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--- |
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license: mit |
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base_model: |
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- zerofata/MS3.2-PaintedFantasy-Visage-v4-34B |
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--- |
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<style> |
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@keyframes fallingleaf { |
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0% { transform: translateY(-20px) rotate(0deg); opacity: 1; } |
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100% { transform: translateY(100vh) rotate(360deg); opacity: 0.3; } |
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} |
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.container { |
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--primary-accent: #7BC4C4; |
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--secondary-accent: #9FD4D4; |
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--accent-warm: #E8A5B5; |
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--red-deep: #2C5F6F; |
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--gold-bright: #B4DDD4; |
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--ink-dark: #000000; |
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--bg-main: #0A1A1A; |
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--bg-container: #0F2525; |
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--bg-card: rgba(15, 30, 35, 0.95); |
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--text-main: #D5E8E8; |
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--text-muted: #9CB9C4; |
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--white: #FFFFFF; |
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--border-color: #6B9BAA; |
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--font-title: 'Inter', sans-serif; |
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--font-body: 'Source Sans Pro', sans-serif; |
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--font-code: 'JetBrains Mono', monospace; |
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font-family: var(--font-body); |
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color: var(--text-main); |
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line-height: 1.7; |
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font-weight: 400; |
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max-width: 1100px; |
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margin: 20px auto; |
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padding: 50px 40px; |
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background-color: var(--bg-main); |
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background-image: |
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radial-gradient(ellipse at top, rgba(20, 45, 60, 0.3), transparent 60%), |
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radial-gradient(ellipse at bottom, rgba(0, 0, 0, 0.8), transparent 50%), |
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linear-gradient(180deg, |
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rgba(15, 30, 40, 0.6) 0%, |
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rgba(12, 25, 35, 0.7) 25%, |
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rgba(10, 20, 28, 0.8) 50%, |
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rgba(8, 16, 22, 0.85) 75%, |
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rgba(6, 12, 18, 0.9) 100%); |
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min-height: calc(100vh - 40px); |
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position: relative; |
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z-index: 2; |
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border-radius: 3px; |
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box-shadow: |
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0 10px 60px rgba(0, 0, 0, 0.8), |
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0 2px 10px rgba(0, 0, 0, 0.9), |
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inset 0 0 120px rgba(20, 40, 55, 0.1); |
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border: 2px solid rgba(30, 60, 75, 0.4); |
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} |
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.container::before { |
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content: ''; |
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position: absolute; |
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top: 0; |
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left: 0; |
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right: 0; |
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bottom: 0; |
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background-image: |
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radial-gradient(circle 3px at 15% 12%, rgba(123, 196, 196, 0.4) 0%, transparent 100%), |
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radial-gradient(circle 2px at 25% 18%, rgba(159, 212, 212, 0.35) 0%, transparent 100%), |
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radial-gradient(circle 4px at 45% 25%, rgba(232, 165, 181, 0.4) 0%, transparent 100%), |
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radial-gradient(circle 3px at 65% 15%, rgba(123, 196, 196, 0.4) 0%, transparent 100%), |
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radial-gradient(circle 2px at 85% 20%, rgba(196, 240, 234, 0.35) 0%, transparent 100%), |
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radial-gradient(circle 3px at 35% 35%, rgba(232, 165, 181, 0.35) 0%, transparent 100%), |
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radial-gradient(circle 2px at 75% 40%, rgba(180, 221, 221, 0.35) 0%, transparent 100%), |
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radial-gradient(circle 4px at 20% 50%, rgba(123, 196, 196, 0.45) 0%, transparent 100%), |
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radial-gradient(circle 3px at 90% 55%, rgba(196, 240, 234, 0.4) 0%, transparent 100%), |
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radial-gradient(circle 2px at 50% 60%, rgba(232, 165, 181, 0.3) 0%, transparent 100%); |
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pointer-events: none; |
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border-radius: 3px; |
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} |
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.container .title-container { |
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background: |
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radial-gradient(circle at 15% 25%, rgba(30, 65, 80, 0.4) 0%, transparent 40%), |
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radial-gradient(circle at 85% 70%, rgba(123, 196, 196, 0.2) 0%, transparent 45%), |
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radial-gradient(circle at 50% 50%, rgba(20, 45, 60, 0.35) 0%, transparent 60%), |
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linear-gradient(135deg, |
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rgba(20, 45, 60, 0.3) 0%, |
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rgba(15, 35, 50, 0.4) 50%, |
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rgba(12, 28, 40, 0.5) 100%); |
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margin-bottom: 50px; |
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border: 3px solid; |
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border-image: linear-gradient(135deg, |
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rgba(123, 196, 196, 0.5) 0%, |
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rgba(159, 212, 212, 0.6) 50%, |
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rgba(123, 196, 196, 0.5) 100%) 1; |
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border-radius: 0; |
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padding: 60px 50px 70px; |
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text-align: center; |
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position: relative; |
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overflow: hidden; |
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box-shadow: |
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0 10px 50px rgba(0, 0, 0, 0.7), |
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0 4px 20px rgba(0, 0, 0, 0.8), |
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inset 0 2px 8px rgba(123, 196, 196, 0.15); |
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} |
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.container .title-container::before { |
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content: ''; |
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position: absolute; |
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top: -50%; |
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right: -10%; |
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width: 300px; |
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height: 300px; |
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background: radial-gradient(circle, rgba(44, 95, 111, 0.3) 0%, transparent 70%); |
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border-radius: 40% 60% 70% 30%; |
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filter: blur(40px); |
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} |
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.container .title-container::after { |
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content: ''; |
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position: absolute; |
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bottom: -30%; |
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left: -5%; |
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width: 250px; |
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height: 250px; |
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background: radial-gradient(circle, rgba(232, 165, 181, 0.2) 0%, transparent 70%); |
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border-radius: 60% 40% 30% 70%; |
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filter: blur(35px); |
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} |
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.container .title-container .title-wrapper { |
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position: relative; |
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} |
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.container .title-main { |
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font-size: 3.5rem; |
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font-weight: 800; |
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margin: 0; |
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letter-spacing: 6px; |
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text-transform: uppercase; |
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font-family: var(--font-title); |
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position: relative; |
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line-height: 1.2; |
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z-index: 1; |
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} |
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.container .title-main .title-prefix { |
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display: block; |
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background: linear-gradient(135deg, #6EC5C5 0%, #E8A5B5 35%, #9FD4D4 70%, #B4E8DD 100%); |
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background-clip: text; |
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-webkit-background-clip: text; |
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-webkit-text-fill-color: transparent; |
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filter: drop-shadow(0 3px 12px rgba(110, 197, 197, 0.6)) drop-shadow(0 0 20px rgba(232, 165, 181, 0.4)); |
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margin-bottom: 15px; |
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font-size: 3.5rem; |
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letter-spacing: 6px; |
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font-weight: 700; |
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position: relative; |
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padding-bottom: 12px; |
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font-family: 'Cinzel', serif; |
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text-transform: uppercase; |
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} |
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.container .title-main .title-prefix::after { |
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content: ''; |
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position: absolute; |
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bottom: 0; |
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left: 50%; |
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transform: translateX(-50%); |
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width: 80px; |
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height: 4px; |
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background: linear-gradient(90deg, |
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transparent 0%, |
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rgba(159, 212, 212, 0.8) 35%, |
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rgba(232, 165, 181, 0.8) 65%, |
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transparent 100%); |
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border-radius: 2px; |
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box-shadow: 0 0 10px rgba(232, 165, 181, 0.5); |
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} |
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.container .title-main .title-version { |
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display: inline-block; |
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background: linear-gradient(135deg, #8DD4D4 0%, #A8E0E0 50%, #C4F0EA 100%); |
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background-clip: text; |
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-webkit-background-clip: text; |
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-webkit-text-fill-color: transparent; |
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filter: drop-shadow(0 2px 8px rgba(141, 212, 212, 0.6)) drop-shadow(0 0 15px rgba(168, 224, 224, 0.5)); |
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font-size: 2.5rem; |
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letter-spacing: 4px; |
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font-weight: 700; |
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position: relative; |
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padding: 5px 15px; |
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border: 2px solid transparent; |
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border-image: linear-gradient(135deg, |
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rgba(141, 212, 212, 0.4), |
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rgba(168, 224, 224, 0.6), |
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rgba(141, 212, 212, 0.4)) 1; |
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font-family: 'Cinzel', serif; |
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text-transform: uppercase; |
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} |
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.container .title-main .title-version::before { |
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content: ''; |
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position: absolute; |
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top: -8px; |
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left: -8px; |
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right: -8px; |
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bottom: -8px; |
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background: linear-gradient(135deg, rgba(141, 212, 212, 0.15), rgba(44, 95, 111, 0.1)); |
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z-index: -1; |
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transform: skew(-2deg); |
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} |
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.container .lemonade-text { |
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background: linear-gradient(135deg, var(--red-deep), var(--primary-accent)); |
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background-clip: text; |
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-webkit-background-clip: text; |
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-webkit-text-fill-color: transparent; |
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filter: drop-shadow(0 2px 6px rgba(232, 107, 142, 0.6)); |
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} |
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.container .title-subtitle { |
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padding-left: 0; |
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margin-top: 15px; |
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} |
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.container .subtitle-text { |
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color: var(--text-muted); |
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font-size: 1.2rem; |
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font-family: var(--font-body); |
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font-style: italic; |
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font-weight: 400; |
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letter-spacing: 2px; |
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text-transform: uppercase; |
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opacity: 0.9; |
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text-shadow: 0 0 10px rgba(159, 212, 212, 0.3); |
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} |
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.container img { |
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max-width: 100%; |
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border: 2px solid rgba(44, 95, 111, 0.5); |
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margin-bottom: 40px; |
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box-shadow: |
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0 8px 30px rgba(0, 0, 0, 0.7), |
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0 3px 12px rgba(123, 196, 196, 0.4), |
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0 0 20px rgba(232, 165, 181, 0.3); |
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border-radius: 2px; |
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position: relative; |
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} |
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.container img::after { |
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content: ''; |
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position: absolute; |
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inset: -5px; |
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background: linear-gradient(135deg, |
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rgba(123, 196, 196, 0.2), |
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rgba(232, 165, 181, 0.15)); |
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filter: blur(8px); |
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z-index: -1; |
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} |
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.container .section-container { |
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margin-bottom: 35px; |
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padding: 30px; |
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background: linear-gradient(135deg, |
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rgba(25, 55, 70, 0.85) 0%, |
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rgba(30, 65, 80, 0.9) 50%, |
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rgba(25, 55, 70, 0.85) 100%); |
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border: 2px solid transparent; |
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border-image: linear-gradient(135deg, |
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rgba(123, 196, 196, 0.6), |
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rgba(159, 212, 212, 0.7), |
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rgba(123, 196, 196, 0.6)) 1; |
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border-radius: 0; |
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box-shadow: |
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0 8px 35px rgba(0, 0, 0, 0.7), |
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inset 0 2px 8px rgba(123, 196, 196, 0.3), |
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0 0 25px rgba(123, 196, 196, 0.3), |
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inset 0 -1px 20px rgba(159, 212, 212, 0.12); |
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position: relative; |
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} |
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.container .section-container::before { |
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content: ''; |
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position: absolute; |
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top: 0; |
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left: 0; |
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right: 0; |
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bottom: 0; |
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background: |
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radial-gradient(circle at 20% 30%, rgba(123, 196, 196, 0.25) 0%, transparent 50%), |
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radial-gradient(circle at 80% 70%, rgba(159, 212, 212, 0.15) 0%, transparent 50%), |
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radial-gradient(circle at 50% 10%, rgba(232, 165, 181, 0.1) 0%, transparent 60%); |
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pointer-events: none; |
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z-index: 0; |
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} |
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.container .section-container:last-of-type { |
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margin-bottom: 0; |
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} |
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.container .section-header { |
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display: flex; |
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align-items: center; |
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padding: 0 0 18px 0; |
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border-bottom: 2px solid rgba(123, 196, 196, 0.4); |
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margin-bottom: 25px; |
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position: relative; |
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z-index: 1; |
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} |
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.container .section-title { |
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font-family: var(--font-title); |
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background: linear-gradient(135deg, |
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#9FD4D4 0%, |
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#B4E8DD 100%); |
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background-clip: text; |
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-webkit-background-clip: text; |
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-webkit-text-fill-color: transparent; |
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font-size: 1.5rem; |
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margin: 0 !important; |
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padding: 0 !important; |
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letter-spacing: 2px; |
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font-weight: 700; |
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text-transform: uppercase; |
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border: none !important; |
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position: relative; |
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display: inline-block; |
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filter: drop-shadow(0 2px 4px rgba(123, 196, 196, 0.5)); |
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} |
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.container .section-title::after { |
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content: ''; |
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position: absolute; |
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bottom: -18px; |
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left: 0; |
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width: 50%; |
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height: 2px; |
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background-image: linear-gradient(to right, |
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rgba(159, 212, 212, 0.7), |
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transparent); |
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opacity: 0.8; |
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} |
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.container .section-content { |
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padding: 0; |
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position: relative; |
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z-index: 1; |
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} |
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.container .subheading { |
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color: var(--secondary-accent); |
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font-size: 1.25rem; |
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margin-top: 28px; |
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margin-bottom: 18px; |
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font-weight: 600; |
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display: block; |
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text-transform: uppercase; |
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letter-spacing: 1.5px; |
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font-family: var(--font-title); |
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border-bottom: 2px solid transparent; |
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border-image: linear-gradient(to right, |
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var(--primary-accent), |
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var(--secondary-accent), |
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transparent) 1; |
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padding-bottom: 10px; |
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text-shadow: 0 0 10px rgba(159, 212, 212, 0.4); |
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} |
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.container .data-box { |
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background: linear-gradient(135deg, |
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rgba(20, 40, 50, 0.6) 0%, |
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rgba(25, 50, 60, 0.7) 100%); |
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padding: 22px; |
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border: 2px solid rgba(107, 155, 170, 0.3); |
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border-left: 5px solid var(--primary-accent); |
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margin-bottom: 20px; |
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box-shadow: |
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0 4px 15px rgba(0, 0, 0, 0.5), |
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inset 0 1px 2px rgba(44, 95, 111, 0.3), |
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0 0 10px rgba(232, 107, 142, 0.15); |
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border-radius: 0; |
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font-size: 1rem; |
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position: relative; |
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} |
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.container .data-box::after { |
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content: ''; |
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position: absolute; |
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top: 0; |
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right: 0; |
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width: 100px; |
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height: 100px; |
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background: radial-gradient(circle at top right, rgba(123, 196, 196, 0.08) 0%, transparent 60%); |
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pointer-events: none; |
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border-radius: 0 0 0 100%; |
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} |
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.container .data-row { |
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display: flex; |
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align-items: center; |
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margin-bottom: 6px; |
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padding: 5px 0; |
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} |
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.container .data-row:last-child { |
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margin-bottom: 0; |
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} |
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.container .data-arrow { |
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color: var(--secondary-accent); |
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font-weight: bold; |
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margin-right: 12px; |
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font-family: var(--font-code); |
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font-size: 1.1rem; |
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text-shadow: 0 0 8px rgba(159, 212, 212, 0.5); |
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} |
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.container .data-label { |
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color: var(--text-main); |
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font-weight: 600; |
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font-family: var(--font-body); |
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margin-right: 10px; |
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min-width: 90px; |
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} |
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.container a { |
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color: var(--primary-accent); |
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text-decoration: none; |
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font-weight: 600; |
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transition: all .2s; |
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text-shadow: 0 0 10px rgba(232, 107, 142, 0.4); |
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} |
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.container .data-row a { |
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border-bottom: 1px dotted var(--primary-accent); |
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} |
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.container a:hover { |
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text-decoration: none; |
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color: var(--secondary-accent); |
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transform: translateY(-1px); |
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filter: drop-shadow(0 2px 6px rgba(159, 212, 212, 0.6)); |
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text-shadow: 0 0 15px rgba(159, 212, 212, 0.8); |
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} |
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.container .data-row a:hover { |
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border-bottom-style: solid; |
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border-bottom-color: var(--secondary-accent); |
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} |
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.container .dropdown-container { |
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margin-top: 20px; |
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} |
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.container .dropdown-summary { |
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cursor: pointer; |
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padding: 10px 0; |
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color: var(--text-muted); |
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|
font-size: 1.1rem; |
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|
font-weight: 700; |
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text-transform: none; |
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font-family: var(--font-title); |
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letter-spacing: 1px; |
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list-style: none; |
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transition: color 0.2s ease; |
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} |
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.container .dropdown-summary:hover { |
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color: var(--primary-accent); |
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} |
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.container .dropdown-arrow { |
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color: var(--primary-accent); |
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margin-right: 10px; |
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transition: transform 0.2s ease; |
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} |
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.container .dropdown-content { |
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margin-top: 15px; |
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padding: 25px; |
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background: linear-gradient(135deg, |
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rgba(15, 30, 35, 0.8) 0%, |
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rgba(20, 40, 50, 0.85) 100%); |
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border: 2px solid rgba(107, 155, 170, 0.3); |
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border-radius: 0; |
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box-shadow: |
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0 4px 15px rgba(0, 0, 0, 0.6), |
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inset 0 1px 3px rgba(44, 95, 111, 0.3), |
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0 0 10px rgba(232, 107, 142, 0.15); |
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} |
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.container .config-title { |
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color: var(--secondary-accent); |
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font-size: 1rem; |
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margin-bottom: 10px; |
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font-family: var(--font-body); |
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text-transform: uppercase; |
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letter-spacing: 1px; |
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font-weight: 700; |
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text-shadow: 0 0 10px rgba(159, 212, 212, 0.4); |
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} |
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.container pre { |
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background: linear-gradient(135deg, |
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rgba(20, 5, 5, 0.9) 0%, |
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rgba(30, 8, 8, 0.95) 100%); |
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padding: 22px; |
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border: 2px solid rgba(139, 0, 0, 0.3); |
|
|
white-space: pre-wrap; |
|
|
word-wrap: break-word; |
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color: var(--text-main); |
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border-radius: 0; |
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box-shadow: |
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|
inset 0 2px 6px rgba(0, 0, 0, 0.5), |
|
|
0 4px 12px rgba(0, 0, 0, 0.6); |
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} |
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.container pre code { |
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background: none; |
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color: inherit; |
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|
padding: 0; |
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border-radius: 0; |
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} |
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.container code { |
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font-family: var(--font-code); |
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|
color: var(--secondary-accent); |
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|
background: rgba(159, 212, 212, 0.15); |
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padding: 3px 7px; |
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border-radius: 2px; |
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|
box-shadow: 0 1px 3px rgba(159, 212, 212, 0.3); |
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|
} |
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body { |
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|
background: linear-gradient(180deg, |
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|
#0A1F2E 0%, |
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#0D2838 15%, |
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#102D3D 30%, |
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#0D2838 50%, |
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#0A1F2E 70%, |
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|
#071520 85%, |
|
|
#040D15 100%); |
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|
background-attachment: fixed; |
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margin: 0; |
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padding: 0; |
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position: relative; |
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|
overflow-x: hidden; |
|
|
} |
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body::before { |
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content: ''; |
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position: fixed; |
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left: -10%; |
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bottom: 0; |
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width: 25%; |
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height: 100%; |
|
|
background-image: |
|
|
radial-gradient(ellipse 80px 200px at 20% 90%, rgba(0, 0, 0, 0.95), transparent), |
|
|
radial-gradient(ellipse 100px 300px at 15% 85%, rgba(0, 0, 0, 0.9), transparent), |
|
|
radial-gradient(ellipse 60px 250px at 25% 88%, rgba(0, 0, 0, 0.92), transparent), |
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|
radial-gradient(ellipse 70px 180px at 10% 92%, rgba(0, 0, 0, 0.93), transparent), |
|
|
linear-gradient(to top, rgba(0, 0, 0, 0.8) 0%, transparent 60%); |
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pointer-events: none; |
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z-index: 1; |
|
|
} |
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body::after { |
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content: ''; |
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position: fixed; |
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right: -10%; |
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bottom: 0; |
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width: 25%; |
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height: 100%; |
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background-image: |
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radial-gradient(ellipse 90px 220px at 80% 88%, rgba(0, 0, 0, 0.95), transparent), |
|
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radial-gradient(ellipse 110px 320px at 85% 83%, rgba(0, 0, 0, 0.9), transparent), |
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radial-gradient(ellipse 65px 260px at 75% 86%, rgba(0, 0, 0, 0.92), transparent), |
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radial-gradient(ellipse 80px 190px at 90% 90%, rgba(0, 0, 0, 0.93), transparent), |
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linear-gradient(to top, rgba(0, 0, 0, 0.8) 0%, transparent 60%); |
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pointer-events: none; |
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z-index: 1; |
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|
} |
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.container p { |
|
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position: relative; |
|
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z-index: 1; |
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|
} |
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</style> |
|
|
<html lang="en"> |
|
|
<head> |
|
|
<meta charset="UTF-8"> |
|
|
<meta name="viewport" content="width=device-width, initial-scale=1.0"> |
|
|
<title>PaintedFantasy-Visage-v4</title> |
|
|
<link rel="preconnect" href="https://fonts.googleapis.com"> |
|
|
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin> |
|
|
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700;800&family=Source+Sans+Pro:ital,wght@0,400;0,600;1,400&family=JetBrains+Mono:wght@400;500&family=Cinzel:wght@400;600;700&display=swap" rel="stylesheet"> |
|
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</head> |
|
|
<body> |
|
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|
|
|
<div class="container"> |
|
|
<div class="title-container"> |
|
|
<div class="title-wrapper"> |
|
|
<h1 class="title-main"> |
|
|
<span class="title-prefix">Painted Fantasy</span> |
|
|
<span class="title-version">Visage v4</span> |
|
|
</h1> |
|
|
</div> |
|
|
</div> |
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|
 |
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|
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<div class="section-container"> |
|
|
<div class="section-header"> |
|
|
<div class="section-indicator"></div> |
|
|
<h2 class="section-title">Overview</h2> |
|
|
</div> |
|
|
<div class="section-content"> |
|
|
<p>Magistral 24B Upscaled to 34B.</p> |
|
|
<p>The latest Magistral model seems pretty good. Has some refreshing prose.</p> |
|
|
<p>This model is an uncensored, creative writing and RP model. It uses a new (still a work in progress) dataset I've been curating based on real character cards.</p> |
|
|
<p>Has some structural repetition, at this point it's a calling card of Mistral models. I think it's better than v3 though.</p> |
|
|
</div> |
|
|
</div> |
|
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|
|
|
<div class="section-container"> |
|
|
<div class="section-header"> |
|
|
<div class="section-indicator"></div> |
|
|
<h2 class="section-title">SillyTavern Settings</h2> |
|
|
</div> |
|
|
<div class="section-content"> |
|
|
<h3 class="subheading">Recommended Roleplay Format</h3> |
|
|
<div class="data-box"> |
|
|
<div class="data-row"> |
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|
<span class="data-arrow">></span> |
|
|
<span class="data-label">Actions:</span> |
|
|
<span>In plaintext</span> |
|
|
</div> |
|
|
<div class="data-row"> |
|
|
<span class="data-arrow">></span> |
|
|
<span class="data-label">Dialogue:</span> |
|
|
<span>"In quotes"</span> |
|
|
</div> |
|
|
<div class="data-row"> |
|
|
<span class="data-arrow">></span> |
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|
<span class="data-label">Thoughts:</span> |
|
|
<span>*In asterisks*</span> |
|
|
</div> |
|
|
</div> |
|
|
<h3 class="subheading">Recommended Samplers</h3> |
|
|
<div class="data-box"> |
|
|
<div class="data-row"> |
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|
<span class="data-arrow">></span> |
|
|
<span class="data-label">Temp:</span> |
|
|
<span>0.8</span> |
|
|
</div> |
|
|
<div class="data-row"> |
|
|
<span class="data-arrow">></span> |
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|
<span class="data-label">MinP:</span> |
|
|
<span>0.05</span> |
|
|
</div> |
|
|
<div class="data-row"> |
|
|
<span class="data-arrow">></span> |
|
|
<span class="data-label">TopP:</span> |
|
|
<span>0.95</span> |
|
|
</div> |
|
|
</div> |
|
|
<h3 class="subheading">Instruct</h3> |
|
|
<div class="data-box"> |
|
|
<p style="margin: 0;">Mistral v7 Tekken</p> |
|
|
</div> |
|
|
</div> |
|
|
</div> |
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|
|
<div class="section-container"> |
|
|
<div class="section-header"> |
|
|
<div class="section-indicator"></div> |
|
|
<h2 class="section-title">Quantizations</h2> |
|
|
</div> |
|
|
<div class="section-content"> |
|
|
<div style="margin-bottom: 20px;"> |
|
|
<h3 class="subheading">EXL3</h3> |
|
|
<div class="data-box"> |
|
|
<div class="data-row"> |
|
|
<span class="data-arrow">></span> |
|
|
<a href="https://huggingface.co/zerofata/MS3.2-PaintedFantasy-Visage-v4-34b-exl3-3bpw">3bpw</a> |
|
|
</div> |
|
|
<div class="data-row"> |
|
|
<span class="data-arrow">></span> |
|
|
<a href="https://huggingface.co/zerofata/MS3.2-PaintedFantasy-Visage-v4-34b-exl3-4bpw">4bpw</a> |
|
|
</div> |
|
|
<div class="data-row"> |
|
|
<span class="data-arrow">></span> |
|
|
<a href="https://huggingface.co/zerofata/MS3.2-PaintedFantasy-Visage-v4-34b-exl3-5bpw">5bpw</a> |
|
|
</div> |
|
|
<div class="data-row"> |
|
|
<span class="data-arrow">></span> |
|
|
<a href="https://huggingface.co/zerofata/MS3.2-PaintedFantasy-Visage-v4-34b-exl3-6bpw">6bpw</a> |
|
|
</div> |
|
|
</div> |
|
|
</div> |
|
|
</div> |
|
|
<div class="section-content"> |
|
|
<div style="margin-bottom: 20px;"> |
|
|
<h3 class="subheading">GGUF</h3> |
|
|
<div class="data-box"> |
|
|
<div class="data-row"> |
|
|
<span class="data-arrow">></span> |
|
|
<a href="https://huggingface.co/mradermacher/MS3.2-PaintedFantasy-Visage-v4-34B-i1-GGUF">iMatrix (mradermacher)</a> |
|
|
</div> |
|
|
</div> |
|
|
</div> |
|
|
</div> |
|
|
</div> |
|
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|
|
|
<div class="section-container"> |
|
|
<div class="section-header"> |
|
|
<div class="section-indicator"></div> |
|
|
<h2 class="section-title">Creation Process</h2> |
|
|
</div> |
|
|
<div class="section-content"> |
|
|
<p>Creation Process: Upscale > CPT > SFT > Merge</p> |
|
|
<p>After upscaling, was pretrained on approx 100MB of light novels and a subset of DCLM records.</p> |
|
|
<p>SFT on approx 10 million tokens, SFW / NSFW RP, stories and creative instruct. I've removed some chat data which I think hurt more than helped and replaced it with conversations from real character cards.</p> |
|
|
<p>Did some experimenting with lora methods. Particularly dora vs rslora. With dora the writing was fantastic, but the model wasn't able to handle its own creativity, even with further RLHF applied. Rslora took the data far better, but was significantly less adept at writing. Merged the two models together, using the stable version as a base, which seems to have successfully combined the positives of both models.</p> |
|
|
<div class="dropdown-container"> |
|
|
<details> |
|
|
<summary class="dropdown-summary"> |
|
|
<span class="dropdown-arrow">></span> |
|
|
Mergekit configs |
|
|
</summary> |
|
|
<div class="dropdown-content"> |
|
|
<div class="config-title">Upscale</div> |
|
|
<pre><code>base_model: Darkhn/Magistral-Small-2509-Text-Only |
|
|
merge_method: passthrough |
|
|
dtype: bfloat16 |
|
|
slices: |
|
|
- sources: |
|
|
- model: Darkhn/Magistral-Small-2509-Text-Only |
|
|
layer_range: [0, 29] |
|
|
- sources: |
|
|
- model: Darkhn/Magistral-Small-2509-Text-Only |
|
|
layer_range: [10, 40]</code></pre> |
|
|
<div class="config-title">Slerp Merge</div> |
|
|
<pre><code>models: |
|
|
- model: ApocalypseParty/Magi-PT-2-SFT-1-DPO-3 |
|
|
- model: ApocalypseParty/Magi-PT-2-SFT-2 |
|
|
merge_method: slerp |
|
|
base_model: ApocalypseParty/Magi-PT-2-SFT-2 |
|
|
parameters: |
|
|
t: [0, 0, 0, 0.1, 0.2] |
|
|
dtype: bfloat16</code></pre> |
|
|
</div> |
|
|
</details> |
|
|
</div> |
|
|
<div class="dropdown-container"> |
|
|
<details> |
|
|
<summary class="dropdown-summary"> |
|
|
<span class="dropdown-arrow">></span> |
|
|
Axolotl configs |
|
|
</summary> |
|
|
<div class="dropdown-content"> |
|
|
<div class="config-title">Pretrain (2*H100)</div> |
|
|
<pre><code># ==================== |
|
|
# MODEL CONFIGURATION |
|
|
# ==================== |
|
|
base_model: ApocalypseParty/magistral-34b |
|
|
model_type: MistralForCausalLM |
|
|
tokenizer_type: AutoTokenizer |
|
|
chat_template: mistral_v7_tekken |
|
|
# ==================== |
|
|
# DATASET CONFIGURATION |
|
|
# ==================== |
|
|
datasets: |
|
|
- path: ./data/text_files_minimal_dataset.jsonl |
|
|
type: completion |
|
|
- path: ./data/filtered_results.jsonl |
|
|
type: completion |
|
|
<br> |
|
|
dataset_prepared_path: |
|
|
train_on_inputs: false # Only train on assistant responses |
|
|
<br> |
|
|
# ==================== |
|
|
# QLORA CONFIGURATION |
|
|
# ==================== |
|
|
adapter: qlora |
|
|
load_in_4bit: true |
|
|
lora_r: 64 |
|
|
lora_alpha: 64 |
|
|
lora_dropout: 0.0 |
|
|
lora_target_linear: true |
|
|
# lora_modules_to_save: # Uncomment only if you added NEW tokens |
|
|
<br> |
|
|
# ==================== |
|
|
# TRAINING PARAMETERS |
|
|
# ==================== |
|
|
num_epochs: 1 |
|
|
micro_batch_size: 4 |
|
|
gradient_accumulation_steps: 1 |
|
|
learning_rate: 3e-5 |
|
|
optimizer: paged_adamw_8bit |
|
|
lr_scheduler: rex |
|
|
warmup_ratio: 0.05 |
|
|
weight_decay: 0.0 |
|
|
max_grad_norm: 1.0 |
|
|
<br> |
|
|
# ==================== |
|
|
# SEQUENCE & PACKING |
|
|
# ==================== |
|
|
sequence_len: 16384 |
|
|
sample_packing: true |
|
|
eval_sample_packing: false |
|
|
pad_to_sequence_len: true |
|
|
<br> |
|
|
# ==================== |
|
|
# HARDWARE OPTIMIZATIONS |
|
|
# ==================== |
|
|
bf16: auto |
|
|
flash_attention: true |
|
|
gradient_checkpointing: offload |
|
|
deepspeed: deepspeed_configs/zero1.json |
|
|
<br> |
|
|
plugins: |
|
|
- axolotl.integrations.liger.LigerPlugin |
|
|
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin |
|
|
cut_cross_entropy: true |
|
|
liger_rope: true |
|
|
liger_rms_norm: true |
|
|
liger_layer_norm: true |
|
|
liger_glu_activation: true |
|
|
liger_cross_entropy: false # Cut Cross Entropy overrides this |
|
|
liger_fused_linear_cross_entropy: false # Cut Cross Entropy overrides this |
|
|
<br> |
|
|
# ==================== |
|
|
# EVALUATION & CHECKPOINTING |
|
|
# ==================== |
|
|
save_strategy: steps |
|
|
save_steps: 40 |
|
|
save_total_limit: 5 # Keep best + last few checkpoints |
|
|
load_best_model_at_end: true |
|
|
greater_is_better: false |
|
|
<br> |
|
|
# ==================== |
|
|
# LOGGING & OUTPUT |
|
|
# ==================== |
|
|
output_dir: ./Magi-PT-2 |
|
|
logging_steps: 1 |
|
|
save_safetensors: true |
|
|
<br> |
|
|
# ==================== |
|
|
# WANDB TRACKING |
|
|
# ==================== |
|
|
wandb_project: Magi-PT |
|
|
# wandb_entity: your_entity |
|
|
wandb_name: Magi-PT-2</code></pre> |
|
|
<div class="config-title">SFT (2*H100)</div> |
|
|
<pre><code>base_model: ApocalypseParty/Magi-PT-2 |
|
|
model_type: MistralForCausalLM |
|
|
tokenizer_type: AutoTokenizer |
|
|
chat_template: mistral_v7_tekken |
|
|
<br> |
|
|
plugins: |
|
|
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin |
|
|
<br> |
|
|
load_in_8bit: true |
|
|
load_in_bit: false |
|
|
deepspeed: deepspeed_configs/zero1.json |
|
|
<br> |
|
|
datasets: |
|
|
- path: ./data/automated_dataset.jsonl |
|
|
type: chat_template |
|
|
- path: ./data/chub_dataset.jsonl |
|
|
type: chat_template |
|
|
- path: ./data/handcrafted_dataset.jsonl |
|
|
type: chat_template |
|
|
- path: ./data/cw_dataset.jsonl |
|
|
type: chat_template |
|
|
- path: ./data/instruct_dataset.jsonl |
|
|
type: chat_template |
|
|
- path: ./data/nsfw_stories.jsonl |
|
|
type: chat_template |
|
|
- path: ./data/stories_dataset.jsonl |
|
|
type: chat_template |
|
|
<br> |
|
|
dataset_prepared_path: last_run_prepared |
|
|
val_set_size: 0 |
|
|
output_dir: ./Magi-PT-2-SFT-2 |
|
|
<br> |
|
|
adapter: lora |
|
|
peft_use_rslora: true |
|
|
lora_model_dir: |
|
|
<br> |
|
|
sequence_len: 8192 |
|
|
sample_packing: true |
|
|
<br> |
|
|
lora_r: 128 |
|
|
lora_alpha: 128 |
|
|
lora_dropout: 0.05 |
|
|
lora_target_linear: true |
|
|
lora_target_modules: |
|
|
- gate_proj |
|
|
- down_proj |
|
|
- up_proj |
|
|
- q_proj |
|
|
- v_proj |
|
|
- k_proj |
|
|
- o_proj |
|
|
<br> |
|
|
wandb_project: Magi-SFT |
|
|
wandb_name: Magi-PT-2-SFT-2 |
|
|
<br> |
|
|
gradient_accumulation_steps: 4 |
|
|
micro_batch_size: 2 |
|
|
num_epochs: 3 |
|
|
optimizer: adamw_bnb_8bit |
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lr_scheduler: cosine |
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learning_rate: 1e-5 |
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<br> |
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bf16: auto |
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tf32: false |
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<br> |
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gradient_checkpointing: true |
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resume_from_checkpoint: |
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logging_steps: 1 |
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flash_attention: true |
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<br> |
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warmup_ratio: 0.05 |
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evals_per_epoch: 1 |
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saves_per_epoch: 2</code></pre> |
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</div> |
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</details> |
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</div> |
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</div> |
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</div> |
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</div> |
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</body> |
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</html> |
