mirror of
https://github.com/jingyaogong/minimind.git
synced 2026-01-13 19:57:20 +08:00
839 lines
26 KiB
HTML
839 lines
26 KiB
HTML
<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>MiniMind - Train LLMs from Scratch</title>
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<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css" rel="stylesheet">
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<style>
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@import url('https://fonts.googleapis.com/css2?family=Press+Start+2P&display=swap');
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:root {
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--mc-grass: #5fa824;
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--mc-stone: #7f8c8d;
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--mc-wood: #8b5a3c;
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--mc-sand: #d4a574;
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--mc-gold: #ffd700;
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--mc-dark: #1e1e1e;
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--mc-light: #f5f5f5;
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--primary: #5e72e4;
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--accent: #11cdef;
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}
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* {
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font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
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}
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.pixel-font {
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font-family: 'Press Start 2P', 'Courier New', monospace;
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}
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body {
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background: linear-gradient(180deg, #87ceeb 0%, #e0f6ff 50%, #d4a574 100%);
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min-height: 100vh;
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padding: 20px;
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margin: 0;
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}
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.container-main {
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background: var(--mc-light);
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border: 4px solid;
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border-color: #fff #ccc #999 #fff;
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box-shadow: inset 1px 1px 0 rgba(255,255,255,.5),
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inset -1px -1px 0 rgba(0,0,0,.5),
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3px 3px 0 rgba(0,0,0,.3);
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padding: 40px;
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max-width: 1200px;
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margin: 0 auto;
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image-rendering: pixelated;
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image-rendering: -moz-crisp-edges;
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image-rendering: crisp-edges;
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}
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/* Logo */
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.logo {
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max-width: 80%;
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height: auto;
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image-rendering: pixelated;
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display: block;
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}
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/* Badge Container - between logo and title */
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.badge-container {
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display: flex;
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flex-wrap: wrap;
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justify-content: center;
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gap: 8px;
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margin: 12px 0 15px 0;
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}
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.badge-container img {
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height: 22px;
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}
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/* Title Section */
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.title-section {
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text-align: center;
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background: var(--mc-grass);
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color: white;
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padding: 20px 20px;
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border: 3px solid;
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border-color: #fff #777 #777 #fff;
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box-shadow: inset 1px 1px 0 rgba(255,255,255,.3),
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inset -1px -1px 0 rgba(0,0,0,.3);
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margin-bottom: 20px;
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}
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.title-section h1 {
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display: none;
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}
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.subtitle {
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display: none;
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}
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.tagline {
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font-size: 0.85rem;
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color: #fff;
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margin: 0;
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line-height: 1.4;
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}
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/* Stats Section */
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.stats-section {
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display: grid;
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grid-template-columns: repeat(auto-fit, minmax(140px, 1fr));
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gap: 12px;
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margin: 20px 0;
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}
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.stat-box {
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background: var(--mc-sand);
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border: 3px solid;
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border-color: #fff #888 #888 #fff;
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box-shadow: inset 1px 1px 0 rgba(255,255,255,.3),
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inset -1px -1px 0 rgba(0,0,0,.3),
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2px 2px 0 rgba(0,0,0,.2);
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padding: 15px;
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text-align: center;
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background: linear-gradient(135deg, #ffd700 0%, #ffed4e 100%);
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}
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.stat-box h3 {
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margin: 0;
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font-size: 1.4rem;
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color: #333;
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text-shadow: 1px 1px 0 rgba(255,255,255,.8);
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}
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.stat-box p {
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margin: 6px 0 0 0;
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font-size: 0.7rem;
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color: #555;
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font-weight: bold;
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}
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/* Feature Cards */
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.features-grid {
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display: grid;
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grid-template-columns: repeat(auto-fit, minmax(280px, 1fr));
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gap: 15px;
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margin: 25px 0;
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}
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.feature-card {
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background: var(--mc-light);
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border: 3px solid;
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border-color: #fff #888 #888 #fff;
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box-shadow: inset 1px 1px 0 rgba(255,255,255,.5),
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inset -1px -1px 0 rgba(0,0,0,.2),
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3px 3px 0 rgba(0,0,0,.2);
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padding: 15px;
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background: linear-gradient(135deg, #f5f5f5 0%, #e8e8e8 100%);
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transition: transform 0.1s, box-shadow 0.1s;
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}
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.feature-card:hover {
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transform: translate(-2px, -2px);
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box-shadow: inset 1px 1px 0 rgba(255,255,255,.5),
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inset -1px -1px 0 rgba(0,0,0,.2),
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5px 5px 0 rgba(0,0,0,.3);
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}
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.feature-card h3 {
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margin: 0 0 8px 0;
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color: var(--mc-grass);
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font-size: 0.9rem;
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}
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.feature-card p {
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margin: 0;
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color: #555;
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font-size: 0.75rem;
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line-height: 1.4;
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}
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/* Section Headers */
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.section-header {
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background: var(--mc-stone);
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color: white;
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padding: 12px 15px;
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border: 3px solid;
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border-color: #fff #555 #555 #fff;
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box-shadow: inset 1px 1px 0 rgba(255,255,255,.3),
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inset -1px -1px 0 rgba(0,0,0,.3);
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margin: 25px 0 15px 0;
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font-size: 1rem;
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font-weight: bold;
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text-shadow: 1px 1px 0 rgba(0,0,0,.5);
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}
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/* Models Table */
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.models-table {
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margin: 18px 0;
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overflow-x: auto;
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}
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.models-table table {
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width: 100%;
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border-collapse: collapse;
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border: 3px solid;
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border-color: #fff #888 #888 #fff;
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box-shadow: inset 1px 1px 0 rgba(255,255,255,.3),
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inset -1px -1px 0 rgba(0,0,0,.3);
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background: var(--mc-light);
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}
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.models-table thead {
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background: var(--mc-wood);
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color: white;
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}
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.models-table thead th {
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padding: 10px;
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text-align: center;
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font-weight: bold;
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font-size: 0.8rem;
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text-shadow: 1px 1px 0 rgba(0,0,0,.5);
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border-right: 2px solid rgba(0,0,0,.2);
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}
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.models-table tbody tr {
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border-bottom: 2px solid #ddd;
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}
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.models-table tbody tr:nth-child(even) {
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background: #f0f0f0;
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}
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.models-table td {
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padding: 8px 10px;
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text-align: center;
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font-size: 0.8rem;
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border-right: 2px solid #ddd;
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}
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.models-table td:last-child {
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border-right: none;
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}
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.models-table tbody tr:hover {
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background: #fffacd !important;
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}
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/* Changelog */
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.changelog-section {
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margin: 18px 0;
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}
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.changelog-item {
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background: var(--mc-light);
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border: 3px solid;
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border-color: #fff #888 #888 #fff;
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box-shadow: inset 1px 1px 0 rgba(255,255,255,.3),
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inset -1px -1px 0 rgba(0,0,0,.2),
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2px 2px 0 rgba(0,0,0,.15);
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padding: 0;
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margin: 8px 0;
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}
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.changelog-header {
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background: var(--mc-gold);
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color: #333;
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padding: 10px 15px;
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cursor: pointer;
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font-weight: bold;
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user-select: none;
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display: flex;
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justify-content: space-between;
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align-items: center;
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transition: background 0.1s;
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border-bottom: 2px solid #ddd;
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font-size: 0.85rem;
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}
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.changelog-header:hover {
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background: #ffed4e;
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}
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.changelog-header.open {
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background: #ffd700;
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}
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.toggle-icon {
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font-size: 0.9rem;
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}
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.changelog-content {
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padding: 12px 15px;
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display: none;
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background: #fafafa;
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font-size: 0.8rem;
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}
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.changelog-item.open .changelog-content {
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display: block;
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}
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.changelog-content ul {
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list-style: none;
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padding-left: 0;
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margin: 0;
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}
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.changelog-content li {
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padding: 4px 0;
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padding-left: 20px;
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position: relative;
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color: #333;
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line-height: 1.3;
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}
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.changelog-content li:before {
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content: "▸";
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position: absolute;
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left: 0;
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color: var(--mc-grass);
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font-weight: bold;
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}
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/* Links Section */
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.links-section {
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background: var(--mc-grass);
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color: white;
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padding: 25px;
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border: 3px solid;
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border-color: #fff #555 #555 #fff;
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box-shadow: inset 1px 1px 0 rgba(255,255,255,.2),
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inset -1px -1px 0 rgba(0,0,0,.3),
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3px 3px 0 rgba(0,0,0,.3);
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margin: 25px 0;
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}
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.links-section h2 {
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text-align: center;
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margin-bottom: 15px;
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font-size: 1rem;
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text-shadow: 1px 1px 0 rgba(0,0,0,.5);
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}
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.links-grid {
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display: grid;
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grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
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gap: 10px;
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}
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.pixel-button {
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background: var(--mc-sand);
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color: #333;
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border: 3px solid;
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border-color: #fff #888 #888 #fff;
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box-shadow: inset 1px 1px 0 rgba(255,255,255,.3),
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inset -1px -1px 0 rgba(0,0,0,.2),
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2px 2px 0 rgba(0,0,0,.2);
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padding: 8px 14px;
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text-decoration: none;
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font-weight: bold;
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text-align: center;
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cursor: pointer;
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display: inline-block;
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transition: all 0.1s;
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font-size: 0.75rem;
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}
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.pixel-button:hover {
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border-color: #888 #fff #fff #888;
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box-shadow: inset -1px -1px 0 rgba(255,255,255,.3),
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inset 1px 1px 0 rgba(0,0,0,.2),
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1px 1px 0 rgba(0,0,0,.1);
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transform: translate(2px, 2px);
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color: #333;
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text-decoration: none;
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}
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.pixel-button:active {
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box-shadow: inset 1px 1px 0 rgba(255,255,255,.3),
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inset -1px -1px 0 rgba(0,0,0,.2);
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transform: translate(3px, 3px);
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}
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/* Why Section */
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||
.why-section {
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background: var(--mc-light);
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border: 3px solid;
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border-color: #fff #888 #888 #fff;
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box-shadow: inset 1px 1px 0 rgba(255,255,255,.3),
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inset -1px -1px 0 rgba(0,0,0,.2);
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padding: 20px;
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margin: 22px 0;
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}
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.why-section h2 {
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color: var(--mc-wood);
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||
margin-bottom: 15px;
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font-size: 1rem;
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}
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||
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.why-section ul {
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||
list-style: none;
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||
padding-left: 0;
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||
margin: 0;
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}
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||
.why-section li {
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||
padding: 4px 0;
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||
padding-left: 22px;
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||
position: relative;
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||
color: #333;
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||
line-height: 1.4;
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||
font-size: 0.8rem;
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||
}
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||
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.why-section li:before {
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||
content: "→";
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||
position: absolute;
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||
left: 0;
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||
color: var(--mc-grass);
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font-weight: bold;
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||
font-size: 0.9rem;
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}
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.quote {
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||
font-style: italic;
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||
color: #666;
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||
border-left: 4px solid var(--mc-grass);
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||
padding-left: 12px;
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||
margin: 15px 0;
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||
font-size: 0.85rem;
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||
}
|
||
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/* Visuals */
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||
.visuals-container {
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display: flex;
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overflow-x: auto;
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gap: 12px;
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||
padding: 15px;
|
||
background: var(--mc-light);
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||
border: 3px solid #ddd;
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||
margin: 20px 0;
|
||
border-radius: 2px;
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||
}
|
||
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.visuals-container img {
|
||
width: auto;
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||
height: auto;
|
||
max-height: 450px;
|
||
border: 2px solid #999;
|
||
image-rendering: auto;
|
||
flex-shrink: 0;
|
||
object-fit: contain;
|
||
}
|
||
|
||
.visuals-container::-webkit-scrollbar {
|
||
height: 10px;
|
||
}
|
||
|
||
.visuals-container::-webkit-scrollbar-track {
|
||
background: #e0e0e0;
|
||
}
|
||
|
||
.visuals-container::-webkit-scrollbar-thumb {
|
||
background: #999;
|
||
border: 1px solid #777;
|
||
}
|
||
|
||
/* Footer */
|
||
.footer {
|
||
text-align: center;
|
||
padding-top: 20px;
|
||
border-top: 3px solid #ccc;
|
||
margin-top: 25px;
|
||
color: #666;
|
||
font-size: 0.75rem;
|
||
}
|
||
|
||
.footer a {
|
||
color: var(--mc-grass);
|
||
text-decoration: none;
|
||
font-weight: bold;
|
||
}
|
||
|
||
.footer a:hover {
|
||
text-decoration: underline;
|
||
}
|
||
|
||
@media (max-width: 768px) {
|
||
.container-main {
|
||
padding: 20px;
|
||
}
|
||
|
||
.title-section h1 {
|
||
font-size: 2.2rem;
|
||
}
|
||
|
||
.stats-section {
|
||
grid-template-columns: repeat(2, 1fr);
|
||
}
|
||
|
||
.features-grid {
|
||
grid-template-columns: 1fr;
|
||
}
|
||
|
||
.visuals-container {
|
||
flex-wrap: wrap;
|
||
}
|
||
|
||
.visuals-container img {
|
||
max-width: 100%;
|
||
height: auto;
|
||
}
|
||
}
|
||
</style>
|
||
</head>
|
||
<body>
|
||
<div class="container-main">
|
||
<!-- Logo -->
|
||
<div align="center" style="padding: 30px 0;">
|
||
<img src="./images/logo.png" alt="MiniMind Logo" class="logo"/>
|
||
</div>
|
||
|
||
<!-- Badges -->
|
||
<div class="badge-container">
|
||
<img src="https://visitor-badge.laobi.icu/badge?page_id=jingyaogong/minimind" alt="Visitor Badge">
|
||
<img src="https://img.shields.io/github/stars/jingyaogong/minimind?style=social" alt="GitHub Stars">
|
||
<img src="https://img.shields.io/github/license/jingyaogong/minimind" alt="License">
|
||
<img src="https://img.shields.io/github/last-commit/jingyaogong/minimind" alt="Last Commit">
|
||
</div>
|
||
|
||
<!-- Title Section -->
|
||
<div class="title-section">
|
||
<p class="tagline pixel-font">
|
||
Train a 26M ChatBot from zero.<br/>
|
||
2 hours. ¥3. One 3090.<br/>
|
||
That's it.
|
||
</p>
|
||
</div>
|
||
|
||
<!-- Key Stats -->
|
||
<div class="stats-section">
|
||
<div class="stat-box">
|
||
<h3>26M</h3>
|
||
<p>Parameters</p>
|
||
</div>
|
||
<div class="stat-box">
|
||
<h3>2h</h3>
|
||
<p>Training</p>
|
||
</div>
|
||
<div class="stat-box">
|
||
<h3>¥3</h3>
|
||
<p>Cost</p>
|
||
</div>
|
||
<div class="stat-box">
|
||
<h3>1/7000</h3>
|
||
<p>vs GPT-3</p>
|
||
</div>
|
||
</div>
|
||
|
||
<!-- What You Get -->
|
||
<div class="section-header pixel-font">✨ What You Get</div>
|
||
<div class="features-grid">
|
||
<div class="feature-card">
|
||
<h3>💰 Ultra-Cheap</h3>
|
||
<p>One 3090. Zero to ChatBot in 2 hours, costing just ¥3.</p>
|
||
</div>
|
||
<div class="feature-card">
|
||
<h3>📦 Full Stack</h3>
|
||
<p>Complete pipeline: Tokenizer → Pretrain → SFT → LoRA → PPO/GRPO/SPO</p>
|
||
</div>
|
||
<div class="feature-card">
|
||
<h3>🔬 Latest RL</h3>
|
||
<p>PPO, GRPO, SPO + YaRN length extrapolation. Native PyTorch implementation.</p>
|
||
</div>
|
||
<div class="feature-card">
|
||
<h3>📖 Learn by Reading</h3>
|
||
<p>Pure PyTorch. No black boxes. Understand every line of code.</p>
|
||
</div>
|
||
<div class="feature-card">
|
||
<h3>🔌 Plug & Play</h3>
|
||
<p>Compatible with vLLM, ollama, llama.cpp, transformers.</p>
|
||
</div>
|
||
<div class="feature-card">
|
||
<h3>⚡ OpenAI API</h3>
|
||
<p>Drop-in replacement for FastGPT, Open-WebUI, Dify.</p>
|
||
</div>
|
||
</div>
|
||
|
||
<!-- Models -->
|
||
<div class="section-header pixel-font">📦 Models</div>
|
||
<div class="models-table">
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th>Model</th>
|
||
<th>Parameters</th>
|
||
<th>Hidden Dim</th>
|
||
<th>Layers</th>
|
||
<th>Memory</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td><strong>MiniMind2-Small</strong></td>
|
||
<td>26M</td>
|
||
<td>512</td>
|
||
<td>8</td>
|
||
<td>~0.5 GB</td>
|
||
</tr>
|
||
<tr>
|
||
<td><strong>MiniMind2</strong></td>
|
||
<td>104M</td>
|
||
<td>768</td>
|
||
<td>16</td>
|
||
<td>~1.0 GB</td>
|
||
</tr>
|
||
<tr>
|
||
<td><strong>MiniMind2-MoE</strong></td>
|
||
<td>145M</td>
|
||
<td>640</td>
|
||
<td>8</td>
|
||
<td>~1.0 GB</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</div>
|
||
|
||
<!-- What's New / Changelog -->
|
||
<div class="section-header pixel-font">📰 What's New</div>
|
||
<div class="changelog-section">
|
||
<div class="changelog-item open">
|
||
<div class="changelog-header open" onclick="toggleChangelog(this)">
|
||
<span>🔥 2025-10-24 (Latest)</span>
|
||
<span class="toggle-icon">−</span>
|
||
</div>
|
||
<div class="changelog-content">
|
||
<ul>
|
||
<li>🔥 RLAIF algorithms: PPO, GRPO, SPO (native PyTorch)</li>
|
||
<li>Checkpoint resume training: auto-save & cross-GPU recovery</li>
|
||
<li>RLAIF dataset: rlaif-mini.jsonl (10K samples); Simplified DPO dataset with Chinese data</li>
|
||
<li>YaRN algorithm for RoPE length extrapolation</li>
|
||
<li>Adaptive Thinking in reasoning models</li>
|
||
<li>Tool Calling & Reasoning tags support</li>
|
||
<li>Complete RLAIF chapter with training curves</li>
|
||
<li>SwanLab integration (WandB alternative for China)</li>
|
||
<li>Code standardization & bug fixes</li>
|
||
</ul>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="changelog-item">
|
||
<div class="changelog-header" onclick="toggleChangelog(this)">
|
||
<span>⚙️ 2025-04-26 (Major Refactor)</span>
|
||
<span class="toggle-icon">+</span>
|
||
</div>
|
||
<div class="changelog-content">
|
||
<ul>
|
||
<li>Model parameter renaming (align with Transformers)</li>
|
||
<li>Generate method refactor (GenerationMixin)</li>
|
||
<li>✅ llama.cpp, vllm, ollama support</li>
|
||
<li>Vocab update: <s></s> → <|im_start|><|im_end|></li>
|
||
<li>Code structure standardization</li>
|
||
</ul>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="changelog-item">
|
||
<div class="changelog-header" onclick="toggleChangelog(this)">
|
||
<span>🎉 2025-02-09 (MiniMind2 Release)</span>
|
||
<span class="toggle-icon">+</span>
|
||
</div>
|
||
<div class="changelog-content">
|
||
<ul>
|
||
<li>Complete codebase rewrite</li>
|
||
<li>MiniMind2 series: 26M, 104M, 145M models</li>
|
||
<li>JSONL data format (no preprocessing needed)</li>
|
||
<li>LoRA from scratch (no peft dependency)</li>
|
||
<li>DPO native PyTorch implementation</li>
|
||
<li>White-box model distillation</li>
|
||
<li>DeepSeek-R1 distillation models</li>
|
||
<li>HQ pretraining data (2h on single 3090)</li>
|
||
</ul>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="changelog-item">
|
||
<div class="changelog-header" onclick="toggleChangelog(this)">
|
||
<span>🎬 2024-10-05 (Vision Multimodal)</span>
|
||
<span class="toggle-icon">+</span>
|
||
</div>
|
||
<div class="changelog-content">
|
||
<ul>
|
||
<li>Vision multimodal support</li>
|
||
<li>MiniMind-V project launch</li>
|
||
</ul>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="changelog-item">
|
||
<div class="changelog-header" onclick="toggleChangelog(this)">
|
||
<span>🔧 2024-09-27 (Data Preprocessing)</span>
|
||
<span class="toggle-icon">+</span>
|
||
</div>
|
||
<div class="changelog-content">
|
||
<ul>
|
||
<li>Pretrain dataset preprocessing update</li>
|
||
<li>Text integrity preservation</li>
|
||
<li>Code cleanup</li>
|
||
</ul>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="changelog-item">
|
||
<div class="changelog-header" onclick="toggleChangelog(this)">
|
||
<span>📝 2024-09-17 (MoE & Tokenizer)</span>
|
||
<span class="toggle-icon">+</span>
|
||
</div>
|
||
<div class="changelog-content">
|
||
<ul>
|
||
<li>MiniMind-V1-MoE model release</li>
|
||
<li>Standardized minimind_tokenizer</li>
|
||
<li>Removed mistral_tokenizer variants</li>
|
||
</ul>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="changelog-item">
|
||
<div class="changelog-header" onclick="toggleChangelog(this)">
|
||
<span>🚀 2024-09-01 (V1 Release)</span>
|
||
<span class="toggle-icon">+</span>
|
||
</div>
|
||
<div class="changelog-content">
|
||
<ul>
|
||
<li>MiniMind-V1 (108M) release</li>
|
||
<li>3 epochs pretrain + 10 epochs SFT</li>
|
||
<li>ModelScope online demo</li>
|
||
</ul>
|
||
</div>
|
||
</div>
|
||
|
||
<div class="changelog-item">
|
||
<div class="changelog-header" onclick="toggleChangelog(this)">
|
||
<span>⭐ 2024-08-27 (First Release)</span>
|
||
<span class="toggle-icon">+</span>
|
||
</div>
|
||
<div class="changelog-content">
|
||
<ul>
|
||
<li>🚀 First open-source release</li>
|
||
<li>MiniMind project launched</li>
|
||
</ul>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
<!-- Visuals -->
|
||
<div class="section-header pixel-font">🎮 Inside MiniMind</div>
|
||
<div align="center">
|
||
<div class="visuals-container">
|
||
<img src="./images/minimind2.gif" alt="Streamlit Demo">
|
||
<img src="./images/LLM-structure.png" alt="LLM Structure">
|
||
<img src="./images/LLM-structure-moe.png" alt="LLM Structure MOE">
|
||
</div>
|
||
</div>
|
||
|
||
<!-- Try It Links -->
|
||
<div class="links-section">
|
||
<h2 class="pixel-font">🎯 Try It Online</h2>
|
||
<div class="links-grid">
|
||
<a href="https://www.modelscope.cn/studios/gongjy/MiniMind-Reasoning" class="pixel-button">
|
||
🍓 Reasoning Model
|
||
</a>
|
||
<a href="https://www.modelscope.cn/studios/gongjy/MiniMind" class="pixel-button">
|
||
💬 Chat Model
|
||
</a>
|
||
<a href="https://www.bilibili.com/video/BV12dHPeqE72/" class="pixel-button">
|
||
📹 Video Demo
|
||
</a>
|
||
</div>
|
||
|
||
<div style="border-top: 3px solid rgba(255,255,255,.3); margin: 25px 0;"></div>
|
||
|
||
<h2 class="pixel-font">📦 Get the Code</h2>
|
||
<div class="links-grid">
|
||
<a href="https://github.com/jingyaogong/minimind" class="pixel-button">
|
||
GitHub
|
||
</a>
|
||
<a href="https://huggingface.co/collections/jingyaogong/minimind-66caf8d999f5c7fa64f399e5" class="pixel-button">
|
||
🤗 HuggingFace
|
||
</a>
|
||
<a href="https://www.modelscope.cn/profile/gongjy" class="pixel-button">
|
||
ModelScope
|
||
</a>
|
||
</div>
|
||
</div>
|
||
|
||
<!-- Why MiniMind -->
|
||
<div class="why-section">
|
||
<h2>💡 Why MiniMind?</h2>
|
||
<ul>
|
||
<li>Train from zero, not just finetune</li>
|
||
<li>Pure PyTorch—no magic, no black boxes</li>
|
||
<li>Understand by building, not by reading docs</li>
|
||
<li>Works on your laptop—no cloud GPUs needed</li>
|
||
<li>2025 RLAIF algorithms: PPO, GRPO, SPO</li>
|
||
<li>OpenAI API compatible—plug into any UI</li>
|
||
<li>Vision support via MiniMind-V</li>
|
||
<li>Code you can actually read and modify</li>
|
||
</ul>
|
||
<p class="quote">
|
||
💭 "Building a Lego plane beats flying first class."
|
||
</p>
|
||
</div>
|
||
|
||
<!-- Footer -->
|
||
<div class="footer">
|
||
<p>
|
||
Timings based on single 3090. YMMV.<br/>
|
||
Apache-2.0 License ·
|
||
<a href="https://github.com/jingyaogong/minimind">GitHub</a>
|
||
</p>
|
||
</div>
|
||
</div>
|
||
|
||
<script>
|
||
function toggleChangelog(element) {
|
||
const item = element.parentElement;
|
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item.classList.toggle('open');
|
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const icon = element.querySelector('.toggle-icon');
|
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if (item.classList.contains('open')) {
|
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icon.textContent = '−';
|
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} else {
|
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icon.textContent = '+';
|
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|
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}
|
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|
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|
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<script src="https://cdn.jsdelivr.net/npm/@popperjs/core@2.11.6/dist/umd/popper.min.js"></script>
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|
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