT-Kernel</a></h1>
+<div style="break-before: page; page-break-before: always;"></div>
+<h1 id="kt-kernel"><a class="header" href="#kt-kernel">KT-Kernel</a></h1>
 <p>High-performance kernel operations for KTransformers, featuring CPU-optimized MoE inference with AMX, AVX, KML and blis (amd library) support.</p>
 <ul>
-<li><a href="en/kt-kernel/kt-kernel_intro.html#note">Note</a></li>
-<li><a href="en/kt-kernel/kt-kernel_intro.html#features">Features</a></li>
-<li><a href="en/kt-kernel/kt-kernel_intro.html#installation">Installation</a>
+<li><a href="#note">Note</a></li>
+<li><a href="#features">Features</a></li>
+<li><a href="#installation">Installation</a>
 <ul>
-<li><a href="en/kt-kernel/kt-kernel_intro.html#prerequisites">Prerequisites</a></li>
-<li><a href="en/kt-kernel/kt-kernel_intro.html#quick-installation-recommended">Quick Installation (Recommended)</a></li>
-<li><a href="en/kt-kernel/kt-kernel_intro.html#manual-configuration-advanced">Manual Configuration (Advanced)</a></li>
+<li><a href="#prerequisites">Prerequisites</a></li>
+<li><a href="#quick-installation-recommended">Quick Installation (Recommended)</a></li>
+<li><a href="#manual-configuration-advanced">Manual Configuration (Advanced)</a></li>
 </ul>
 </li>
-<li><a href="en/kt-kernel/kt-kernel_intro.html#verification">Verification</a></li>
-<li><a href="en/kt-kernel/kt-kernel_intro.html#integration-with-sglang">Integration with SGLang</a>
+<li><a href="#verification">Verification</a></li>
+<li><a href="#integration-with-sglang">Integration with SGLang</a>
 <ul>
-<li><a href="en/kt-kernel/kt-kernel_intro.html#installation-steps">Installation Steps</a></li>
-<li><a href="en/kt-kernel/kt-kernel_intro.html#complete-example-qwen3-30b-a3b">Complete Example: Qwen3-30B-A3B</a></li>
-<li><a href="en/kt-kernel/kt-kernel_intro.html#kt-kernel-parameters">KT-Kernel Parameters</a></li>
+<li><a href="#installation-steps">Installation Steps</a></li>
+<li><a href="#complete-example-qwen3-30b-a3b">Complete Example: Qwen3-30B-A3B</a></li>
+<li><a href="#kt-kernel-parameters">KT-Kernel Parameters</a></li>
 </ul>
 </li>
-<li><a href="en/kt-kernel/kt-kernel_intro.html#direct-python-api-usage">Direct Python API Usage</a>
+<li><a href="#direct-python-api-usage">Direct Python API Usage</a>
 <ul>
-<li><a href="en/kt-kernel/kt-kernel_intro.html#advanced-options">Advanced Options</a></li>
+<li><a href="#advanced-options">Advanced Options</a></li>
 </ul>
 </li>
-<li><a href="en/kt-kernel/kt-kernel_intro.html#build-configuration">Build Configuration</a>
+<li><a href="#build-configuration">Build Configuration</a>
 <ul>
-<li><a href="en/kt-kernel/kt-kernel_intro.html#manual-installation">Manual Installation</a></li>
+<li><a href="#manual-installation">Manual Installation</a></li>
 </ul>
 </li>
-<li><a href="en/kt-kernel/kt-kernel_intro.html#error-troubleshooting">Error Troubleshooting</a>
+<li><a href="#error-troubleshooting">Error Troubleshooting</a>
 <ul>
-<li><a href="en/kt-kernel/kt-kernel_intro.html#cuda-not-found">CUDA Not Found</a></li>
-<li><a href="en/kt-kernel/kt-kernel_intro.html#hwloc-not-found">hwloc Not Found</a></li>
+<li><a href="#cuda-not-found">CUDA Not Found</a></li>
+<li><a href="#hwloc-not-found">hwloc Not Found</a></li>
 </ul>
 </li>
-<li><a href="en/kt-kernel/kt-kernel_intro.html#weight-quantization">Weight Quantization</a>
+<li><a href="#weight-quantization">Weight Quantization</a>
 <ul>
-<li><a href="en/kt-kernel/kt-kernel_intro.html#cpu-weights-for-cold-experts-on-cpu">CPU Weights (for "cold" experts on CPU)</a></li>
-<li><a href="en/kt-kernel/kt-kernel_intro.html#gpu-weights-for-hot-experts-on-gpu">GPU Weights (for "hot" experts on GPU)</a></li>
+<li><a href="#cpu-weights-for-cold-experts-on-cpu">CPU Weights (for “cold” experts on CPU)</a></li>
+<li><a href="#gpu-weights-for-hot-experts-on-gpu">GPU Weights (for “hot” experts on GPU)</a></li>
 </ul>
 </li>
-<li><a href="en/kt-kernel/kt-kernel_intro.html#before-commit">Before Commit!</a></li>
+<li><a href="#before-commit">Before Commit!</a></li>
 </ul>
 <h2 id="note"><a class="header" href="#note">Note</a></h2>
 <p><strong>Current Support Status:</strong></p>
@@ -401,12 +419,12 @@ export CPUINFER_ENABLE_AMX=OFF       # Options: ON, OFF
 # Run with manual mode (build only)
 ./install.sh build --manual
 </code></pre>
-<p>For advanced build options and binary distribution, see the <a href="en/kt-kernel/kt-kernel_intro.html#build-configuration">Build Configuration</a> section. If you encounter issues, refer to <a href="en/kt-kernel/kt-kernel_intro.html#error-troubleshooting">Error Troubleshooting</a>.</p>
+<p>For advanced build options and binary distribution, see the <a href="#build-configuration">Build Configuration</a> section. If you encounter issues, refer to <a href="#error-troubleshooting">Error Troubleshooting</a>.</p>
 <h2 id="verification"><a class="header" href="#verification">Verification</a></h2>
 <pre><code class="language-bash">python -c "from kt_kernel import KTMoEWrapper; print('✓ kt-kernel installed successfully')"
 </code></pre>
 <h2 id="integration-with-sglang"><a class="header" href="#integration-with-sglang">Integration with SGLang</a></h2>
-<p>KT-Kernel can be used standalone via <a href="en/kt-kernel/kt-kernel_intro.html#direct-python-api-usage">Direct Python API</a> or integrated with SGLang for production deployment. This section describes SGLang integration to enable CPU-GPU heterogeneous inference, where "hot" experts run on GPU and "cold" experts run on CPU for optimal resource utilization.</p>
+<p>KT-Kernel can be used standalone via <a href="#direct-python-api-usage">Direct Python API</a> or integrated with SGLang for production deployment. This section describes SGLang integration to enable CPU-GPU heterogeneous inference, where “hot” experts run on GPU and “cold” experts run on CPU for optimal resource utilization.</p>
 <h3 id="installation-steps"><a class="header" href="#installation-steps">Installation Steps</a></h3>
 <h4 id="1-install-sglang"><a class="header" href="#1-install-sglang">1. Install SGLang</a></h4>
 <pre><code class="language-bash">git clone https://github.com/sgl-project/sglang.git
@@ -426,8 +444,8 @@ pip install -e "python[all]"
 <p><strong>Supported input formats:</strong> FP8, FP16, BF16 → INT4/INT8.</p>
 <p>For more details, see:</p>
 <ul>
-<li><a href="en/kt-kernel/kt-kernel_intro.html#cpu-weights-for-cold-experts-on-cpu">CPU Weights conversion</a></li>
-<li><a href="en/kt-kernel/kt-kernel_intro.html#gpu-weights-for-hot-experts-on-gpu">GPU Weights quantization</a></li>
+<li><a href="#cpu-weights-for-cold-experts-on-cpu">CPU Weights conversion</a></li>
+<li><a href="#gpu-weights-for-hot-experts-on-gpu">GPU Weights quantization</a></li>
 </ul>
 <p><strong>Note:</strong> LLAMAFILE backend supports GGUF format directly, but this feature is still in preview.</p>
 <h4 id="3-launch-sglang-server"><a class="header" href="#3-launch-sglang-server">3. Launch SGLang Server</a></h4>
@@ -451,7 +469,7 @@ pip install -e "python[all]"
   --kt-num-gpu-experts 32 \
   --kt-max-deferred-experts-per-token 2
 </code></pre>
-<p>See <a href="en/kt-kernel/kt-kernel_intro.html#kt-kernel-parameters">KT-Kernel Parameters</a> section below for detailed parameter tuning guidelines.</p>
+<p>See <a href="#kt-kernel-parameters">KT-Kernel Parameters</a> section below for detailed parameter tuning guidelines.</p>
 <h3 id="complete-example-qwen3-30b-a3b"><a class="header" href="#complete-example-qwen3-30b-a3b">Complete Example: Qwen3-30B-A3B</a></h3>
 <p>This example demonstrates the full workflow from downloading weights to launching the server.</p>
 <p><strong>Hardware Configuration:</strong></p>
@@ -511,14 +529,20 @@ hf download Qwen/Qwen3-30B-A3B --local-dir /mnt/data/models/Qwen3-30B-A3B
   --kt-max-deferred-experts-per-token 2
 </code></pre>
 <h3 id="kt-kernel-parameters"><a class="header" href="#kt-kernel-parameters">KT-Kernel Parameters</a></h3>
-<div class="table-wrapper"><table><thead><tr><th>Parameter</th><th>Description</th><th>Example Value</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>Parameter</th><th>Description</th><th>Example Value</th></tr>
+</thead>
+<tbody>
 <tr><td><code>--kt-method</code></td><td>CPU inference backend method</td><td><code>AMXINT4</code>, <code>AMXINT8</code>, or <code>LLAMAFILE</code> (preview)</td></tr>
 <tr><td><code>--kt-weight-path</code></td><td>Path to quantized CPU weights</td><td><code>/path/to/cpu-weights</code></td></tr>
 <tr><td><code>--kt-cpuinfer</code></td><td>Number of CPU inference threads</td><td><code>64</code> (adjust based on CPU cores)</td></tr>
 <tr><td><code>--kt-threadpool-count</code></td><td>Number of thread pools for parallel execution</td><td><code>2</code> (typically 1-4)</td></tr>
 <tr><td><code>--kt-num-gpu-experts</code></td><td>Number of experts to keep on GPU</td><td><code>32</code> (remaining experts go to CPU)</td></tr>
 <tr><td><code>--kt-max-deferred-experts-per-token</code></td><td>Number of experts per token to defer for pipelined execution</td><td><code>2</code> (0 to disable, 1-2 recommended)</td></tr>
-</tbody></table>
+</tbody>
+</table>
 </div>
 <p><strong>Parameter Guidelines:</strong></p>
 <ul>
@@ -640,13 +664,19 @@ KTMoEWrapper.clear_buffer_cache()
 </ul>
 <h4 id="2-set-build-configuration"><a class="header" href="#2-set-build-configuration">2. Set Build Configuration</a></h4>
 <p><strong>Core Options:</strong></p>
-<div class="table-wrapper"><table><thead><tr><th>Variable</th><th>Options</th><th>Description</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>Variable</th><th>Options</th><th>Description</th></tr>
+</thead>
+<tbody>
 <tr><td><code>CPUINFER_CPU_INSTRUCT</code></td><td><code>NATIVE</code>, <code>AVX512</code>, <code>AVX2</code>, <code>FANCY</code></td><td>CPU instruction set to use</td></tr>
 <tr><td><code>CPUINFER_ENABLE_AMX</code></td><td><code>ON</code>, <code>OFF</code></td><td>Enable Intel AMX support</td></tr>
 <tr><td><code>CPUINFER_BUILD_TYPE</code></td><td><code>Release</code>, <code>Debug</code>, <code>RelWithDebInfo</code></td><td>Build type (default: <code>Release</code>)</td></tr>
 <tr><td><code>CPUINFER_PARALLEL</code></td><td>Number</td><td>Parallel build jobs (default: auto-detect)</td></tr>
 <tr><td><code>CPUINFER_VERBOSE</code></td><td><code>0</code>, <code>1</code></td><td>Verbose build output (default: <code>0</code>)</td></tr>
-</tbody></table>
+</tbody>
+</table>
 </div>
 <p><strong>Instruction Set Details:</strong></p>
 <ul>
@@ -698,7 +728,7 @@ sudo make install
 </code></pre>
 <h2 id="weight-quantization"><a class="header" href="#weight-quantization">Weight Quantization</a></h2>
 <p>KT-Kernel provides weight quantization tools for CPU-GPU hybrid inference (e.g., integrating with SGLang). Both tools work together to enable heterogeneous expert placement across CPUs and GPUs.</p>
-<h3 id="cpu-weights-for-cold-experts-on-cpu"><a class="header" href="#cpu-weights-for-cold-experts-on-cpu">CPU Weights (for "cold" experts on CPU)</a></h3>
+<h3 id="cpu-weights-for-cold-experts-on-cpu"><a class="header" href="#cpu-weights-for-cold-experts-on-cpu">CPU Weights (for “cold” experts on CPU)</a></h3>
 <p>Quantize weights to INT4/INT8 format optimized for AMX inference:</p>
 <pre><code class="language-bash">python scripts/convert_cpu_weights.py \
   --input-path /path/to/model \
@@ -707,7 +737,7 @@ sudo make install
   --quant-method int4
 </code></pre>
 <p><strong>Supported formats:</strong> FP8, FP16, BF16 → INT4/INT8</p>
-<h3 id="gpu-weights-for-hot-experts-on-gpu"><a class="header" href="#gpu-weights-for-hot-experts-on-gpu">GPU Weights (for "hot" experts on GPU)</a></h3>
+<h3 id="gpu-weights-for-hot-experts-on-gpu"><a class="header" href="#gpu-weights-for-hot-experts-on-gpu">GPU Weights (for “hot” experts on GPU)</a></h3>
 <p>Apply GPTQ quantization to model weights:</p>
 <pre><code class="language-bash"># Install additional dependencies first
 pip install accelerate transformers llmcompressor datasets
@@ -719,7 +749,7 @@ python scripts/convert_gpu_weights.py \
   --quant_type W4A16
 </code></pre>
 <p><strong>Supported types:</strong> W4A16 (GPTQ4), W8A16 (GPTQ8)</p>
-<hr />
+<hr>
 <p>For detailed documentation, advanced options, and low-memory mode, see <a href="en/kt-kernel/scripts/README.html">scripts/README.md</a>.</p>
 <h2 id="before-commit"><a class="header" href="#before-commit">Before Commit!</a></h2>
 <p>your msg should match: Conventional Commits (https://www.conventionalcommits.org/) <br>and format your code before commit:</p>
@@ -734,27 +764,28 @@ rm -rf build
 <p>and you may need black for python format:</p>
 <pre><code class="language-shell">conda install black
 </code></pre>
-<div style="break-before: page; page-break-before: always;"></div><ul>
-<li><a href="en/SFT/KTransformers-Fine-Tuning_User-Guide.html#introduction">Introduction</a>
+<div style="break-before: page; page-break-before: always;"></div>
 <ul>
-<li><a href="en/SFT/KTransformers-Fine-Tuning_User-Guide.html#fine-tuning-results-examples">Fine-Tuning Results (Examples)</a></li>
+<li><a href="#introduction">Introduction</a>
+<ul>
+<li><a href="#fine-tuning-results-examples">Fine-Tuning Results (Examples)</a></li>
 </ul>
 </li>
-<li><a href="en/SFT/KTransformers-Fine-Tuning_User-Guide.html#quick-to-start">Quick to Start</a>
+<li><a href="#quick-to-start">Quick to Start</a>
 <ul>
-<li><a href="en/SFT/KTransformers-Fine-Tuning_User-Guide.html#environment-setup">Environment Setup</a></li>
-<li><a href="en/SFT/KTransformers-Fine-Tuning_User-Guide.html#core-feature-1-use-ktransformers-backend-to-fine-tune-ultra-large-moe-models">Core Feature 1: Use KTransformers backend to fine-tune ultra-large MoE models</a></li>
-<li><a href="en/SFT/KTransformers-Fine-Tuning_User-Guide.html#core-feature-2-chat-with-the-fine-tuned-model-base--lora-adapter">Core Feature 2: Chat with the fine-tuned model (base + LoRA adapter)</a></li>
-<li><a href="en/SFT/KTransformers-Fine-Tuning_User-Guide.html#core-feature-3-batch-inference--metrics-base--lora-adapter">Core Feature 3: Batch inference + metrics (base + LoRA adapter)</a></li>
+<li><a href="#environment-setup">Environment Setup</a></li>
+<li><a href="#core-feature-1-use-ktransformers-backend-to-fine-tune-ultra-large-moe-models">Core Feature 1: Use KTransformers backend to fine-tune ultra-large MoE models</a></li>
+<li><a href="#core-feature-2-chat-with-the-fine-tuned-model-base--lora-adapter">Core Feature 2: Chat with the fine-tuned model (base + LoRA adapter)</a></li>
+<li><a href="#core-feature-3-batch-inference--metrics-base--lora-adapter">Core Feature 3: Batch inference + metrics (base + LoRA adapter)</a></li>
 </ul>
 </li>
-<li><a href="en/SFT/KTransformers-Fine-Tuning_User-Guide.html#kt-fine-tuning-speed-user-side-view">KT Fine-Tuning Speed (User-Side View)</a>
+<li><a href="#kt-fine-tuning-speed-user-side-view">KT Fine-Tuning Speed (User-Side View)</a>
 <ul>
-<li><a href="en/SFT/KTransformers-Fine-Tuning_User-Guide.html#end-to-end-performance">End-to-End Performance</a></li>
-<li><a href="en/SFT/KTransformers-Fine-Tuning_User-Guide.html#gpucpu-memory-footprint">GPU/CPU Memory Footprint</a></li>
+<li><a href="#end-to-end-performance">End-to-End Performance</a></li>
+<li><a href="#gpucpu-memory-footprint">GPU/CPU Memory Footprint</a></li>
 </ul>
 </li>
-<li><a href="en/SFT/KTransformers-Fine-Tuning_User-Guide.html#conclusion">Conclusion</a></li>
+<li><a href="#conclusion">Conclusion</a></li>
 </ul>
 <h1 id="ktransformers-fine-tuning--llama-factory-integration--user-guide"><a class="header" href="#ktransformers-fine-tuning--llama-factory-integration--user-guide">KTransformers Fine-Tuning × LLaMA-Factory Integration – User Guide</a></h1>
 <p><strong>MadSys Lab, KVCache-AI Team, Approaching AI, LLaMA-Factory Team</strong></p>
@@ -762,45 +793,71 @@ rm -rf build
 <p>From <strong>DeepSeek-V3/R1</strong> to <strong>Qwen3-MoE</strong> and <strong>Kimi-K2</strong>, each wave of open-sourced large models brings leaps in performance and scale. However, many researchers and developers are constrained by expensive GPUs and models with tens or even hundreds of billions of parameters, making it <strong>hard to fine-tune very large models under limited resources</strong>. To bridge this gap, we propose a practical approach: combining <strong>KTransformers</strong> with <strong>LLaMA-Factory</strong>. With just <strong>2–4 RTX 4090s</strong> and a high-memory CPU, you can fine-tune ultra-large MoE models like DeepSeek-671B.</p>
 <p>Our goal is to give resource-constrained researchers a <strong>local path to explore fine-tuning ultra-large models</strong>, and also a fast way to customize smaller models (e.g., 14B/30B) for specific scenarios. We validate the setup using <strong>stylized dialogue</strong>, <strong>Westernized translation tone</strong>, and <strong>medical Q&amp;A</strong> as representative tasks, showing that <strong>personalized adaptation can be achieved within hours</strong>.</p>
 <p>As shown below, LLaMA-Factory is the unified orchestration/configuration layer for the whole fine-tuning workflow—handling data, training scheduling, LoRA injection, and inference interfaces. <strong>KTransformers</strong> acts as a pluggable high-performance backend that takes over core operators like Attention/MoE under the same training configs, enabling efficient <strong>GPU+CPU heterogeneous cooperation</strong>.</p>
-<p><img src="en/SFT/../../assets/image-20251011010558909.png" alt="image-20251011010558909" /></p>
+<p><img src="assets/image-20251011010558909.png" alt="image-20251011010558909"></p>
 <p>Within LLaMA-Factory, we compared LoRA fine-tuning with <strong>HuggingFace</strong>, <strong>Unsloth</strong>, and <strong>KTransformers</strong> backends. KTransformers is the <strong>only workable 4090-class solution</strong> for ultra-large MoE models (e.g., 671B) and also delivers higher throughput and lower GPU memory on smaller MoE models (e.g., DeepSeek-14B).</p>
-<div class="table-wrapper"><table><thead><tr><th>Under LoRA (BF16) + <a href="https://github.com/mindsRiverPonder/LLM-practice">NekoQA-10K stylized dialogue</a></th><th>HuggingFace Backend</th><th>Unsloth Backend</th><th>KTransformers Backend</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>Under LoRA (BF16) + <a href="https://github.com/mindsRiverPonder/LLM-practice">NekoQA-10K stylized dialogue</a></th><th>HuggingFace Backend</th><th>Unsloth Backend</th><th>KTransformers Backend</th></tr>
+</thead>
+<tbody>
 <tr><td>[14B-DeepSeekV2-Lite] LoRA fine-tuning throughput</td><td>303.58 token/s</td><td>455.37 token/s</td><td>530.38 token/s</td></tr>
 <tr><td>[14B-DeepSeekV2-Lite] GPU memory</td><td>32.12 GB</td><td>9.64 GB</td><td>6.08 GB</td></tr>
-<tr><td>[671B-DeepSeekV3] LoRA fine-tuning throughput</td><td><font color='red'>Too Huge to run</font></td><td><font color='red'>NOT SUPPORT</font></td><td>40.35 token/s</td></tr>
-<tr><td>[671B-DeepSeekV3] GPU memory (sum across GPUs)</td><td>theoretical 1400 GB †</td><td><font color='red'>NOT SUPPORT</font></td><td>70 GB †</td></tr>
-</tbody></table>
+<tr><td>[671B-DeepSeekV3] LoRA fine-tuning throughput</td><td><font color="red">Too Huge to run</font></td><td><font color="red">NOT SUPPORT</font></td><td>40.35 token/s</td></tr>
+<tr><td>[671B-DeepSeekV3] GPU memory (sum across GPUs)</td><td>theoretical 1400 GB †</td><td><font color="red">NOT SUPPORT</font></td><td>70 GB †</td></tr>
+</tbody>
+</table>
 </div>
 <p>† <strong>1400 GB</strong> is a <strong>theoretical</strong> FP16 full-parameter resident footprint (not runnable). <strong>70 GB</strong> is the <strong>measured peak</strong> with KT strategy (Attention on GPU + layered MoE offload).</p>
-<p><img src="en/SFT/../../assets/image-compare_model.png" alt="按照模型划分的对比图_02" /></p>
+<p><img src="assets/image-compare_model.png" alt="按照模型划分的对比图_02"></p>
 <h3 id="fine-tuning-results-examples"><a class="header" href="#fine-tuning-results-examples">Fine-Tuning Results (Examples)</a></h3>
 <h4 id="stylized-dialogue-catgirl-tone"><a class="header" href="#stylized-dialogue-catgirl-tone">Stylized Dialogue (CatGirl tone)</a></h4>
 <p>Dataset: <a href="https://zhuanlan.zhihu.com/p/1934983798233231689">NekoQA-10K</a>. Goal: improve style consistency and recognizability.</p>
 <p>The figure compares responses from the base vs. fine-tuned models. The fine-tuned model maintains the target tone and address terms more consistently (red boxes), validating the effectiveness of <strong>style-transfer fine-tuning</strong>.</p>
-<p><img src="en/SFT/../../assets/image-20251016175046882.png" alt="image-20251016175046882" /></p>
+<p><img src="assets/image-20251016175046882.png" alt="image-20251016175046882"></p>
 <h4 id="benchmarks"><a class="header" href="#benchmarks">Benchmarks</a></h4>
 <p>We use:</p>
 <p>(1) <a href="https://github.com/Benson114/Translational-Style-ChatLLM">Translational-Style-ChatLLM</a>, which asks for an exaggerated, Westernized translation tone—clear, stylized customization.</p>
 <p>(2) <a href="https://aclanthology.org/2025.acl-long.96/">AfriMed-QA</a> (ACL 2025), a medical dataset for African contexts with strong domain specificity, including multiple-choice and short-answer sub-tasks—well-suited for vertical fine-tuning evaluation.</p>
 <p>The tables show metrics before vs. after LoRA fine-tuning. We observe <strong>large improvements</strong> across metrics, verifying fine-tuning effectiveness:</p>
-<div class="table-wrapper"><table><thead><tr><th>Translational-Style dataset</th><th>BLEU-1</th><th>BLEU-2</th><th>BLEU-3</th><th>BLEU-4</th><th>ROUGE-1</th><th>ROUGE-2</th><th>ROUGE-L</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>Translational-Style dataset</th><th>BLEU-1</th><th>BLEU-2</th><th>BLEU-3</th><th>BLEU-4</th><th>ROUGE-1</th><th>ROUGE-2</th><th>ROUGE-L</th></tr>
+</thead>
+<tbody>
 <tr><td>V2-Lite (no LoRA)</td><td>20.66</td><td>8.33</td><td>4.54</td><td>2.89</td><td>22.71</td><td>4.52</td><td>19.19</td></tr>
 <tr><td><strong>KT-LoRA fine-tuned V2-Lite</strong></td><td><strong>35.41</strong></td><td><strong>22.44</strong></td><td><strong>15.42</strong></td><td><strong>11.18</strong></td><td><strong>42.03</strong></td><td><strong>18.38</strong></td><td><strong>33.10</strong></td></tr>
 <tr><td>V3 base (no LoRA)</td><td>8.49</td><td>3.34</td><td>1.62</td><td>0.96</td><td>15.91</td><td>2.55</td><td>10.07</td></tr>
 <tr><td><strong>KT-LoRA fine-tuned V3</strong></td><td><strong>37.02</strong></td><td><strong>23.70</strong></td><td><strong>16.21</strong></td><td><strong>11.49</strong></td><td><strong>43.43</strong></td><td><strong>18.96</strong></td><td><strong>34.54</strong></td></tr>
-</tbody></table>
-</div><div class="table-wrapper"><table><thead><tr><th>AfriMed-QA (short answer)</th><th>BLEU-1</th><th>BLEU-2</th><th>BLEU-3</th><th>BLEU-4</th><th>ROUGE-1</th><th>ROUGE-2</th><th>ROUGE-L</th></tr></thead><tbody>
+</tbody>
+</table>
+</div>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>AfriMed-QA (short answer)</th><th>BLEU-1</th><th>BLEU-2</th><th>BLEU-3</th><th>BLEU-4</th><th>ROUGE-1</th><th>ROUGE-2</th><th>ROUGE-L</th></tr>
+</thead>
+<tbody>
 <tr><td>V2-Lite (no LoRA)</td><td>13.58</td><td>11.12</td><td>9.10</td><td>7.23</td><td>22.48</td><td>7.81</td><td>11.73</td></tr>
 <tr><td><strong>KT-LoRA fine-tuned V2-Lite</strong></td><td><strong>35.90</strong></td><td><strong>27.63</strong></td><td><strong>22.99</strong></td><td><strong>19.15</strong></td><td><strong>35.25</strong></td><td><strong>17.50</strong></td><td><strong>28.44</strong></td></tr>
 <tr><td>V3 base (no LoRA)</td><td>12.75</td><td>10.27</td><td>8.05</td><td>5.99</td><td>20.33</td><td>5.65</td><td>10.11</td></tr>
 <tr><td><strong>KT-LoRA fine-tuned V3</strong></td><td><strong>42.42</strong></td><td><strong>34.12</strong></td><td><strong>28.95</strong></td><td><strong>24.54</strong></td><td><strong>41.97</strong></td><td><strong>22.37</strong></td><td><strong>33.28</strong></td></tr>
-</tbody></table>
-</div><div class="table-wrapper"><table><thead><tr><th>AfriMed-QA (multiple choice)</th><th>Accuracy</th></tr></thead><tbody>
+</tbody>
+</table>
+</div>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>AfriMed-QA (multiple choice)</th><th>Accuracy</th></tr>
+</thead>
+<tbody>
 <tr><td>V2-Lite (no LoRA)</td><td>0.0645</td></tr>
 <tr><td><strong>KT-LoRA fine-tuned V2-Lite</strong></td><td><strong>0.4812</strong></td></tr>
 <tr><td>V3 base (no LoRA)</td><td>0.5833</td></tr>
 <tr><td><strong>KT-LoRA fine-tuned V3</strong></td><td><strong>0.7930</strong></td></tr>
-</tbody></table>
+</tbody>
+</table>
 </div>
 <p>Even for ultra-large MoE models, <strong>KTransformers-backed fine-tuning</strong> achieves strong task performance quickly.</p>
 <h2 id="quick-to-start"><a class="header" href="#quick-to-start">Quick to Start</a></h2>
@@ -890,12 +947,18 @@ cpu_infer: 32
 chunk_size: 8192
 </code></pre>
 <p><code>kt_optimize_rule</code> controls <strong>placement strategy</strong>. See also <a href="https://github.com/kvcache-ai/ktransformers/tree/main/ktransformers/optimize/optimize_rules">ktransformers/optimize_rules</a>. Naming hints (<code>*</code> = wildcard):</p>
-<div class="table-wrapper"><table><thead><tr><th>Pattern</th><th>Meaning</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>Pattern</th><th>Meaning</th></tr>
+</thead>
+<tbody>
 <tr><td>DeepSeek-V2-Lite-Chat-* / DeepSeek-V3-Chat-*</td><td>Target model variants</td></tr>
 <tr><td><em>-sft-</em></td><td>Strategy for fine-tuning; others are for inference</td></tr>
 <tr><td><em>-amx-</em></td><td>Use AMX on CPU; otherwise use <strong>llamafile</strong></td></tr>
 <tr><td><em>-multi-gpu-X</em></td><td>Model parallel on X GPUs (X omitted → default 2 GPUs)</td></tr>
-</tbody></table>
+</tbody>
+</table>
 </div>
 <p>Example: <code>DeepSeek-V3-Chat-sft-amx-multi-gpu.yaml</code> = V3-Chat fine-tuning with AMX and 2-GPU model parallel.</p>
 <p>We recommend <strong>AMX acceleration</strong> where available (<code>lscpu | grep amx</code>). AMX supports BF16/INT8. Example:</p>
@@ -912,7 +975,7 @@ chunk_size: 8192
       backend: "AMXInt8" # or "AMXBF16" or "llamafile" (default)
 </code></pre>
 <p>Outputs go to <code>output_dir</code> in safetensors format plus adapter metadata for later loading.</p>
-<p><img src="en/SFT/../../assets/image-20251016171537997.png" alt="image-20251016171537997" /></p>
+<p><img src="assets/image-20251016171537997.png" alt="image-20251016171537997"></p>
 <h3 id="core-feature-2-chat-with-the-fine-tuned-model-base--lora-adapter"><a class="header" href="#core-feature-2-chat-with-the-fine-tuned-model-base--lora-adapter">Core Feature 2: Chat with the fine-tuned model (base + LoRA adapter)</a></h3>
 <p>Run the command: <code>llamafactory-cli chat examples/inference/deepseek3_lora_sft_kt.yaml</code>.</p>
 <p>Use the safetensors adapter trained with KT for inference.</p>
@@ -929,7 +992,7 @@ chunk_size: 8192
 </code></pre>
 <p>We also support <strong>GGUF</strong> adapters: for safetensors, set the <strong>directory</strong>; for GGUF, set the <strong>file path</strong> in <code>adapter_name_or_path</code>.</p>
 <p>During loading, LLaMA-Factory maps layer names to KT’s naming. You’ll see logs like <code>Loaded adapter weight: XXX -&gt; XXX</code>:</p>
-<p><img src="en/SFT/../../assets/image-20251016171526210.png" alt="image-20251016171526210" /></p>
+<p><img src="assets/image-20251016171526210.png" alt="image-20251016171526210"></p>
 <h3 id="core-feature-3-batch-inference--metrics-base--lora-adapter"><a class="header" href="#core-feature-3-batch-inference--metrics-base--lora-adapter">Core Feature 3: Batch inference + metrics (base + LoRA adapter)</a></h3>
 <p>Run the command: <code>API_PORT=8000 llamafactory-cli api examples/inference/deepseek3_lora_sft_kt.yaml</code>.
 Invoke the KT fine-tuned adapter to provide the API; the usage logic of other APIs is consistent with the native LLaMA-Factory approach.</p>
@@ -965,25 +1028,27 @@ chunk_size: 8192
 <h2 id="conclusion"><a class="header" href="#conclusion">Conclusion</a></h2>
 <p>By integrating <strong>KTransformers LoRA fine-tuning</strong> into <strong>LLaMA-Factory</strong>, we provide a practical guide for efficient training and deployment of MoE LLMs. KT brings cutting-edge optimizations (DeepSeek/Qwen/Kimi support with AMX-accelerated kernels), and LoRA enables customization under very low GPU memory. LLaMA-Factory offers a friendly, unified interface.</p>
 <p>This integration (akin to Unsloth-style speedups) means even models with tens to hundreds of billions of parameters can be fine-tuned and deployed with low latency on commodity hardware. You get <strong>memory savings, speed-ups, and usability</strong> together. We encourage you to try LLaMA-Factory + KT for your next MoE project and follow this guide. Feedback is welcome!</p>
-<div style="break-before: page; page-break-before: always;"></div><h1 id="kt-sft-docs"><a class="header" href="#kt-sft-docs">kt-sft Docs</a></h1>
-<div style="break-before: page; page-break-before: always;"></div><ul>
-<li><a href="en/SFT/KTransformers-Fine-Tuning_Developer-Technical-Notes.html#introduction">Introduction</a></li>
-<li><a href="en/SFT/KTransformers-Fine-Tuning_Developer-Technical-Notes.html#overall-view-of-the-kt-fine-tuning-framework">Overall View of the KT Fine-Tuning Framework</a>
+<div style="break-before: page; page-break-before: always;"></div>
+<h1 id="kt-sft-docs"><a class="header" href="#kt-sft-docs">kt-sft Docs</a></h1>
+<div style="break-before: page; page-break-before: always;"></div>
 <ul>
-<li><a href="en/SFT/KTransformers-Fine-Tuning_Developer-Technical-Notes.html#attention-lora--kt-coexist">Attention (LoRA + KT coexist)</a></li>
-<li><a href="en/SFT/KTransformers-Fine-Tuning_Developer-Technical-Notes.html#moe-operator-encapsulation--backward">MoE (operator encapsulation + backward)</a></li>
-<li><a href="en/SFT/KTransformers-Fine-Tuning_Developer-Technical-Notes.html#multi-gpu-loadingtraining-placement-strategy-instead-of-dataparallel">Multi-GPU Loading/Training: Placement strategy instead of DataParallel</a></li>
+<li><a href="#introduction-1">Introduction</a></li>
+<li><a href="#overall-view-of-the-kt-fine-tuning-framework">Overall View of the KT Fine-Tuning Framework</a>
+<ul>
+<li><a href="#attention-lora--kt-coexist">Attention (LoRA + KT coexist)</a></li>
+<li><a href="#moe-operator-encapsulation--backward">MoE (operator encapsulation + backward)</a></li>
+<li><a href="#multi-gpu-loadingtraining-placement-strategy-instead-of-dataparallel">Multi-GPU Loading/Training: Placement strategy instead of DataParallel</a></li>
 </ul>
 </li>
-<li><a href="en/SFT/KTransformers-Fine-Tuning_Developer-Technical-Notes.html#kt-lora-fine-tuning-evaluation">KT-LoRA Fine-Tuning Evaluation</a>
+<li><a href="#kt-lora-fine-tuning-evaluation">KT-LoRA Fine-Tuning Evaluation</a>
 <ul>
-<li><a href="en/SFT/KTransformers-Fine-Tuning_Developer-Technical-Notes.html#setup">Setup</a></li>
-<li><a href="en/SFT/KTransformers-Fine-Tuning_Developer-Technical-Notes.html#results">Results</a></li>
-<li><a href="en/SFT/KTransformers-Fine-Tuning_Developer-Technical-Notes.html#speed-tests">Speed Tests</a></li>
-<li><a href="en/SFT/KTransformers-Fine-Tuning_Developer-Technical-Notes.html#memory-footprint">Memory Footprint</a></li>
+<li><a href="#setup">Setup</a></li>
+<li><a href="#results">Results</a></li>
+<li><a href="#speed-tests">Speed Tests</a></li>
+<li><a href="#memory-footprint">Memory Footprint</a></li>
 </ul>
 </li>
-<li><a href="en/SFT/KTransformers-Fine-Tuning_Developer-Technical-Notes.html#conclusion">Conclusion</a></li>
+<li><a href="#conclusion-1">Conclusion</a></li>
 </ul>
 <h1 id="ktransformers-fine-tuning--llama-factory-integration--developer-technical-notes"><a class="header" href="#ktransformers-fine-tuning--llama-factory-integration--developer-technical-notes">KTransformers Fine-Tuning × LLaMA-Factory Integration – Developer Technical Notes</a></h1>
 <p><strong>MadSys Lab, KVCache-AI Team, Approaching AI, LLaMA-Factory Team</strong></p>
@@ -991,18 +1056,24 @@ chunk_size: 8192
 <p>Recent open-source LLMs—from DeepSeek-V3/R1 to Qwen-MoE and Kimi-K2—have surged in performance and scale. Yet due to <strong>compute and memory constraints</strong>, it is difficult for typical researchers to fine-tune trillion-parameter-class models. We therefore integrate <strong>KTransformers</strong> with <strong>LLaMA-Factory</strong> so that, with <strong>2–4 RTX 4090 GPUs</strong> and sufficient CPU memory, one can fine-tune ultra-large Mixture-of-Experts (MoE) models such as DeepSeek-671B.</p>
 <p>This architecture bridges resource gaps, enabling <strong>local fine-tuning of ultra-large models</strong>, while also supporting <strong>efficient scenario customization</strong> at 14B/30B scales. We validate on stylized dialogue, Westernized translation tone, and medical Q&amp;A, achieving rapid adaptation within hours.</p>
 <p>Architecturally, LLaMA-Factory orchestrates data/config/training, LoRA insertion, and inference; KTransformers is a pluggable, high-performance operator backend that takes over Attention and MoE under the same training code, enabling <strong>GPU+CPU heterogeneity</strong> to accelerate training and reduce GPU memory.</p>
-<p><img src="en/SFT/../../assets/image-20251011010558909.png" alt="image-20251011010558909" /></p>
+<p><img src="assets/image-20251011010558909.png" alt="image-20251011010558909"></p>
 <p>We evaluated LoRA fine-tuning with HuggingFace default, Unsloth, and KTransformers backends (same settings and data). <strong>KTransformers</strong> is currently the only solution feasible on <strong>2–4×24GB 4090s</strong> for <strong>671B-scale MoE</strong>, and also shows higher throughput and lower GPU memory for 14B MoEs.</p>
-<div class="table-wrapper"><table><thead><tr><th>Under LoRA (BF16) + <a href="https://github.com/mindsRiverPonder/LLM-practice">NekoQA-10K stylized dialogue</a></th><th>HuggingFace Backend</th><th>Unsloth Backend</th><th>KTransformers Backend</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>Under LoRA (BF16) + <a href="https://github.com/mindsRiverPonder/LLM-practice">NekoQA-10K stylized dialogue</a></th><th>HuggingFace Backend</th><th>Unsloth Backend</th><th>KTransformers Backend</th></tr>
+</thead>
+<tbody>
 <tr><td>[14B-DeepSeekV2-Lite] LoRA fine-tuning throughput</td><td>303.58 token/s</td><td>455.37 token/s</td><td>530.38 token/s</td></tr>
 <tr><td>[14B-DeepSeekV2-Lite] GPU memory</td><td>32.12 GB</td><td>9.64 GB</td><td>6.08 GB</td></tr>
-<tr><td>[671B-DeepSeekV3] LoRA fine-tuning throughput</td><td><font color='red'>Too Huge to run</font></td><td><font color='red'>NOT SUPPORT</font></td><td>40.35 token/s</td></tr>
-<tr><td>[671B-DeepSeekV3] GPU memory (sum across GPUs)</td><td>theoretical 1400 GB †</td><td><font color='red'>NOT SUPPORT</font></td><td>70 GB †</td></tr>
-</tbody></table>
+<tr><td>[671B-DeepSeekV3] LoRA fine-tuning throughput</td><td><font color="red">Too Huge to run</font></td><td><font color="red">NOT SUPPORT</font></td><td>40.35 token/s</td></tr>
+<tr><td>[671B-DeepSeekV3] GPU memory (sum across GPUs)</td><td>theoretical 1400 GB †</td><td><font color="red">NOT SUPPORT</font></td><td>70 GB †</td></tr>
+</tbody>
+</table>
 </div>
 <p>† The <strong>1400 GB</strong> is the <strong>theoretical</strong> FP16 full-resident footprint (not runnable). <strong>70 GB</strong> is the <strong>measured peak</strong> with KT (Attention on GPU + layered MoE offload).</p>
 <p>From the table above, it can be seen that for the 14B model, the KTransformers backend achieves approximately 75% higher throughput than the default HuggingFace solution, while using only about one-fifth of the GPU memory. For the 671B model, both HuggingFace and Unsloth fail to run on a single 4090 GPU, whereas KTransformers is able to perform LoRA fine-tuning at 40 tokens/s, keeping the GPU memory usage within 70 GB.</p>
-<p><img src="en/SFT/../../assets/image-compare_model.png" alt="按照模型划分的对比图_02" /></p>
+<p><img src="assets/image-compare_model.png" alt="按照模型划分的对比图_02"></p>
 <h2 id="overall-view-of-the-kt-fine-tuning-framework"><a class="header" href="#overall-view-of-the-kt-fine-tuning-framework">Overall View of the KT Fine-Tuning Framework</a></h2>
 <p>We detail how KTransformers takes over core operators in LLaMA-Factory’s fine-tuning framework to optimize Attention and MoE.</p>
 <p>DeepSeek-V3/V2 MoE models comprise a small-parameter dense Attention part and a large-parameter sparse MoE part. For illustration, consider layer 2 of DeepSeek-V2-Lite-Chat (from which each layer includes both Attention and MoE). Attention compute and KV cache mainly reside on the GPU; the heavyweight MoE part is primarily executed on the CPU. We first cover <strong>Attention replacement and inheritance</strong>, then <strong>MoE encapsulation and backend interfacing</strong>, and finally <strong>multi-GPU placement</strong>.</p>
@@ -1012,9 +1083,9 @@ chunk_size: 8192
 <li><strong>Inheritance:</strong> <code>KTransformersLinearLora</code> retains KT’s high-performance paths (<code>prefill_linear</code>/<code>generate_linear</code>) while accepting LoRA parameters (<code>lora_A/lora_B</code>).</li>
 <li><strong>Replacement:</strong> During preparation, we replace original <code>KTransformersLinear</code> layers (Q/K/V/O) with <code>KTransformersLinearLora</code>, preserving KT optimizations while enabling LoRA trainability.</li>
 </ul>
-<p><img src="en/SFT/../../assets/image-20251016182810716.png" alt="image-20251016182810716" /></p>
+<p><img src="assets/image-20251016182810716.png" alt="image-20251016182810716"></p>
 <p>After replacement, LoRA is inserted at Q/K/V/O linear transforms (left), and <code>KTransformersLinearLora</code> contains both KT fast paths and LoRA matrices (right).</p>
-<p><img src="en/SFT/../../assets/image-20251016182920722.png" alt="image-20251016182920722" /></p>
+<p><img src="assets/image-20251016182920722.png" alt="image-20251016182920722"></p>
 <h3 id="moe-operator-encapsulation--backward"><a class="header" href="#moe-operator-encapsulation--backward">MoE (operator encapsulation + backward)</a></h3>
 <h4 id="encapsulation"><a class="header" href="#encapsulation">Encapsulation</a></h4>
 <p>Given large parameters and sparse compute, we encapsulate the expert computation as a <strong>differentiable black-box operator</strong>—transparent upstream, replaceable downstream.</p>
@@ -1022,10 +1093,10 @@ chunk_size: 8192
 <li><strong>Upstream (PyTorch graph):</strong> we register a custom Autograd Function so the MoE layer appears as <strong>a single node</strong>. In the left figure (red box), only <code>KSFTExpertsCPU</code> is visible; on the right, the unencapsulated graph expands routing, dispatch, and FFN experts. Encapsulation makes the MoE layer behave like a standard <code>nn.Module</code> with gradients.</li>
 <li><strong>Downstream (backend):</strong> inside the Autograd Function, pybind11 calls C++ extensions for forward/backward. Multiple <strong>pluggable backends</strong> exist (AMX BF16/INT8; <strong>llamafile</strong>). The backend can be switched via YAML (e.g., <code>"backend": "AMXBF16"</code> vs. <code>"llamafile"</code>).</li>
 </ul>
-<p><img src="en/SFT/../../assets/image-20250801174623919.png" alt="image-20250801174623919" /></p>
+<p><img src="assets/image-20250801174623919.png" alt="image-20250801174623919"></p>
 <h4 id="backward-cpu"><a class="header" href="#backward-cpu">Backward (CPU)</a></h4>
 <p>MoE backward frequently needs the transposed weights $W^\top$. To avoid repeated runtime transposes, we <strong>precompute/cache</strong> $W^\top$ at load time (blue box). We also <strong>cache necessary intermediate activations</strong> (e.g., expert projections, red box) to reuse in backward and reduce recomputation. We provide backward implementations for <strong>llamafile</strong> and <strong>AMX (INT8/BF16)</strong>, with NUMA-aware optimizations.</p>
-<img src="en/SFT/../../assets/image-20251016182942726.png" alt="image-20251016182942726" style="zoom:33%;" />
+<img src="assets/image-20251016182942726.png" alt="image-20251016182942726" style="zoom:33%;" />
 <h3 id="multi-gpu-loadingtraining-placement-strategy-instead-of-dataparallel"><a class="header" href="#multi-gpu-loadingtraining-placement-strategy-instead-of-dataparallel">Multi-GPU Loading/Training: Placement strategy instead of DataParallel</a></h3>
 <p>To lower <strong>per-GPU memory peaks</strong> on 2–4 GPUs, we use <strong>model parallelism + explicit placement</strong>, not DataParallel (which duplicates the whole model on each GPU).</p>
 <p>Key changes:</p>
@@ -1041,31 +1112,50 @@ chunk_size: 8192
 <h3 id="results"><a class="header" href="#results">Results</a></h3>
 <h4 id="stylized-dialogue-catgirl-tone-1"><a class="header" href="#stylized-dialogue-catgirl-tone-1">Stylized Dialogue (CatGirl tone)</a></h4>
 <p>Dataset: <a href="https://zhuanlan.zhihu.com/p/1934983798233231689">NekoQA-10K</a>. The fine-tuned model consistently exhibits the target style (red boxes) versus neutral/rational base (blue). This shows <strong>KT-LoRA injects style features</strong> into the generation distribution with low GPU cost.</p>
-<p><img src="en/SFT/../../assets/image-20251016175848143.png" alt="image-20251016175848143" /></p>
+<p><img src="assets/image-20251016175848143.png" alt="image-20251016175848143"></p>
 <h4 id="translational-style-benchmark-generative"><a class="header" href="#translational-style-benchmark-generative">Translational-Style benchmark (generative)</a></h4>
 <p>Dataset: <a href="https://github.com/Benson114/Translational-Style-ChatLLM">Translational-Style-ChatLLM</a>. Metrics: BLEU-1/2/3/4, ROUGE-1/2/L.</p>
-<div class="table-wrapper"><table><thead><tr><th>Translational-Style dataset</th><th>BLEU-1</th><th>BLEU-2</th><th>BLEU-3</th><th>BLEU-4</th><th>ROUGE-1</th><th>ROUGE-2</th><th>ROUGE-L</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>Translational-Style dataset</th><th>BLEU-1</th><th>BLEU-2</th><th>BLEU-3</th><th>BLEU-4</th><th>ROUGE-1</th><th>ROUGE-2</th><th>ROUGE-L</th></tr>
+</thead>
+<tbody>
 <tr><td>V2-Lite (no LoRA)</td><td>20.66</td><td>8.33</td><td>4.54</td><td>2.89</td><td>22.71</td><td>4.52</td><td>19.19</td></tr>
 <tr><td><strong>KT-LoRA fine-tuned V2-Lite</strong></td><td><strong>35.41</strong></td><td><strong>22.44</strong></td><td><strong>15.42</strong></td><td><strong>11.18</strong></td><td><strong>42.03</strong></td><td><strong>18.38</strong></td><td><strong>33.10</strong></td></tr>
 <tr><td>V3 base (no LoRA)</td><td>8.49</td><td>3.34</td><td>1.62</td><td>0.96</td><td>15.91</td><td>2.55</td><td>10.07</td></tr>
 <tr><td><strong>KT-LoRA fine-tuned V3</strong></td><td><strong>37.02</strong></td><td><strong>23.70</strong></td><td><strong>16.21</strong></td><td><strong>11.49</strong></td><td><strong>43.43</strong></td><td><strong>18.96</strong></td><td><strong>34.54</strong></td></tr>
-</tbody></table>
+</tbody>
+</table>
 </div>
 <p>As shown by the test results in the tables above, under a unified workflow and placement strategy, <strong>both model scales exhibit consistent gains after fine-tuning</strong>, supporting the usability and effectiveness of the “KT backend + LoRA fine-tuning” combination for generative style control. At the same time, this indicates that KT’s heterogeneous placement and operator optimizations can stably support small-sample adaptation in the style domain.</p>
 <h4 id="medical-vertical-benchmark-afrimed-saqmcq"><a class="header" href="#medical-vertical-benchmark-afrimed-saqmcq">Medical Vertical Benchmark (AfriMed-SAQ/MCQ)</a></h4>
 <p>The dataset adopts <a href="https://aclanthology.org/2025.acl-long.96/">AfriMed-QA</a> (ACL 2025), a domain-specific dataset for the medical field in Africa with strong scenario customization characteristics, comprising two formats—multiple-choice questions (MCQ) and short-answer questions (SAQ)—which in this case serve as the evaluation for vertical-domain fine-tuning. In terms of evaluation criteria, BLEU/ROUGE are used for SAQ, and Accuracy is used for MCQ.</p>
-<div class="table-wrapper"><table><thead><tr><th>AfriMed-QA (SAQ)</th><th>BLEU-1</th><th>BLEU-2</th><th>BLEU-3</th><th>BLEU-4</th><th>ROUGE-1</th><th>ROUGE-2</th><th>ROUGE-L</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>AfriMed-QA (SAQ)</th><th>BLEU-1</th><th>BLEU-2</th><th>BLEU-3</th><th>BLEU-4</th><th>ROUGE-1</th><th>ROUGE-2</th><th>ROUGE-L</th></tr>
+</thead>
+<tbody>
 <tr><td>V2-Lite (no LoRA)</td><td>13.58</td><td>11.12</td><td>9.10</td><td>7.23</td><td>22.48</td><td>7.81</td><td>11.73</td></tr>
 <tr><td><strong>KT-LoRA fine-tuned V2-Lite</strong></td><td><strong>35.90</strong></td><td><strong>27.63</strong></td><td><strong>22.99</strong></td><td><strong>19.15</strong></td><td><strong>35.25</strong></td><td><strong>17.50</strong></td><td><strong>28.44</strong></td></tr>
 <tr><td>V3 base (no LoRA)</td><td>12.75</td><td>10.27</td><td>8.05</td><td>5.99</td><td>20.33</td><td>5.65</td><td>10.11</td></tr>
 <tr><td><strong>KT-LoRA fine-tuned V3</strong></td><td><strong>42.42</strong></td><td><strong>34.12</strong></td><td><strong>28.95</strong></td><td><strong>24.54</strong></td><td><strong>41.97</strong></td><td><strong>22.37</strong></td><td><strong>33.28</strong></td></tr>
-</tbody></table>
-</div><div class="table-wrapper"><table><thead><tr><th>AfriMed-QA (MCQ)</th><th>Accuracy</th></tr></thead><tbody>
+</tbody>
+</table>
+</div>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>AfriMed-QA (MCQ)</th><th>Accuracy</th></tr>
+</thead>
+<tbody>
 <tr><td>V2-Lite (no LoRA)</td><td>0.0645</td></tr>
 <tr><td><strong>KT-LoRA fine-tuned V2-Lite</strong></td><td><strong>0.4812</strong></td></tr>
 <tr><td>V3 base (no LoRA)</td><td>0.5833</td></tr>
 <tr><td><strong>KT-LoRA fine-tuned V3</strong></td><td><strong>0.7930</strong></td></tr>
-</tbody></table>
+</tbody>
+</table>
 </div>
 <p>As shown in the tables above, (1) DeepSeek-V3 (671B) after KT-LoRA fine-tuning achieves clearly higher performance than the fine-tuned DeepSeek-V2-Lite (14B) on both MCQ and SAQ, and it also surpasses the V3 base model. Within our small-scale setting, this preliminarily indicates that KT-LoRA fine-tuning of ultra-large-parameter models has practical significance in vertical domains.</p>
 <p>(2) Across both SAQ/MCQ sub-tasks, KT-LoRA delivers consistent gains, indicating that—with KT’s heterogeneous placement and backend operator support—LoRA fine-tuning can effectively inject the key knowledge points of vertical domains such as medicine into the model.</p>
@@ -1078,10 +1168,16 @@ chunk_size: 8192
 <p><code>step_time</code>：time per optimization step (tensor movement + Attention + MoE + others).</p>
 <p><code>tokens_per_step = GAS × qlen</code>；<code>token/s = tokens_per_step / step_time</code>。 We use <code>GAS=16</code>, <code>qlen=512</code> → <code>tokens_per_step=8192</code>.</p>
 <p><strong>Measured</strong></p>
-<div class="table-wrapper"><table><thead><tr><th>Model</th><th>step_time (s)</th><th>tokens/step</th><th>token/s</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>Model</th><th>step_time (s)</th><th>tokens/step</th><th>token/s</th></tr>
+</thead>
+<tbody>
 <tr><td>DeepSeek-V3-671B</td><td>203</td><td>8192</td><td><strong>40.35</strong></td></tr>
 <tr><td>DeepSeek-V2-Lite-14B</td><td>36</td><td>8192</td><td><strong>227.6</strong></td></tr>
-</tbody></table>
+</tbody>
+</table>
 </div>
 <h4 id="moe-compute-deepseek-v3-671b"><a class="header" href="#moe-compute-deepseek-v3-671b">MoE Compute (DeepSeek-V3-671B)</a></h4>
 <p><strong>Theory</strong></p>
@@ -1102,9 +1198,15 @@ $$</li>
 <p><strong>Measured (MoE TFLOPS on CPU)</strong></p>
 <p>If the <strong>MoE-only</strong> time per step is <code>t_moe</code> (seconds), $\text{TFLOPS} = \text{FLOPs}_{\text{per-step}} / \text{step_per_second}.$</p>
 <p>Use MoE-phase time, not full <code>step_time</code>, to get MoE throughput.</p>
-<div class="table-wrapper"><table><thead><tr><th>TFLOPS</th><th>Forward</th><th>Backward</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>TFLOPS</th><th>Forward</th><th>Backward</th></tr>
+</thead>
+<tbody>
 <tr><td>Average</td><td>17.55</td><td>18.41</td></tr>
-</tbody></table>
+</tbody>
+</table>
 </div>
 <h3 id="memory-footprint"><a class="header" href="#memory-footprint">Memory Footprint</a></h3>
 <ul>
@@ -1114,23 +1216,24 @@ $$</li>
 <h2 id="conclusion-1"><a class="header" href="#conclusion-1">Conclusion</a></h2>
 <p>Integrating <strong>KTransformers LoRA</strong> with <strong>LLaMA-Factory</strong> provides a practical path to efficiently train and deploy MoE LLMs. KT contributes placement strategies and operator optimizations (DeepSeek/Qwen/Kimi support with AMX-accelerated kernels), and LoRA enables customization with very low GPU memory; LLaMA-Factory supplies a coherent user-level interface.</p>
 <p>This means even tens-to-hundreds-of-billion-parameter MoE models can be fine-tuned and served with low latency on ordinary hardware. The approach balances <strong>memory savings</strong>, <strong>speed</strong>, and <strong>usability</strong>, turning ultra-large models into tools that developers can actually wield.</p>
-<div style="break-before: page; page-break-before: always;"></div><h1 id="tutorial-inject-operator-step-by-step"><a class="header" href="#tutorial-inject-operator-step-by-step">Tutorial: Inject Operator Step by Step</a></h1>
+<div style="break-before: page; page-break-before: always;"></div>
+<h1 id="tutorial-inject-operator-step-by-step"><a class="header" href="#tutorial-inject-operator-step-by-step">Tutorial: Inject Operator Step by Step</a></h1>
 <blockquote>
 <p>Author: Azure-Tang</p>
 </blockquote>
 <h2 id="tldr"><a class="header" href="#tldr">TL;DR</a></h2>
 <p>This tutorial will guide you through the process of injecting custom operators into a model using the KTransformers framework. We will use the DeepSeekV2-Chat model as an example to demonstrate how to inject custom operators into the model step by step. The tutorial will cover the following topics:</p>
 <ul>
-<li><a href="en/SFT/injection_tutorial.html#tldr">TL;DR</a></li>
-<li><a href="en/SFT/injection_tutorial.html#how-to-write-injection-rules">How to Write Injection Rules</a></li>
-<li><a href="en/SFT/injection_tutorial.html#understanding-model-structure">Understanding Model Structure</a></li>
-<li><a href="en/SFT/injection_tutorial.html#matrix-absorption-based-mla-injection">Matrix Absorption-based MLA Injection</a></li>
-<li><a href="en/SFT/injection_tutorial.html#injection-of-routed-experts">Injection of Routed Experts</a></li>
-<li><a href="en/SFT/injection_tutorial.html#injection-of-linear-layers">Injection of Linear Layers</a></li>
-<li><a href="en/SFT/injection_tutorial.html#injection-of-modules-with-pre-calculated-buffers">Injection of Modules with Pre-calculated Buffers</a></li>
-<li><a href="en/SFT/injection_tutorial.html#specifying-running-devices-for-modules">Specifying Running Devices for Modules</a></li>
-<li><a href="en/SFT/injection_tutorial.html#muti-gpu">Muti-GPU</a></li>
-<li><a href="en/SFT/injection_tutorial.html#how-to-write-a-new-operator-and-inject-into-the-model">How to Write a New Operator and Inject into the Model</a></li>
+<li><a href="#tldr">TL;DR</a></li>
+<li><a href="#how-to-write-injection-rules">How to Write Injection Rules</a></li>
+<li><a href="#understanding-model-structure">Understanding Model Structure</a></li>
+<li><a href="#matrix-absorption-based-mla-injection">Matrix Absorption-based MLA Injection</a></li>
+<li><a href="#injection-of-routed-experts">Injection of Routed Experts</a></li>
+<li><a href="#injection-of-linear-layers">Injection of Linear Layers</a></li>
+<li><a href="#injection-of-modules-with-pre-calculated-buffers">Injection of Modules with Pre-calculated Buffers</a></li>
+<li><a href="#specifying-running-devices-for-modules">Specifying Running Devices for Modules</a></li>
+<li><a href="#muti-gpu">Muti-GPU</a></li>
+<li><a href="#how-to-write-a-new-operator-and-inject-into-the-model">How to Write a New Operator and Inject into the Model</a></li>
 </ul>
 <h2 id="how-to-write-injection-rules"><a class="header" href="#how-to-write-injection-rules">How to Write Injection Rules</a></h2>
 <p>The basic form of the injection rules for the Inject framework is as follows:</p>
@@ -1165,19 +1268,26 @@ $$</li>
 <p>Fortunately, knowing the structure of a model is very simple. Open the file list on the <a href="https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite-Chat/tree/main">deepseek-ai/DeepSeek-V2-Lite</a> homepage, and you can see the following files:</p>
 <p align="center">
   <picture>
-    <img alt="Inject-Struction" src="en/SFT/../../assets/model_structure_guild.png" width=60%>
+    <img alt="Inject-Struction" src="assets/model_structure_guild.png" width="60%">
   </picture>
 </p>
+
 <p>From the <code>.saftensors</code> file, we can see the name of each layer’s weights, corresponding to the match.name attribute in the injection rules.
 From the <code>modeling_deepseek.py</code> file, we can see the specific implementation of each module class, with the class name corresponding to the match.class attribute in the injection rules.</p>
 <p>The structure of the DeepSeekV2 model from the <code>.saftensors</code> and <code>modeling_deepseek.py</code> files is as follows:</p>
 <p align="center">
   <picture>
-    <img alt="Inject-Struction" src="en/SFT/../../assets/deepseekv2_structure.png" width=60%>
+    <img alt="Inject-Struction" src="assets/deepseekv2_structure.png" width="60%">
   </picture>
 </p>
+
 <p>Supported operators and their corresponding classes are as follows:</p>
-<div class="table-wrapper"><table><thead><tr><th>match</th><th>replace</th><th>backends</th><th>descriptions</th></tr></thead><tbody>
+<div class="table-wrapper">
+<table>
+<thead>
+<tr><th>match</th><th>replace</th><th>backends</th><th>descriptions</th></tr>
+</thead>
+<tbody>
 <tr><td>Linear</td><td>KTransformersLinear</td><td>KLinearMarlin</td><td>Marlin as backend</td></tr>
 <tr><td></td><td></td><td>KLinearTorch</td><td>pytorch as backend</td></tr>
 <tr><td></td><td></td><td>KLinearCPUInfer</td><td>llamafile as backend</td></tr>
@@ -1192,7 +1302,8 @@ From the <code>modeling_deepseek.py</code> file, we can see the specific impleme
 <tr><td></td><td>KDeepseekV2Model</td><td>KDeepseekV2Model</td><td>Model for DeepseekV2</td></tr>
 <tr><td>RoPE</td><td>RotaryEmbedding</td><td>RotaryEmbedding</td><td>RoPE module</td></tr>
 <tr><td></td><td>YarnRotaryEmbedding</td><td>YarnRotaryEmbedding</td><td>RoPE module</td></tr>
-</tbody></table>
+</tbody>
+</table>
 </div>
 <p>Then we start step-by-step injection of custom modules, our targets are:</p>
 <ul>
@@ -1271,9 +1382,10 @@ From the <code>modeling_deepseek.py</code> file, we can see the specific impleme
 DeepseekV2-Chat got 60 layers, if we got 2 GPUs, we can allocate 30 layers to each GPU. Complete multi GPU rule examples <a href="https://github.com/kvcache-ai/ktransformers/blob/main/ktransformers/optimize/optimize_rules/DeepSeek-V2-Chat-multi-gpu.yaml">here</a>.</p>
 <p align="center">
   <picture>
-    <img alt="Inject-Struction" src="en/SFT/../../assets/multi_gpu.png" width=60%>
+    <img alt="Inject-Struction" src="assets/multi_gpu.png" width="60%">
   </picture>
 </p>
+
 <p>First of all, for multi-GPU, we have to inject an new operator <code>KDeepseekV2Model</code>. And set division of the layers to different GPUs. For our case, we have to set the <code>transfer_map</code> in the <code>KDeepseekV2Model</code> operatoras as follows:</p>
 <pre><code class="language-yaml">- match:
     name: "^model$"
@@ -1411,8 +1523,9 @@ DeepseekV2-Chat got 60 layers, if we got 2 GPUs, we can allocate 30 layers to ea
         return x
 </code></pre>
 <p>Note that the <code>self.load_weight</code> function is provided by the KLinearBase class to help users load weights from a gguf file into the module. The implementation details of KLinearBase can be found on <a href="https://github.com/kvcache-ai/ktransformers/blob/44f57270c9514d79fab224186d90ccf61059331a/ktransformers/operators/linear.py#L31">GITHUB</a>.</p>
-<div style="break-before: page; page-break-before: always;"></div><h1 id="kt-kernel-docs"><a class="header" href="#kt-kernel-docs">kt-kernel Docs</a></h1>
-<p>To be written...</p>
+<div style="break-before: page; page-break-before: always;"></div>
+<h1 id="kt-kernel-docs"><a class="header" href="#kt-kernel-docs">kt-kernel Docs</a></h1>
+<p>To be written…</p>
 <div style="break-before: page; page-break-before: always;"></div><!-- omit in toc -->
 <h1 id="see-the-issue-faq-page"><a class="header" href="#see-the-issue-faq-page">see the issue <a href="https://github.com/kvcache-ai/ktransformers/issues/1608">FAQ page</a></a></h1>
 
@@ -1433,6 +1546,11 @@ DeepseekV2-Chat got 60 layers, if we got 2 GPUs, we can allocate 30 layers to ea
 
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Flexible Framework for Experiencing Cutting-edge LLM Inference/Fine-tune Optimizations 🎯 Overview | 🚀 kt-kernel | 🎓 kt-sft | 🔥 Citation | 🚀 Roadmap(2025Q4) KTransformers is a research project focused on efficient inference and fine-tuning of large language models through CPU-GPU heterogeneous computing. The project has evolved into two core modules: kt-kernel and kt-sft.","breadcrumbs":"Introduction » » 🎯 Overview","id":"0","title":"🎯 Overview"},"1":{"body":"Nov 6, 2025: Support Kimi-K2-Thinking inference ( Tutorial) and fine-tune ( Tutorial) Nov 4, 2025: KTransformers Fine-Tuning × LLaMA-Factory Integration. ( Tutorial) Oct 27, 2025: Support Ascend NPU. ( Tutorial) Oct 10, 2025: Integrating into SGLang. ( Roadmap, Blog) Sept 11, 2025: Support Qwen3-Next. ( Tutorial) Sept 05, 2025: Support Kimi-K2-0905. ( Tutorial) July 26, 2025: Support SmallThinker and GLM4-MoE. ( Tutorial) July 11, 2025: Support Kimi-K2. ( Tutorial) June 30, 2025: Support 3-layer (GPU-CPU-Disk) prefix cache reuse. May 14, 2025: Support Intel Arc GPU ( Tutorial). Apr 29, 2025: Support AMX-Int8、 AMX-BF16 and Qwen3MoE ( Tutorial) Apr 9, 2025: Experimental support for LLaMA 4 models ( Tutorial). Apr 2, 2025: Support Multi-concurrency. ( Tutorial). Mar 15, 2025: Support ROCm on AMD GPU ( Tutorial). Mar 5, 2025: Support unsloth 1.58/2.51 bits weights and IQ1_S/FP8 hybrid weights. Support 139K Longer Context for DeepSeek-V3 and R1 in 24GB VRAM. Feb 25, 2025: Support FP8 GPU kernel for DeepSeek-V3 and R1; Longer Context. Feb 15, 2025: Longer Context (from 4K to 8K for 24GB VRAM) & Slightly Faster Speed （+15%, up to 16 Tokens/s), update docs and online books. Feb 10, 2025: Support Deepseek-R1 and V3 on single (24GB VRAM)/multi gpu and 382G DRAM, up to 3~28x speedup. For detailed show case and reproduction tutorial, see here. Aug 28, 2024: Decrease DeepseekV2’s required VRAM from 21G to 11G. Aug 15, 2024: Update detailed tutorial for injection and multi-GPU. Aug 14, 2024: Support llamfile as linear backend. Aug 12, 2024: Support multiple GPU; Support new model: mixtral 8*7B and 8*22B; Support q2k, q3k, q5k dequant on gpu. Aug 9, 2024: Support windows native.","breadcrumbs":"Introduction » » 🔥 Updates","id":"1","title":"🔥 Updates"},"10":{"body":"Current Support Status: ✅ Intel CPUs with AMX: Fully supported ⚠️ Universal CPU with llamafile: In preview, not yet fully complete ⚠️ AMD CPUs with BLIS: Upcoming, not yet fully integrated","breadcrumbs":"For kt-kernel » Note","id":"10","title":"Note"},"11":{"body":"AMX Optimization: Intel AMX (Advanced Matrix Extensions) support for INT4/INT8 quantized MoE inference Multi-Backend: Unified KTMoEWrapper API supporting multiple backends (AMXINT4, AMXINT8, LLAMAFILE*) Flexible Backends: AVX512, AVX2 via pluggable backend architecture Efficient MoE: Optimized Mixture-of-Experts operations with NUMA-aware memory management Async Execution: Non-blocking submit_forward / sync_forward API for improved pipelining Easy Integration: Clean Python API with automatic backend selection Note: *LLAMAFILE backend support is currently in preview and not yet fully complete.","breadcrumbs":"For kt-kernel » Features","id":"11","title":"Features"},"12":{"body":"","breadcrumbs":"For kt-kernel » Installation","id":"12","title":"Installation"},"13":{"body":"First, initialize git submodules: git submodule update --init --recursive","breadcrumbs":"For kt-kernel » Prerequisites","id":"13","title":"Prerequisites"},"14":{"body":"Step 0: Create and activate a conda environment (recommended): conda create -n kt-kernel python=3.11 -y\\nconda activate kt-kernel You can now install in two clear steps using the same script. Option A: Two-step (explicit) # 1) Install system prerequisites (cmake, hwloc, pkg-config)\\n./install.sh deps # 2) Build and install kt-kernel (auto-detects CPU)\\n# By default, the script cleans the local ./build directory before compiling.\\n./install.sh build Option B: One-step (deps + build) ./install.sh The install script will: Auto-detect CPU capabilities (AMX support) Install cmake via conda (if available) Install system dependencies ( libhwloc-dev, pkg-config) based on your OS What gets configured automatically: AMX CPU detected → NATIVE + AMX=ON No AMX detected → NATIVE + AMX=OFF ⚠️ Important for LLAMAFILE backend users: If you have an AMX-capable CPU and plan to use the LLAMAFILE backend, do NOT use auto-detection. Use manual mode with AVX512 or AVX2 instead of NATIVE to avoid compilation issues (see below).","breadcrumbs":"For kt-kernel » Quick Installation (Recommended)","id":"14","title":"Quick Installation (Recommended)"},"15":{"body":"If you need specific build options (e.g., for LLAMAFILE backend, compatibility, or binary distribution): # Example for LLAMAFILE backend on AMX CPU with AVX512\\nexport CPUINFER_CPU_INSTRUCT=AVX512 # Options: NATIVE, AVX512, AVX2\\nexport CPUINFER_ENABLE_AMX=OFF # Options: ON, OFF # Run with manual mode (build only)\\n./install.sh build --manual For advanced build options and binary distribution, see the Build Configuration section. If you encounter issues, refer to Error Troubleshooting.","breadcrumbs":"For kt-kernel » Manual Configuration (Advanced)","id":"15","title":"Manual Configuration (Advanced)"},"16":{"body":"python -c \\"from kt_kernel import KTMoEWrapper; print(\'✓ kt-kernel installed successfully\')\\"","breadcrumbs":"For kt-kernel » Verification","id":"16","title":"Verification"},"17":{"body":"KT-Kernel can be used standalone via Direct Python API or integrated with SGLang for production deployment. This section describes SGLang integration to enable CPU-GPU heterogeneous inference, where “hot” experts run on GPU and “cold” experts run on CPU for optimal resource utilization.","breadcrumbs":"For kt-kernel » Integration with SGLang","id":"17","title":"Integration with SGLang"},"18":{"body":"1. Install SGLang git clone https://github.com/sgl-project/sglang.git\\ncd sglang\\npip install -e \\"python[all]\\" 2. Prepare Weights You need both GPU weights and CPU weights for heterogeneous inference: GPU Weights: Use the original / quantized model weights. CPU Weights: Quantize to AMX-optimized format using the conversion script: python scripts/convert_cpu_weights.py \\\\ --input-path /path/to/model \\\\ --input-type bf16 \\\\ # Depends on your GPU weights type: fp8, fp16, or bf16 --output /path/to/cpu-weights \\\\ --quant-method int8 # or int4 Supported input formats: FP8, FP16, BF16 → INT4/INT8. For more details, see: CPU Weights conversion GPU Weights quantization Note: LLAMAFILE backend supports GGUF format directly, but this feature is still in preview. 3. Launch SGLang Server Start the SGLang server with your normal SGLang parameters, and add the following KT-Kernel specific parameters to enable CPU-GPU heterogeneous inference: KT-Kernel Parameters to Add: --kt-method: Backend method (AMXINT4, AMXINT8, or LLAMAFILE) --kt-weight-path: Path to the converted CPU weights --kt-cpuinfer: Number of CPU inference threads (set to physical cores) --kt-threadpool-count: Number of thread pools (set to NUMA node count) --kt-num-gpu-experts: Number of experts to keep on GPU --kt-max-deferred-experts-per-token: Deferred experts for pipelined execution Example: python -m sglang.launch_server \\\\ [your normal SGLang parameters...] \\\\ --kt-method AMXINT8 \\\\ --kt-weight-path /path/to/cpu-weights \\\\ --kt-cpuinfer 64 \\\\ --kt-threadpool-count 2 \\\\ --kt-num-gpu-experts 32 \\\\ --kt-max-deferred-experts-per-token 2 See KT-Kernel Parameters section below for detailed parameter tuning guidelines.","breadcrumbs":"For kt-kernel » Installation Steps","id":"18","title":"Installation Steps"},"19":{"body":"This example demonstrates the full workflow from downloading weights to launching the server. Hardware Configuration: GPU: NVIDIA RTX 4090 24GB CPU: 2x Intel Xeon Gold 6454S (64 physical cores total, 128 threads, 2 NUMA nodes) Model: Qwen3-30B-A3B GPU Weights: BF16 original weights CPU Weights: AMXINT8 quantized How to verify your system configuration: # Check CPU configuration\\nlscpu | grep -E \\"^CPU\\\\(s\\\\)|Thread\\\\(s\\\\) per core|Socket\\\\(s\\\\)|NUMA node\\\\(s\\\\)\\"\\n# Expected output example:\\nCPU(s): 128\\nThread(s) per core: 2\\nSocket(s): 2\\nNUMA node(s): 2\\n# → Physical cores = CPU(s) / Thread(s) per core = 128 / 2 = 64 Parameter Rationale: --kt-cpuinfer 64: Set to physical cores (64), not hyperthreads (128) --kt-threadpool-count 2: 2 NUMA nodes detected (dual-socket system) --kt-num-gpu-experts 32: With 24GB GPU memory, we can fit ~32 experts on GPU for this model (varies by model architecture and actual memory usage) --kt-max-deferred-experts-per-token 2: Enable pipelined execution - allows CPU to process next batch while GPU completes current batch Step 1: Download model weights # Install huggingface-cli if not already installed\\npip install huggingface-hub # Download model from Hugging Face\\nhf download Qwen/Qwen3-30B-A3B --local-dir /mnt/data/models/Qwen3-30B-A3B Step 2: Convert to CPU weights (AMXINT8) python scripts/convert_cpu_weights.py \\\\ --input-path /mnt/data/models/Qwen3-30B-A3B \\\\ --input-type bf16 \\\\ --output /mnt/data/models/Qwen3-30B-A3B-INT8 \\\\ --quant-method int8 Step 3: Launch SGLang server python -m sglang.launch_server \\\\ --host 0.0.0.0 \\\\ --port 8000 \\\\ --model /mnt/data/models/Qwen3-30B-A3B \\\\ --trust-remote-code \\\\ --mem-fraction-static 0.92 \\\\ --chunked-prefill-size 4096 \\\\ --served-model-name Qwen3-30B-A3B \\\\ --enable-mixed-chunk \\\\ --kt-method AMXINT8 \\\\ --kt-weight-path /mnt/data/models/Qwen3-30B-A3B-INT8 \\\\ --kt-cpuinfer 64 \\\\ --kt-threadpool-count 2 \\\\ --kt-num-gpu-experts 32 \\\\ --kt-max-deferred-experts-per-token 2","breadcrumbs":"For kt-kernel » Complete Example: Qwen3-30B-A3B","id":"19","title":"Complete Example: Qwen3-30B-A3B"},"2":{"body":"","breadcrumbs":"Introduction » » 📦 Core Modules","id":"2","title":"📦 Core Modules"},"20":{"body":"Parameter Description Example Value --kt-method CPU inference backend method AMXINT4, AMXINT8, or LLAMAFILE (preview) --kt-weight-path Path to quantized CPU weights /path/to/cpu-weights --kt-cpuinfer Number of CPU inference threads 64 (adjust based on CPU cores) --kt-threadpool-count Number of thread pools for parallel execution 2 (typically 1-4) --kt-num-gpu-experts Number of experts to keep on GPU 32 (remaining experts go to CPU) --kt-max-deferred-experts-per-token Number of experts per token to defer for pipelined execution 2 (0 to disable, 1-2 recommended) Parameter Guidelines: kt-method: Choose based on your CPU and weight format: AMXINT4: Best performance on AMX CPUs with INT4 quantized weights (May cause huge accuracy drop for some models, e.g., Qwen3-30B-A3B) AMXINT8: Higher accuracy with INT8 quantized weights on AMX CPUs LLAMAFILE: Preview support for GGUF format (not fully complete) kt-cpuinfer: Set to the number of physical CPU cores (not hyperthreads). Check physical cores: lscpu | grep -E \\"^CPU\\\\(s\\\\)|Thread\\\\(s\\\\) per core\\" Physical cores = CPU(s) / Thread(s) per core Example: If CPU(s)=128 and Thread(s) per core=2, then physical cores = 64 Important: Do NOT set to hyperthread count - this will degrade performance kt-threadpool-count: Set to the number of NUMA nodes. Check NUMA count: lscpu | grep \\"NUMA node(s)\\" Or use: numactl --hardware | grep \\"available\\" Note: NUMA node count is NOT necessarily the number of physical CPUs It represents memory domains, which may be divided within a single CPU or across multiple CPUs Use the NUMA node count from lscpu, regardless of physical CPU count Typical values: 1-2 for single-socket, 2-4 for dual-socket systems This enables better memory bandwidth utilization across NUMA domains kt-num-gpu-experts: Determine based on GPU memory and profiling: More GPU experts = lower latency but higher GPU memory usage (May cause OOM) kt-max-deferred-experts-per-token: Enables pipelined execution: 0: Synchronous execution (simpler, higher latency) 1-2: Deferred execution (better latency, requires tuning) - recommended 3-4: Higher deferred count (possible but rarely beneficial)","breadcrumbs":"For kt-kernel » KT-Kernel Parameters","id":"20","title":"KT-Kernel Parameters"},"21":{"body":"For standalone usage without SGLang, you can use KT-Kernel directly via Python API: from kt_kernel import KTMoEWrapper # Initialize the MoE wrapper\\nwrapper = KTMoEWrapper( layer_idx=0, num_experts=8, num_experts_per_tok=2, hidden_size=4096, moe_intermediate_size=14336, num_gpu_experts=2, cpuinfer_threads=32, threadpool_count=2, weight_path=\\"/path/to/weights\\", chunked_prefill_size=512, method=\\"AMXINT4\\" # Options: \\"AMXINT4\\", \\"AMXINT8\\", \\"LLAMAFILE\\" (preview)\\n) # Load weights (from disk - pre-quantized)\\nwrapper.load_weights(physical_to_logical_map) # Or load weights from tensors (online quantization)\\nwrapper.load_weights_from_tensors(gate_proj, up_proj, down_proj, physical_to_logical_map) # Run inference\\noutput = wrapper.forward(hidden_states, topk_ids, topk_weights, cuda_stream) # Or use async API for better performance\\nwrapper.submit_forward(hidden_states, topk_ids, topk_weights, cuda_stream)\\n# ... do other work ...\\noutput = wrapper.sync_forward(hidden_states, cuda_stream)","breadcrumbs":"For kt-kernel » Direct Python API Usage","id":"21","title":"Direct Python API Usage"},"22":{"body":"# Initialize with additional options\\nwrapper = KTMoEWrapper( layer_idx=0, num_experts=8, num_experts_per_tok=2, hidden_size=4096, moe_intermediate_size=14336, num_gpu_experts=2, cpuinfer_threads=32, threadpool_count=2, weight_path=\\"/path/to/weights\\", chunked_prefill_size=512, method=\\"AMXINT4\\", cpu_save=False, # Keep weights in CPU memory after loading max_deferred_experts_per_token=0 # Number of experts to defer (for pipelined execution)\\n) # Pre-allocate buffers for specific batch sizes (improves performance)\\nKTMoEWrapper.set_capture_batch_sizes([1, 2, 4, 8, 16]) # Query captured batch sizes\\nbatch_sizes = KTMoEWrapper.get_capture_batch_sizes() # Clear buffer cache to free memory\\nKTMoEWrapper.clear_buffer_cache()","breadcrumbs":"For kt-kernel » Advanced Options","id":"22","title":"Advanced Options"},"23":{"body":"","breadcrumbs":"For kt-kernel » Build Configuration","id":"23","title":"Build Configuration"},"24":{"body":"If you prefer manual installation without the install.sh script, follow these steps: 1. Install System Dependencies Prerequisites: cmake (recommended: conda install -y cmake) libhwloc-dev and pkg-config 2. Set Build Configuration Core Options: Variable Options Description CPUINFER_CPU_INSTRUCT NATIVE, AVX512, AVX2, FANCY CPU instruction set to use CPUINFER_ENABLE_AMX ON, OFF Enable Intel AMX support CPUINFER_BUILD_TYPE Release, Debug, RelWithDebInfo Build type (default: Release) CPUINFER_PARALLEL Number Parallel build jobs (default: auto-detect) CPUINFER_VERBOSE 0, 1 Verbose build output (default: 0) Instruction Set Details: NATIVE: Auto-detect and use all available CPU instructions ( -march=native) - Recommended for best performance AVX512: Explicit AVX512 support for Skylake-SP and Cascade Lake AVX2: AVX2 support for maximum compatibility FANCY: AVX512 with full extensions (AVX512F/BW/DQ/VL/VNNI) for Ice Lake+ and Zen 4+. Use this when building pre-compiled binaries to distribute to users with modern CPUs. For local builds, prefer NATIVE for better performance. Example Configurations: # Maximum performance on AMX CPU\\nexport CPUINFER_CPU_INSTRUCT=NATIVE\\nexport CPUINFER_ENABLE_AMX=ON # AVX512 CPU without AMX\\nexport CPUINFER_CPU_INSTRUCT=AVX512\\nexport CPUINFER_ENABLE_AMX=OFF # Compatibility build\\nexport CPUINFER_CPU_INSTRUCT=AVX2\\nexport CPUINFER_ENABLE_AMX=OFF # Debug build for development\\nexport CPUINFER_BUILD_TYPE=Debug\\nexport CPUINFER_VERBOSE=1 3. Build and Install # Editable installation (for development)\\npip install -e . # Standard installation\\npip install .","breadcrumbs":"For kt-kernel » Manual Installation","id":"24","title":"Manual Installation"},"25":{"body":"","breadcrumbs":"For kt-kernel » Error Troubleshooting","id":"25","title":"Error Troubleshooting"},"26":{"body":"-- Looking for a CUDA compiler - NOTFOUND CMake Error at CMakeLists.txt:389 (message): KTRANSFORMERS_USE_CUDA=ON but CUDA compiler not found Make sure you have the CUDA toolkit installed and nvcc is in your system PATH. Try export CMAKE_ARGS=\\"-D CMAKE_CUDA_COMPILER=$(which nvcc)\\" and reinstall again.","breadcrumbs":"For kt-kernel » CUDA Not Found","id":"26","title":"CUDA Not Found"},"27":{"body":"Run sudo apt install libhwloc-dev if on a Debian-based system or build from source: https://www.open-mpi.org/projects/hwloc/. wget https://download.open-mpi.org/release/hwloc/v2.12/hwloc-2.12.2.tar.gz\\ntar -xzf hwloc-2.12.2.tar.gz\\ncd hwloc-2.12.2\\n./configure\\nmake\\nsudo make install","breadcrumbs":"For kt-kernel » hwloc Not Found","id":"27","title":"hwloc Not Found"},"28":{"body":"KT-Kernel provides weight quantization tools for CPU-GPU hybrid inference (e.g., integrating with SGLang). Both tools work together to enable heterogeneous expert placement across CPUs and GPUs.","breadcrumbs":"For kt-kernel » Weight Quantization","id":"28","title":"Weight Quantization"},"29":{"body":"Quantize weights to INT4/INT8 format optimized for AMX inference: python scripts/convert_cpu_weights.py \\\\ --input-path /path/to/model \\\\ --input-type bf16 \\\\ --output /path/to/output \\\\ --quant-method int4 Supported formats: FP8, FP16, BF16 → INT4/INT8","breadcrumbs":"For kt-kernel » CPU Weights (for “cold” experts on CPU)","id":"29","title":"CPU Weights (for “cold” experts on CPU)"},"3":{"body":"CPU-optimized kernel operations for heterogeneous LLM inference. Key Features: AMX/AVX Acceleration: Intel AMX and AVX512/AVX2 optimized kernels for INT4/INT8 quantized inference MoE Optimization: Efficient Mixture-of-Experts inference with NUMA-aware memory management Quantization Support: CPU-side INT4/INT8 quantized weights, GPU-side GPTQ support Easy Integration: Clean Python API for SGLang and other frameworks Quick Start: cd kt-kernel\\npip install . Use Cases: CPU-GPU hybrid inference for large MoE models Integration with SGLang for production serving Heterogeneous expert placement (hot experts on GPU, cold experts on CPU) Performance Examples: Model Hardware Configuration Total Throughput Output Throughput DeepSeek-R1-0528 (FP8) 8×L20 GPU + Xeon Gold 6454S 227.85 tokens/s 87.58 tokens/s (8-way concurrency) 👉 Full Documentation →","breadcrumbs":"Introduction » » 🚀 kt-kernel - High-Performance Inference Kernels","id":"3","title":"🚀 kt-kernel - High-Performance Inference Kernels"},"30":{"body":"Apply GPTQ quantization to model weights: # Install additional dependencies first\\npip install accelerate transformers llmcompressor datasets # Quantize GPU weights\\npython scripts/convert_gpu_weights.py \\\\ --model_id /path/to/model \\\\ --output_dir /path/to/output \\\\ --quant_type W4A16 Supported types: W4A16 (GPTQ4), W8A16 (GPTQ8) For detailed documentation, advanced options, and low-memory mode, see scripts/README.md.","breadcrumbs":"For kt-kernel » GPU Weights (for “hot” experts on GPU)","id":"30","title":"GPU Weights (for “hot” experts on GPU)"},"31":{"body":"your msg should match: Conventional Commits (https://www.conventionalcommits.org/) and format your code before commit: cmake -B build\\ncd build\\nmake format and you may need a new clang-format at least 18, use this command in conda env: conda install -c conda-forge clang-format=18\\nrm -rf build and you may need black for python format: conda install black","breadcrumbs":"For kt-kernel » Before Commit!","id":"31","title":"Before Commit!"},"32":{"body":"Introduction Fine-Tuning Results (Examples) Quick to Start Environment Setup Core Feature 1: Use KTransformers backend to fine-tune ultra-large MoE models Core Feature 2: Chat with the fine-tuned model (base + LoRA adapter) Core Feature 3: Batch inference + metrics (base + LoRA adapter) KT Fine-Tuning Speed (User-Side View) End-to-End Performance GPU/CPU Memory Footprint Conclusion MadSys Lab, KVCache-AI Team, Approaching AI, LLaMA-Factory Team","breadcrumbs":"For kt-sft » KTransformers Fine-Tuning × LLaMA-Factory Integration – User Guide","id":"32","title":"KTransformers Fine-Tuning × LLaMA-Factory Integration – User Guide"},"33":{"body":"From DeepSeek-V3/R1 to Qwen3-MoE and Kimi-K2, each wave of open-sourced large models brings leaps in performance and scale. However, many researchers and developers are constrained by expensive GPUs and models with tens or even hundreds of billions of parameters, making it hard to fine-tune very large models under limited resources. To bridge this gap, we propose a practical approach: combining KTransformers with LLaMA-Factory. With just 2–4 RTX 4090s and a high-memory CPU, you can fine-tune ultra-large MoE models like DeepSeek-671B. Our goal is to give resource-constrained researchers a local path to explore fine-tuning ultra-large models, and also a fast way to customize smaller models (e.g., 14B/30B) for specific scenarios. We validate the setup using stylized dialogue, Westernized translation tone, and medical Q&A as representative tasks, showing that personalized adaptation can be achieved within hours. As shown below, LLaMA-Factory is the unified orchestration/configuration layer for the whole fine-tuning workflow—handling data, training scheduling, LoRA injection, and inference interfaces. KTransformers acts as a pluggable high-performance backend that takes over core operators like Attention/MoE under the same training configs, enabling efficient GPU+CPU heterogeneous cooperation. Within LLaMA-Factory, we compared LoRA fine-tuning with HuggingFace, Unsloth, and KTransformers backends. KTransformers is the only workable 4090-class solution for ultra-large MoE models (e.g., 671B) and also delivers higher throughput and lower GPU memory on smaller MoE models (e.g., DeepSeek-14B). Under LoRA (BF16) + NekoQA-10K stylized dialogue HuggingFace Backend Unsloth Backend KTransformers Backend [14B-DeepSeekV2-Lite] LoRA fine-tuning throughput 303.58 token/s 455.37 token/s 530.38 token/s [14B-DeepSeekV2-Lite] GPU memory 32.12 GB 9.64 GB 6.08 GB [671B-DeepSeekV3] LoRA fine-tuning throughput Too Huge to run NOT SUPPORT 40.35 token/s [671B-DeepSeekV3] GPU memory (sum across GPUs) theoretical 1400 GB † NOT SUPPORT 70 GB † † 1400 GB is a theoretical FP16 full-parameter resident footprint (not runnable). 70 GB is the measured peak with KT strategy (Attention on GPU + layered MoE offload).","breadcrumbs":"For kt-sft » Introduction","id":"33","title":"Introduction"},"34":{"body":"Stylized Dialogue (CatGirl tone) Dataset: NekoQA-10K. Goal: improve style consistency and recognizability. The figure compares responses from the base vs. fine-tuned models. The fine-tuned model maintains the target tone and address terms more consistently (red boxes), validating the effectiveness of style-transfer fine-tuning. Benchmarks We use: (1) Translational-Style-ChatLLM, which asks for an exaggerated, Westernized translation tone—clear, stylized customization. (2) AfriMed-QA (ACL 2025), a medical dataset for African contexts with strong domain specificity, including multiple-choice and short-answer sub-tasks—well-suited for vertical fine-tuning evaluation. The tables show metrics before vs. after LoRA fine-tuning. We observe large improvements across metrics, verifying fine-tuning effectiveness: Translational-Style dataset BLEU-1 BLEU-2 BLEU-3 BLEU-4 ROUGE-1 ROUGE-2 ROUGE-L V2-Lite (no LoRA) 20.66 8.33 4.54 2.89 22.71 4.52 19.19 KT-LoRA fine-tuned V2-Lite 35.41 22.44 15.42 11.18 42.03 18.38 33.10 V3 base (no LoRA) 8.49 3.34 1.62 0.96 15.91 2.55 10.07 KT-LoRA fine-tuned V3 37.02 23.70 16.21 11.49 43.43 18.96 34.54 AfriMed-QA (short answer) BLEU-1 BLEU-2 BLEU-3 BLEU-4 ROUGE-1 ROUGE-2 ROUGE-L V2-Lite (no LoRA) 13.58 11.12 9.10 7.23 22.48 7.81 11.73 KT-LoRA fine-tuned V2-Lite 35.90 27.63 22.99 19.15 35.25 17.50 28.44 V3 base (no LoRA) 12.75 10.27 8.05 5.99 20.33 5.65 10.11 KT-LoRA fine-tuned V3 42.42 34.12 28.95 24.54 41.97 22.37 33.28 AfriMed-QA (multiple choice) Accuracy V2-Lite (no LoRA) 0.0645 KT-LoRA fine-tuned V2-Lite 0.4812 V3 base (no LoRA) 0.5833 KT-LoRA fine-tuned V3 0.7930 Even for ultra-large MoE models, KTransformers-backed fine-tuning achieves strong task performance quickly.","breadcrumbs":"For kt-sft » Fine-Tuning Results (Examples)","id":"34","title":"Fine-Tuning Results (Examples)"},"35":{"body":"This section shows how to install and use LLaMA-Factory + KTransformers for fine-tuning and inference: Environment setup Fine-tune ultra-large MoE models with KTransformers backend Load the fine-tuned model (base + LoRA adapter) for chat/inference Batch inference and metric evaluation","breadcrumbs":"For kt-sft » Quick to Start","id":"35","title":"Quick to Start"},"36":{"body":"According to the following example, install both the KTransformers and LLaMA-Factory environments simultaneously.\\nThis time, to simplify the installation process of KTransformers, we have specially packaged a wheel file to avoid local compilation.\\nThe detailed installation steps are as follows:\\n(Note: Make sure your local Python version, Torch version, CUDA version, and the KTransformers wheel filename correspond correctly.) # 1. Create a conda environment\\nconda create -n Kllama python=3.12 # choose from : [3.10, 3.11, 3.12, 3.13]\\nconda install -y -c conda-forge libstdcxx-ng gcc_impl_linux-64\\nconda install -y -c nvidia/label/cuda-11.8.0 cuda-runtime # 2. Install the LLaMA-Factory environment\\ngit clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git\\ncd LLaMA-Factory\\npip install -e \\".[torch,metrics]\\" --no-build-isolation # 3. Install the KTransformers wheel that matches your Torch and Python versions, from https://github.com/kvcache-ai/ktransformers/releases/tag/v0.4.1 (Note: The CUDA version can differ from that in the wheel filename.)\\npip install ktransformers-0.4.1+cu128torch27fancy-cp312-cp312-linux_x86_64.whl # 4. Install flash-attention, download the corresponding file based on your Python and Torch versions from: https://github.com/Dao-AILab/flash-attention/releases\\npip install flash_attn-2.8.3+cu12torch2.7cxx11abiTRUE-cp312-cp312-linux_x86_64.whl\\n# abi=True/False can find from below\\n# import torch\\n# print(torch._C._GLIBCXX_USE_CXX11_ABI) # 5. (Optional) If you want to use flash_infer (otherwise it defaults to triton)\\ngit clone https://github.com/kvcache-ai/custom_flashinfer.git\\npip install custom_flashinfer/ Usage tip: In LLaMA-Factory YAML, set use_kt: true and pick a kt_optimize_rule file to have KTransformers handle the core compute. The features below show typical configs.","breadcrumbs":"For kt-sft » Environment Setup","id":"36","title":"Environment Setup"},"37":{"body":"Run the command: USE_KT=1 llamafactory-cli train examples/train_lora/deepseek3_lora_sft_kt.yaml. Note: You must provide a BF16 model. DeepSeek-V3-671B is released in FP8 by default; convert with DeepSeek-V3/inference/fp8_cast_bf16.py. ### model\\nmodel_name_or_path: opensourcerelease/DeepSeek-V3-bf16\\ntrust_remote_code: true ### method\\nstage: sft\\ndo_train: true\\nfinetuning_type: lora\\nlora_rank: 8\\nlora_target: all ### dataset\\ndataset: identity\\ntemplate: deepseek\\ncutoff_len: 2048\\nmax_samples: 100000\\noverwrite_cache: true\\npreprocessing_num_workers: 16\\ndataloader_num_workers: 4 ### output\\noutput_dir: saves/Kllama_deepseekV3\\nlogging_steps: 10\\nsave_steps: 500\\nplot_loss: true\\noverwrite_output_dir: true\\nsave_only_model: false\\nreport_to: none # choices: [none, wandb, tensorboard, swanlab, mlflow] ### train\\nper_device_train_batch_size: 1\\ngradient_accumulation_steps: 8\\nlearning_rate: 1.0e-4\\nnum_train_epochs: 3.0\\nlr_scheduler_type: cosine\\nwarmup_ratio: 0.1\\nbf16: true\\nddp_timeout: 180000000\\nresume_from_checkpoint: null ### ktransformers\\nuse_kt: true # use KTransformers as LoRA sft backend\\nkt_optimize_rule: examples/kt_optimize_rules/DeepSeek-V3-Chat-sft-amx-multi-gpu.yaml\\ncpu_infer: 32\\nchunk_size: 8192 kt_optimize_rule controls placement strategy. See also ktransformers/optimize_rules. Naming hints ( * = wildcard): Pattern Meaning DeepSeek-V2-Lite-Chat-* / DeepSeek-V3-Chat-* Target model variants -sft- Strategy for fine-tuning; others are for inference -amx- Use AMX on CPU; otherwise use llamafile -multi-gpu-X Model parallel on X GPUs (X omitted → default 2 GPUs) Example: DeepSeek-V3-Chat-sft-amx-multi-gpu.yaml = V3-Chat fine-tuning with AMX and 2-GPU model parallel. We recommend AMX acceleration where available ( lscpu | grep amx). AMX supports BF16/INT8. Example: - match: name: \\"^model\\\\\\\\.layers\\\\\\\\..*\\\\\\\\.mlp\\\\\\\\.experts$\\" replace: class: ktransformers.operators.experts.KTransformersExperts # custom MoE Kernel with expert parallelism kwargs: prefill_device: \\"cpu\\" prefill_op: \\"KExpertsTorch\\" generate_device: \\"cpu\\" generate_op: \\"KSFTExpertsCPU\\" out_device: \\"cuda\\" backend: \\"AMXInt8\\" # or \\"AMXBF16\\" or \\"llamafile\\" (default) Outputs go to output_dir in safetensors format plus adapter metadata for later loading.","breadcrumbs":"For kt-sft » Core Feature 1: Use KTransformers backend to fine-tune ultra-large MoE models","id":"37","title":"Core Feature 1: Use KTransformers backend to fine-tune ultra-large MoE models"},"38":{"body":"Run the command: llamafactory-cli chat examples/inference/deepseek3_lora_sft_kt.yaml. Use the safetensors adapter trained with KT for inference. model_name_or_path: opensourcerelease/DeepSeek-V3-bf16\\nadapter_name_or_path: saves/Kllama_deepseekV3\\ntemplate: deepseek\\ninfer_backend: ktransformers # choices: [huggingface, vllm, sglang, ktransformers]\\ntrust_remote_code: true use_kt: true # use KTransformers as LoRA sft backend to inference\\nkt_optimize_rule: examples/kt_optimize_rules/DeepSeek-V3-Chat-sft-amx-multi-gpu.yaml\\ncpu_infer: 32\\nchunk_size: 8192 We also support GGUF adapters: for safetensors, set the directory; for GGUF, set the file path in adapter_name_or_path. During loading, LLaMA-Factory maps layer names to KT’s naming. You’ll see logs like Loaded adapter weight: XXX -> XXX:","breadcrumbs":"For kt-sft » Core Feature 2: Chat with the fine-tuned model (base + LoRA adapter)","id":"38","title":"Core Feature 2: Chat with the fine-tuned model (base + LoRA adapter)"},"39":{"body":"Run the command: API_PORT=8000 llamafactory-cli api examples/inference/deepseek3_lora_sft_kt.yaml.\\nInvoke the KT fine-tuned adapter to provide the API; the usage logic of other APIs is consistent with the native LLaMA-Factory approach. model_name_or_path: opensourcerelease/DeepSeek-V3-bf16\\nadapter_name_or_path: saves/Kllama_deepseekV3\\ntemplate: deepseek\\ninfer_backend: ktransformers # choices: [huggingface, vllm, sglang, ktransformers]\\ntrust_remote_code: true use_kt: true # use KTransformers as LoRA sft backend to inference\\nkt_optimize_rule: examples/kt_optimize_rules/DeepSeek-V3-Chat-sft-amx-multi-gpu.yaml\\ncpu_infer: 32\\nchunk_size: 8192","breadcrumbs":"For kt-sft » Core Feature 3: Batch inference + metrics (base + LoRA adapter)","id":"39","title":"Core Feature 3: Batch inference + metrics (base + LoRA adapter)"},"4":{"body":"KTransformers × LLaMA-Factory integration for ultra-large MoE model fine-tuning. Key Features: Resource Efficient: Fine-tune 671B DeepSeek-V3 with just 70GB GPU memory + 1.3TB RAM LoRA Support: Full LoRA fine-tuning with heterogeneous acceleration LLaMA-Factory Integration: Seamless integration with popular fine-tuning framework Production Ready: Chat, batch inference, and metrics evaluation Performance Examples: Model Configuration Throughput GPU Memory DeepSeek-V3 (671B) LoRA + AMX ~40 tokens/s 70GB (multi-GPU) DeepSeek-V2-Lite (14B) LoRA + AMX ~530 tokens/s 6GB Quick Start: cd kt-sft\\n# Install environment following kt-sft/README.md\\nUSE_KT=1 llamafactory-cli train examples/train_lora/deepseek3_lora_sft_kt.yaml 👉 Full Documentation →","breadcrumbs":"Introduction » » 🎓 kt-sft - Fine-Tuning Framework","id":"4","title":"🎓 kt-sft - Fine-Tuning Framework"},"40":{"body":"","breadcrumbs":"For kt-sft » KT Fine-Tuning Speed (User-Side View)","id":"40","title":"KT Fine-Tuning Speed (User-Side View)"},"41":{"body":"Definitions step_time: wall-clock time for a full optimization step (tensor movement + Attention + MoE + other compute). tokens_per_step = GAS × qlen; token/s = tokens_per_step / step_time. Settings: GAS=16, qlen=512 (→ tokens_per_step = 8192); LoRA ( r=8, alpha=32, dropout=0.1); AMX enabled; GPU: RTX 4090, CPU: Intel Xeon Platinum 8488C. Measured DeepSeek-V3-671B: step_time = 203 s → token/s ≈ 8192 / 203 ≈ 40.35 DeepSeek-V2-Lite-14B: step_time = 36 s → token/s ≈ 8192 / 36 ≈ 227.6","breadcrumbs":"For kt-sft » End-to-End Performance","id":"41","title":"End-to-End Performance"},"42":{"body":"DeepSeek-V3 (671B; 61 layers with 58 MoE): ~ 70 GB total GPU VRAM (multi-GPU), ~ 1.2–1.3 TB CPU RAM. DeepSeek-V2-Lite (14B; 27 layers with 26 MoE): ~ 5.5 GB GPU VRAM, ~ 30 GB CPU RAM.","breadcrumbs":"For kt-sft » GPU/CPU Memory Footprint","id":"42","title":"GPU/CPU Memory Footprint"},"43":{"body":"By integrating KTransformers LoRA fine-tuning into LLaMA-Factory, we provide a practical guide for efficient training and deployment of MoE LLMs. KT brings cutting-edge optimizations (DeepSeek/Qwen/Kimi support with AMX-accelerated kernels), and LoRA enables customization under very low GPU memory. LLaMA-Factory offers a friendly, unified interface. This integration (akin to Unsloth-style speedups) means even models with tens to hundreds of billions of parameters can be fine-tuned and deployed with low latency on commodity hardware. You get memory savings, speed-ups, and usability together. We encourage you to try LLaMA-Factory + KT for your next MoE project and follow this guide. Feedback is welcome!","breadcrumbs":"For kt-sft » Conclusion","id":"43","title":"Conclusion"},"44":{"body":"","breadcrumbs":"kt-sft part » kt-sft Docs","id":"44","title":"kt-sft Docs"},"45":{"body":"Introduction Overall View of the KT Fine-Tuning Framework Attention (LoRA + KT coexist) MoE (operator encapsulation + backward) Multi-GPU Loading/Training: Placement strategy instead of DataParallel KT-LoRA Fine-Tuning Evaluation Setup Results Speed Tests Memory Footprint Conclusion MadSys Lab, KVCache-AI Team, Approaching AI, LLaMA-Factory Team","breadcrumbs":"kt-sft part » kt-sft developer tech notes » KTransformers Fine-Tuning × LLaMA-Factory Integration – Developer Technical Notes","id":"45","title":"KTransformers Fine-Tuning × LLaMA-Factory Integration – Developer Technical Notes"},"46":{"body":"Recent open-source LLMs—from DeepSeek-V3/R1 to Qwen-MoE and Kimi-K2—have surged in performance and scale. Yet due to compute and memory constraints, it is difficult for typical researchers to fine-tune trillion-parameter-class models. We therefore integrate KTransformers with LLaMA-Factory so that, with 2–4 RTX 4090 GPUs and sufficient CPU memory, one can fine-tune ultra-large Mixture-of-Experts (MoE) models such as DeepSeek-671B. This architecture bridges resource gaps, enabling local fine-tuning of ultra-large models, while also supporting efficient scenario customization at 14B/30B scales. We validate on stylized dialogue, Westernized translation tone, and medical Q&A, achieving rapid adaptation within hours. Architecturally, LLaMA-Factory orchestrates data/config/training, LoRA insertion, and inference; KTransformers is a pluggable, high-performance operator backend that takes over Attention and MoE under the same training code, enabling GPU+CPU heterogeneity to accelerate training and reduce GPU memory. We evaluated LoRA fine-tuning with HuggingFace default, Unsloth, and KTransformers backends (same settings and data). KTransformers is currently the only solution feasible on 2–4×24GB 4090s for 671B-scale MoE, and also shows higher throughput and lower GPU memory for 14B MoEs. Under LoRA (BF16) + NekoQA-10K stylized dialogue HuggingFace Backend Unsloth Backend KTransformers Backend [14B-DeepSeekV2-Lite] LoRA fine-tuning throughput 303.58 token/s 455.37 token/s 530.38 token/s [14B-DeepSeekV2-Lite] GPU memory 32.12 GB 9.64 GB 6.08 GB [671B-DeepSeekV3] LoRA fine-tuning throughput Too Huge to run NOT SUPPORT 40.35 token/s [671B-DeepSeekV3] GPU memory (sum across GPUs) theoretical 1400 GB † NOT SUPPORT 70 GB † † The 1400 GB is the theoretical FP16 full-resident footprint (not runnable). 70 GB is the measured peak with KT (Attention on GPU + layered MoE offload). From the table above, it can be seen that for the 14B model, the KTransformers backend achieves approximately 75% higher throughput than the default HuggingFace solution, while using only about one-fifth of the GPU memory. For the 671B model, both HuggingFace and Unsloth fail to run on a single 4090 GPU, whereas KTransformers is able to perform LoRA fine-tuning at 40 tokens/s, keeping the GPU memory usage within 70 GB.","breadcrumbs":"kt-sft part » kt-sft developer tech notes » Introduction","id":"46","title":"Introduction"},"47":{"body":"We detail how KTransformers takes over core operators in LLaMA-Factory’s fine-tuning framework to optimize Attention and MoE. DeepSeek-V3/V2 MoE models comprise a small-parameter dense Attention part and a large-parameter sparse MoE part. For illustration, consider layer 2 of DeepSeek-V2-Lite-Chat (from which each layer includes both Attention and MoE). Attention compute and KV cache mainly reside on the GPU; the heavyweight MoE part is primarily executed on the CPU. We first cover Attention replacement and inheritance, then MoE encapsulation and backend interfacing, and finally multi-GPU placement.","breadcrumbs":"kt-sft part » kt-sft developer tech notes » Overall View of the KT Fine-Tuning Framework","id":"47","title":"Overall View of the KT Fine-Tuning Framework"},"48":{"body":"KTransformers provides operator injection ( BaseInjectedModule), and PEFT provides LoRA layer insertion. For fine-tuning, we design KTransformersLinearLora, inheriting from both KTransformersLinear and LoraLayer: Inheritance: KTransformersLinearLora retains KT’s high-performance paths ( prefill_linear/ generate_linear) while accepting LoRA parameters ( lora_A/lora_B). Replacement: During preparation, we replace original KTransformersLinear layers (Q/K/V/O) with KTransformersLinearLora, preserving KT optimizations while enabling LoRA trainability. After replacement, LoRA is inserted at Q/K/V/O linear transforms (left), and KTransformersLinearLora contains both KT fast paths and LoRA matrices (right).","breadcrumbs":"kt-sft part » kt-sft developer tech notes » Attention (LoRA + KT coexist)","id":"48","title":"Attention (LoRA + KT coexist)"},"49":{"body":"Encapsulation Given large parameters and sparse compute, we encapsulate the expert computation as a differentiable black-box operator—transparent upstream, replaceable downstream. Upstream (PyTorch graph): we register a custom Autograd Function so the MoE layer appears as a single node. In the left figure (red box), only KSFTExpertsCPU is visible; on the right, the unencapsulated graph expands routing, dispatch, and FFN experts. Encapsulation makes the MoE layer behave like a standard nn.Module with gradients. Downstream (backend): inside the Autograd Function, pybind11 calls C++ extensions for forward/backward. Multiple pluggable backends exist (AMX BF16/INT8; llamafile). The backend can be switched via YAML (e.g., \\"backend\\": \\"AMXBF16\\" vs. \\"llamafile\\"). Backward (CPU) MoE backward frequently needs the transposed weights $W^\\\\top$. To avoid repeated runtime transposes, we precompute/cache $W^\\\\top$ at load time (blue box). We also cache necessary intermediate activations (e.g., expert projections, red box) to reuse in backward and reduce recomputation. We provide backward implementations for llamafile and AMX (INT8/BF16), with NUMA-aware optimizations.","breadcrumbs":"kt-sft part » kt-sft developer tech notes » MoE (operator encapsulation + backward)","id":"49","title":"MoE (operator encapsulation + backward)"},"5":{"body":"If you use KTransformers in your research, please cite our paper: @inproceedings{10.1145/3731569.3764843, title = {KTransformers: Unleashing the Full Potential of CPU/GPU Hybrid Inference for MoE Models}, author = {Chen, Hongtao and Xie, Weiyu and Zhang, Boxin and Tang, Jingqi and Wang, Jiahao and Dong, Jianwei and Chen, Shaoyuan and Yuan, Ziwei and Lin, Chen and Qiu, Chengyu and Zhu, Yuening and Ou, Qingliang and Liao, Jiaqi and Chen, Xianglin and Ai, Zhiyuan and Wu, Yongwei and Zhang, Mingxing}, booktitle = {Proceedings of the ACM SIGOPS 31st Symposium on Operating Systems Principles}, year = {2025}\\n}","breadcrumbs":"Introduction » » 🔥 Citation","id":"5","title":"🔥 Citation"},"50":{"body":"To lower per-GPU memory peaks on 2–4 GPUs, we use model parallelism + explicit placement, not DataParallel (which duplicates the whole model on each GPU). Key changes: KTrainer: takes over .to(device) to prevent “move whole model to a single GPU”. Using KT’s optimize-rule YAML, each layer declares device: cuda:0/cuda:1/... and is constructed directly on the target GPU (no extra copies). Disable automatic DataParallel: when USE_KT=1, we disable automatic DP wrappers from LLaMA-Factory/HF Trainer to avoid duplication and keep full control over sharding. Gradient aggregation: gradients are reduced to cuda:0. Intermediate activations stay local; only necessary tensors are transferred, cutting communication/activation overhead. Thus, we keep KT placement strategies under multi-GPU fine-tuning. Users choose a kt_optimize_rule with multi-gpu. For DeepSeek-671B, DeepSeek-V3-Chat-sft-amx-multi-gpu.yaml is a typical 2-GPU plan: KV/attention parts on each GPU; MoE experts sharded on CPU; both GPUs share the workload.","breadcrumbs":"kt-sft part » kt-sft developer tech notes » Multi-GPU Loading/Training: Placement strategy instead of DataParallel","id":"50","title":"Multi-GPU Loading/Training: Placement strategy instead of DataParallel"},"51":{"body":"","breadcrumbs":"kt-sft part » kt-sft developer tech notes » KT-LoRA Fine-Tuning Evaluation","id":"51","title":"KT-LoRA Fine-Tuning Evaluation"},"52":{"body":"LLaMA-Factory orchestration, KTransformers backend, LoRA (rank=8, α=32, dropout=0.1, BF16), GAS=16, qlen=512, with the same KT optimize rule as training. We evaluate (a) stylized dialogue transfer and (b) two small-scale representative benchmarks: Translational-Style (generative) and AfriMed-QA (medical vertical; SAQ and MCQ). AMX is enabled; GPUs: 2×48GB RTX 4090; CPU: Intel Xeon Platinum 8488C.","breadcrumbs":"kt-sft part » kt-sft developer tech notes » Setup","id":"52","title":"Setup"},"53":{"body":"Stylized Dialogue (CatGirl tone) Dataset: NekoQA-10K. The fine-tuned model consistently exhibits the target style (red boxes) versus neutral/rational base (blue). This shows KT-LoRA injects style features into the generation distribution with low GPU cost. Translational-Style benchmark (generative) Dataset: Translational-Style-ChatLLM. Metrics: BLEU-1/2/3/4, ROUGE-1/2/L. Translational-Style dataset BLEU-1 BLEU-2 BLEU-3 BLEU-4 ROUGE-1 ROUGE-2 ROUGE-L V2-Lite (no LoRA) 20.66 8.33 4.54 2.89 22.71 4.52 19.19 KT-LoRA fine-tuned V2-Lite 35.41 22.44 15.42 11.18 42.03 18.38 33.10 V3 base (no LoRA) 8.49 3.34 1.62 0.96 15.91 2.55 10.07 KT-LoRA fine-tuned V3 37.02 23.70 16.21 11.49 43.43 18.96 34.54 As shown by the test results in the tables above, under a unified workflow and placement strategy, both model scales exhibit consistent gains after fine-tuning, supporting the usability and effectiveness of the “KT backend + LoRA fine-tuning” combination for generative style control. At the same time, this indicates that KT’s heterogeneous placement and operator optimizations can stably support small-sample adaptation in the style domain. Medical Vertical Benchmark (AfriMed-SAQ/MCQ) The dataset adopts AfriMed-QA (ACL 2025), a domain-specific dataset for the medical field in Africa with strong scenario customization characteristics, comprising two formats—multiple-choice questions (MCQ) and short-answer questions (SAQ)—which in this case serve as the evaluation for vertical-domain fine-tuning. In terms of evaluation criteria, BLEU/ROUGE are used for SAQ, and Accuracy is used for MCQ. AfriMed-QA (SAQ) BLEU-1 BLEU-2 BLEU-3 BLEU-4 ROUGE-1 ROUGE-2 ROUGE-L V2-Lite (no LoRA) 13.58 11.12 9.10 7.23 22.48 7.81 11.73 KT-LoRA fine-tuned V2-Lite 35.90 27.63 22.99 19.15 35.25 17.50 28.44 V3 base (no LoRA) 12.75 10.27 8.05 5.99 20.33 5.65 10.11 KT-LoRA fine-tuned V3 42.42 34.12 28.95 24.54 41.97 22.37 33.28 AfriMed-QA (MCQ) Accuracy V2-Lite (no LoRA) 0.0645 KT-LoRA fine-tuned V2-Lite 0.4812 V3 base (no LoRA) 0.5833 KT-LoRA fine-tuned V3 0.7930 As shown in the tables above, (1) DeepSeek-V3 (671B) after KT-LoRA fine-tuning achieves clearly higher performance than the fine-tuned DeepSeek-V2-Lite (14B) on both MCQ and SAQ, and it also surpasses the V3 base model. Within our small-scale setting, this preliminarily indicates that KT-LoRA fine-tuning of ultra-large-parameter models has practical significance in vertical domains. (2) Across both SAQ/MCQ sub-tasks, KT-LoRA delivers consistent gains, indicating that—with KT’s heterogeneous placement and backend operator support—LoRA fine-tuning can effectively inject the key knowledge points of vertical domains such as medicine into the model. Limitations At present, most of our testing is conducted on single datasets and at small scale (≤ 20k examples), with the goal of providing existence evidence of system effectiveness for KT-LoRA fine-tuning, rather than drawing generalized conclusions about algorithmic generalization or scaling laws. Our report primarily presents representative figures; to support stronger algorithmic claims, larger sample sizes, multi-lingual/multi-domain datasets, and multi-seed repeated experiments would be required—these are beyond the scope of this work. We also warmly welcome everyone to join the open-source LLaMA-Factory KT fine-tuning project. If you have additional test results, we especially welcome you to record them in the shared spreadsheet below, and to include the corresponding kt_optimize_rule files, dataset examples, training/evaluation YAMLs, and detailed GPU-memory and CPU configurations for community reference and reproducibility~!","breadcrumbs":"kt-sft part » kt-sft developer tech notes » Results","id":"53","title":"Results"},"54":{"body":"End-to-End Performance Definitions step_time：time per optimization step (tensor movement + Attention + MoE + others). tokens_per_step = GAS × qlen； token/s = tokens_per_step / step_time。 We use GAS=16, qlen=512 → tokens_per_step=8192. Measured Model step_time (s) tokens/step token/s DeepSeek-V3-671B 203 8192 40.35 DeepSeek-V2-Lite-14B 36 8192 227.6 MoE Compute (DeepSeek-V3-671B) Theory MoE per-layer, per-token FLOPs (forward+backward) approx.:\\n$$\\n\\\\text{FLOPs}_{\\\\text{per-layer, per-token}} \\\\approx c \\\\cdot k \\\\cdot H \\\\cdot I\\n$$ ​ with $k = 8$（Top-k），$H = 7168$（hidden size），$I = 2048$（intermediate size），$c\\\\approx16$（≈6 forward + ≈10 backward matmuls）。 Per-step across all MoE layers:\\n$$\\n\\\\text{FLOPs} {\\\\text{per-step}} \\\\approx c \\\\cdot qlen \\\\cdot k \\\\cdot H \\\\cdot I \\\\cdot L{\\\\text{MoE}}\\n$$ ​ Plugging $c=16, qlen=512, k=8, H=7168, I=2048, L_{MoE}=58$，$\\\\text{FLOPs}_{\\\\text{per-step}} \\\\approx 55.8\\\\ \\\\text{TFLOPs}$. Measured (MoE TFLOPS on CPU) If the MoE-only time per step is t_moe (seconds), $\\\\text{TFLOPS} = \\\\text{FLOPs}_{\\\\text{per-step}} / \\\\text{step_per_second}.$ Use MoE-phase time, not full step_time, to get MoE throughput. TFLOPS Forward Backward Average 17.55 18.41","breadcrumbs":"kt-sft part » kt-sft developer tech notes » Speed Tests","id":"54","title":"Speed Tests"},"55":{"body":"DeepSeek-V3 (671B; 58 MoE layers out of 61): ~ 70 GB total GPU, ~ 1.2–1.3 TB host memory. DeepSeek-V2-Lite (14B; 26 MoE layers out of 27): ~ 5 GB GPU, ~ 30 GB host memory.","breadcrumbs":"kt-sft part » kt-sft developer tech notes » Memory Footprint","id":"55","title":"Memory Footprint"},"56":{"body":"Integrating KTransformers LoRA with LLaMA-Factory provides a practical path to efficiently train and deploy MoE LLMs. KT contributes placement strategies and operator optimizations (DeepSeek/Qwen/Kimi support with AMX-accelerated kernels), and LoRA enables customization with very low GPU memory; LLaMA-Factory supplies a coherent user-level interface. This means even tens-to-hundreds-of-billion-parameter MoE models can be fine-tuned and served with low latency on ordinary hardware. The approach balances memory savings, speed, and usability, turning ultra-large models into tools that developers can actually wield.","breadcrumbs":"kt-sft part » kt-sft developer tech notes » Conclusion","id":"56","title":"Conclusion"},"57":{"body":"Author: Azure-Tang","breadcrumbs":"kt-sft part » Injection Tutorial » Tutorial: Inject Operator Step by Step","id":"57","title":"Tutorial: Inject Operator Step by Step"},"58":{"body":"This tutorial will guide you through the process of injecting custom operators into a model using the KTransformers framework. We will use the DeepSeekV2-Chat model as an example to demonstrate how to inject custom operators into the model step by step. The tutorial will cover the following topics: TL;DR How to Write Injection Rules Understanding Model Structure Matrix Absorption-based MLA Injection Injection of Routed Experts Injection of Linear Layers Injection of Modules with Pre-calculated Buffers Specifying Running Devices for Modules Muti-GPU How to Write a New Operator and Inject into the Model","breadcrumbs":"kt-sft part » Injection Tutorial » TL;DR","id":"58","title":"TL;DR"},"59":{"body":"The basic form of the injection rules for the Inject framework is as follows: - match: name: \\"^model\\\\\\\\.layers\\\\\\\\..*\\\\\\\\.*$\\" # Target module name class: torch.nn.Linear # Target module replace: class: \\"default\\" kwargs: generate_device: \\"cuda:0\\" # your_op_param_1: 1234 # your_op_param_2: 5678 recursive: True match: This field marks the matching rules, which can appear in two forms, name and class. These two matching rules can appear together or separately; they only match when both criteria are met. replace: class: Python class that can be imported to replace the target module. If no replacement is desired, set to default. kwargs: List of parameters needed for module initialization. generate_device: The device for this module, can be set to “cpu”, “cuda”, “cuda:1”, etc. recursive: Whether to recursively inject this module’s submodules, default is True. For the recursive field: Some modules contain multiple submodules, such as the Self-attention module typically includes q/k/v/o four linear modules. If we replace the self-attention module but do not want the internal linear modules to be covered by other rules, set this rule to False.","breadcrumbs":"kt-sft part » Injection Tutorial » How to Write Injection Rules","id":"59","title":"How to Write Injection Rules"},"6":{"body":"Developed and maintained by: MADSys Lab @ Tsinghua University Approaching.AI Community contributors We welcome contributions! Please feel free to submit issues and pull requests.","breadcrumbs":"Introduction » » 👥 Contributors & Team","id":"6","title":"👥 Contributors & Team"},"60":{"body":"Using deepseek-ai/DeepSeek-V2-Lite-Chat as an example, we can follow the above rules step by step to inject our custom module and run it. KTransformers offers a high degree of flexibility, allowing you to replace/experiment with basic operators. However, it also requires users to clearly understand the structure of the model they are running. Fortunately, knowing the structure of a model is very simple. Open the file list on the deepseek-ai/DeepSeek-V2-Lite homepage, and you can see the following files: From the .saftensors file, we can see the name of each layer’s weights, corresponding to the match.name attribute in the injection rules.\\nFrom the modeling_deepseek.py file, we can see the specific implementation of each module class, with the class name corresponding to the match.class attribute in the injection rules. The structure of the DeepSeekV2 model from the .saftensors and modeling_deepseek.py files is as follows: Supported operators and their corresponding classes are as follows: match replace backends descriptions Linear KTransformersLinear KLinearMarlin Marlin as backend KLinearTorch pytorch as backend KLinearCPUInfer llamafile as backend KLinearFP8 Triton fp8_gemm kernel. Requires GPU be able to caluculate fp8 data experts KTransformersExperts KExpertsTorch pytorch as backend KExpertsMarlin Marlin as backend KExpertsCPU llamafile as backend Attention KDeepseekV2Attention KDeepseekV2Attention MLA implementation MoE KMistralSparseMoEBlock KQwen2MoeSparseMoeBlock MoE for Qwen2 KDeepseekV2MoE KDeepseekV2MoE MoE for DeepseekV2 Model KQwen2MoeModel KQwen2MoeModel Model for Qwen2 KDeepseekV2Model KDeepseekV2Model Model for DeepseekV2 RoPE RotaryEmbedding RotaryEmbedding RoPE module YarnRotaryEmbedding YarnRotaryEmbedding RoPE module Then we start step-by-step injection of custom modules, our targets are: Replace the linear module with custom Marlin linear module. Replace the self-attention module with a custom Absorption-based MLA module. Replace the experts module with a custom Experts module. Replace the MoE module with a custom MoE module. Replace the RoPE module with a custom RoPE module. Set the running device for each module. The full implementation of the injection rules can be found in the here.","breadcrumbs":"kt-sft part » Injection Tutorial » Understanding Model Structure","id":"60","title":"Understanding Model Structure"},"61":{"body":"For the injection of the Attention module, we only need to use a regular expression to match the module names used in transformers and replace them with our own MLA module implementation. The YAML injection rule is as follows: - match: name: \\"^model\\\\\\\\.layers\\\\\\\\..*\\\\\\\\.self_attn$\\" # Regular expression replace: class: ktransformers.operators.attention.KDeepseekV2Attention # Optimized MLA implementation As you can see, each rule in the YAML file has two parts: match and replace. The match part specifies the module to be replaced, and the replace part specifies the module to be injected into the model along with the initialization keywords.","breadcrumbs":"kt-sft part » Injection Tutorial » Matrix Absorption-based MLA Injection","id":"61","title":"Matrix Absorption-based MLA Injection"},"62":{"body":"For Routed Experts (corresponding to the exps in the diagram), the module we inject is CPUInfer, which is wrapped in the wrapper module KTransformersExperts. KTransformersExperts has multiple implementations, and we need to specify keywords to tell the wrapper module which implementation we want to use and how we plan to use it. In the source code of the transformer, MoE is implemented using nn.ModuleList. We do not want KTransformers to traverse all submodules in the list and inject them one by one, so in this rule, we set recursive: False to prevent recursive injection into the submodules of this module. The YAML rule is as follows: - match: name: \\"^model\\\\\\\\.layers\\\\\\\\..*\\\\\\\\.mlp\\\\\\\\.experts$\\" replace: class: ktransformers.operators.experts.KTransformersExperts # Custom MoE kernel with expert parallelism kwargs: generate_device: \\"cpu\\" generate_op: \\"MLPCPUExperts\\" out_device: \\"cuda\\" recursive: False # Don\'t recursively inject submodules of this module If we inject Routed Experts as a custom module, we cannot use the interfaces in the original nn.ModuleList. Therefore, it is necessary to modify the forward function in the FFN module. The simplest method is to implement a new module with a custom forward function and inject it. - match: class: ktransformers.models.modeling_deepseek.DeepseekV2MoE replace: class: ktransformers.operators.experts.KDeepseekV2MoE # MLP module with custom forward function","breadcrumbs":"kt-sft part » Injection Tutorial » Injection of Routed Experts","id":"62","title":"Injection of Routed Experts"},"63":{"body":"For the remaining linear layer modules, we aim to use quantized operators to save storage space while improving performance. Since there is no current research on using MLA and quantization together, we do not want to inject linear into the MLA operator. Therefore, we can modify the regular expression and add a type check in the match part of the rule. Only modules that match both the name and class simultaneously will be injected. We also need to pass some keywords similar to the injection of Routed Experts. The YAML rule is as follows: - match: name: \\"^model\\\\\\\\.layers\\\\\\\\.(?!.*self_attn).*$\\" # Regular expression class: torch.nn.Linear # Only match modules matching name and class simultaneously replace: class: ktransformers.operators.linear.KTransformersLinear # Optimized kernel on quantized data types kwargs: generate_device: \\"cuda\\" generate_op: \\"QuantizedLinearMarlin\\"","breadcrumbs":"kt-sft part » Injection Tutorial » Injection of Linear Layers","id":"63","title":"Injection of Linear Layers"},"64":{"body":"To avoid occupying resources when initializing the injected original model, we use torch’s meta device to initialize the original model. The RoPE module pre-calculates some buffers during initialization, but no calculations are performed when using the meta device. Therefore, we need to compensate for the calculation of the buffer when loading the model. Simply, we inject a custom module into the rotary embedding module, which performs pre-calculation during loading. The YAML rule is as follows: - match: class: ktransformers.models.modeling_deepseek.DeepseekV2YarnRotaryEmbedding replace: class: ktransformers.operators.RoPE.YarnRotaryEmbedding","breadcrumbs":"kt-sft part » Injection Tutorial » Injection of Modules with Pre-calculated Buffers","id":"64","title":"Injection of Modules with Pre-calculated Buffers"},"65":{"body":"Finally, we set a fallback basic attribute generate_device for all modules: - match: name: \\"^model\\\\\\\\.layers\\\\\\\\..*\\\\\\\\.|^lm_head\\" replace: class: \\"default\\" kwargs: generate_device: \\"cuda\\" - match: name: \\"^model.embed_tokens\\" replace: class: \\"default\\" kwargs: generate_device: \\"cpu\\" Through these two rules, we place all previously unmatched layers (and their submodules) and lm_head on cuda, and the embedding on cpu. Note that the properties of a module will be determined by the first rule it matches. For example, if you later set a new replace.kwargs.generate_device in an injected module, the device set earlier will take precedence. If your computer has multiple cards, you can also configure the model to multiple cards.","breadcrumbs":"kt-sft part » Injection Tutorial » Specifying Running Devices for Modules","id":"65","title":"Specifying Running Devices for Modules"},"66":{"body":"If you have multiple GPUs, you can set the device for each module to different GPUs.\\nDeepseekV2-Chat got 60 layers, if we got 2 GPUs, we can allocate 30 layers to each GPU. Complete multi GPU rule examples here. First of all, for multi-GPU, we have to inject an new operator KDeepseekV2Model. And set division of the layers to different GPUs. For our case, we have to set the transfer_map in the KDeepseekV2Model operatoras as follows: - match: name: \\"^model$\\" replace: class: \\"ktransformers.operators.models.KDeepseekV2Model\\" kwargs: transfer_map: 30: \\"cuda:1\\" And we have to set the device for each module in the model. For example, for routed experts, the yaml for one GPU is: - match: name: \\"^model\\\\\\\\.layers\\\\\\\\..*\\\\\\\\.mlp\\\\\\\\.experts$\\" replace: class: ktransformers.operators.experts.KTransformersExperts # Custom MoE kernel with expert parallelism kwargs: generate_device: \\"cuda:0\\" generate_op: \\"MLPCUDAExperts\\" out_device: \\"cuda:0\\" recursive: False # Don\'t recursively inject submodules of this module But for two GPUs, we need to set the device for each module in the model. # allcate 0-29 layers‘s out_device to cuda:0\\n- match: name: \\"^model\\\\\\\\.layers\\\\\\\\.(0|[1-9]|[12][0-9])\\\\\\\\.mlp\\\\\\\\.experts$\\" replace: class: ktransformers.operators.experts.KTransformersExperts # custom MoE Kernel with expert paralleism kwargs: generate_device: \\"cpu\\" generate_op: \\"KExpertsCPU\\" out_device: \\"cuda:0\\" recursive: False # don\'t recursively inject submodules of this module # allocate 30-59 layers‘s out_device to cuda:1\\n- match: name: \\"^model\\\\\\\\.layers\\\\\\\\.([345][0-9])\\\\\\\\.mlp\\\\\\\\.experts$\\" replace: class: ktransformers.operators.experts.KTransformersExperts # custom MoE Kernel with expert paralleism kwargs: generate_device: \\"cpu\\" generate_op: \\"KExpertsCPU\\" out_device: \\"cuda:1\\" recursive: False # don\'t recursively inject submodules of this module For other modules, we can set the device in the same way.","breadcrumbs":"kt-sft part » Injection Tutorial » Muti-GPU","id":"66","title":"Muti-GPU"},"67":{"body":"In this section, we will explain how to write an operator that can be injected, using the implementation of a new linear as an example. First, all injectable operators need to inherit from the BaseInjectedModule class, which inherits some attributes required by our injection framework. Its initialization function needs to meet the following basic format: class LinearTorchInject(BaseInjectedModule): def __init__( self, key: str, gguf_loader: GGUFLoader, config: PretrainedConfig, orig_module: nn.Module = None, generate_device: str = \\"cuda\\", **kwargs, ): super().__init__(key, gguf_loader, config, orig_module, generate_device, **kwargs) If users have other parameters that need to be passed to this class, they can also be included in the init function and re-passed in the kwargs parameter in the yaml file. For example, if our operator wants to pass a parameter my_param, the init function can be written as: class LinearTorchInject(BaseInjectedModule): def __init__( self, key: str, gguf_loader: GGUFLoader, config: PretrainedConfig, orig_module: nn.Module = None, generate_device: str = \\"cuda\\", my_param: bool = True, **kwargs, ): super().__init__(key, gguf_loader, config, orig_module, generate_device, **kwargs) self.my_param = my_param Then our injection rule can be written as: - match: name: \\"^model\\\\\\\\.layers\\\\\\\\..*$\\" # Regular expression matches the module name. class: torch.nn.Linear # Type restrictions can be added. replace: class: ktransformers.operators.linear.LinearTorchInject # Inject module path kwargs: # Extra parameters generate_device: \\"cuda\\" my_param: True For the linear module, it is also necessary to read weights from a gguf file. We provide the KLinearBase class to help users read weights from gguf files. Users only need to inherit and implement the load, unload, and forward functions. Therefore, a fully injectable linear class would look like this: class LinearTorchInject(BaseInjectedModule, KLinearBase): def __init__( self, key: str, gguf_loader: GGUFLoader, config: PretrainedConfig, orig_module: nn.Module = None, generate_device: str = \\"cuda\\", **kwargs, ): super().__init__(key, gguf_loader, config, orig_module, generate_device, **kwargs) KLinearBase.__init__(self) self.has_bias = False self.dtype = torch.get_default_dtype() self.w = None self.has_bias = False def load(self, w: dict | nn.Parameter | tuple | None = None, device: str|None = None): if device is None: device = self.device if w is None: w = self.load_weight(device=device) if isinstance(w, nn.Parameter): self.w = w.to(dtype=self.dtype).view(self.out_features, self.in_features).T self.has_bias = False elif isinstance(w, tuple): self.w = w[0].to(dtype=self.dtype).view(self.out_features, self.in_features).T self.bias = w[1].to(dtype=self.dtype) self.has_bias = True else: raise ValueError(\\"Invalid weight type\\") self.w = self.w.to(device) if self.has_bias: self.bias = self.bias.to(device) def unload(self): if self.w is not None: self.w = None if self.has_bias: self.bias = None def forward(self, x: torch.Tensor) -> torch.Tensor: dtype = x.dtype out_device = x.device x = x.to(device=self.device, dtype=self.dtype) x = x @ self.w if self.has_bias: x = x + self.bias x = x.to(dtype=dtype, device=out_device) return x Note that the self.load_weight function is provided by the KLinearBase class to help users load weights from a gguf file into the module. The implementation details of KLinearBase can be found on GITHUB.","breadcrumbs":"kt-sft part » Injection Tutorial » How to Write a New Operator and Inject into the Model","id":"67","title":"How to Write a New Operator and Inject into the Model"},"68":{"body":"To be written…","breadcrumbs":"kt-kernel part » kt-kernel Docs","id":"68","title":"kt-kernel Docs"},"69":{"body":"","breadcrumbs":"FAQ » see the issue FAQ page","id":"69","title":"see the issue FAQ page"},"7":{"body":"GitHub Issues: Report bugs or request features WeChat Group: See archive/WeChatGroup.png","breadcrumbs":"Introduction » » 💬 Community & Support","id":"7","title":"💬 Community & Support"},"8":{"body":"The original integrated KTransformers framework has been archived to the archive/ directory for reference. The project now focuses on the two core modules above for better modularity and maintainability. For the original documentation with full quick-start guides and examples, see: archive/README.md (English) archive/README_ZH.md (中文)","breadcrumbs":"Introduction » » 📦 KT original Code","id":"8","title":"📦 KT original Code"},"9":{"body":"High-performance kernel operations for KTransformers, featuring CPU-optimized MoE inference with AMX, AVX, KML and blis (amd library) support. Note Features Installation Prerequisites Quick Installation (Recommended) Manual Configuration (Advanced) Verification Integration with SGLang Installation Steps Complete Example: Qwen3-30B-A3B KT-Kernel Parameters Direct Python API Usage Advanced Options Build Configuration Manual Installation Error Troubleshooting CUDA Not Found hwloc Not Found Weight Quantization CPU Weights (for “cold” experts on CPU) GPU Weights (for “hot” experts on GPU) Before Commit!","breadcrumbs":"For kt-kernel » 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Flexible Framework for Experiencing Cutting-edge LLM Inference/Fine-tune Optimizations 🎯 Overview | 🚀 kt-kernel | 🎓 kt-sft | 🔥 Citation | 🚀 Roadmap(2025Q4) KTransformers is a research project focused on efficient inference and fine-tuning of large language models through CPU-GPU heterogeneous computing. The project has evolved into two core modules : kt-kernel and kt-sft .","breadcrumbs":"Introduction » 🎯 Overview","id":"0","title":"🎯 Overview"},"1":{"body":"Nov 6, 2025 : Support Kimi-K2-Thinking inference ( Tutorial ) and fine-tune ( Tutorial ) Nov 4, 2025 : KTransformers Fine-Tuning × LLaMA-Factory Integration. ( Tutorial ) Oct 27, 2025 : Support Ascend NPU. ( Tutorial ) Oct 10, 2025 : Integrating into SGLang. ( Roadmap , Blog ) Sept 11, 2025 : Support Qwen3-Next. ( Tutorial ) Sept 05, 2025 : Support Kimi-K2-0905. ( Tutorial ) July 26, 2025 : Support SmallThinker and GLM4-MoE. ( Tutorial ) July 11, 2025 : Support Kimi-K2. ( Tutorial ) June 30, 2025 : Support 3-layer (GPU-CPU-Disk) prefix cache reuse. May 14, 2025 : Support Intel Arc GPU ( Tutorial ). Apr 29, 2025 : Support AMX-Int8、 AMX-BF16 and Qwen3MoE ( Tutorial ) Apr 9, 2025 : Experimental support for LLaMA 4 models ( Tutorial ). Apr 2, 2025 : Support Multi-concurrency. ( Tutorial ). Mar 15, 2025 : Support ROCm on AMD GPU ( Tutorial ). Mar 5, 2025 : Support unsloth 1.58/2.51 bits weights and IQ1_S/FP8 hybrid weights. Support 139K Longer Context for DeepSeek-V3 and R1 in 24GB VRAM. Feb 25, 2025 : Support FP8 GPU kernel for DeepSeek-V3 and R1; Longer Context . Feb 15, 2025 : Longer Context (from 4K to 8K for 24GB VRAM) & Slightly Faster Speed （+15%, up to 16 Tokens/s), update docs and online books . Feb 10, 2025 : Support Deepseek-R1 and V3 on single (24GB VRAM)/multi gpu and 382G DRAM, up to 3~28x speedup. For detailed show case and reproduction tutorial, see here . Aug 28, 2024 : Decrease DeepseekV2\'s required VRAM from 21G to 11G. Aug 15, 2024 : Update detailed tutorial for injection and multi-GPU. Aug 14, 2024 : Support llamfile as linear backend. Aug 12, 2024 : Support multiple GPU; Support new model: mixtral 8*7B and 8*22B; Support q2k, q3k, q5k dequant on gpu. Aug 9, 2024 : Support windows native.","breadcrumbs":"Introduction » 🔥 Updates","id":"1","title":"🔥 Updates"},"10":{"body":"Current Support Status: ✅ Intel CPUs with AMX : Fully supported ⚠️ Universal CPU with llamafile : In preview, not yet fully complete ⚠️ AMD CPUs with BLIS : Upcoming, not yet fully integrated","breadcrumbs":"For kt-kernel » Note","id":"10","title":"Note"},"11":{"body":"AMX Optimization : Intel AMX (Advanced Matrix Extensions) support for INT4/INT8 quantized MoE inference Multi-Backend : Unified KTMoEWrapper API supporting multiple backends (AMXINT4, AMXINT8, LLAMAFILE*) Flexible Backends : AVX512, AVX2 via pluggable backend architecture Efficient MoE : Optimized Mixture-of-Experts operations with NUMA-aware memory management Async Execution : Non-blocking submit_forward / sync_forward API for improved pipelining Easy Integration : Clean Python API with automatic backend selection Note : *LLAMAFILE backend support is currently in preview and not yet fully complete.","breadcrumbs":"For kt-kernel » Features","id":"11","title":"Features"},"12":{"body":"","breadcrumbs":"For kt-kernel » Installation","id":"12","title":"Installation"},"13":{"body":"First, initialize git submodules: git submodule update --init --recursive","breadcrumbs":"For kt-kernel » Prerequisites","id":"13","title":"Prerequisites"},"14":{"body":"Step 0: Create and activate a conda environment (recommended): conda create -n kt-kernel python=3.11 -y\\nconda activate kt-kernel You can now install in two clear steps using the same script. Option A: Two-step (explicit) # 1) Install system prerequisites (cmake, hwloc, pkg-config)\\n./install.sh deps # 2) Build and install kt-kernel (auto-detects CPU)\\n# By default, the script cleans the local ./build directory before compiling.\\n./install.sh build Option B: One-step (deps + build) ./install.sh The install script will: Auto-detect CPU capabilities (AMX support) Install cmake via conda (if available) Install system dependencies (libhwloc-dev, pkg-config) based on your OS What gets configured automatically: AMX CPU detected → NATIVE + AMX=ON No AMX detected → NATIVE + AMX=OFF ⚠️ Important for LLAMAFILE backend users: If you have an AMX-capable CPU and plan to use the LLAMAFILE backend, do NOT use auto-detection. Use manual mode with AVX512 or AVX2 instead of NATIVE to avoid compilation issues (see below).","breadcrumbs":"For kt-kernel » Quick Installation (Recommended)","id":"14","title":"Quick Installation (Recommended)"},"15":{"body":"If you need specific build options (e.g., for LLAMAFILE backend, compatibility, or binary distribution): # Example for LLAMAFILE backend on AMX CPU with AVX512\\nexport CPUINFER_CPU_INSTRUCT=AVX512 # Options: NATIVE, AVX512, AVX2\\nexport CPUINFER_ENABLE_AMX=OFF # Options: ON, OFF # Run with manual mode (build only)\\n./install.sh build --manual For advanced build options and binary distribution, see the Build Configuration section. If you encounter issues, refer to Error Troubleshooting .","breadcrumbs":"For kt-kernel » Manual Configuration (Advanced)","id":"15","title":"Manual Configuration (Advanced)"},"16":{"body":"python -c \\"from kt_kernel import KTMoEWrapper; print(\'✓ kt-kernel installed successfully\')\\"","breadcrumbs":"For kt-kernel » Verification","id":"16","title":"Verification"},"17":{"body":"KT-Kernel can be used standalone via Direct Python API or integrated with SGLang for production deployment. This section describes SGLang integration to enable CPU-GPU heterogeneous inference, where \\"hot\\" experts run on GPU and \\"cold\\" experts run on CPU for optimal resource utilization.","breadcrumbs":"For kt-kernel » Integration with SGLang","id":"17","title":"Integration with SGLang"},"18":{"body":"1. Install SGLang git clone https://github.com/sgl-project/sglang.git\\ncd sglang\\npip install -e \\"python[all]\\" 2. Prepare Weights You need both GPU weights and CPU weights for heterogeneous inference: GPU Weights: Use the original / quantized model weights. CPU Weights: Quantize to AMX-optimized format using the conversion script: python scripts/convert_cpu_weights.py \\\\ --input-path /path/to/model \\\\ --input-type bf16 \\\\ # Depends on your GPU weights type: fp8, fp16, or bf16 --output /path/to/cpu-weights \\\\ --quant-method int8 # or int4 Supported input formats: FP8, FP16, BF16 → INT4/INT8. For more details, see: CPU Weights conversion GPU Weights quantization Note: LLAMAFILE backend supports GGUF format directly, but this feature is still in preview. 3. Launch SGLang Server Start the SGLang server with your normal SGLang parameters, and add the following KT-Kernel specific parameters to enable CPU-GPU heterogeneous inference: KT-Kernel Parameters to Add: --kt-method: Backend method (AMXINT4, AMXINT8, or LLAMAFILE) --kt-weight-path: Path to the converted CPU weights --kt-cpuinfer: Number of CPU inference threads (set to physical cores) --kt-threadpool-count: Number of thread pools (set to NUMA node count) --kt-num-gpu-experts: Number of experts to keep on GPU --kt-max-deferred-experts-per-token: Deferred experts for pipelined execution Example: python -m sglang.launch_server \\\\ [your normal SGLang parameters...] \\\\ --kt-method AMXINT8 \\\\ --kt-weight-path /path/to/cpu-weights \\\\ --kt-cpuinfer 64 \\\\ --kt-threadpool-count 2 \\\\ --kt-num-gpu-experts 32 \\\\ --kt-max-deferred-experts-per-token 2 See KT-Kernel Parameters section below for detailed parameter tuning guidelines.","breadcrumbs":"For kt-kernel » Installation Steps","id":"18","title":"Installation Steps"},"19":{"body":"This example demonstrates the full workflow from downloading weights to launching the server. Hardware Configuration: GPU : NVIDIA RTX 4090 24GB CPU : 2x Intel Xeon Gold 6454S (64 physical cores total, 128 threads, 2 NUMA nodes) Model : Qwen3-30B-A3B GPU Weights : BF16 original weights CPU Weights : AMXINT8 quantized How to verify your system configuration: # Check CPU configuration\\nlscpu | grep -E \\"^CPU\\\\(s\\\\)|Thread\\\\(s\\\\) per core|Socket\\\\(s\\\\)|NUMA node\\\\(s\\\\)\\"\\n# Expected output example:\\nCPU(s): 128\\nThread(s) per core: 2\\nSocket(s): 2\\nNUMA node(s): 2\\n# → Physical cores = CPU(s) / Thread(s) per core = 128 / 2 = 64 Parameter Rationale: --kt-cpuinfer 64: Set to physical cores (64), not hyperthreads (128) --kt-threadpool-count 2: 2 NUMA nodes detected (dual-socket system) --kt-num-gpu-experts 32: With 24GB GPU memory, we can fit ~32 experts on GPU for this model (varies by model architecture and actual memory usage) --kt-max-deferred-experts-per-token 2: Enable pipelined execution - allows CPU to process next batch while GPU completes current batch Step 1: Download model weights # Install huggingface-cli if not already installed\\npip install huggingface-hub # Download model from Hugging Face\\nhf download Qwen/Qwen3-30B-A3B --local-dir /mnt/data/models/Qwen3-30B-A3B Step 2: Convert to CPU weights (AMXINT8) python scripts/convert_cpu_weights.py \\\\ --input-path /mnt/data/models/Qwen3-30B-A3B \\\\ --input-type bf16 \\\\ --output /mnt/data/models/Qwen3-30B-A3B-INT8 \\\\ --quant-method int8 Step 3: Launch SGLang server python -m sglang.launch_server \\\\ --host 0.0.0.0 \\\\ --port 8000 \\\\ --model /mnt/data/models/Qwen3-30B-A3B \\\\ --trust-remote-code \\\\ --mem-fraction-static 0.92 \\\\ --chunked-prefill-size 4096 \\\\ --served-model-name Qwen3-30B-A3B \\\\ --enable-mixed-chunk \\\\ --kt-method AMXINT8 \\\\ --kt-weight-path /mnt/data/models/Qwen3-30B-A3B-INT8 \\\\ --kt-cpuinfer 64 \\\\ --kt-threadpool-count 2 \\\\ --kt-num-gpu-experts 32 \\\\ --kt-max-deferred-experts-per-token 2","breadcrumbs":"For kt-kernel » Complete Example: Qwen3-30B-A3B","id":"19","title":"Complete Example: Qwen3-30B-A3B"},"2":{"body":"","breadcrumbs":"Introduction » 📦 Core Modules","id":"2","title":"📦 Core Modules"},"20":{"body":"Parameter Description Example Value --kt-method CPU inference backend method AMXINT4, AMXINT8, or LLAMAFILE (preview) --kt-weight-path Path to quantized CPU weights /path/to/cpu-weights --kt-cpuinfer Number of CPU inference threads 64 (adjust based on CPU cores) --kt-threadpool-count Number of thread pools for parallel execution 2 (typically 1-4) --kt-num-gpu-experts Number of experts to keep on GPU 32 (remaining experts go to CPU) --kt-max-deferred-experts-per-token Number of experts per token to defer for pipelined execution 2 (0 to disable, 1-2 recommended) Parameter Guidelines: kt-method : Choose based on your CPU and weight format: AMXINT4: Best performance on AMX CPUs with INT4 quantized weights (May cause huge accuracy drop for some models, e.g., Qwen3-30B-A3B) AMXINT8: Higher accuracy with INT8 quantized weights on AMX CPUs LLAMAFILE: Preview support for GGUF format (not fully complete) kt-cpuinfer : Set to the number of physical CPU cores (not hyperthreads). Check physical cores: lscpu | grep -E \\"^CPU\\\\(s\\\\)|Thread\\\\(s\\\\) per core\\" Physical cores = CPU(s) / Thread(s) per core Example: If CPU(s)=128 and Thread(s) per core=2, then physical cores = 64 Important : Do NOT set to hyperthread count - this will degrade performance kt-threadpool-count : Set to the number of NUMA nodes . Check NUMA count: lscpu | grep \\"NUMA node(s)\\" Or use: numactl --hardware | grep \\"available\\" Note : NUMA node count is NOT necessarily the number of physical CPUs It represents memory domains, which may be divided within a single CPU or across multiple CPUs Use the NUMA node count from lscpu, regardless of physical CPU count Typical values: 1-2 for single-socket, 2-4 for dual-socket systems This enables better memory bandwidth utilization across NUMA domains kt-num-gpu-experts : Determine based on GPU memory and profiling: More GPU experts = lower latency but higher GPU memory usage (May cause OOM) kt-max-deferred-experts-per-token : Enables pipelined execution: 0: Synchronous execution (simpler, higher latency) 1-2: Deferred execution (better latency, requires tuning) - recommended 3-4: Higher deferred count (possible but rarely beneficial)","breadcrumbs":"For kt-kernel » KT-Kernel Parameters","id":"20","title":"KT-Kernel Parameters"},"21":{"body":"For standalone usage without SGLang, you can use KT-Kernel directly via Python API: from kt_kernel import KTMoEWrapper # Initialize the MoE wrapper\\nwrapper = KTMoEWrapper( layer_idx=0, num_experts=8, num_experts_per_tok=2, hidden_size=4096, moe_intermediate_size=14336, num_gpu_experts=2, cpuinfer_threads=32, threadpool_count=2, weight_path=\\"/path/to/weights\\", chunked_prefill_size=512, method=\\"AMXINT4\\" # Options: \\"AMXINT4\\", \\"AMXINT8\\", \\"LLAMAFILE\\" (preview)\\n) # Load weights (from disk - pre-quantized)\\nwrapper.load_weights(physical_to_logical_map) # Or load weights from tensors (online quantization)\\nwrapper.load_weights_from_tensors(gate_proj, up_proj, down_proj, physical_to_logical_map) # Run inference\\noutput = wrapper.forward(hidden_states, topk_ids, topk_weights, cuda_stream) # Or use async API for better performance\\nwrapper.submit_forward(hidden_states, topk_ids, topk_weights, cuda_stream)\\n# ... do other work ...\\noutput = wrapper.sync_forward(hidden_states, cuda_stream)","breadcrumbs":"For kt-kernel » Direct Python API Usage","id":"21","title":"Direct Python API Usage"},"22":{"body":"# Initialize with additional options\\nwrapper = KTMoEWrapper( layer_idx=0, num_experts=8, num_experts_per_tok=2, hidden_size=4096, moe_intermediate_size=14336, num_gpu_experts=2, cpuinfer_threads=32, threadpool_count=2, weight_path=\\"/path/to/weights\\", chunked_prefill_size=512, method=\\"AMXINT4\\", cpu_save=False, # Keep weights in CPU memory after loading max_deferred_experts_per_token=0 # Number of experts to defer (for pipelined execution)\\n) # Pre-allocate buffers for specific batch sizes (improves performance)\\nKTMoEWrapper.set_capture_batch_sizes([1, 2, 4, 8, 16]) # Query captured batch sizes\\nbatch_sizes = KTMoEWrapper.get_capture_batch_sizes() # Clear buffer cache to free memory\\nKTMoEWrapper.clear_buffer_cache()","breadcrumbs":"For kt-kernel » Advanced Options","id":"22","title":"Advanced Options"},"23":{"body":"","breadcrumbs":"For kt-kernel » Build Configuration","id":"23","title":"Build Configuration"},"24":{"body":"If you prefer manual installation without the install.sh script, follow these steps: 1. Install System Dependencies Prerequisites: cmake (recommended: conda install -y cmake) libhwloc-dev and pkg-config 2. Set Build Configuration Core Options: Variable Options Description CPUINFER_CPU_INSTRUCT NATIVE, AVX512, AVX2, FANCY CPU instruction set to use CPUINFER_ENABLE_AMX ON, OFF Enable Intel AMX support CPUINFER_BUILD_TYPE Release, Debug, RelWithDebInfo Build type (default: Release) CPUINFER_PARALLEL Number Parallel build jobs (default: auto-detect) CPUINFER_VERBOSE 0, 1 Verbose build output (default: 0) Instruction Set Details: NATIVE : Auto-detect and use all available CPU instructions (-march=native) - Recommended for best performance AVX512 : Explicit AVX512 support for Skylake-SP and Cascade Lake AVX2 : AVX2 support for maximum compatibility FANCY : AVX512 with full extensions (AVX512F/BW/DQ/VL/VNNI) for Ice Lake+ and Zen 4+. Use this when building pre-compiled binaries to distribute to users with modern CPUs. For local builds, prefer NATIVE for better performance. Example Configurations: # Maximum performance on AMX CPU\\nexport CPUINFER_CPU_INSTRUCT=NATIVE\\nexport CPUINFER_ENABLE_AMX=ON # AVX512 CPU without AMX\\nexport CPUINFER_CPU_INSTRUCT=AVX512\\nexport CPUINFER_ENABLE_AMX=OFF # Compatibility build\\nexport CPUINFER_CPU_INSTRUCT=AVX2\\nexport CPUINFER_ENABLE_AMX=OFF # Debug build for development\\nexport CPUINFER_BUILD_TYPE=Debug\\nexport CPUINFER_VERBOSE=1 3. Build and Install # Editable installation (for development)\\npip install -e . # Standard installation\\npip install .","breadcrumbs":"For kt-kernel » Manual Installation","id":"24","title":"Manual Installation"},"25":{"body":"","breadcrumbs":"For kt-kernel » Error Troubleshooting","id":"25","title":"Error Troubleshooting"},"26":{"body":"-- Looking for a CUDA compiler - NOTFOUND CMake Error at CMakeLists.txt:389 (message): KTRANSFORMERS_USE_CUDA=ON but CUDA compiler not found Make sure you have the CUDA toolkit installed and nvcc is in your system PATH. Try export CMAKE_ARGS=\\"-D CMAKE_CUDA_COMPILER=$(which nvcc)\\" and reinstall again.","breadcrumbs":"For kt-kernel » CUDA Not Found","id":"26","title":"CUDA Not Found"},"27":{"body":"Run sudo apt install libhwloc-dev if on a Debian-based system or build from source: https://www.open-mpi.org/projects/hwloc/. wget https://download.open-mpi.org/release/hwloc/v2.12/hwloc-2.12.2.tar.gz\\ntar -xzf hwloc-2.12.2.tar.gz\\ncd hwloc-2.12.2\\n./configure\\nmake\\nsudo make install","breadcrumbs":"For kt-kernel » hwloc Not Found","id":"27","title":"hwloc Not Found"},"28":{"body":"KT-Kernel provides weight quantization tools for CPU-GPU hybrid inference (e.g., integrating with SGLang). Both tools work together to enable heterogeneous expert placement across CPUs and GPUs.","breadcrumbs":"For kt-kernel » Weight Quantization","id":"28","title":"Weight Quantization"},"29":{"body":"Quantize weights to INT4/INT8 format optimized for AMX inference: python scripts/convert_cpu_weights.py \\\\ --input-path /path/to/model \\\\ --input-type bf16 \\\\ --output /path/to/output \\\\ --quant-method int4 Supported formats: FP8, FP16, BF16 → INT4/INT8","breadcrumbs":"For kt-kernel » CPU Weights (for \\"cold\\" experts on CPU)","id":"29","title":"CPU Weights (for \\"cold\\" experts on CPU)"},"3":{"body":"CPU-optimized kernel operations for heterogeneous LLM inference. Key Features: AMX/AVX Acceleration : Intel AMX and AVX512/AVX2 optimized kernels for INT4/INT8 quantized inference MoE Optimization : Efficient Mixture-of-Experts inference with NUMA-aware memory management Quantization Support : CPU-side INT4/INT8 quantized weights, GPU-side GPTQ support Easy Integration : Clean Python API for SGLang and other frameworks Quick Start: cd kt-kernel\\npip install . Use Cases: CPU-GPU hybrid inference for large MoE models Integration with SGLang for production serving Heterogeneous expert placement (hot experts on GPU, cold experts on CPU) Performance Examples: Model Hardware Configuration Total Throughput Output Throughput DeepSeek-R1-0528 (FP8) 8×L20 GPU + Xeon Gold 6454S 227.85 tokens/s 87.58 tokens/s (8-way concurrency) 👉 Full Documentation →","breadcrumbs":"Introduction » 🚀 kt-kernel - High-Performance Inference Kernels","id":"3","title":"🚀 kt-kernel - High-Performance Inference Kernels"},"30":{"body":"Apply GPTQ quantization to model weights: # Install additional dependencies first\\npip install accelerate transformers llmcompressor datasets # Quantize GPU weights\\npython scripts/convert_gpu_weights.py \\\\ --model_id /path/to/model \\\\ --output_dir /path/to/output \\\\ --quant_type W4A16 Supported types: W4A16 (GPTQ4), W8A16 (GPTQ8) For detailed documentation, advanced options, and low-memory mode, see scripts/README.md .","breadcrumbs":"For kt-kernel » GPU Weights (for \\"hot\\" experts on GPU)","id":"30","title":"GPU Weights (for \\"hot\\" experts on GPU)"},"31":{"body":"your msg should match: Conventional Commits (https://www.conventionalcommits.org/) and format your code before commit: cmake -B build\\ncd build\\nmake format and you may need a new clang-format at least 18, use this command in conda env: conda install -c conda-forge clang-format=18\\nrm -rf build and you may need black for python format: conda install black","breadcrumbs":"For kt-kernel » Before Commit!","id":"31","title":"Before Commit!"},"32":{"body":"Introduction Fine-Tuning Results (Examples) Quick to Start Environment Setup Core Feature 1: Use KTransformers backend to fine-tune ultra-large MoE models Core Feature 2: Chat with the fine-tuned model (base + LoRA adapter) Core Feature 3: Batch inference + metrics (base + LoRA adapter) KT Fine-Tuning Speed (User-Side View) End-to-End Performance GPU/CPU Memory Footprint Conclusion MadSys Lab, KVCache-AI Team, Approaching AI, LLaMA-Factory Team","breadcrumbs":"For kt-sft » KTransformers Fine-Tuning × LLaMA-Factory Integration – User Guide","id":"32","title":"KTransformers Fine-Tuning × LLaMA-Factory Integration – User Guide"},"33":{"body":"From DeepSeek-V3/R1 to Qwen3-MoE and Kimi-K2 , each wave of open-sourced large models brings leaps in performance and scale. However, many researchers and developers are constrained by expensive GPUs and models with tens or even hundreds of billions of parameters, making it hard to fine-tune very large models under limited resources . To bridge this gap, we propose a practical approach: combining KTransformers with LLaMA-Factory . With just 2–4 RTX 4090s and a high-memory CPU, you can fine-tune ultra-large MoE models like DeepSeek-671B. Our goal is to give resource-constrained researchers a local path to explore fine-tuning ultra-large models , and also a fast way to customize smaller models (e.g., 14B/30B) for specific scenarios. We validate the setup using stylized dialogue , Westernized translation tone , and medical Q&A as representative tasks, showing that personalized adaptation can be achieved within hours . As shown below, LLaMA-Factory is the unified orchestration/configuration layer for the whole fine-tuning workflow—handling data, training scheduling, LoRA injection, and inference interfaces. KTransformers acts as a pluggable high-performance backend that takes over core operators like Attention/MoE under the same training configs, enabling efficient GPU+CPU heterogeneous cooperation . image-20251011010558909 Within LLaMA-Factory, we compared LoRA fine-tuning with HuggingFace , Unsloth , and KTransformers backends. KTransformers is the only workable 4090-class solution for ultra-large MoE models (e.g., 671B) and also delivers higher throughput and lower GPU memory on smaller MoE models (e.g., DeepSeek-14B). Under LoRA (BF16) + NekoQA-10K stylized dialogue HuggingFace Backend Unsloth Backend KTransformers Backend [14B-DeepSeekV2-Lite] LoRA fine-tuning throughput 303.58 token/s 455.37 token/s 530.38 token/s [14B-DeepSeekV2-Lite] GPU memory 32.12 GB 9.64 GB 6.08 GB [671B-DeepSeekV3] LoRA fine-tuning throughput Too Huge to run NOT SUPPORT 40.35 token/s [671B-DeepSeekV3] GPU memory (sum across GPUs) theoretical 1400 GB † NOT SUPPORT 70 GB † † 1400 GB is a theoretical FP16 full-parameter resident footprint (not runnable). 70 GB is the measured peak with KT strategy (Attention on GPU + layered MoE offload). 按照模型划分的对比图_02","breadcrumbs":"For kt-sft » Introduction","id":"33","title":"Introduction"},"34":{"body":"Stylized Dialogue (CatGirl tone) Dataset: NekoQA-10K . Goal: improve style consistency and recognizability. The figure compares responses from the base vs. fine-tuned models. The fine-tuned model maintains the target tone and address terms more consistently (red boxes), validating the effectiveness of style-transfer fine-tuning . image-20251016175046882 Benchmarks We use: (1) Translational-Style-ChatLLM , which asks for an exaggerated, Westernized translation tone—clear, stylized customization. (2) AfriMed-QA (ACL 2025), a medical dataset for African contexts with strong domain specificity, including multiple-choice and short-answer sub-tasks—well-suited for vertical fine-tuning evaluation. The tables show metrics before vs. after LoRA fine-tuning. We observe large improvements across metrics, verifying fine-tuning effectiveness: Translational-Style dataset BLEU-1 BLEU-2 BLEU-3 BLEU-4 ROUGE-1 ROUGE-2 ROUGE-L V2-Lite (no LoRA) 20.66 8.33 4.54 2.89 22.71 4.52 19.19 KT-LoRA fine-tuned V2-Lite 35.41 22.44 15.42 11.18 42.03 18.38 33.10 V3 base (no LoRA) 8.49 3.34 1.62 0.96 15.91 2.55 10.07 KT-LoRA fine-tuned V3 37.02 23.70 16.21 11.49 43.43 18.96 34.54 AfriMed-QA (short answer) BLEU-1 BLEU-2 BLEU-3 BLEU-4 ROUGE-1 ROUGE-2 ROUGE-L V2-Lite (no LoRA) 13.58 11.12 9.10 7.23 22.48 7.81 11.73 KT-LoRA fine-tuned V2-Lite 35.90 27.63 22.99 19.15 35.25 17.50 28.44 V3 base (no LoRA) 12.75 10.27 8.05 5.99 20.33 5.65 10.11 KT-LoRA fine-tuned V3 42.42 34.12 28.95 24.54 41.97 22.37 33.28 AfriMed-QA (multiple choice) Accuracy V2-Lite (no LoRA) 0.0645 KT-LoRA fine-tuned V2-Lite 0.4812 V3 base (no LoRA) 0.5833 KT-LoRA fine-tuned V3 0.7930 Even for ultra-large MoE models, KTransformers-backed fine-tuning achieves strong task performance quickly.","breadcrumbs":"For kt-sft » Fine-Tuning Results (Examples)","id":"34","title":"Fine-Tuning Results (Examples)"},"35":{"body":"This section shows how to install and use LLaMA-Factory + KTransformers for fine-tuning and inference: Environment setup Fine-tune ultra-large MoE models with KTransformers backend Load the fine-tuned model (base + LoRA adapter) for chat/inference Batch inference and metric evaluation","breadcrumbs":"For kt-sft » Quick to Start","id":"35","title":"Quick to Start"},"36":{"body":"According to the following example, install both the KTransformers and LLaMA-Factory environments simultaneously. This time, to simplify the installation process of KTransformers, we have specially packaged a wheel file to avoid local compilation. The detailed installation steps are as follows: (Note: Make sure your local Python version , Torch version , CUDA version , and the KTransformers wheel filename correspond correctly.) # 1. Create a conda environment\\nconda create -n Kllama python=3.12 # choose from : [3.10, 3.11, 3.12, 3.13]\\nconda install -y -c conda-forge libstdcxx-ng gcc_impl_linux-64\\nconda install -y -c nvidia/label/cuda-11.8.0 cuda-runtime # 2. Install the LLaMA-Factory environment\\ngit clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git\\ncd LLaMA-Factory\\npip install -e \\".[torch,metrics]\\" --no-build-isolation # 3. Install the KTransformers wheel that matches your Torch and Python versions, from https://github.com/kvcache-ai/ktransformers/releases/tag/v0.4.1 (Note: The CUDA version can differ from that in the wheel filename.)\\npip install ktransformers-0.4.1+cu128torch27fancy-cp312-cp312-linux_x86_64.whl # 4. Install flash-attention, download the corresponding file based on your Python and Torch versions from: https://github.com/Dao-AILab/flash-attention/releases\\npip install flash_attn-2.8.3+cu12torch2.7cxx11abiTRUE-cp312-cp312-linux_x86_64.whl\\n# abi=True/False can find from below\\n# import torch\\n# print(torch._C._GLIBCXX_USE_CXX11_ABI) # 5. (Optional) If you want to use flash_infer (otherwise it defaults to triton)\\ngit clone https://github.com/kvcache-ai/custom_flashinfer.git\\npip install custom_flashinfer/ Usage tip: In LLaMA-Factory YAML, set use_kt: true and pick a kt_optimize_rule file to have KTransformers handle the core compute. The features below show typical configs.","breadcrumbs":"For kt-sft » Environment Setup","id":"36","title":"Environment Setup"},"37":{"body":"Run the command: USE_KT=1 llamafactory-cli train examples/train_lora/deepseek3_lora_sft_kt.yaml. Note: You must provide a BF16 model. DeepSeek-V3-671B is released in FP8 by default; convert with DeepSeek-V3/inference/fp8_cast_bf16.py . ### model\\nmodel_name_or_path: opensourcerelease/DeepSeek-V3-bf16\\ntrust_remote_code: true ### method\\nstage: sft\\ndo_train: true\\nfinetuning_type: lora\\nlora_rank: 8\\nlora_target: all ### dataset\\ndataset: identity\\ntemplate: deepseek\\ncutoff_len: 2048\\nmax_samples: 100000\\noverwrite_cache: true\\npreprocessing_num_workers: 16\\ndataloader_num_workers: 4 ### output\\noutput_dir: saves/Kllama_deepseekV3\\nlogging_steps: 10\\nsave_steps: 500\\nplot_loss: true\\noverwrite_output_dir: true\\nsave_only_model: false\\nreport_to: none # choices: [none, wandb, tensorboard, swanlab, mlflow] ### train\\nper_device_train_batch_size: 1\\ngradient_accumulation_steps: 8\\nlearning_rate: 1.0e-4\\nnum_train_epochs: 3.0\\nlr_scheduler_type: cosine\\nwarmup_ratio: 0.1\\nbf16: true\\nddp_timeout: 180000000\\nresume_from_checkpoint: null ### ktransformers\\nuse_kt: true # use KTransformers as LoRA sft backend\\nkt_optimize_rule: examples/kt_optimize_rules/DeepSeek-V3-Chat-sft-amx-multi-gpu.yaml\\ncpu_infer: 32\\nchunk_size: 8192 kt_optimize_rule controls placement strategy . See also ktransformers/optimize_rules . Naming hints (* = wildcard): Pattern Meaning DeepSeek-V2-Lite-Chat-* / DeepSeek-V3-Chat-* Target model variants -sft- Strategy for fine-tuning; others are for inference -amx- Use AMX on CPU; otherwise use llamafile -multi-gpu-X Model parallel on X GPUs (X omitted → default 2 GPUs) Example: DeepSeek-V3-Chat-sft-amx-multi-gpu.yaml = V3-Chat fine-tuning with AMX and 2-GPU model parallel. We recommend AMX acceleration where available (lscpu | grep amx). AMX supports BF16/INT8. Example: - match: name: \\"^model\\\\\\\\.layers\\\\\\\\..*\\\\\\\\.mlp\\\\\\\\.experts$\\" replace: class: ktransformers.operators.experts.KTransformersExperts # custom MoE Kernel with expert parallelism kwargs: prefill_device: \\"cpu\\" prefill_op: \\"KExpertsTorch\\" generate_device: \\"cpu\\" generate_op: \\"KSFTExpertsCPU\\" out_device: \\"cuda\\" backend: \\"AMXInt8\\" # or \\"AMXBF16\\" or \\"llamafile\\" (default) Outputs go to output_dir in safetensors format plus adapter metadata for later loading. image-20251016171537997","breadcrumbs":"For kt-sft » Core Feature 1: Use KTransformers backend to fine-tune ultra-large MoE models","id":"37","title":"Core Feature 1: Use KTransformers backend to fine-tune ultra-large MoE models"},"38":{"body":"Run the command: llamafactory-cli chat examples/inference/deepseek3_lora_sft_kt.yaml. Use the safetensors adapter trained with KT for inference. model_name_or_path: opensourcerelease/DeepSeek-V3-bf16\\nadapter_name_or_path: saves/Kllama_deepseekV3\\ntemplate: deepseek\\ninfer_backend: ktransformers # choices: [huggingface, vllm, sglang, ktransformers]\\ntrust_remote_code: true use_kt: true # use KTransformers as LoRA sft backend to inference\\nkt_optimize_rule: examples/kt_optimize_rules/DeepSeek-V3-Chat-sft-amx-multi-gpu.yaml\\ncpu_infer: 32\\nchunk_size: 8192 We also support GGUF adapters: for safetensors, set the directory ; for GGUF, set the file path in adapter_name_or_path. During loading, LLaMA-Factory maps layer names to KT’s naming. You’ll see logs like Loaded adapter weight: XXX -> XXX: image-20251016171526210","breadcrumbs":"For kt-sft » Core Feature 2: Chat with the fine-tuned model (base + LoRA adapter)","id":"38","title":"Core Feature 2: Chat with the fine-tuned model (base + LoRA adapter)"},"39":{"body":"Run the command: API_PORT=8000 llamafactory-cli api examples/inference/deepseek3_lora_sft_kt.yaml. Invoke the KT fine-tuned adapter to provide the API; the usage logic of other APIs is consistent with the native LLaMA-Factory approach. model_name_or_path: opensourcerelease/DeepSeek-V3-bf16\\nadapter_name_or_path: saves/Kllama_deepseekV3\\ntemplate: deepseek\\ninfer_backend: ktransformers # choices: [huggingface, vllm, sglang, ktransformers]\\ntrust_remote_code: true use_kt: true # use KTransformers as LoRA sft backend to inference\\nkt_optimize_rule: examples/kt_optimize_rules/DeepSeek-V3-Chat-sft-amx-multi-gpu.yaml\\ncpu_infer: 32\\nchunk_size: 8192","breadcrumbs":"For kt-sft » Core Feature 3: Batch inference + metrics (base + LoRA adapter)","id":"39","title":"Core Feature 3: Batch inference + metrics (base + LoRA adapter)"},"4":{"body":"KTransformers × LLaMA-Factory integration for ultra-large MoE model fine-tuning. image-20251011010558909 Key Features: Resource Efficient : Fine-tune 671B DeepSeek-V3 with just 70GB GPU memory + 1.3TB RAM LoRA Support : Full LoRA fine-tuning with heterogeneous acceleration LLaMA-Factory Integration : Seamless integration with popular fine-tuning framework Production Ready : Chat, batch inference, and metrics evaluation Performance Examples: Model Configuration Throughput GPU Memory DeepSeek-V3 (671B) LoRA + AMX ~40 tokens/s 70GB (multi-GPU) DeepSeek-V2-Lite (14B) LoRA + AMX ~530 tokens/s 6GB Quick Start: cd kt-sft\\n# Install environment following kt-sft/README.md\\nUSE_KT=1 llamafactory-cli train examples/train_lora/deepseek3_lora_sft_kt.yaml 👉 Full Documentation →","breadcrumbs":"Introduction » 🎓 kt-sft - Fine-Tuning Framework","id":"4","title":"🎓 kt-sft - Fine-Tuning Framework"},"40":{"body":"","breadcrumbs":"For kt-sft » KT Fine-Tuning Speed (User-Side View)","id":"40","title":"KT Fine-Tuning Speed (User-Side View)"},"41":{"body":"Definitions step_time: wall-clock time for a full optimization step (tensor movement + Attention + MoE + other compute). tokens_per_step = GAS × qlen; token/s = tokens_per_step / step_time. Settings: GAS=16, qlen=512 (→ tokens_per_step = 8192); LoRA (r=8, alpha=32, dropout=0.1); AMX enabled; GPU: RTX 4090, CPU: Intel Xeon Platinum 8488C. Measured DeepSeek-V3-671B: step_time = 203 s → token/s ≈ 8192 / 203 ≈ 40.35 DeepSeek-V2-Lite-14B: step_time = 36 s → token/s ≈ 8192 / 36 ≈ 227.6","breadcrumbs":"For kt-sft » End-to-End Performance","id":"41","title":"End-to-End Performance"},"42":{"body":"DeepSeek-V3 (671B; 61 layers with 58 MoE): ~ 70 GB total GPU VRAM (multi-GPU), ~ 1.2–1.3 TB CPU RAM. DeepSeek-V2-Lite (14B; 27 layers with 26 MoE): ~ 5.5 GB GPU VRAM, ~ 30 GB CPU RAM.","breadcrumbs":"For kt-sft » GPU/CPU Memory Footprint","id":"42","title":"GPU/CPU Memory Footprint"},"43":{"body":"By integrating KTransformers LoRA fine-tuning into LLaMA-Factory , we provide a practical guide for efficient training and deployment of MoE LLMs. KT brings cutting-edge optimizations (DeepSeek/Qwen/Kimi support with AMX-accelerated kernels), and LoRA enables customization under very low GPU memory. LLaMA-Factory offers a friendly, unified interface. This integration (akin to Unsloth-style speedups) means even models with tens to hundreds of billions of parameters can be fine-tuned and deployed with low latency on commodity hardware. You get memory savings, speed-ups, and usability together. We encourage you to try LLaMA-Factory + KT for your next MoE project and follow this guide. Feedback is welcome!","breadcrumbs":"For kt-sft » Conclusion","id":"43","title":"Conclusion"},"44":{"body":"","breadcrumbs":"kt-sft part » kt-sft Docs","id":"44","title":"kt-sft Docs"},"45":{"body":"Introduction Overall View of the KT Fine-Tuning Framework Attention (LoRA + KT coexist) MoE (operator encapsulation + backward) Multi-GPU Loading/Training: Placement strategy instead of DataParallel KT-LoRA Fine-Tuning Evaluation Setup Results Speed Tests Memory Footprint Conclusion MadSys Lab, KVCache-AI Team, Approaching AI, LLaMA-Factory Team","breadcrumbs":"kt-sft part » kt-sft developer tech notes » KTransformers Fine-Tuning × LLaMA-Factory Integration – Developer Technical Notes","id":"45","title":"KTransformers Fine-Tuning × LLaMA-Factory Integration – Developer Technical Notes"},"46":{"body":"Recent open-source LLMs—from DeepSeek-V3/R1 to Qwen-MoE and Kimi-K2—have surged in performance and scale. Yet due to compute and memory constraints , it is difficult for typical researchers to fine-tune trillion-parameter-class models. We therefore integrate KTransformers with LLaMA-Factory so that, with 2–4 RTX 4090 GPUs and sufficient CPU memory, one can fine-tune ultra-large Mixture-of-Experts (MoE) models such as DeepSeek-671B. This architecture bridges resource gaps, enabling local fine-tuning of ultra-large models , while also supporting efficient scenario customization at 14B/30B scales. We validate on stylized dialogue, Westernized translation tone, and medical Q&A, achieving rapid adaptation within hours. Architecturally, LLaMA-Factory orchestrates data/config/training, LoRA insertion, and inference; KTransformers is a pluggable, high-performance operator backend that takes over Attention and MoE under the same training code, enabling GPU+CPU heterogeneity to accelerate training and reduce GPU memory. image-20251011010558909 We evaluated LoRA fine-tuning with HuggingFace default, Unsloth, and KTransformers backends (same settings and data). KTransformers is currently the only solution feasible on 2–4×24GB 4090s for 671B-scale MoE , and also shows higher throughput and lower GPU memory for 14B MoEs. Under LoRA (BF16) + NekoQA-10K stylized dialogue HuggingFace Backend Unsloth Backend KTransformers Backend [14B-DeepSeekV2-Lite] LoRA fine-tuning throughput 303.58 token/s 455.37 token/s 530.38 token/s [14B-DeepSeekV2-Lite] GPU memory 32.12 GB 9.64 GB 6.08 GB [671B-DeepSeekV3] LoRA fine-tuning throughput Too Huge to run NOT SUPPORT 40.35 token/s [671B-DeepSeekV3] GPU memory (sum across GPUs) theoretical 1400 GB † NOT SUPPORT 70 GB † † The 1400 GB is the theoretical FP16 full-resident footprint (not runnable). 70 GB is the measured peak with KT (Attention on GPU + layered MoE offload). From the table above, it can be seen that for the 14B model, the KTransformers backend achieves approximately 75% higher throughput than the default HuggingFace solution, while using only about one-fifth of the GPU memory. For the 671B model, both HuggingFace and Unsloth fail to run on a single 4090 GPU, whereas KTransformers is able to perform LoRA fine-tuning at 40 tokens/s, keeping the GPU memory usage within 70 GB. 按照模型划分的对比图_02","breadcrumbs":"kt-sft part » kt-sft developer tech notes » Introduction","id":"46","title":"Introduction"},"47":{"body":"We detail how KTransformers takes over core operators in LLaMA-Factory’s fine-tuning framework to optimize Attention and MoE. DeepSeek-V3/V2 MoE models comprise a small-parameter dense Attention part and a large-parameter sparse MoE part. For illustration, consider layer 2 of DeepSeek-V2-Lite-Chat (from which each layer includes both Attention and MoE). Attention compute and KV cache mainly reside on the GPU; the heavyweight MoE part is primarily executed on the CPU. We first cover Attention replacement and inheritance , then MoE encapsulation and backend interfacing , and finally multi-GPU placement .","breadcrumbs":"kt-sft part » kt-sft developer tech notes » Overall View of the KT Fine-Tuning Framework","id":"47","title":"Overall View of the KT Fine-Tuning Framework"},"48":{"body":"KTransformers provides operator injection (BaseInjectedModule), and PEFT provides LoRA layer insertion. For fine-tuning, we design KTransformersLinearLora, inheriting from both KTransformersLinear and LoraLayer: Inheritance: KTransformersLinearLora retains KT’s high-performance paths (prefill_linear/generate_linear) while accepting LoRA parameters (lora_A/lora_B). Replacement: During preparation, we replace original KTransformersLinear layers (Q/K/V/O) with KTransformersLinearLora, preserving KT optimizations while enabling LoRA trainability. image-20251016182810716 After replacement, LoRA is inserted at Q/K/V/O linear transforms (left), and KTransformersLinearLora contains both KT fast paths and LoRA matrices (right). image-20251016182920722","breadcrumbs":"kt-sft part » kt-sft developer tech notes » Attention (LoRA + KT coexist)","id":"48","title":"Attention (LoRA + KT coexist)"},"49":{"body":"Encapsulation Given large parameters and sparse compute, we encapsulate the expert computation as a differentiable black-box operator —transparent upstream, replaceable downstream. Upstream (PyTorch graph): we register a custom Autograd Function so the MoE layer appears as a single node . In the left figure (red box), only KSFTExpertsCPU is visible; on the right, the unencapsulated graph expands routing, dispatch, and FFN experts. Encapsulation makes the MoE layer behave like a standard nn.Module with gradients. Downstream (backend): inside the Autograd Function, pybind11 calls C++ extensions for forward/backward. Multiple pluggable backends exist (AMX BF16/INT8; llamafile ). The backend can be switched via YAML (e.g., \\"backend\\": \\"AMXBF16\\" vs. \\"llamafile\\"). image-20250801174623919 Backward (CPU) MoE backward frequently needs the transposed weights $W^\\\\top$. To avoid repeated runtime transposes, we precompute/cache $W^\\\\top$ at load time (blue box). We also cache necessary intermediate activations (e.g., expert projections, red box) to reuse in backward and reduce recomputation. We provide backward implementations for llamafile and AMX (INT8/BF16) , with NUMA-aware optimizations.","breadcrumbs":"kt-sft part » kt-sft developer tech notes » MoE (operator encapsulation + backward)","id":"49","title":"MoE (operator encapsulation + backward)"},"5":{"body":"If you use KTransformers in your research, please cite our paper: @inproceedings{10.1145/3731569.3764843, title = {KTransformers: Unleashing the Full Potential of CPU/GPU Hybrid Inference for MoE Models}, author = {Chen, Hongtao and Xie, Weiyu and Zhang, Boxin and Tang, Jingqi and Wang, Jiahao and Dong, Jianwei and Chen, Shaoyuan and Yuan, Ziwei and Lin, Chen and Qiu, Chengyu and Zhu, Yuening and Ou, Qingliang and Liao, Jiaqi and Chen, Xianglin and Ai, Zhiyuan and Wu, Yongwei and Zhang, Mingxing}, booktitle = {Proceedings of the ACM SIGOPS 31st Symposium on Operating Systems Principles}, year = {2025}\\n}","breadcrumbs":"Introduction » 🔥 Citation","id":"5","title":"🔥 Citation"},"50":{"body":"To lower per-GPU memory peaks on 2–4 GPUs, we use model parallelism + explicit placement , not DataParallel (which duplicates the whole model on each GPU). Key changes: KTrainer: takes over .to(device) to prevent “move whole model to a single GPU”. Using KT’s optimize-rule YAML, each layer declares device: cuda:0/cuda:1/... and is constructed directly on the target GPU (no extra copies). Disable automatic DataParallel: when USE_KT=1, we disable automatic DP wrappers from LLaMA-Factory/HF Trainer to avoid duplication and keep full control over sharding. Gradient aggregation: gradients are reduced to cuda:0. Intermediate activations stay local; only necessary tensors are transferred, cutting communication/activation overhead. Thus, we keep KT placement strategies under multi-GPU fine-tuning. Users choose a kt_optimize_rule with multi-gpu. For DeepSeek-671B, DeepSeek-V3-Chat-sft-amx-multi-gpu.yaml is a typical 2-GPU plan: KV/attention parts on each GPU; MoE experts sharded on CPU; both GPUs share the workload.","breadcrumbs":"kt-sft part » kt-sft developer tech notes » Multi-GPU Loading/Training: Placement strategy instead of DataParallel","id":"50","title":"Multi-GPU Loading/Training: Placement strategy instead of DataParallel"},"51":{"body":"","breadcrumbs":"kt-sft part » kt-sft developer tech notes » KT-LoRA Fine-Tuning Evaluation","id":"51","title":"KT-LoRA Fine-Tuning Evaluation"},"52":{"body":"LLaMA-Factory orchestration, KTransformers backend, LoRA (rank=8, α=32, dropout=0.1, BF16), GAS=16, qlen=512, with the same KT optimize rule as training. We evaluate (a) stylized dialogue transfer and (b) two small-scale representative benchmarks: Translational-Style (generative) and AfriMed-QA (medical vertical; SAQ and MCQ ). AMX is enabled; GPUs: 2×48GB RTX 4090; CPU: Intel Xeon Platinum 8488C.","breadcrumbs":"kt-sft part » kt-sft developer tech notes » Setup","id":"52","title":"Setup"},"53":{"body":"Stylized Dialogue (CatGirl tone) Dataset: NekoQA-10K . The fine-tuned model consistently exhibits the target style (red boxes) versus neutral/rational base (blue). This shows KT-LoRA injects style features into the generation distribution with low GPU cost. image-20251016175848143 Translational-Style benchmark (generative) Dataset: Translational-Style-ChatLLM . Metrics: BLEU-1/2/3/4, ROUGE-1/2/L. Translational-Style dataset BLEU-1 BLEU-2 BLEU-3 BLEU-4 ROUGE-1 ROUGE-2 ROUGE-L V2-Lite (no LoRA) 20.66 8.33 4.54 2.89 22.71 4.52 19.19 KT-LoRA fine-tuned V2-Lite 35.41 22.44 15.42 11.18 42.03 18.38 33.10 V3 base (no LoRA) 8.49 3.34 1.62 0.96 15.91 2.55 10.07 KT-LoRA fine-tuned V3 37.02 23.70 16.21 11.49 43.43 18.96 34.54 As shown by the test results in the tables above, under a unified workflow and placement strategy, both model scales exhibit consistent gains after fine-tuning , supporting the usability and effectiveness of the “KT backend + LoRA fine-tuning” combination for generative style control. At the same time, this indicates that KT’s heterogeneous placement and operator optimizations can stably support small-sample adaptation in the style domain. Medical Vertical Benchmark (AfriMed-SAQ/MCQ) The dataset adopts AfriMed-QA (ACL 2025), a domain-specific dataset for the medical field in Africa with strong scenario customization characteristics, comprising two formats—multiple-choice questions (MCQ) and short-answer questions (SAQ)—which in this case serve as the evaluation for vertical-domain fine-tuning. In terms of evaluation criteria, BLEU/ROUGE are used for SAQ, and Accuracy is used for MCQ. AfriMed-QA (SAQ) BLEU-1 BLEU-2 BLEU-3 BLEU-4 ROUGE-1 ROUGE-2 ROUGE-L V2-Lite (no LoRA) 13.58 11.12 9.10 7.23 22.48 7.81 11.73 KT-LoRA fine-tuned V2-Lite 35.90 27.63 22.99 19.15 35.25 17.50 28.44 V3 base (no LoRA) 12.75 10.27 8.05 5.99 20.33 5.65 10.11 KT-LoRA fine-tuned V3 42.42 34.12 28.95 24.54 41.97 22.37 33.28 AfriMed-QA (MCQ) Accuracy V2-Lite (no LoRA) 0.0645 KT-LoRA fine-tuned V2-Lite 0.4812 V3 base (no LoRA) 0.5833 KT-LoRA fine-tuned V3 0.7930 As shown in the tables above, (1) DeepSeek-V3 (671B) after KT-LoRA fine-tuning achieves clearly higher performance than the fine-tuned DeepSeek-V2-Lite (14B) on both MCQ and SAQ, and it also surpasses the V3 base model. Within our small-scale setting, this preliminarily indicates that KT-LoRA fine-tuning of ultra-large-parameter models has practical significance in vertical domains. (2) Across both SAQ/MCQ sub-tasks, KT-LoRA delivers consistent gains, indicating that—with KT’s heterogeneous placement and backend operator support—LoRA fine-tuning can effectively inject the key knowledge points of vertical domains such as medicine into the model. Limitations At present, most of our testing is conducted on single datasets and at small scale (≤ 20k examples), with the goal of providing existence evidence of system effectiveness for KT-LoRA fine-tuning , rather than drawing generalized conclusions about algorithmic generalization or scaling laws. Our report primarily presents representative figures; to support stronger algorithmic claims, larger sample sizes, multi-lingual/multi-domain datasets, and multi-seed repeated experiments would be required—these are beyond the scope of this work. We also warmly welcome everyone to join the open-source LLaMA-Factory KT fine-tuning project. If you have additional test results, we especially welcome you to record them in the shared spreadsheet below, and to include the corresponding kt_optimize_rule files, dataset examples, training/evaluation YAMLs, and detailed GPU-memory and CPU configurations for community reference and reproducibility~!","breadcrumbs":"kt-sft part » kt-sft developer tech notes » Results","id":"53","title":"Results"},"54":{"body":"End-to-End Performance Definitions step_time：time per optimization step (tensor movement + Attention + MoE + others). tokens_per_step = GAS × qlen；token/s = tokens_per_step / step_time。 We use GAS=16, qlen=512 → tokens_per_step=8192. Measured Model step_time (s) tokens/step token/s DeepSeek-V3-671B 203 8192 40.35 DeepSeek-V2-Lite-14B 36 8192 227.6 MoE Compute (DeepSeek-V3-671B) Theory MoE per-layer, per-token FLOPs (forward+backward) approx.: $$ \\\\text{FLOPs}_{\\\\text{per-layer, per-token}} \\\\approx c \\\\cdot k \\\\cdot H \\\\cdot I $$ ​ with $k = 8$（Top-k），$H = 7168$（hidden size），$I = 2048$（intermediate size），$c\\\\approx16$（≈6 forward + ≈10 backward matmuls）。 Per-step across all MoE layers: $$ \\\\text{FLOPs} {\\\\text{per-step}} \\\\approx c \\\\cdot qlen \\\\cdot k \\\\cdot H \\\\cdot I \\\\cdot L {\\\\text{MoE}} $$ ​ Plugging $c=16, qlen=512, k=8, H=7168, I=2048, L_{MoE}=58$，$\\\\text{FLOPs}_{\\\\text{per-step}} \\\\approx 55.8\\\\ \\\\text{TFLOPs}$. Measured (MoE TFLOPS on CPU) If the MoE-only time per step is t_moe (seconds), $\\\\text{TFLOPS} = \\\\text{FLOPs}_{\\\\text{per-step}} / \\\\text{step_per_second}.$ Use MoE-phase time, not full step_time, to get MoE throughput. TFLOPS Forward Backward Average 17.55 18.41","breadcrumbs":"kt-sft part » kt-sft developer tech notes » Speed Tests","id":"54","title":"Speed Tests"},"55":{"body":"DeepSeek-V3 (671B; 58 MoE layers out of 61): ~ 70 GB total GPU, ~ 1.2–1.3 TB host memory. DeepSeek-V2-Lite (14B; 26 MoE layers out of 27): ~ 5 GB GPU, ~ 30 GB host memory.","breadcrumbs":"kt-sft part » kt-sft developer tech notes » Memory Footprint","id":"55","title":"Memory Footprint"},"56":{"body":"Integrating KTransformers LoRA with LLaMA-Factory provides a practical path to efficiently train and deploy MoE LLMs. KT contributes placement strategies and operator optimizations (DeepSeek/Qwen/Kimi support with AMX-accelerated kernels), and LoRA enables customization with very low GPU memory; LLaMA-Factory supplies a coherent user-level interface. This means even tens-to-hundreds-of-billion-parameter MoE models can be fine-tuned and served with low latency on ordinary hardware. The approach balances memory savings , speed , and usability , turning ultra-large models into tools that developers can actually wield.","breadcrumbs":"kt-sft part » kt-sft developer tech notes » Conclusion","id":"56","title":"Conclusion"},"57":{"body":"Author: Azure-Tang","breadcrumbs":"kt-sft part » Injection Tutorial » Tutorial: Inject Operator Step by Step","id":"57","title":"Tutorial: Inject Operator Step by Step"},"58":{"body":"This tutorial will guide you through the process of injecting custom operators into a model using the KTransformers framework. We will use the DeepSeekV2-Chat model as an example to demonstrate how to inject custom operators into the model step by step. The tutorial will cover the following topics: TL;DR How to Write Injection Rules Understanding Model Structure Matrix Absorption-based MLA Injection Injection of Routed Experts Injection of Linear Layers Injection of Modules with Pre-calculated Buffers Specifying Running Devices for Modules Muti-GPU How to Write a New Operator and Inject into the Model","breadcrumbs":"kt-sft part » Injection Tutorial » TL;DR","id":"58","title":"TL;DR"},"59":{"body":"The basic form of the injection rules for the Inject framework is as follows: - match: name: \\"^model\\\\\\\\.layers\\\\\\\\..*\\\\\\\\.*$\\" # Target module name class: torch.nn.Linear # Target module replace: class: \\"default\\" kwargs: generate_device: \\"cuda:0\\" # your_op_param_1: 1234 # your_op_param_2: 5678 recursive: True match: This field marks the matching rules, which can appear in two forms, name and class. These two matching rules can appear together or separately; they only match when both criteria are met. replace: class: Python class that can be imported to replace the target module. If no replacement is desired, set to default. kwargs: List of parameters needed for module initialization. generate_device: The device for this module, can be set to “cpu”, “cuda”, “cuda:1”, etc. recursive: Whether to recursively inject this module’s submodules, default is True. For the recursive field: Some modules contain multiple submodules, such as the Self-attention module typically includes q/k/v/o four linear modules. If we replace the self-attention module but do not want the internal linear modules to be covered by other rules, set this rule to False.","breadcrumbs":"kt-sft part » Injection Tutorial » How to Write Injection Rules","id":"59","title":"How to Write Injection Rules"},"6":{"body":"Developed and maintained by: MADSys Lab @ Tsinghua University Approaching.AI Community contributors We welcome contributions! Please feel free to submit issues and pull requests.","breadcrumbs":"Introduction » 👥 Contributors & Team","id":"6","title":"👥 Contributors & Team"},"60":{"body":"Using deepseek-ai/DeepSeek-V2-Lite-Chat as an example, we can follow the above rules step by step to inject our custom module and run it. KTransformers offers a high degree of flexibility, allowing you to replace/experiment with basic operators. However, it also requires users to clearly understand the structure of the model they are running. Fortunately, knowing the structure of a model is very simple. Open the file list on the deepseek-ai/DeepSeek-V2-Lite homepage, and you can see the following files: From the .saftensors file, we can see the name of each layer’s weights, corresponding to the match.name attribute in the injection rules. From the modeling_deepseek.py file, we can see the specific implementation of each module class, with the class name corresponding to the match.class attribute in the injection rules. The structure of the DeepSeekV2 model from the .saftensors and modeling_deepseek.py files is as follows: Supported operators and their corresponding classes are as follows: match replace backends descriptions Linear KTransformersLinear KLinearMarlin Marlin as backend KLinearTorch pytorch as backend KLinearCPUInfer llamafile as backend KLinearFP8 Triton fp8_gemm kernel. Requires GPU be able to caluculate fp8 data experts KTransformersExperts KExpertsTorch pytorch as backend KExpertsMarlin Marlin as backend KExpertsCPU llamafile as backend Attention KDeepseekV2Attention KDeepseekV2Attention MLA implementation MoE KMistralSparseMoEBlock KQwen2MoeSparseMoeBlock MoE for Qwen2 KDeepseekV2MoE KDeepseekV2MoE MoE for DeepseekV2 Model KQwen2MoeModel KQwen2MoeModel Model for Qwen2 KDeepseekV2Model KDeepseekV2Model Model for DeepseekV2 RoPE RotaryEmbedding RotaryEmbedding RoPE module YarnRotaryEmbedding YarnRotaryEmbedding RoPE module Then we start step-by-step injection of custom modules, our targets are: Replace the linear module with custom Marlin linear module. Replace the self-attention module with a custom Absorption-based MLA module. Replace the experts module with a custom Experts module. Replace the MoE module with a custom MoE module. Replace the RoPE module with a custom RoPE module. Set the running device for each module. The full implementation of the injection rules can be found in the here .","breadcrumbs":"kt-sft part » Injection Tutorial » Understanding Model Structure","id":"60","title":"Understanding Model Structure"},"61":{"body":"For the injection of the Attention module, we only need to use a regular expression to match the module names used in transformers and replace them with our own MLA module implementation. The YAML injection rule is as follows: - match: name: \\"^model\\\\\\\\.layers\\\\\\\\..*\\\\\\\\.self_attn$\\" # Regular expression replace: class: ktransformers.operators.attention.KDeepseekV2Attention # Optimized MLA implementation As you can see, each rule in the YAML file has two parts: match and replace. The match part specifies the module to be replaced, and the replace part specifies the module to be injected into the model along with the initialization keywords.","breadcrumbs":"kt-sft part » Injection Tutorial » Matrix Absorption-based MLA Injection","id":"61","title":"Matrix Absorption-based MLA Injection"},"62":{"body":"For Routed Experts (corresponding to the exps in the diagram), the module we inject is CPUInfer, which is wrapped in the wrapper module KTransformersExperts. KTransformersExperts has multiple implementations, and we need to specify keywords to tell the wrapper module which implementation we want to use and how we plan to use it. In the source code of the transformer, MoE is implemented using nn.ModuleList. We do not want KTransformers to traverse all submodules in the list and inject them one by one, so in this rule, we set recursive: False to prevent recursive injection into the submodules of this module. The YAML rule is as follows: - match: name: \\"^model\\\\\\\\.layers\\\\\\\\..*\\\\\\\\.mlp\\\\\\\\.experts$\\" replace: class: ktransformers.operators.experts.KTransformersExperts # Custom MoE kernel with expert parallelism kwargs: generate_device: \\"cpu\\" generate_op: \\"MLPCPUExperts\\" out_device: \\"cuda\\" recursive: False # Don\'t recursively inject submodules of this module If we inject Routed Experts as a custom module, we cannot use the interfaces in the original nn.ModuleList. Therefore, it is necessary to modify the forward function in the FFN module. The simplest method is to implement a new module with a custom forward function and inject it. - match: class: ktransformers.models.modeling_deepseek.DeepseekV2MoE replace: class: ktransformers.operators.experts.KDeepseekV2MoE # MLP module with custom forward function","breadcrumbs":"kt-sft part » Injection Tutorial » Injection of Routed Experts","id":"62","title":"Injection of Routed Experts"},"63":{"body":"For the remaining linear layer modules, we aim to use quantized operators to save storage space while improving performance. Since there is no current research on using MLA and quantization together, we do not want to inject linear into the MLA operator. Therefore, we can modify the regular expression and add a type check in the match part of the rule. Only modules that match both the name and class simultaneously will be injected. We also need to pass some keywords similar to the injection of Routed Experts. The YAML rule is as follows: - match: name: \\"^model\\\\\\\\.layers\\\\\\\\.(?!.*self_attn).*$\\" # Regular expression class: torch.nn.Linear # Only match modules matching name and class simultaneously replace: class: ktransformers.operators.linear.KTransformersLinear # Optimized kernel on quantized data types kwargs: generate_device: \\"cuda\\" generate_op: \\"QuantizedLinearMarlin\\"","breadcrumbs":"kt-sft part » Injection Tutorial » Injection of Linear Layers","id":"63","title":"Injection of Linear Layers"},"64":{"body":"To avoid occupying resources when initializing the injected original model, we use torch’s meta device to initialize the original model. The RoPE module pre-calculates some buffers during initialization, but no calculations are performed when using the meta device. Therefore, we need to compensate for the calculation of the buffer when loading the model. Simply, we inject a custom module into the rotary embedding module, which performs pre-calculation during loading. The YAML rule is as follows: - match: class: ktransformers.models.modeling_deepseek.DeepseekV2YarnRotaryEmbedding replace: class: ktransformers.operators.RoPE.YarnRotaryEmbedding","breadcrumbs":"kt-sft part » Injection Tutorial » Injection of Modules with Pre-calculated Buffers","id":"64","title":"Injection of Modules with Pre-calculated Buffers"},"65":{"body":"Finally, we set a fallback basic attribute generate_device for all modules: - match: name: \\"^model\\\\\\\\.layers\\\\\\\\..*\\\\\\\\.|^lm_head\\" replace: class: \\"default\\" kwargs: generate_device: \\"cuda\\" - match: name: \\"^model.embed_tokens\\" replace: class: \\"default\\" kwargs: generate_device: \\"cpu\\" Through these two rules, we place all previously unmatched layers (and their submodules) and lm_head on cuda, and the embedding on cpu. Note that the properties of a module will be determined by the first rule it matches. For example, if you later set a new replace.kwargs.generate_device in an injected module, the device set earlier will take precedence. If your computer has multiple cards, you can also configure the model to multiple cards.","breadcrumbs":"kt-sft part » Injection Tutorial » Specifying Running Devices for Modules","id":"65","title":"Specifying Running Devices for Modules"},"66":{"body":"If you have multiple GPUs, you can set the device for each module to different GPUs. DeepseekV2-Chat got 60 layers, if we got 2 GPUs, we can allocate 30 layers to each GPU. Complete multi GPU rule examples here . First of all, for multi-GPU, we have to inject an new operator KDeepseekV2Model. And set division of the layers to different GPUs. For our case, we have to set the transfer_map in the KDeepseekV2Model operatoras as follows: - match: name: \\"^model$\\" replace: class: \\"ktransformers.operators.models.KDeepseekV2Model\\" kwargs: transfer_map: 30: \\"cuda:1\\" And we have to set the device for each module in the model. For example, for routed experts, the yaml for one GPU is: - match: name: \\"^model\\\\\\\\.layers\\\\\\\\..*\\\\\\\\.mlp\\\\\\\\.experts$\\" replace: class: ktransformers.operators.experts.KTransformersExperts # Custom MoE kernel with expert parallelism kwargs: generate_device: \\"cuda:0\\" generate_op: \\"MLPCUDAExperts\\" out_device: \\"cuda:0\\" recursive: False # Don\'t recursively inject submodules of this module But for two GPUs, we need to set the device for each module in the model. # allcate 0-29 layers‘s out_device to cuda:0\\n- match: name: \\"^model\\\\\\\\.layers\\\\\\\\.(0|[1-9]|[12][0-9])\\\\\\\\.mlp\\\\\\\\.experts$\\" replace: class: ktransformers.operators.experts.KTransformersExperts # custom MoE Kernel with expert paralleism kwargs: generate_device: \\"cpu\\" generate_op: \\"KExpertsCPU\\" out_device: \\"cuda:0\\" recursive: False # don\'t recursively inject submodules of this module # allocate 30-59 layers‘s out_device to cuda:1\\n- match: name: \\"^model\\\\\\\\.layers\\\\\\\\.([345][0-9])\\\\\\\\.mlp\\\\\\\\.experts$\\" replace: class: ktransformers.operators.experts.KTransformersExperts # custom MoE Kernel with expert paralleism kwargs: generate_device: \\"cpu\\" generate_op: \\"KExpertsCPU\\" out_device: \\"cuda:1\\" recursive: False # don\'t recursively inject submodules of this module For other modules, we can set the device in the same way.","breadcrumbs":"kt-sft part » Injection Tutorial » Muti-GPU","id":"66","title":"Muti-GPU"},"67":{"body":"In this section, we will explain how to write an operator that can be injected, using the implementation of a new linear as an example. First, all injectable operators need to inherit from the BaseInjectedModule class, which inherits some attributes required by our injection framework. Its initialization function needs to meet the following basic format: class LinearTorchInject(BaseInjectedModule): def __init__( self, key: str, gguf_loader: GGUFLoader, config: PretrainedConfig, orig_module: nn.Module = None, generate_device: str = \\"cuda\\", **kwargs, ): super().__init__(key, gguf_loader, config, orig_module, generate_device, **kwargs) If users have other parameters that need to be passed to this class, they can also be included in the init function and re-passed in the kwargs parameter in the yaml file. For example, if our operator wants to pass a parameter my_param, the init function can be written as: class LinearTorchInject(BaseInjectedModule): def __init__( self, key: str, gguf_loader: GGUFLoader, config: PretrainedConfig, orig_module: nn.Module = None, generate_device: str = \\"cuda\\", my_param: bool = True, **kwargs, ): super().__init__(key, gguf_loader, config, orig_module, generate_device, **kwargs) self.my_param = my_param Then our injection rule can be written as: - match: name: \\"^model\\\\\\\\.layers\\\\\\\\..*$\\" # Regular expression matches the module name. class: torch.nn.Linear # Type restrictions can be added. replace: class: ktransformers.operators.linear.LinearTorchInject # Inject module path kwargs: # Extra parameters generate_device: \\"cuda\\" my_param: True For the linear module, it is also necessary to read weights from a gguf file. We provide the KLinearBase class to help users read weights from gguf files. Users only need to inherit and implement the load, unload, and forward functions. Therefore, a fully injectable linear class would look like this: class LinearTorchInject(BaseInjectedModule, KLinearBase): def __init__( self, key: str, gguf_loader: GGUFLoader, config: PretrainedConfig, orig_module: nn.Module = None, generate_device: str = \\"cuda\\", **kwargs, ): super().__init__(key, gguf_loader, config, orig_module, generate_device, **kwargs) KLinearBase.__init__(self) self.has_bias = False self.dtype = torch.get_default_dtype() self.w = None self.has_bias = False def load(self, w: dict | nn.Parameter | tuple | None = None, device: str|None = None): if device is None: device = self.device if w is None: w = self.load_weight(device=device) if isinstance(w, nn.Parameter): self.w = w.to(dtype=self.dtype).view(self.out_features, self.in_features).T self.has_bias = False elif isinstance(w, tuple): self.w = w[0].to(dtype=self.dtype).view(self.out_features, self.in_features).T self.bias = w[1].to(dtype=self.dtype) self.has_bias = True else: raise ValueError(\\"Invalid weight type\\") self.w = self.w.to(device) if self.has_bias: self.bias = self.bias.to(device) def unload(self): if self.w is not None: self.w = None if self.has_bias: self.bias = None def forward(self, x: torch.Tensor) -> torch.Tensor: dtype = x.dtype out_device = x.device x = x.to(device=self.device, dtype=self.dtype) x = x @ self.w if self.has_bias: x = x + self.bias x = x.to(dtype=dtype, device=out_device) return x Note that the self.load_weight function is provided by the KLinearBase class to help users load weights from a gguf file into the module. The implementation details of KLinearBase can be found on GITHUB .","breadcrumbs":"kt-sft part » Injection Tutorial » How to Write a New Operator and Inject into the Model","id":"67","title":"How to Write a New Operator and Inject into the Model"},"68":{"body":"To be written...","breadcrumbs":"kt-kernel part » kt-kernel Docs","id":"68","title":"kt-kernel Docs"},"69":{"body":"","breadcrumbs":"FAQ » see the issue FAQ page","id":"69","title":"see the issue FAQ page"},"7":{"body":"GitHub Issues : Report bugs or request features WeChat Group : See archive/WeChatGroup.png","breadcrumbs":"Introduction » 💬 Community & Support","id":"7","title":"💬 Community & Support"},"8":{"body":"The original integrated KTransformers framework has been archived to the archive/ directory for reference. The project now focuses on the two core modules above for better modularity and maintainability. For the original documentation with full quick-start guides and examples, see: archive/README.md (English) archive/README_ZH.md (中文)","breadcrumbs":"Introduction » 📦 KT original Code","id":"8","title":"📦 KT original Code"},"9":{"body":"High-performance kernel operations for KTransformers, featuring CPU-optimized MoE inference with AMX, AVX, KML and blis (amd library) support. Note Features Installation Prerequisites Quick Installation (Recommended) Manual Configuration (Advanced) Verification Integration with SGLang Installation Steps Complete Example: Qwen3-30B-A3B KT-Kernel Parameters Direct Python API Usage Advanced Options Build Configuration Manual Installation Error Troubleshooting CUDA Not Found hwloc Not Found Weight Quantization CPU Weights (for \\"cold\\" experts on CPU) GPU Weights (for \\"hot\\" experts on GPU) Before Commit!","breadcrumbs":"For kt-kernel » 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diff --git a/theme-dawn.js b/theme-dawn-4493f9c8.js
similarity index 100%
rename from theme-dawn.js
rename to theme-dawn-4493f9c8.js
diff --git a/theme-tomorrow_night.js b/theme-tomorrow_night-9dbe62a9.js
similarity index 100%
rename from theme-tomorrow_night.js
rename to theme-tomorrow_night-9dbe62a9.js
diff --git a/toc-6a9a54e6.js b/toc-6a9a54e6.js
new file mode 100644
index 00000000..ea02f8d1
--- /dev/null
+++ b/toc-6a9a54e6.js
@@ -0,0 +1,446 @@
+// Populate the sidebar
+//
+// This is a script, and not included directly in the page, to control the total size of the book.
+// The TOC contains an entry for each page, so if each page includes a copy of the TOC,
+// the total size of the page becomes O(n**2).
+class MDBookSidebarScrollbox extends HTMLElement {
+    constructor() {
+        super();
+    }
+    connectedCallback() {
+        this.innerHTML = '<ol class="chapter"><li class="chapter-item "><span class="chapter-link-wrapper"><a href="index.html">Introduction</a></span></li><li class="chapter-item "><li class="part-title">Install &amp; Usage</li></li><li class="chapter-item "><span class="chapter-link-wrapper"><a href="en/kt-kernel/kt-kernel_intro.html"><strong aria-hidden="true">1.</strong> For kt-kernel</a></span></li><li class="chapter-item "><span class="chapter-link-wrapper"><a href="en/SFT/KTransformers-Fine-Tuning_User-Guide.html"><strong aria-hidden="true">2.</strong> For kt-sft</a></span></li><li class="chapter-item "><li class="part-title">Tutorial</li></li><li class="chapter-item "><span class="chapter-link-wrapper"><a href="en/SFT/index.html"><strong aria-hidden="true">3.</strong> kt-sft part</a><a class="chapter-fold-toggle"><div>❱</div></a></span><ol class="section"><li class="chapter-item "><span class="chapter-link-wrapper"><a href="en/SFT/KTransformers-Fine-Tuning_Developer-Technical-Notes.html"><strong aria-hidden="true">3.1.</strong> kt-sft developer tech notes</a></span></li><li class="chapter-item "><span class="chapter-link-wrapper"><a href="en/SFT/injection_tutorial.html"><strong aria-hidden="true">3.2.</strong> Injection Tutorial</a></span></li></ol><li class="chapter-item "><span class="chapter-link-wrapper"><a href="en/kt-kernel/index.html"><strong aria-hidden="true">4.</strong> kt-kernel part</a></span></li><li class="chapter-item "><li class="part-title">FAQ</li></li><li class="chapter-item "><span class="chapter-link-wrapper"><a href="en/FAQ.html"><strong aria-hidden="true">5.</strong> FAQ</a></span></li></ol>';
+        // Set the current, active page, and reveal it if it's hidden
+        let current_page = document.location.href.toString().split('#')[0].split('?')[0];
+        if (current_page.endsWith('/')) {
+            current_page += 'index.html';
+        }
+        const links = Array.prototype.slice.call(this.querySelectorAll('a'));
+        const l = links.length;
+        for (let i = 0; i < l; ++i) {
+            const link = links[i];
+            const href = link.getAttribute('href');
+            if (href && !href.startsWith('#') && !/^(?:[a-z+]+:)?\/\//.test(href)) {
+                link.href = path_to_root + href;
+            }
+            // The 'index' page is supposed to alias the first chapter in the book.
+            if (link.href === current_page
+                || i === 0
+                && path_to_root === ''
+                && current_page.endsWith('/index.html')) {
+                link.classList.add('active');
+                let parent = link.parentElement;
+                while (parent) {
+                    if (parent.tagName === 'LI' && parent.classList.contains('chapter-item')) {
+                        parent.classList.add('expanded');
+                    }
+                    parent = parent.parentElement;
+                }
+            }
+        }
+        // Track and set sidebar scroll position
+        this.addEventListener('click', e => {
+            if (e.target.tagName === 'A') {
+                sessionStorage.setItem('sidebar-scroll', this.scrollTop);
+            }
+        }, { passive: true });
+        const sidebarScrollTop = sessionStorage.getItem('sidebar-scroll');
+        sessionStorage.removeItem('sidebar-scroll');
+        if (sidebarScrollTop) {
+            // preserve sidebar scroll position when navigating via links within sidebar
+            this.scrollTop = sidebarScrollTop;
+        } else {
+            // scroll sidebar to current active section when navigating via
+            // 'next/previous chapter' buttons
+            const activeSection = document.querySelector('#mdbook-sidebar .active');
+            if (activeSection) {
+                activeSection.scrollIntoView({ block: 'center' });
+            }
+        }
+        // Toggle buttons
+        const sidebarAnchorToggles = document.querySelectorAll('.chapter-fold-toggle');
+        function toggleSection(ev) {
+            ev.currentTarget.parentElement.parentElement.classList.toggle('expanded');
+        }
+        Array.from(sidebarAnchorToggles).forEach(el => {
+            el.addEventListener('click', toggleSection);
+        });
+    }
+}
+window.customElements.define('mdbook-sidebar-scrollbox', MDBookSidebarScrollbox);
+
+
+// ---------------------------------------------------------------------------
+// Support for dynamically adding headers to the sidebar.
+
+(function() {
+    // This is used to detect which direction the page has scrolled since the
+    // last scroll event.
+    let lastKnownScrollPosition = 0;
+    // This is the threshold in px from the top of the screen where it will
+    // consider a header the "current" header when scrolling down.
+    const defaultDownThreshold = 150;
+    // Same as defaultDownThreshold, except when scrolling up.
+    const defaultUpThreshold = 300;
+    // The threshold is a virtual horizontal line on the screen where it
+    // considers the "current" header to be above the line. The threshold is
+    // modified dynamically to handle headers that are near the bottom of the
+    // screen, and to slightly offset the behavior when scrolling up vs down.
+    let threshold = defaultDownThreshold;
+    // This is used to disable updates while scrolling. This is needed when
+    // clicking the header in the sidebar, which triggers a scroll event. It
+    // is somewhat finicky to detect when the scroll has finished, so this
+    // uses a relatively dumb system of disabling scroll updates for a short
+    // time after the click.
+    let disableScroll = false;
+    // Array of header elements on the page.
+    let headers;
+    // Array of li elements that are initially collapsed headers in the sidebar.
+    // I'm not sure why eslint seems to have a false positive here.
+    // eslint-disable-next-line prefer-const
+    let headerToggles = [];
+    // This is a debugging tool for the threshold which you can enable in the console.
+    let thresholdDebug = false;
+
+    // Updates the threshold based on the scroll position.
+    function updateThreshold() {
+        const scrollTop = window.pageYOffset || document.documentElement.scrollTop;
+        const windowHeight = window.innerHeight;
+        const documentHeight = document.documentElement.scrollHeight;
+
+        // The number of pixels below the viewport, at most documentHeight.
+        // This is used to push the threshold down to the bottom of the page
+        // as the user scrolls towards the bottom.
+        const pixelsBelow = Math.max(0, documentHeight - (scrollTop + windowHeight));
+        // The number of pixels above the viewport, at least defaultDownThreshold.
+        // Similar to pixelsBelow, this is used to push the threshold back towards
+        // the top when reaching the top of the page.
+        const pixelsAbove = Math.max(0, defaultDownThreshold - scrollTop);
+        // How much the threshold should be offset once it gets close to the
+        // bottom of the page.
+        const bottomAdd = Math.max(0, windowHeight - pixelsBelow - defaultDownThreshold);
+        let adjustedBottomAdd = bottomAdd;
+
+        // Adjusts bottomAdd for a small document. The calculation above
+        // assumes the document is at least twice the windowheight in size. If
+        // it is less than that, then bottomAdd needs to be shrunk
+        // proportional to the difference in size.
+        if (documentHeight < windowHeight * 2) {
+            const maxPixelsBelow = documentHeight - windowHeight;
+            const t = 1 - pixelsBelow / Math.max(1, maxPixelsBelow);
+            const clamp = Math.max(0, Math.min(1, t));
+            adjustedBottomAdd *= clamp;
+        }
+
+        let scrollingDown = true;
+        if (scrollTop < lastKnownScrollPosition) {
+            scrollingDown = false;
+        }
+
+        if (scrollingDown) {
+            // When scrolling down, move the threshold up towards the default
+            // downwards threshold position. If near the bottom of the page,
+            // adjustedBottomAdd will offset the threshold towards the bottom
+            // of the page.
+            const amountScrolledDown = scrollTop - lastKnownScrollPosition;
+            const adjustedDefault = defaultDownThreshold + adjustedBottomAdd;
+            threshold = Math.max(adjustedDefault, threshold - amountScrolledDown);
+        } else {
+            // When scrolling up, move the threshold down towards the default
+            // upwards threshold position. If near the bottom of the page,
+            // quickly transition the threshold back up where it normally
+            // belongs.
+            const amountScrolledUp = lastKnownScrollPosition - scrollTop;
+            const adjustedDefault = defaultUpThreshold - pixelsAbove
+                + Math.max(0, adjustedBottomAdd - defaultDownThreshold);
+            threshold = Math.min(adjustedDefault, threshold + amountScrolledUp);
+        }
+
+        if (documentHeight <= windowHeight) {
+            threshold = 0;
+        }
+
+        if (thresholdDebug) {
+            const id = 'mdbook-threshold-debug-data';
+            let data = document.getElementById(id);
+            if (data === null) {
+                data = document.createElement('div');
+                data.id = id;
+                data.style.cssText = `
+                    position: fixed;
+                    top: 50px;
+                    right: 10px;
+                    background-color: 0xeeeeee;
+                    z-index: 9999;
+                    pointer-events: none;
+                `;
+                document.body.appendChild(data);
+            }
+            data.innerHTML = `
+                <table>
+                  <tr><td>documentHeight</td><td>${documentHeight.toFixed(1)}</td></tr>
+                  <tr><td>windowHeight</td><td>${windowHeight.toFixed(1)}</td></tr>
+                  <tr><td>scrollTop</td><td>${scrollTop.toFixed(1)}</td></tr>
+                  <tr><td>pixelsAbove</td><td>${pixelsAbove.toFixed(1)}</td></tr>
+                  <tr><td>pixelsBelow</td><td>${pixelsBelow.toFixed(1)}</td></tr>
+                  <tr><td>bottomAdd</td><td>${bottomAdd.toFixed(1)}</td></tr>
+                  <tr><td>adjustedBottomAdd</td><td>${adjustedBottomAdd.toFixed(1)}</td></tr>
+                  <tr><td>scrollingDown</td><td>${scrollingDown}</td></tr>
+                  <tr><td>threshold</td><td>${threshold.toFixed(1)}</td></tr>
+                </table>
+            `;
+            drawDebugLine();
+        }
+
+        lastKnownScrollPosition = scrollTop;
+    }
+
+    function drawDebugLine() {
+        if (!document.body) {
+            return;
+        }
+        const id = 'mdbook-threshold-debug-line';
+        const existingLine = document.getElementById(id);
+        if (existingLine) {
+            existingLine.remove();
+        }
+        const line = document.createElement('div');
+        line.id = id;
+        line.style.cssText = `
+            position: fixed;
+            top: ${threshold}px;
+            left: 0;
+            width: 100vw;
+            height: 2px;
+            background-color: red;
+            z-index: 9999;
+            pointer-events: none;
+        `;
+        document.body.appendChild(line);
+    }
+
+    function mdbookEnableThresholdDebug() {
+        thresholdDebug = true;
+        updateThreshold();
+        drawDebugLine();
+    }
+
+    window.mdbookEnableThresholdDebug = mdbookEnableThresholdDebug;
+
+    // Updates which headers in the sidebar should be expanded. If the current
+    // header is inside a collapsed group, then it, and all its parents should
+    // be expanded.
+    function updateHeaderExpanded(currentA) {
+        // Add expanded to all header-item li ancestors.
+        let current = currentA.parentElement;
+        while (current) {
+            if (current.tagName === 'LI' && current.classList.contains('header-item')) {
+                current.classList.add('expanded');
+            }
+            current = current.parentElement;
+        }
+    }
+
+    // Updates which header is marked as the "current" header in the sidebar.
+    // This is done with a virtual Y threshold, where headers at or below
+    // that line will be considered the current one.
+    function updateCurrentHeader() {
+        if (!headers || !headers.length) {
+            return;
+        }
+
+        // Reset the classes, which will be rebuilt below.
+        const els = document.getElementsByClassName('current-header');
+        for (const el of els) {
+            el.classList.remove('current-header');
+        }
+        for (const toggle of headerToggles) {
+            toggle.classList.remove('expanded');
+        }
+
+        // Find the last header that is above the threshold.
+        let lastHeader = null;
+        for (const header of headers) {
+            const rect = header.getBoundingClientRect();
+            if (rect.top <= threshold) {
+                lastHeader = header;
+            } else {
+                break;
+            }
+        }
+        if (lastHeader === null) {
+            lastHeader = headers[0];
+            const rect = lastHeader.getBoundingClientRect();
+            const windowHeight = window.innerHeight;
+            if (rect.top >= windowHeight) {
+                return;
+            }
+        }
+
+        // Get the anchor in the summary.
+        const href = '#' + lastHeader.id;
+        const a = [...document.querySelectorAll('.header-in-summary')]
+            .find(element => element.getAttribute('href') === href);
+        if (!a) {
+            return;
+        }
+
+        a.classList.add('current-header');
+
+        updateHeaderExpanded(a);
+    }
+
+    // Updates which header is "current" based on the threshold line.
+    function reloadCurrentHeader() {
+        if (disableScroll) {
+            return;
+        }
+        updateThreshold();
+        updateCurrentHeader();
+    }
+
+
+    // When clicking on a header in the sidebar, this adjusts the threshold so
+    // that it is located next to the header. This is so that header becomes
+    // "current".
+    function headerThresholdClick(event) {
+        // See disableScroll description why this is done.
+        disableScroll = true;
+        setTimeout(() => {
+            disableScroll = false;
+        }, 100);
+        // requestAnimationFrame is used to delay the update of the "current"
+        // header until after the scroll is done, and the header is in the new
+        // position.
+        requestAnimationFrame(() => {
+            requestAnimationFrame(() => {
+                // Closest is needed because if it has child elements like <code>.
+                const a = event.target.closest('a');
+                const href = a.getAttribute('href');
+                const targetId = href.substring(1);
+                const targetElement = document.getElementById(targetId);
+                if (targetElement) {
+                    threshold = targetElement.getBoundingClientRect().bottom;
+                    updateCurrentHeader();
+                }
+            });
+        });
+    }
+
+    // Takes the nodes from the given head and copies them over to the
+    // destination, along with some filtering.
+    function filterHeader(source, dest) {
+        const clone = source.cloneNode(true);
+        clone.querySelectorAll('mark').forEach(mark => {
+            mark.replaceWith(...mark.childNodes);
+        });
+        dest.append(...clone.childNodes);
+    }
+
+    // Scans page for headers and adds them to the sidebar.
+    document.addEventListener('DOMContentLoaded', function() {
+        const activeSection = document.querySelector('#mdbook-sidebar .active');
+        if (activeSection === null) {
+            return;
+        }
+
+        const main = document.getElementsByTagName('main')[0];
+        headers = Array.from(main.querySelectorAll('h2, h3, h4, h5, h6'))
+            .filter(h => h.id !== '' && h.children.length && h.children[0].tagName === 'A');
+
+        if (headers.length === 0) {
+            return;
+        }
+
+        // Build a tree of headers in the sidebar.
+
+        const stack = [];
+
+        const firstLevel = parseInt(headers[0].tagName.charAt(1));
+        for (let i = 1; i < firstLevel; i++) {
+            const ol = document.createElement('ol');
+            ol.classList.add('section');
+            if (stack.length > 0) {
+                stack[stack.length - 1].ol.appendChild(ol);
+            }
+            stack.push({level: i + 1, ol: ol});
+        }
+
+        // The level where it will start folding deeply nested headers.
+        const foldLevel = 3;
+
+        for (let i = 0; i < headers.length; i++) {
+            const header = headers[i];
+            const level = parseInt(header.tagName.charAt(1));
+
+            const currentLevel = stack[stack.length - 1].level;
+            if (level > currentLevel) {
+                // Begin nesting to this level.
+                for (let nextLevel = currentLevel + 1; nextLevel <= level; nextLevel++) {
+                    const ol = document.createElement('ol');
+                    ol.classList.add('section');
+                    const last = stack[stack.length - 1];
+                    const lastChild = last.ol.lastChild;
+                    // Handle the case where jumping more than one nesting
+                    // level, which doesn't have a list item to place this new
+                    // list inside of.
+                    if (lastChild) {
+                        lastChild.appendChild(ol);
+                    } else {
+                        last.ol.appendChild(ol);
+                    }
+                    stack.push({level: nextLevel, ol: ol});
+                }
+            } else if (level < currentLevel) {
+                while (stack.length > 1 && stack[stack.length - 1].level >= level) {
+                    stack.pop();
+                }
+            }
+
+            const li = document.createElement('li');
+            li.classList.add('header-item');
+            li.classList.add('expanded');
+            if (level < foldLevel) {
+                li.classList.add('expanded');
+            }
+            const span = document.createElement('span');
+            span.classList.add('chapter-link-wrapper');
+            const a = document.createElement('a');
+            span.appendChild(a);
+            a.href = '#' + header.id;
+            a.classList.add('header-in-summary');
+            filterHeader(header.children[0], a);
+            a.addEventListener('click', headerThresholdClick);
+            const nextHeader = headers[i + 1];
+            if (nextHeader !== undefined) {
+                const nextLevel = parseInt(nextHeader.tagName.charAt(1));
+                if (nextLevel > level && level >= foldLevel) {
+                    const toggle = document.createElement('a');
+                    toggle.classList.add('chapter-fold-toggle');
+                    toggle.classList.add('header-toggle');
+                    toggle.addEventListener('click', () => {
+                        li.classList.toggle('expanded');
+                    });
+                    const toggleDiv = document.createElement('div');
+                    toggleDiv.textContent = '❱';
+                    toggle.appendChild(toggleDiv);
+                    span.appendChild(toggle);
+                    headerToggles.push(li);
+                }
+            }
+            li.appendChild(span);
+
+            const currentParent = stack[stack.length - 1];
+            currentParent.ol.appendChild(li);
+        }
+
+        const onThisPage = document.createElement('div');
+        onThisPage.classList.add('on-this-page');
+        onThisPage.append(stack[0].ol);
+        const activeItemSpan = activeSection.parentElement;
+        activeItemSpan.after(onThisPage);
+    });
+
+    document.addEventListener('DOMContentLoaded', reloadCurrentHeader);
+    document.addEventListener('scroll', reloadCurrentHeader, { passive: true });
+})();
+
diff --git a/toc.html b/toc.html
index 2bba0cf1..977d9b24 100644
--- a/toc.html
+++ b/toc.html
@@ -17,16 +17,15 @@
         <!-- Custom HTML head -->
         <meta name="viewport" content="width=device-width, initial-scale=1">
         <meta name="theme-color" content="#ffffff">
-        <link rel="stylesheet" href="css/variables.css">
-        <link rel="stylesheet" href="css/general.css">
-        <link rel="stylesheet" href="css/chrome.css">
-        <link rel="stylesheet" href="css/print.css" media="print">
+        <link rel="stylesheet" href="css/variables-8adf115d.css">
+        <link rel="stylesheet" href="css/general-2459343d.css">
+        <link rel="stylesheet" href="css/chrome-9dfbd86b.css">
+        <link rel="stylesheet" href="css/print-9e4910d8.css" media="print">
         <!-- Fonts -->
-        <link rel="stylesheet" href="FontAwesome/css/font-awesome.css">
-        <link rel="stylesheet" href="fonts/fonts.css">
+        <link rel="stylesheet" href="fonts/fonts-9644e21d.css">
         <!-- Custom theme stylesheets -->
     </head>
     <body class="sidebar-iframe-inner">
-        <ol class="chapter"><li class="chapter-item affix "><a href="index.html" target="_parent">Introduction</a></li><li class="chapter-item affix "><li class="part-title">Install &amp; Usage</li><li class="chapter-item "><a href="en/kt-kernel/kt-kernel_intro.html" target="_parent"><strong aria-hidden="true">1.</strong> For kt-kernel</a></li><li class="chapter-item "><a href="en/SFT/KTransformers-Fine-Tuning_User-Guide.html" target="_parent"><strong aria-hidden="true">2.</strong> For kt-sft</a></li><li class="chapter-item affix "><li class="part-title">Tutorial</li><li class="chapter-item "><a href="en/SFT/index.html" target="_parent"><strong aria-hidden="true">3.</strong> kt-sft part</a><a class="toggle"><div>❱</div></a></li><li><ol class="section"><li class="chapter-item "><a href="en/SFT/KTransformers-Fine-Tuning_Developer-Technical-Notes.html" target="_parent"><strong aria-hidden="true">3.1.</strong> kt-sft developer tech notes</a></li><li class="chapter-item "><a href="en/SFT/injection_tutorial.html" target="_parent"><strong aria-hidden="true">3.2.</strong> Injection Tutorial</a></li></ol></li><li class="chapter-item "><a href="en/kt-kernel/index.html" target="_parent"><strong aria-hidden="true">4.</strong> kt-kernel part</a></li><li class="chapter-item affix "><li class="part-title">FAQ</li><li class="chapter-item "><a href="en/FAQ.html" target="_parent"><strong aria-hidden="true">5.</strong> FAQ</a></li></ol>
+        <ol class="chapter"><li class="chapter-item "><span class="chapter-link-wrapper"><a href="index.html" target="_parent">Introduction</a></span></li><li class="chapter-item "><li class="part-title">Install &amp; Usage</li></li><li class="chapter-item "><span class="chapter-link-wrapper"><a href="en/kt-kernel/kt-kernel_intro.html" target="_parent"><strong aria-hidden="true">1.</strong> For kt-kernel</a></span></li><li class="chapter-item "><span class="chapter-link-wrapper"><a href="en/SFT/KTransformers-Fine-Tuning_User-Guide.html" target="_parent"><strong aria-hidden="true">2.</strong> For kt-sft</a></span></li><li class="chapter-item "><li class="part-title">Tutorial</li></li><li class="chapter-item "><span class="chapter-link-wrapper"><a href="en/SFT/index.html" target="_parent"><strong aria-hidden="true">3.</strong> kt-sft part</a><a class="chapter-fold-toggle"><div>❱</div></a></span><ol class="section"><li class="chapter-item "><span class="chapter-link-wrapper"><a href="en/SFT/KTransformers-Fine-Tuning_Developer-Technical-Notes.html" target="_parent"><strong aria-hidden="true">3.1.</strong> kt-sft developer tech notes</a></span></li><li class="chapter-item "><span class="chapter-link-wrapper"><a href="en/SFT/injection_tutorial.html" target="_parent"><strong aria-hidden="true">3.2.</strong> Injection Tutorial</a></span></li></ol><li class="chapter-item "><span class="chapter-link-wrapper"><a href="en/kt-kernel/index.html" target="_parent"><strong aria-hidden="true">4.</strong> kt-kernel part</a></span></li><li class="chapter-item "><li class="part-title">FAQ</li></li><li class="chapter-item "><span class="chapter-link-wrapper"><a href="en/FAQ.html" target="_parent"><strong aria-hidden="true">5.</strong> FAQ</a></span></li></ol>
     </body>
 </html>
diff --git a/toc.js b/toc.js
deleted file mode 100644
index 9c5171f6..00000000
--- a/toc.js
+++ /dev/null
@@ -1,70 +0,0 @@
-// Populate the sidebar
-//
-// This is a script, and not included directly in the page, to control the total size of the book.
-// The TOC contains an entry for each page, so if each page includes a copy of the TOC,
-// the total size of the page becomes O(n**2).
-class MDBookSidebarScrollbox extends HTMLElement {
-    constructor() {
-        super();
-    }
-    connectedCallback() {
-        this.innerHTML = '<ol class="chapter"><li class="chapter-item affix "><a href="index.html">Introduction</a></li><li class="chapter-item affix "><li class="part-title">Install &amp; Usage</li><li class="chapter-item "><a href="en/kt-kernel/kt-kernel_intro.html"><strong aria-hidden="true">1.</strong> For kt-kernel</a></li><li class="chapter-item "><a href="en/SFT/KTransformers-Fine-Tuning_User-Guide.html"><strong aria-hidden="true">2.</strong> For kt-sft</a></li><li class="chapter-item affix "><li class="part-title">Tutorial</li><li class="chapter-item "><a href="en/SFT/index.html"><strong aria-hidden="true">3.</strong> kt-sft part</a><a class="toggle"><div>❱</div></a></li><li><ol class="section"><li class="chapter-item "><a href="en/SFT/KTransformers-Fine-Tuning_Developer-Technical-Notes.html"><strong aria-hidden="true">3.1.</strong> kt-sft developer tech notes</a></li><li class="chapter-item "><a href="en/SFT/injection_tutorial.html"><strong aria-hidden="true">3.2.</strong> Injection Tutorial</a></li></ol></li><li class="chapter-item "><a href="en/kt-kernel/index.html"><strong aria-hidden="true">4.</strong> kt-kernel part</a></li><li class="chapter-item affix "><li class="part-title">FAQ</li><li class="chapter-item "><a href="en/FAQ.html"><strong aria-hidden="true">5.</strong> FAQ</a></li></ol>';
-        // Set the current, active page, and reveal it if it's hidden
-        let current_page = document.location.href.toString().split("#")[0].split("?")[0];
-        if (current_page.endsWith("/")) {
-            current_page += "index.html";
-        }
-        var links = Array.prototype.slice.call(this.querySelectorAll("a"));
-        var l = links.length;
-        for (var i = 0; i < l; ++i) {
-            var link = links[i];
-            var href = link.getAttribute("href");
-            if (href && !href.startsWith("#") && !/^(?:[a-z+]+:)?\/\//.test(href)) {
-                link.href = path_to_root + href;
-            }
-            // The "index" page is supposed to alias the first chapter in the book.
-            if (link.href === current_page || (i === 0 && path_to_root === "" && current_page.endsWith("/index.html"))) {
-                link.classList.add("active");
-                var parent = link.parentElement;
-                if (parent && parent.classList.contains("chapter-item")) {
-                    parent.classList.add("expanded");
-                }
-                while (parent) {
-                    if (parent.tagName === "LI" && parent.previousElementSibling) {
-                        if (parent.previousElementSibling.classList.contains("chapter-item")) {
-                            parent.previousElementSibling.classList.add("expanded");
-                        }
-                    }
-                    parent = parent.parentElement;
-                }
-            }
-        }
-        // Track and set sidebar scroll position
-        this.addEventListener('click', function(e) {
-            if (e.target.tagName === 'A') {
-                sessionStorage.setItem('sidebar-scroll', this.scrollTop);
-            }
-        }, { passive: true });
-        var sidebarScrollTop = sessionStorage.getItem('sidebar-scroll');
-        sessionStorage.removeItem('sidebar-scroll');
-        if (sidebarScrollTop) {
-            // preserve sidebar scroll position when navigating via links within sidebar
-            this.scrollTop = sidebarScrollTop;
-        } else {
-            // scroll sidebar to current active section when navigating via "next/previous chapter" buttons
-            var activeSection = document.querySelector('#sidebar .active');
-            if (activeSection) {
-                activeSection.scrollIntoView({ block: 'center' });
-            }
-        }
-        // Toggle buttons
-        var sidebarAnchorToggles = document.querySelectorAll('#sidebar a.toggle');
-        function toggleSection(ev) {
-            ev.currentTarget.parentElement.classList.toggle('expanded');
-        }
-        Array.from(sidebarAnchorToggles).forEach(function (el) {
-            el.addEventListener('click', toggleSection);
-        });
-    }
-}
-window.customElements.define("mdbook-sidebar-scrollbox", MDBookSidebarScrollbox);
diff --git a/tomorrow-night.css b/tomorrow-night-4c0ae647.css
similarity index 100%
rename from tomorrow-night.css
rename to tomorrow-night-4c0ae647.css
