Download index.html from AxiomicLabs/Open_SLM_Leaderboard: direct link, hf CLI and curl.
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https://huggingface.co/spaces/AxiomicLabs/Open_SLM_Leaderboard/resolve/main/index.html
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hf download hf://spaces/AxiomicLabs/Open_SLM_Leaderboard/index.html
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curl -L -o index.html https://huggingface.co/spaces/AxiomicLabs/Open_SLM_Leaderboard/resolve/main/index.html
209 kB
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>Open SLM Leaderboard</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=DM+Sans:ital,opsz,wght@0,9..40,300;0,9..40,400;0,9..40,500;0,9..40,600;0,9..40,700;1,9..40,400&family=Space+Mono:wght@400;700&display=swap" rel="stylesheet"> | |
| <script src="https://cdn.jsdelivr.net/npm/chart.js@4"></script> | |
| <style> | |
| :root { | |
| --leader-gold: #d7bd83; | |
| --leader-gold-hover: #f0dca8; | |
| --bg: #0a0a0b; | |
| --bg-alt: #111113; | |
| --surface: #141416; | |
| --surface-hover: #1a1a1d; | |
| --border: #232326; | |
| --border-subtle: #19191c; | |
| --border-strong: #2e2e32; | |
| --text: #e8e8e8; | |
| --text-secondary: #6b6b6e; | |
| --text-muted: #454548; | |
| --accent: #c2b6ff; | |
| --accent-muted: #7a6fb0; | |
| --accent-soft: rgba(194, 182, 255, 0.08); | |
| --chart-tooltip-background: rgba(20, 20, 22, 0.96); | |
| --chart-tooltip-border: rgba(194, 182, 255, 0.25); | |
| --best: #4ade80; | |
| --worst: #454548; | |
| } | |
| * { margin: 0; padding: 0; box-sizing: border-box; } | |
| html { scroll-behavior: smooth; } | |
| body { | |
| font-family: 'DM Sans', system-ui, -apple-system, sans-serif; | |
| background: var(--bg); | |
| color: var(--text); | |
| line-height: 1.6; | |
| -webkit-font-smoothing: antialiased; | |
| } | |
| .container { max-width: 1100px; margin: 0 auto; padding: 0 40px; } | |
| /* βββ Hero βββ */ | |
| .hero { padding: 80px 0 60px; text-align: center; } | |
| .hero h1 { | |
| font-size: clamp(44px, 7vw, 80px); | |
| font-weight: 300; | |
| letter-spacing: -3px; | |
| line-height: 1.0; | |
| } | |
| .hero h1 span { color: var(--accent); } | |
| .hero-sub { | |
| font-size: 17px; | |
| color: var(--text-secondary); | |
| margin-top: 18px; | |
| max-width: 520px; | |
| margin-left: auto; | |
| margin-right: auto; | |
| font-weight: 400; | |
| letter-spacing: -0.2px; | |
| } | |
| .hero-sub a { | |
| color: var(--accent); | |
| text-decoration: none; | |
| border-bottom: 1px solid transparent; | |
| transition: border-color 0.15s; | |
| } | |
| .hero-sub a:hover { | |
| border-bottom-color: var(--accent); | |
| } | |
| .hero-note { | |
| margin-top: 40px; | |
| background: var(--surface); | |
| border: 1px solid var(--border-subtle); | |
| border-left: 3px solid var(--accent); | |
| border-radius: 0 10px 10px 0; | |
| padding: 20px 28px; | |
| text-align: left; | |
| font-size: 14px; | |
| color: var(--text-secondary); | |
| line-height: 1.7; | |
| } | |
| .hero-note strong { color: var(--text); font-weight: 600; } | |
| .hero-note a { | |
| color: var(--accent); | |
| text-decoration: none; | |
| border-bottom: 1px solid transparent; | |
| transition: border-color 0.15s; | |
| } | |
| .hero-note a:hover { border-bottom-color: var(--accent); } | |
| /* βββ Section Headings βββ */ | |
| .section-title { | |
| font-size: clamp(36px, 5vw, 56px); | |
| font-weight: 300; | |
| letter-spacing: -2px; | |
| line-height: 1.05; | |
| margin-bottom: 8px; | |
| } | |
| .section-sub { | |
| font-size: 14px; | |
| color: var(--text-secondary); | |
| margin-bottom: 24px; | |
| font-weight: 400; | |
| } | |
| /* βββ Highlights βββ */ | |
| .overview-strip { | |
| display: flex; | |
| align-items: center; | |
| justify-content: space-between; | |
| flex-wrap: wrap; | |
| gap: 20px; | |
| padding: 0; | |
| margin-top: 36px; | |
| } | |
| .overview-counts { display: flex; flex-wrap: wrap; gap: 18px 32px; } | |
| .overview-stat { display: flex; align-items: baseline; gap: 10px; } | |
| .overview-stat strong { | |
| color: var(--text); | |
| font: 500 26px/1 'DM Sans', sans-serif; | |
| letter-spacing: -.8px; | |
| } | |
| .overview-stat > span, .overview-scope { color: #91919b; font-size: 12px; } | |
| .overview-scope { display: flex; gap: 10px; } | |
| .size-leaders-heading { display: flex; align-items: baseline; justify-content: space-between; gap: 20px; margin-bottom: 18px; } | |
| .size-leaders-title { color: var(--text); font-size: 24px; font-weight: 500; letter-spacing: -.6px; } | |
| .size-leaders-heading .section-sub { margin-bottom: 0; color: #91919b; } | |
| /* Notable Releases */ | |
| .release-head { | |
| display: flex; | |
| justify-content: space-between; | |
| gap: 20px; | |
| align-items: flex-end; | |
| margin-bottom: 14px; | |
| } | |
| .release-grid { | |
| display: grid; | |
| grid-template-columns: repeat(4, minmax(0, 1fr)); | |
| gap: 12px; | |
| } | |
| .release-card { | |
| min-height: 136px; | |
| display: flex; | |
| flex-direction: column; | |
| background: var(--surface); | |
| border: 1px solid var(--border-subtle); | |
| border-top: 2px solid var(--accent); | |
| border-radius: 8px; | |
| padding: 16px; | |
| transition: background 0.15s, border-color 0.15s; | |
| } | |
| .release-card:hover { | |
| background: var(--surface-hover); | |
| border-color: var(--border); | |
| } | |
| .release-kicker { | |
| display: flex; | |
| justify-content: space-between; | |
| gap: 8px; | |
| align-items: center; | |
| margin-bottom: 12px; | |
| font-family: 'Space Mono', monospace; | |
| font-size: 10px; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| color: var(--text-muted); | |
| } | |
| .release-pill { | |
| flex: 0 0 auto; | |
| border: 1px solid rgba(194,182,255,0.30); | |
| border-radius: 4px; | |
| padding: 1px 6px; | |
| color: var(--accent); | |
| background: var(--accent-soft); | |
| letter-spacing: 0.04em; | |
| } | |
| .release-title { | |
| color: var(--text); | |
| text-decoration: none; | |
| font-size: 19px; | |
| font-weight: 500; | |
| line-height: 1.15; | |
| letter-spacing: -0.35px; | |
| overflow-wrap: anywhere; | |
| transition: color 0.15s; | |
| } | |
| .release-title:hover { color: var(--accent); } | |
| .release-blurb { | |
| margin-top: 10px; | |
| color: var(--text-secondary); | |
| font-size: 13px; | |
| line-height: 1.55; | |
| } | |
| .release-org { | |
| display: block; | |
| margin-top: 6px; | |
| color: var(--text-muted); | |
| font-size: 11px; | |
| } | |
| .release-meta { | |
| display: block; | |
| margin-top: auto; | |
| padding-top: 16px; | |
| color: var(--text-muted); | |
| font-family: 'Space Mono', monospace; | |
| font-size: 10px; | |
| letter-spacing: 0.04em; | |
| text-transform: uppercase; | |
| } | |
| .release-meta a { | |
| color: var(--text-secondary); | |
| text-decoration: none; | |
| border-bottom: 1px solid transparent; | |
| transition: color 0.15s, border-color 0.15s; | |
| } | |
| .release-meta a:hover { | |
| color: var(--accent); | |
| border-bottom-color: var(--accent); | |
| } | |
| .release-stats { | |
| display: grid; | |
| grid-template-columns: 1fr 1fr; | |
| gap: 8px; | |
| margin-top: auto; | |
| padding-top: 16px; | |
| } | |
| .release-stat { | |
| border-top: 1px solid var(--border-subtle); | |
| padding-top: 8px; | |
| } | |
| .release-stat-label { | |
| display: block; | |
| color: var(--text-muted); | |
| font-family: 'Space Mono', monospace; | |
| font-size: 9px; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| } | |
| .release-stat-value { | |
| display: block; | |
| margin-top: 2px; | |
| color: var(--text-secondary); | |
| font-family: 'Space Mono', monospace; | |
| font-size: 12px; | |
| } | |
| .release-stat-value.best { | |
| color: var(--best); | |
| font-weight: 700; | |
| } | |
| /* βββ Param Filters βββ */ | |
| .filter-bar { | |
| display: flex; | |
| gap: 8px; | |
| margin-bottom: 12px; | |
| flex-wrap: wrap; | |
| } | |
| .filter-group { | |
| margin-bottom: 18px; | |
| } | |
| .leaderboard-filter-row { | |
| display: flex; | |
| align-items: flex-end; | |
| justify-content: space-between; | |
| gap: 24px; | |
| margin-bottom: 18px; | |
| } | |
| .leaderboard-filter-row .filter-group { | |
| flex: 1 1 auto; | |
| min-width: 0; | |
| margin-bottom: 0; | |
| } | |
| .leaderboard-search-controls { | |
| flex: 0 1 300px; | |
| min-width: 220px; | |
| } | |
| .leaderboard-search { display: block; } | |
| .size-mode-toggle { | |
| display: flex; | |
| gap: 4px; | |
| padding: 3px; | |
| margin-bottom: 12px; | |
| border: 1px solid var(--border); | |
| border-radius: 8px; | |
| background: var(--surface); | |
| } | |
| .size-mode-toggle .filter-btn { flex: 1; } | |
| .size-mode-toggle .filter-btn:focus-visible { | |
| outline: 2px solid var(--accent); | |
| outline-offset: 2px; | |
| } | |
| .leaderboard-search-input { | |
| display: block; | |
| width: 100%; | |
| height: 32px; | |
| padding: 6px 11px; | |
| border: 1px solid var(--border); | |
| border-radius: 6px; | |
| outline: none; | |
| background: var(--surface); | |
| color: var(--text); | |
| font: 11px/1.2 'Space Mono', monospace; | |
| transition: border-color .15s, background .15s, box-shadow .15s; | |
| } | |
| .leaderboard-search-input::placeholder { color: var(--text-muted); } | |
| .leaderboard-search-input:hover { border-color: var(--border-strong); } | |
| .leaderboard-search-input:focus { | |
| border-color: var(--accent-muted); | |
| background: var(--surface-hover); | |
| box-shadow: 0 0 0 3px var(--accent-soft); | |
| } | |
| @media (max-width: 620px) { | |
| .leaderboard-filter-row { | |
| display: block; | |
| } | |
| .leaderboard-search-controls { | |
| display: block; | |
| min-width: 0; | |
| margin-top: 14px; | |
| } | |
| } | |
| .filter-label { | |
| display: block; | |
| font-family: 'Space Mono', monospace; | |
| font-size: 10px; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| color: var(--text-muted); | |
| margin-bottom: 8px; | |
| } | |
| .filter-btn { | |
| font-family: 'Space Mono', monospace; | |
| font-size: 11px; | |
| font-weight: 400; | |
| letter-spacing: 0.02em; | |
| padding: 5px 12px; | |
| border-radius: 6px; | |
| border: 1px solid var(--border); | |
| background: transparent; | |
| color: var(--text-secondary); | |
| cursor: pointer; | |
| transition: all 0.15s; | |
| } | |
| .filter-btn:hover { | |
| border-color: var(--accent-muted); | |
| color: var(--text); | |
| } | |
| .filter-btn.active { | |
| border-color: var(--accent); | |
| background: var(--accent-soft); | |
| color: var(--accent); | |
| } | |
| /* βββ Table βββ */ | |
| .table-wrap { | |
| overflow-x: auto; | |
| border: 1px solid var(--border-subtle); | |
| border-radius: 10px; | |
| background: var(--surface); | |
| } | |
| .leaderboard-table-shell { | |
| position: relative; | |
| } | |
| .leaderboard-table-shell > .table-wrap { | |
| border-top-right-radius: 0; | |
| } | |
| .extra-columns-tab { | |
| position: absolute; | |
| z-index: 2; | |
| top: 0; | |
| right: -28px; | |
| width: 29px; | |
| height: 42px; | |
| display: inline-flex; | |
| align-items: center; | |
| justify-content: center; | |
| padding: 0; | |
| border: 1px solid var(--border-subtle); | |
| border-left: 0; | |
| border-radius: 0 9px 9px 0; | |
| background: var(--surface); | |
| color: var(--text-muted); | |
| cursor: pointer; | |
| box-shadow: 5px 0 14px rgba(0,0,0,0.10); | |
| transition: color 0.15s, border-color 0.15s, background 0.15s, transform 0.15s; | |
| } | |
| .extra-columns-tab:hover, | |
| .leaderboard-table-shell.extras-open .extra-columns-tab { | |
| color: var(--accent); | |
| border-color: var(--accent-muted); | |
| background: var(--accent-soft); | |
| } | |
| .extra-columns-tab:hover { transform: translateX(2px); } | |
| .extra-columns-glyph { | |
| display: inline-block; | |
| font-family: 'Space Mono', monospace; | |
| font-size: 18px; | |
| line-height: 1; | |
| transition: transform 0.18s; | |
| } | |
| .leaderboard-table-shell.extras-open .extra-columns-glyph { | |
| transform: rotate(180deg); | |
| } | |
| #leaderboard-table .extra-col { display: none; } | |
| .leaderboard-table-shell.extras-open #leaderboard-table .extra-col { display: table-cell; } | |
| .leaderboard-table-shell.extras-open #leaderboard-table .extra-col-start { | |
| border-left: 1px solid var(--border-strong); | |
| } | |
| #leaderboard-table thead th { | |
| height: 41px; | |
| box-sizing: border-box; | |
| } | |
| table { width: 100%; border-collapse: collapse; font-size: 14px; } | |
| thead th { | |
| padding: 12px 12px; | |
| text-align: center; | |
| font-size: 10px; | |
| font-weight: 600; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| color: var(--text-muted); | |
| border-bottom: 1px solid var(--border); | |
| cursor: pointer; | |
| user-select: none; | |
| white-space: nowrap; | |
| transition: color 0.15s; | |
| font-family: 'Space Mono', monospace; | |
| } | |
| thead th:hover { color: var(--accent); } | |
| thead th:first-child, tbody td:first-child { text-align: left; } | |
| thead th .sort-indicator { | |
| margin-left: 3px; | |
| opacity: 0; | |
| font-size: 9px; | |
| color: var(--accent); | |
| } | |
| thead th.sorted { color: var(--accent); } | |
| thead th.sorted .sort-indicator { opacity: 1; } | |
| tbody tr { | |
| border-bottom: 1px solid var(--border-subtle); | |
| transition: background 0.12s; | |
| } | |
| tbody tr:last-child { border-bottom: none; } | |
| tbody tr:hover { background: var(--accent-soft); } | |
| tbody td { | |
| padding: 12px 12px; | |
| text-align: center; | |
| vertical-align: middle; | |
| } | |
| .td-rank { | |
| font-weight: 700; | |
| font-size: 12px; | |
| color: var(--accent-muted); | |
| width: 32px; | |
| font-family: 'Space Mono', monospace; | |
| } | |
| .org-title-line { | |
| display: inline-flex; | |
| align-items: center; | |
| gap: 8px; | |
| } | |
| .rank-move { | |
| display: inline-flex; | |
| align-items: center; | |
| justify-content: center; | |
| min-width: 30px; | |
| height: 17px; | |
| padding: 0 5px; | |
| border-radius: 4px; | |
| border: 1px solid var(--border-subtle); | |
| color: var(--text-muted); | |
| background: rgba(255,255,255,0.03); | |
| font-size: 9px; | |
| line-height: 1; | |
| } | |
| .rank-move.up { | |
| color: var(--best); | |
| border-color: rgba(74,222,128,0.28); | |
| background: rgba(74,222,128,0.08); | |
| } | |
| .rank-move.down { | |
| color: #f87171; | |
| border-color: rgba(248,113,113,0.26); | |
| background: rgba(248,113,113,0.07); | |
| } | |
| .rank-move.new { | |
| color: var(--accent-muted); | |
| border-color: rgba(194,182,255,0.30); | |
| background: rgba(194,182,255,0.08); | |
| } | |
| .td-model { | |
| text-align: left; | |
| font-weight: 500; | |
| font-size: 14px; | |
| color: var(--text); | |
| } | |
| .td-model a { | |
| color: #d6d6da; | |
| text-decoration: none; | |
| transition: color 0.15s; | |
| } | |
| .td-model a:hover { color: var(--accent); } | |
| .td-model .rank-move { | |
| margin-left: 7px; | |
| vertical-align: 1px; | |
| } | |
| .td-model .model-org { | |
| display: block; | |
| font-size: 11px; | |
| color: var(--text-muted); | |
| font-weight: 400; | |
| margin-top: 1px; | |
| } | |
| .td-model .model-org a { | |
| color: var(--text-muted); | |
| text-decoration: none; | |
| transition: color 0.15s, border-color 0.15s; | |
| border-bottom: 1px solid transparent; | |
| } | |
| .td-model .model-org a:hover { | |
| color: var(--accent-muted); | |
| border-bottom-color: var(--accent-muted); | |
| } | |
| .td-score { | |
| font-family: 'Space Mono', monospace; | |
| font-size: 12px; | |
| letter-spacing: -0.2px; | |
| } | |
| .td-score.best { | |
| color: var(--best); | |
| font-weight: 700; | |
| } | |
| .td-score.na { | |
| color: var(--text-muted); | |
| font-style: normal; | |
| } | |
| .td-params { | |
| font-family: 'Space Mono', monospace; | |
| font-size: 11px; | |
| color: var(--text-secondary); | |
| } | |
| .org-badge { | |
| display: inline-block; | |
| font-size: 10px; | |
| font-weight: 600; | |
| padding: 2px 7px; | |
| border-radius: 4px; | |
| letter-spacing: 0.03em; | |
| font-family: 'Space Mono', monospace; | |
| } | |
| /* βββ Scatter Plot βββ */ | |
| .legend-bar { | |
| display: flex; | |
| gap: 20px; | |
| margin-bottom: 24px; | |
| flex-wrap: wrap; | |
| } | |
| .legend-item { | |
| display: flex; | |
| align-items: center; | |
| gap: 6px; | |
| font-size: 12px; | |
| color: var(--text-secondary); | |
| } | |
| .legend-dot { width: 8px; height: 8px; border-radius: 50%; } | |
| .chart-card { | |
| background: var(--surface); | |
| border: 1px solid var(--border-subtle); | |
| border-radius: 10px; | |
| padding: 20px; | |
| } | |
| .chart-card h3 { | |
| font-size: 12px; | |
| font-weight: 500; | |
| color: var(--text-secondary); | |
| margin-bottom: 14px; | |
| letter-spacing: 0.02em; | |
| } | |
| .chart-card canvas { | |
| max-height: 460px; | |
| } | |
| .index-method-card { | |
| border-left-color: var(--best); | |
| } | |
| .index-formula { | |
| margin: 18px 0; | |
| padding: 14px 16px; | |
| border: 1px solid var(--border-subtle); | |
| border-radius: 7px; | |
| background: rgba(255,255,255,0.018); | |
| color: var(--text); | |
| font-family: 'Space Mono', monospace; | |
| font-size: 12px; | |
| line-height: 1.7; | |
| overflow-x: auto; | |
| } | |
| .index-weight-grid { | |
| display: grid; | |
| grid-template-columns: repeat(4, minmax(0, 1fr)); | |
| gap: 10px; | |
| margin: 18px 0; | |
| } | |
| .index-weight { | |
| padding: 13px 14px; | |
| border: 1px solid var(--border-subtle); | |
| border-radius: 7px; | |
| background: rgba(255,255,255,0.012); | |
| } | |
| .index-weight strong, | |
| .index-weight span { | |
| display: block; | |
| } | |
| .index-weight strong { | |
| color: var(--text); | |
| font-size: 13px; | |
| font-weight: 500; | |
| margin-bottom: 5px; | |
| } | |
| .index-weight span { | |
| color: var(--text-muted); | |
| font-family: 'Space Mono', monospace; | |
| font-size: 10px; | |
| line-height: 1.5; | |
| } | |
| .index-method-note { | |
| color: var(--text-muted) ; | |
| font-size: 12px ; | |
| } | |
| /* βββ About βββ */ | |
| .about-box { | |
| background: var(--surface); | |
| border: 1px solid var(--border-subtle); | |
| border-left: 3px solid var(--accent); | |
| border-radius: 0 10px 10px 0; | |
| padding: 28px 32px; | |
| } | |
| .about-box h3 { | |
| font-size: 20px; | |
| font-weight: 500; | |
| margin-bottom: 10px; | |
| color: var(--text); | |
| letter-spacing: -0.5px; | |
| } | |
| .about-box p { | |
| font-size: 14px; | |
| color: var(--text-secondary); | |
| line-height: 1.7; | |
| } | |
| .about-box a { | |
| color: var(--accent); | |
| text-decoration: none; | |
| border-bottom: 1px solid transparent; | |
| transition: border-color 0.15s; | |
| } | |
| .about-box a:hover { border-bottom-color: var(--accent); } | |
| /* βββ Footer βββ */ | |
| footer { | |
| margin-top: 100px; | |
| margin-bottom: 90px; | |
| padding: 32px 0; | |
| border-top: 1px solid var(--border-subtle); | |
| display: flex; | |
| justify-content: space-between; | |
| align-items: center; | |
| font-size: 11px; | |
| color: var(--text-muted); | |
| font-family: 'Space Mono', monospace; | |
| } | |
| footer a { | |
| color: var(--text-secondary); | |
| text-decoration: none; | |
| margin-left: 16px; | |
| transition: color 0.15s; | |
| } | |
| footer a:hover { color: var(--accent); } | |
| /* βββ Side Nav Rail βββ */ | |
| .side-nav { | |
| position: fixed; | |
| left: 28px; | |
| top: 50%; | |
| transform: translateY(-50%); | |
| z-index: 50; | |
| display: flex; | |
| flex-direction: column; | |
| gap: 2px; | |
| } | |
| .side-nav a { | |
| display: flex; | |
| align-items: center; | |
| gap: 11px; | |
| padding: 5px 0; | |
| font-family: 'Space Mono', monospace; | |
| font-size: 11px; | |
| letter-spacing: 0.04em; | |
| color: var(--text-muted); | |
| text-decoration: none; | |
| transition: color 0.15s; | |
| } | |
| .side-nav-dot { | |
| width: 7px; | |
| height: 7px; | |
| border-radius: 50%; | |
| border: 1px solid var(--border-strong); | |
| flex: 0 0 auto; | |
| transition: background 0.15s, border-color 0.15s, box-shadow 0.15s; | |
| } | |
| .side-nav-label { | |
| transition: color 0.15s; | |
| } | |
| .side-nav a:hover { color: var(--text-secondary); } | |
| .side-nav a:hover .side-nav-dot { border-color: var(--accent-muted); } | |
| .side-nav a.active { color: var(--accent); } | |
| .side-nav a.active .side-nav-dot { | |
| background: var(--accent); | |
| border-color: var(--accent); | |
| box-shadow: 0 0 0 3px var(--accent-soft); | |
| } | |
| @media (max-width: 1279px) { | |
| .side-nav { display: none; } | |
| } | |
| /* βββ Compare βββ */ | |
| .th-compare { | |
| cursor: default ; | |
| width: 60px; | |
| } | |
| .th-compare:hover { color: var(--text-muted) ; } | |
| .td-compare { width: 60px; text-align: center ; } | |
| .compare-toggle { | |
| width: 26px; | |
| height: 26px; | |
| border-radius: 6px; | |
| border: 1px solid var(--border); | |
| background: transparent; | |
| color: var(--text-secondary); | |
| cursor: pointer; | |
| font-family: 'Space Mono', monospace; | |
| font-size: 14px; | |
| line-height: 1; | |
| display: inline-flex; | |
| align-items: center; | |
| justify-content: center; | |
| transition: all 0.15s; | |
| } | |
| .compare-toggle:hover { | |
| border-color: var(--accent-muted); | |
| color: var(--text); | |
| } | |
| .compare-toggle.active { | |
| border-color: var(--accent); | |
| background: var(--accent-soft); | |
| color: var(--accent); | |
| } | |
| .compare-head { | |
| display: flex; | |
| justify-content: space-between; | |
| align-items: flex-end; | |
| gap: 20px; | |
| flex-wrap: wrap; | |
| } | |
| .compare-head-actions { | |
| display: flex; | |
| align-items: center; | |
| gap: 12px; | |
| margin-bottom: 24px; | |
| } | |
| .compare-count { | |
| font-family: 'Space Mono', monospace; | |
| font-size: 11px; | |
| color: var(--accent-muted); | |
| margin-left: 4px; | |
| } | |
| .compare-limit-note { | |
| font-family: 'Space Mono', monospace; | |
| font-size: 10px; | |
| letter-spacing: 0.04em; | |
| color: var(--text-muted); | |
| opacity: 0; | |
| transition: opacity 0.2s, color 0.2s; | |
| } | |
| .compare-limit-note.show { | |
| opacity: 1; | |
| color: var(--accent); | |
| } | |
| .compare-empty { | |
| background: var(--surface); | |
| border: 1px dashed var(--border); | |
| border-radius: 10px; | |
| padding: 44px 28px; | |
| text-align: center; | |
| color: var(--text-secondary); | |
| } | |
| .compare-empty p { font-size: 15px; } | |
| .compare-empty-hint { font-size: 13px; color: var(--text-muted); margin-top: 8px; } | |
| .compare-empty-plus { | |
| display: inline-flex; | |
| align-items: center; | |
| justify-content: center; | |
| width: 18px; height: 18px; | |
| border-radius: 4px; | |
| border: 1px solid var(--accent); | |
| color: var(--accent); | |
| font-family: 'Space Mono', monospace; | |
| font-size: 12px; | |
| vertical-align: middle; | |
| } | |
| .compare-table-wrap { overflow-x: auto; } | |
| .compare-table { width: 100%; border-collapse: collapse; } | |
| .compare-table th, .compare-table td { | |
| padding: 14px 16px; | |
| text-align: center; | |
| border-bottom: 1px solid var(--border-subtle); | |
| } | |
| .compare-table thead th { border-bottom: 1px solid var(--border); vertical-align: bottom; } | |
| .compare-metric-head { | |
| text-align: left ; | |
| font-family: 'Space Mono', monospace; | |
| font-size: 10px; | |
| letter-spacing: 0.08em; | |
| text-transform: uppercase; | |
| color: var(--text-muted); | |
| font-weight: 600; | |
| } | |
| .compare-model-head { | |
| position: relative; | |
| min-width: 140px; | |
| } | |
| .compare-remove { | |
| position: absolute; | |
| top: 6px; right: 6px; | |
| width: 18px; height: 18px; | |
| border-radius: 4px; | |
| border: 1px solid var(--border); | |
| background: transparent; | |
| color: var(--text-muted); | |
| cursor: pointer; | |
| font-size: 12px; | |
| line-height: 1; | |
| display: inline-flex; | |
| align-items: center; | |
| justify-content: center; | |
| transition: all 0.15s; | |
| } | |
| .compare-remove:hover { color: #f87171; border-color: rgba(248,113,113,0.4); } | |
| .compare-model-name { | |
| display: block; | |
| color: var(--text); | |
| text-decoration: none; | |
| font-size: 14px; | |
| font-weight: 500; | |
| letter-spacing: -0.2px; | |
| overflow-wrap: anywhere; | |
| transition: color 0.15s; | |
| } | |
| .compare-model-name:hover { color: var(--accent); } | |
| .compare-model-org { | |
| display: block; | |
| margin-top: 3px; | |
| font-size: 11px; | |
| color: var(--text-muted); | |
| } | |
| .compare-model-org a { color: var(--text-muted); text-decoration: none; border-bottom: 1px solid transparent; } | |
| .compare-model-org a:hover { color: var(--accent-muted); border-bottom-color: var(--accent-muted); } | |
| .compare-metric { | |
| text-align: left ; | |
| font-size: 13px; | |
| color: var(--text-secondary); | |
| font-weight: 500; | |
| } | |
| .compare-cell { | |
| font-family: 'Space Mono', monospace; | |
| font-size: 13px; | |
| color: var(--text-secondary); | |
| } | |
| .compare-cell.params { color: var(--text); } | |
| .compare-cell.best { color: var(--best); font-weight: 700; } | |
| .compare-cell.na { color: var(--text-muted); } | |
| .compare-table tbody tr:hover { background: var(--accent-soft); } | |
| .compare-table tbody tr:last-child td { border-bottom: none; } | |
| /* Sticky compare bar */ | |
| .compare-bar { | |
| position: fixed; | |
| left: 0; right: 0; bottom: 0; | |
| z-index: 60; | |
| padding: 0 20px 18px; | |
| pointer-events: none; | |
| transform: translateY(140%); | |
| transition: transform 0.25s ease; | |
| } | |
| .compare-bar.show { transform: translateY(0); } | |
| .compare-bar-inner { | |
| pointer-events: auto; | |
| max-width: 1020px; | |
| margin: 0 auto; | |
| display: flex; | |
| align-items: center; | |
| gap: 14px; | |
| flex-wrap: wrap; | |
| background: rgba(20,20,22,0.92); | |
| backdrop-filter: blur(12px); | |
| border: 1px solid var(--border-strong); | |
| border-radius: 12px; | |
| padding: 12px 16px; | |
| box-shadow: 0 10px 40px rgba(0,0,0,0.45); | |
| } | |
| .compare-bar-label { | |
| font-family: 'Space Mono', monospace; | |
| font-size: 11px; | |
| letter-spacing: 0.04em; | |
| color: var(--accent); | |
| white-space: nowrap; | |
| } | |
| .compare-bar-chips { | |
| display: flex; | |
| gap: 6px; | |
| flex-wrap: wrap; | |
| flex: 1 1 auto; | |
| } | |
| .compare-chip { | |
| display: inline-flex; | |
| align-items: center; | |
| gap: 6px; | |
| font-size: 12px; | |
| color: var(--text); | |
| background: rgba(255,255,255,0.03); | |
| border: 1px solid var(--border); | |
| border-radius: 6px; | |
| padding: 3px 6px 3px 9px; | |
| } | |
| .compare-chip-dot { width: 7px; height: 7px; border-radius: 50%; flex: 0 0 auto; } | |
| .compare-chip-x { | |
| border: none; | |
| background: transparent; | |
| color: var(--text-muted); | |
| cursor: pointer; | |
| font-size: 14px; | |
| line-height: 1; | |
| padding: 0 2px; | |
| transition: color 0.15s; | |
| } | |
| .compare-chip-x:hover { color: #f87171; } | |
| .compare-bar-actions { | |
| display: flex; | |
| align-items: center; | |
| gap: 14px; | |
| white-space: nowrap; | |
| } | |
| .compare-bar-view { | |
| font-family: 'Space Mono', monospace; | |
| font-size: 12px; | |
| color: var(--accent); | |
| text-decoration: none; | |
| background: none; | |
| border: 0; | |
| border-bottom: 1px solid transparent; | |
| padding: 0; | |
| cursor: pointer; | |
| transition: border-color 0.15s; | |
| } | |
| .compare-bar-view:hover { border-bottom-color: var(--accent); } | |
| .compare-bar-clear { | |
| font-family: 'Space Mono', monospace; | |
| font-size: 11px; | |
| color: var(--text-secondary); | |
| background: transparent; | |
| border: 1px solid var(--border); | |
| border-radius: 6px; | |
| padding: 4px 10px; | |
| cursor: pointer; | |
| transition: all 0.15s; | |
| } | |
| .compare-bar-clear:hover { color: var(--text); border-color: var(--accent-muted); } | |
| /* βββ Mobile βββ */ | |
| @media (max-width: 768px) { | |
| .container { padding: 0 20px; } | |
| .hero { padding: 60px 0 40px; } | |
| .release-head { display: block; } | |
| .release-grid { grid-template-columns: repeat(2, minmax(0, 1fr)); } | |
| .chart-card canvas { max-height: 360px; } | |
| .index-weight-grid { grid-template-columns: repeat(2, minmax(0, 1fr)); } | |
| footer { flex-direction: column; gap: 12px; text-align: center; } | |
| footer a { margin: 0 8px; } | |
| .compare-head { display: block; } | |
| .compare-head-actions { margin-top: 12px; } | |
| .compare-bar-inner { gap: 10px; padding: 10px 12px; } | |
| .compare-bar-actions { width: 100%; justify-content: space-between; } | |
| .extra-columns-tab { | |
| top: 0; | |
| right: -20px; | |
| width: 29px; | |
| height: 42px; | |
| border: 1px solid var(--border-subtle); | |
| border-left: 0; | |
| border-radius: 0 9px 9px 0; | |
| box-shadow: 5px 0 14px rgba(0,0,0,0.10); | |
| } | |
| .extra-columns-tab:hover { transform: none; } | |
| } | |
| @media (max-width: 520px) { | |
| .release-grid { grid-template-columns: 1fr; } | |
| .index-weight-grid { grid-template-columns: 1fr; } | |
| } | |
| </style> | |
| <style> | |
| .filter-leader-grid { | |
| display: grid; | |
| grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); | |
| gap: 8px; | |
| width: 100%; | |
| margin: 10px 0 22px; | |
| } | |
| #benchmark-leader-grid { grid-template-columns: repeat(6, minmax(0, 1fr)); } | |
| .filter-leader-card { | |
| min-width: 0; | |
| position: relative; | |
| padding: 13px 14px 14px; | |
| background: var(--surface); | |
| border: 1px solid var(--border-subtle); | |
| border-top: 2px solid var(--border-strong); | |
| border-radius: 8px; | |
| cursor: pointer; | |
| transition: background .15s ease, border-color .15s ease, transform .15s ease; | |
| } | |
| .filter-leader-card:hover, | |
| .filter-leader-card:focus-visible { | |
| background: var(--surface-hover); | |
| border-color: var(--accent-muted); | |
| outline: none; | |
| transform: translateY(-2px); | |
| } | |
| .filter-leader-card.active { | |
| background: var(--accent-soft); | |
| border-color: var(--accent-muted); | |
| border-top-color: var(--accent); | |
| } | |
| .filter-leader-kicker { | |
| display: flex; | |
| justify-content: space-between; | |
| gap: 8px; | |
| color: var(--text-muted); | |
| font: 700 9px/1.2 'Space Mono', monospace; | |
| letter-spacing: .08em; | |
| text-transform: uppercase; | |
| } | |
| .filter-leader-kicker b { color: var(--best); } | |
| .filter-leader-name { | |
| display: block; | |
| margin-top: 9px; | |
| color: var(--text); | |
| font-size: 13px; | |
| font-weight: 600; | |
| line-height: 1.25; | |
| text-decoration: none; | |
| overflow-wrap: anywhere; | |
| } | |
| .filter-leader-name:hover { color: var(--accent); } | |
| .filter-leader-score { | |
| display: block; | |
| margin-top: 8px; | |
| color: var(--best); | |
| font: 700 16px/1 'Space Mono', monospace; | |
| } | |
| .filter-leader-meta { | |
| display: block; | |
| margin-top: 7px; | |
| color: var(--text-muted); | |
| font-size: 10px; | |
| line-height: 1.35; | |
| } | |
| @media (max-width: 1100px) { | |
| .filter-leader-grid, | |
| #benchmark-leader-grid { grid-template-columns: repeat(3, minmax(0, 1fr)); } | |
| } | |
| @media (max-width: 620px) { | |
| .filter-leader-grid, | |
| #benchmark-leader-grid { grid-template-columns: repeat(2, minmax(0, 1fr)); } | |
| } | |
| body.variant-seven { | |
| background: | |
| radial-gradient(circle at 50% 0, rgba(194,182,255,.10), transparent 36rem), | |
| var(--bg); | |
| } | |
| body.variant-seven .container { | |
| max-width: 1360px; | |
| padding: 0 58px; | |
| display: grid; | |
| grid-template-columns: repeat(12, minmax(0, 1fr)); | |
| gap: 22px; | |
| } | |
| body.variant-seven .container > * { min-width: 0; } | |
| body.variant-seven .hero { | |
| grid-column: 1 / -1; | |
| padding: 112px 0 32px; | |
| border-bottom: 1px solid var(--accent-muted); | |
| } | |
| body.variant-seven .hero::before { | |
| content: 'Axiomic Labs'; | |
| display: block; | |
| margin-bottom: 28px; | |
| color: var(--accent); | |
| font: 700 10px/1 'Space Mono', monospace; | |
| letter-spacing: .2em; | |
| } | |
| body.variant-seven .hero h1 { font-size: clamp(64px, 9vw, 126px); } | |
| body.variant-seven #about { | |
| grid-column: 1 / span 4; | |
| padding-top: 48px ; | |
| } | |
| body.variant-seven #int-index-method { | |
| grid-column: 5 / -1; | |
| padding-top: 48px ; | |
| } | |
| body.variant-seven #about .about-box, | |
| body.variant-seven #int-index-method .about-box { | |
| height: 100%; | |
| border-radius: 0; | |
| border-top: 2px solid var(--accent); | |
| } | |
| body.variant-seven #highlights, | |
| body.variant-seven #new-releases, | |
| body.variant-seven #leaderboard, | |
| body.variant-seven #compare, | |
| body.variant-seven #score-chart, | |
| body.variant-seven #charts, | |
| body.variant-seven #org-performance, | |
| body.variant-seven footer { grid-column: 1 / -1; } | |
| body.variant-seven #highlights { padding-top: 32px ; } | |
| body.variant-seven #new-releases { padding-top: 88px ; } | |
| body.variant-seven #new-releases .release-grid { grid-template-columns: repeat(2, minmax(0, 1fr)); } | |
| body.variant-seven #new-releases .release-card { | |
| min-height: 180px; | |
| padding: 24px; | |
| border-radius: 0; | |
| border-left: 3px solid var(--accent); | |
| border-top-color: var(--border); | |
| } | |
| body.variant-seven #leaderboard, | |
| body.variant-seven #compare, | |
| body.variant-seven #score-chart, | |
| body.variant-seven #charts, | |
| body.variant-seven #org-performance { padding-top: 96px ; } | |
| body.variant-seven .section-title { | |
| font-size: clamp(48px, 6vw, 76px); | |
| letter-spacing: -.06em; | |
| } | |
| body.variant-seven #leaderboard .leaderboard-table-shell { border-radius: 0 0 18px 0; } | |
| body.variant-seven #score-chart, | |
| body.variant-seven #charts { | |
| padding: 36px ; | |
| background: var(--bg-alt); | |
| border: 1px solid var(--border); | |
| border-radius: 0 0 28px 0; | |
| } | |
| body.variant-seven #score-chart .chart-card, | |
| body.variant-seven #charts .chart-card, | |
| body.variant-seven #org-performance .chart-card { background: transparent; border: 0; padding: 18px 0 0; } | |
| body.variant-seven footer { margin-top: 88px; } | |
| @media (max-width: 900px) { | |
| body.variant-seven .container { display: block; padding: 0 22px; } | |
| body.variant-seven .hero { padding: 68px 0 50px; } | |
| body.variant-seven #new-releases .release-grid { grid-template-columns: repeat(2, minmax(0, 1fr)); } | |
| } | |
| @media (max-width: 560px) { | |
| body.variant-seven #new-releases .release-grid { grid-template-columns: 1fr; } | |
| body.variant-seven #score-chart, | |
| body.variant-seven #charts { padding: 22px ; } | |
| } | |
| </style> | |
| <base target="_blank"> | |
| <style> | |
| /* index7.1 refinements */ | |
| #leaderboard-body .td-model > a.category-leader { | |
| color: var(--leader-gold); | |
| text-shadow: 0 0 7px rgba(215, 189, 131, .23), 0 0 12px rgba(215, 189, 131, .07); | |
| } | |
| #leaderboard-body .td-model > a.category-leader:hover { color: var(--leader-gold-hover); } | |
| .category-leader-key { color: var(--leader-gold); } | |
| #highlight-leader-grid { grid-template-columns: repeat(4, minmax(0, 1fr)); gap: 12px; margin: 0; } | |
| #highlight-leader-grid .filter-leader-card { | |
| display: flex; | |
| flex-direction: column; | |
| min-height: 174px; | |
| padding: 18px; | |
| border-radius: 4px; | |
| border: 1px solid var(--border-subtle); | |
| border-left: 3px solid var(--leader-org-color); | |
| background: var(--surface); | |
| box-shadow: none; | |
| } | |
| #highlight-leader-grid .filter-leader-card.active { | |
| border-color: var(--border); | |
| border-left-color: var(--leader-org-color); | |
| } | |
| #highlight-leader-grid .filter-leader-card:hover, | |
| #highlight-leader-grid .filter-leader-card:focus-visible { | |
| border-color: var(--leader-org-color); | |
| background: var(--surface-hover); | |
| transform: none; | |
| } | |
| #highlight-leader-grid .filter-leader-card:focus-visible { outline: 2px solid var(--leader-org-color); outline-offset: 3px; } | |
| #highlight-leader-grid .filter-leader-kicker { color: #91919b; font-size: 10px; letter-spacing: .08em; } | |
| #highlight-leader-grid .filter-leader-name { min-height: 44px; margin-top: 14px; color: var(--text); font-size: 18px; font-weight: 500; letter-spacing: -.3px; } | |
| #highlight-leader-grid .filter-leader-name:hover { color: var(--text); } | |
| .highlight-leader-footer { display: grid; grid-template-columns: minmax(0, 1fr) auto; align-items: end; gap: 10px; margin-top: auto; padding-top: 14px; } | |
| .highlight-leader-score { text-align: right; } | |
| #highlight-leader-grid .filter-leader-score { margin-top: 0; font-size: 22px; } | |
| #highlight-leader-grid .size-score-label { display: block; margin-top: 5px; color: #91919b; font-size: 10px; } | |
| #highlight-leader-grid .filter-leader-meta { margin-top: 0; color: #91919b; font-size: 11px; overflow-wrap: anywhere; } | |
| .highlight-leader-org, .highlight-leader-params { display: block; } | |
| .highlight-leader-params { margin-top: 4px; color: var(--text-secondary); } | |
| @media (max-width: 900px) { | |
| #highlight-leader-grid { grid-template-columns: repeat(3, minmax(0, 1fr)); } | |
| } | |
| @media (max-width: 620px) { | |
| #highlight-leader-grid { grid-template-columns: repeat(2, minmax(0, 1fr)); } | |
| #highlight-leader-grid .filter-leader-card { padding: 14px; } | |
| #highlight-leader-grid .filter-leader-name { font-size: 16px; } | |
| #highlight-leader-grid .filter-leader-score { font-size: 20px; } | |
| #highlight-leader-grid .filter-leader-meta { font-size: 10px; } | |
| .size-leaders-heading { align-items: flex-start; flex-direction: column; gap: 12px; } | |
| .overview-counts { gap: 18px 24px; } | |
| } | |
| body.variant-seven-one #new-releases .release-card { | |
| border-left: 1px solid var(--border-subtle); | |
| } | |
| body.variant-seven-one #new-releases .release-card:hover { | |
| border-left-color: var(--border); | |
| } | |
| body.variant-seven-one .release-carousel-nav { | |
| display: flex; | |
| align-items: center; | |
| gap: 8px; | |
| padding-bottom: 24px; | |
| } | |
| body.variant-seven-one .release-carousel-button { | |
| width: 38px; | |
| height: 38px; | |
| display: inline-flex; | |
| align-items: center; | |
| justify-content: center; | |
| border: 1px solid var(--border-strong); | |
| border-radius: 50%; | |
| background: var(--surface); | |
| color: var(--text); | |
| font: 400 17px/1 'Space Mono', monospace; | |
| cursor: pointer; | |
| transition: color .15s ease, border-color .15s ease, background .15s ease, transform .15s ease; | |
| } | |
| body.variant-seven-one .release-carousel-button:hover:not(:disabled) { | |
| color: var(--accent); | |
| border-color: var(--accent-muted); | |
| background: var(--accent-soft); | |
| transform: translateY(-2px); | |
| } | |
| body.variant-seven-one .release-carousel-button:disabled { | |
| color: var(--text-muted); | |
| cursor: default; | |
| opacity: .32; | |
| } | |
| body.variant-seven-one .release-carousel-status { | |
| min-width: 54px; | |
| color: var(--text-muted); | |
| text-align: center; | |
| font: 700 9px/1 'Space Mono', monospace; | |
| letter-spacing: .1em; | |
| } | |
| body.variant-seven-one #new-releases .release-grid { | |
| display: block; | |
| overflow: hidden; | |
| transition: height .38s cubic-bezier(.2,.8,.2,1); | |
| touch-action: pan-y; | |
| } | |
| body.variant-seven-one .release-carousel-track { | |
| display: flex; | |
| align-items: flex-start; | |
| width: 100%; | |
| transition: transform .5s cubic-bezier(.2,.8,.2,1); | |
| will-change: transform; | |
| } | |
| body.variant-seven-one .release-carousel-page { | |
| flex: 0 0 100%; | |
| min-width: 0; | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 12px; | |
| } | |
| body.variant-seven-one .release-carousel-page .release-card { | |
| height: 100%; | |
| } | |
| body.variant-seven-one #score-chart, | |
| body.variant-seven-one #charts { | |
| padding: 96px 0 0 ; | |
| background: transparent; | |
| border: 0; | |
| border-radius: 0; | |
| } | |
| body.variant-seven-one .filter-leader-card.active { | |
| background: var(--accent-soft); | |
| background: color-mix(in srgb, var(--leader-org-color, var(--accent)) 10%, var(--surface)); | |
| border-color: var(--leader-org-color, var(--accent)); | |
| box-shadow: | |
| inset 0 0 0 1px color-mix(in srgb, var(--leader-org-color, var(--accent)) 24%, transparent), | |
| 0 12px 30px color-mix(in srgb, var(--leader-org-color, var(--accent)) 9%, transparent); | |
| } | |
| body.variant-seven-one footer > div:nth-child(2) { | |
| display: flex; | |
| flex-wrap: wrap; | |
| align-items: center; | |
| justify-content: center; | |
| gap: 8px 16px; | |
| } | |
| body.variant-seven-one footer > div:nth-child(2) a { | |
| flex: 0 0 auto; | |
| margin-left: 0; | |
| white-space: nowrap; | |
| } | |
| body.variant-seven-one #rules { | |
| grid-column: 1 / -1; | |
| padding-top: 96px ; | |
| } | |
| body.variant-seven-one .rules-grid { | |
| display: grid; | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| gap: 12px; | |
| } | |
| body.variant-seven-one .rule-card { | |
| min-height: 170px; | |
| padding: 22px; | |
| background: var(--surface); | |
| border: 1px solid var(--border-subtle); | |
| border-top: 2px solid var(--border-strong); | |
| border-radius: 0 0 18px 0; | |
| } | |
| body.variant-seven-one .rule-number { | |
| display: block; | |
| margin-bottom: 28px; | |
| color: var(--accent-muted); | |
| font: 700 10px/1 'Space Mono', monospace; | |
| letter-spacing: .14em; | |
| } | |
| body.variant-seven-one .rule-card h3 { | |
| margin-bottom: 8px; | |
| color: var(--text); | |
| font-size: 18px; | |
| font-weight: 500; | |
| letter-spacing: -.3px; | |
| } | |
| body.variant-seven-one .rule-card p { | |
| color: var(--text-secondary); | |
| font-size: 13px; | |
| line-height: 1.6; | |
| } | |
| body.variant-seven-one .rule-card a { | |
| color: var(--accent); | |
| text-decoration: none; | |
| } | |
| body.variant-seven-one .rule-card a:hover { | |
| text-decoration: underline; | |
| } | |
| body.variant-seven-one .filter-leader-kicker b { | |
| display: none; | |
| } | |
| body.variant-seven-one .extra-columns-tab { | |
| overflow: hidden; | |
| } | |
| body.variant-seven-one .extra-columns-glyph { | |
| transform: rotate(0deg) scale(1); | |
| transform-origin: 50% 50%; | |
| transition: transform .42s cubic-bezier(.2,.8,.2,1), color .2s ease; | |
| will-change: transform; | |
| } | |
| body.variant-seven-one .leaderboard-table-shell.extras-open .extra-columns-glyph { | |
| transform: rotate(180deg) scale(1.12); | |
| } | |
| body.variant-seven-one .leaderboard-table-shell.extras-open > .table-wrap { | |
| animation: leaderboard-columns-open .48s cubic-bezier(.2,.8,.2,1) both; | |
| } | |
| body.variant-seven-one .leaderboard-table-shell.extras-closing > .table-wrap { | |
| animation: leaderboard-columns-close .42s cubic-bezier(.4,0,.2,1) both; | |
| } | |
| body.variant-seven-one .leaderboard-table-shell.extras-open #leaderboard-table .extra-col { | |
| animation: leaderboard-cell-open .42s cubic-bezier(.2,.8,.2,1) both; | |
| } | |
| body.variant-seven-one .leaderboard-table-shell.extras-open #leaderboard-table .extra-col:nth-child(11) { | |
| animation-delay: .04s; | |
| } | |
| body.variant-seven-one .leaderboard-table-shell.extras-open #leaderboard-table .extra-col:nth-child(12) { | |
| animation-delay: .09s; | |
| } | |
| body.variant-seven-one .leaderboard-table-shell.extras-open #leaderboard-table .extra-col:nth-child(13) { | |
| animation-delay: .14s; | |
| } | |
| body.variant-seven-one .leaderboard-table-shell.extras-closing #leaderboard-table .extra-col { | |
| display: table-cell; | |
| animation: leaderboard-cell-close .3s cubic-bezier(.4,0,1,1) both; | |
| } | |
| body.variant-seven-one .leaderboard-table-shell.extras-closing #leaderboard-table .extra-col-start { | |
| border-left: 1px solid var(--border-strong); | |
| } | |
| @keyframes leaderboard-columns-open { | |
| from { | |
| clip-path: inset(0 24% 0 0); | |
| opacity: .82; | |
| } | |
| to { | |
| clip-path: inset(0 0 0 0); | |
| opacity: 1; | |
| } | |
| } | |
| @keyframes leaderboard-columns-close { | |
| from { | |
| clip-path: inset(0 0 0 0); | |
| opacity: 1; | |
| } | |
| to { | |
| clip-path: inset(0 24% 0 0); | |
| opacity: .82; | |
| } | |
| } | |
| @keyframes leaderboard-cell-open { | |
| from { | |
| opacity: 0; | |
| transform: translateX(-18px); | |
| } | |
| to { | |
| opacity: 1; | |
| transform: translateX(0); | |
| } | |
| } | |
| @keyframes leaderboard-cell-close { | |
| from { | |
| opacity: 1; | |
| transform: translateX(0); | |
| } | |
| to { | |
| opacity: 0; | |
| transform: translateX(14px); | |
| } | |
| } | |
| @media (prefers-reduced-motion: reduce) { | |
| body.variant-seven-one .extra-columns-glyph, | |
| body.variant-seven-one .leaderboard-table-shell > .table-wrap, | |
| body.variant-seven-one .leaderboard-table-shell #leaderboard-table .extra-col { | |
| animation-duration: .01ms ; | |
| transition-duration: .01ms; | |
| } | |
| body.variant-seven-one #new-releases .release-grid, | |
| body.variant-seven-one .release-carousel-track { | |
| transition-duration: .01ms; | |
| } | |
| } | |
| @media (max-width: 900px) { | |
| body.variant-seven-one .rules-grid { | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| } | |
| } | |
| @media (max-width: 1600px) { | |
| body.variant-seven-one .side-nav { | |
| display: none; | |
| } | |
| } | |
| @media (max-width: 560px) { | |
| body.variant-seven-one .rules-grid { | |
| grid-template-columns: 1fr; | |
| } | |
| body.variant-seven-one .release-head { | |
| display: flex; | |
| align-items: flex-end; | |
| } | |
| body.variant-seven-one .release-carousel-page { | |
| grid-template-columns: 1fr; | |
| } | |
| body.variant-seven-one .release-carousel-nav { | |
| padding-bottom: 24px; | |
| } | |
| } | |
| </style> | |
| <link rel="stylesheet" href="leaderboard-history.css"> | |
| </head> | |
| <body class="variant-seven variant-seven-one"> | |
| <!-- SIDE NAV RAIL --> | |
| <nav id="side-nav" class="side-nav" aria-label="Section navigation"></nav> | |
| <div class="container"> | |
| <!-- HERO --> | |
| <section class="hero"> | |
| <h1>Open <span>SLM Leaderboard</span></h1> | |
| <p class="hero-sub"> | |
| A leaderboard for sub-400M parameter language models, evaluated using LM-eval harness | |
| or a custom benchmark script available here | |
| <a href="https://huggingface.co/datasets/AxiomicLabs/ArithMark-3.0" target="_blank" rel="noopener noreferrer">ArithMark-3.0</a>. | |
| </p> | |
| <p class="hero-sub"> | |
| To support our work and help us keep this leaderboard up to date, please consider giving the space a like and follow! | |
| </p> | |
| <div class="overview-strip" id="overview-strip" aria-label="Leaderboard overview"></div> | |
| </section> | |
| <!-- HIGHLIGHTS --> | |
| <section id="highlights" style="padding-top:20px;"> | |
| <div class="size-leaders-heading"> | |
| <h2 class="size-leaders-title">Leaders by size</h2> | |
| <p class="section-sub">Ranked by Intelligence Index</p> | |
| </div> | |
| <div class="filter-leader-grid" id="highlight-leader-grid" aria-label="Intelligence Index leaders by model size"></div> | |
| </section> | |
| <!-- NOTABLE NEW RELEASES --> | |
| <section id="new-releases" style="padding-top:40px;"> | |
| <div class="release-head"> | |
| <div> | |
| <h2 class="section-title">Notable New Releases</h2> | |
| <p class="section-sub">Short notes on new models and leaderboard shifts.</p> | |
| </div> | |
| <div class="release-carousel-nav" aria-label="Release pages"> | |
| <button class="release-carousel-button" id="release-prev" type="button" aria-label="Previous four releases">←</button> | |
| <span class="release-carousel-status" id="release-page-status" aria-live="polite">01 / 01</span> | |
| <button class="release-carousel-button" id="release-next" type="button" aria-label="Next four releases">→</button> | |
| </div> | |
| </div> | |
| <div class="release-grid"> | |
| <article class="release-card" data-org="axiomiclabs"> | |
| <div class="release-kicker"> | |
| <span>14/10/2026</span> | |
| <span class="release-pill">update</span> | |
| </div> | |
| <a class="release-title" href="#leaderboard">Open SLM expands to sub-400M models</a> | |
| <p class="release-blurb">The leaderboard now welcomes models below 400M parameters, expanding the previous 150M limit. Switch to Extended above Search models to use the <250M filter or select All to explore the full leaderboard.</p> | |
| <span class="release-meta">Leaderboard Update · Eligibility</span> | |
| </article> | |
| <article class="release-card" data-org="axiomiclabs"> | |
| <div class="release-kicker"> | |
| <span>10/10/2026</span> | |
| <span class="release-pill">new</span> | |
| </div> | |
| <a class="release-title" href="https://huggingface.co/AxiomicLabs/GPT-X3-150M" target="_blank">New Outright #1</a> | |
| <p class="release-blurb">GPT-X3-150M has taken the #1 spot from cagliostro, becoming the first ever model to surpass 30 on the Intelligence Index</p> | |
| <span class="release-meta">Axiomic Labs · 149M params</span> | |
| </article> | |
| <article class="release-card" data-org="benchlabs"> | |
| <div class="release-kicker"> | |
| <span>05/10/2026</span> | |
| <span class="release-pill">new</span> | |
| </div> | |
| <a class="release-title" href="https://huggingface.co/bench-labs/cagliostro-v3.5" target="_blank">We have a NEW #1</a> | |
| <p class="release-blurb">Bench Labs' cagliostro-v3.5 has taken the #1 spot, becoming the first model to defeat Huggingface's SmolLM2. </p> | |
| <span class="release-meta">Bench Labs · 146M params</span> | |
| </article> | |
| <article class="release-card" data-org="benchlabs"> | |
| <div class="release-kicker"> | |
| <span>20/09/2026</span> | |
| <span class="release-pill">new</span> | |
| </div> | |
| <a class="release-title" href="https://huggingface.co/bench-labs/cagliostro-v3" target="_blank">Bench Labs advances the SLM frontier</a> | |
| <p class="release-blurb">Bench Labs' cagliostro-v3 hits Intelligence Index 26.13, a 146M-param model trained on 75b tokens, from scratch on a single consumer GPU. It places 2nd on the leaderboard, making it the most capable SLM we've seen trained on consumer hardware. </p> | |
| <span class="release-meta">Bench Labs · 146M params</span> | |
| </article> | |
| <article class="release-card" data-org="axiomiclabs"> | |
| <div class="release-kicker"> | |
| <span>26/08/2026</span> | |
| <span class="release-pill">update</span> | |
| </div> | |
| <a class="release-title" href="https://huggingface.co/spaces/AxiomicLabs/Open_SLM_Leaderboard" target="_blank">Open SLM UI gets a catalogue refresh</a> | |
| <p class="release-blurb">The leaderboard UI now brings filter leaders, animated benchmark expansion, Intelligence Index fit scores, and paged release notes into one clearer workspace.</p> | |
| <span class="release-meta">Leaderboard Update · UI</span> | |
| </article> | |
| <article class="release-card" data-org="axiomiclabs"> | |
| <div class="release-kicker"> | |
| <span>12/07/2026</span> | |
| <span class="release-pill">change</span> | |
| </div> | |
| <a class="release-title" href="https://huggingface.co/spaces/AxiomicLabs/Open_SLM_Leaderboard" target="_blank">Leaderboard Math Changes</a> | |
| <p class="release-blurb">ArithMark-3 is now the primary math benchmark for ranking, weighting subject to change. ArithMark-2 remains available in the expanded columns.</p> | |
| <span class="release-meta">Leaderboard Change Β· Math</span> | |
| </article> | |
| <article class="release-card" data-org="ucr"> | |
| <div class="release-kicker"> | |
| <span>01/07/2026</span> | |
| <span class="release-pill">new</span> | |
| </div> | |
| <a class="release-title" href="https://huggingface.co/UniversalComputingResearch/Atom2.7m" target="_blank">Atom2.7m posts standout ArithMark 2.0 result</a> | |
| <p class="release-blurb">UCR's 2.74M SLM uses custom tokenization and digit features to reach 69.4% ArithMark accuracy, setting the leaderboard's #1 score</p> | |
| <span class="release-meta">UCR · 2.7M params</span> | |
| </article> | |
| <article class="release-card" data-org="axiomiclabs"> | |
| <div class="release-kicker"> | |
| <span>19/06/2026</span> | |
| <span class="release-pill">new</span> | |
| </div> | |
| <a class="release-title" href="https://huggingface.co/AxiomicLabs/GPT-S2-5M" target="_blank">Axiomic Labs GPT-S2 takes <10M #1 spot</a> | |
| <p class="release-blurb">Built on Axiomic Labs' T-X4 refresh-gated XSA stack, GPT-S2-5M pushes into the sub-10M bracket and lands at the top, dethroning SLM-10M.</p> | |
| <span class="release-meta">Axiomic Labs Β· 5.4M params</span> | |
| </article> | |
| <article class="release-card" data-org="veyraai"> | |
| <div class="release-kicker"> | |
| <span>16/06/2026</span> | |
| <span class="release-pill">new</span> | |
| </div> | |
| <a class="release-title" href="https://huggingface.co/veyra-ai/Veyra2-Apricot-50M-Base" target="_blank">Veyra2-Apricot-50M-Base takes the <50M lead</a> | |
| <p class="release-blurb">Veyra AI's 49M Apricot checkpoint posts a 37.63 average, setting the strongest score in the sub-50M bracket.</p> | |
| <span class="release-meta">veyra-ai Β· 49M params</span> | |
| </article> | |
| </div> | |
| </section> | |
| <!-- LEADERBOARD TABLE --> | |
| <section id="leaderboard" style="padding-top:40px;"> | |
| <h2 class="section-title">Leaderboard</h2> | |
| <p class="section-sub">Zero-shot evaluation. Higher is better for all columns. <span class="category-leader-key">Gold names</span> mark size category leaders. Click any header to sort.</p> | |
| <div class="leaderboard-filter-row"> | |
| <div class="filter-group"> | |
| <span class="filter-label">Model size</span> | |
| <div class="filter-bar" id="filter-bar"></div> | |
| </div> | |
| <div class="leaderboard-search-controls"> | |
| <div class="size-mode-toggle" role="group" aria-label="Model size categories"> | |
| <button class="filter-btn active" id="size-mode-default" type="button" onclick="setSizeMode('default')" aria-pressed="true" aria-controls="filter-bar size-leader-grid highlight-leader-grid" title="Under 150M and smaller categories">Default</button> | |
| <button class="filter-btn" id="size-mode-extended" type="button" onclick="setSizeMode('extended')" aria-pressed="false" aria-controls="filter-bar size-leader-grid highlight-leader-grid" title="Default categories plus All sizes and under 250M">Extended</button> | |
| </div> | |
| <label class="leaderboard-search"> | |
| <span class="filter-label">Search models</span> | |
| <input class="leaderboard-search-input" id="leaderboard-search" type="search" placeholder="Name or organizationβ¦" autocomplete="off" spellcheck="false"> | |
| </label> | |
| </div> | |
| </div> | |
| <div class="filter-leader-grid" id="size-leader-grid" aria-label="Best model in every size filter"></div> | |
| <div class="leaderboard-table-shell" id="leaderboard-table-shell"> | |
| <button class="extra-columns-tab" id="extra-columns-toggle" type="button" onclick="toggleExtraColumns()" aria-label="Show more columns" aria-expanded="false" aria-controls="leaderboard-table" title="Show more columns"> | |
| <span class="extra-columns-glyph" aria-hidden="true">›</span> | |
| </button> | |
| <div class="table-wrap"> | |
| <table id="leaderboard-table"> | |
| <thead> | |
| <tr> | |
| <th class="th-compare">Compare</th> | |
| <th onclick="sortTable('rank')"># <span class="sort-indicator">βΌ</span></th> | |
| <th onclick="sortTable('name')">Model <span class="sort-indicator"></span></th> | |
| <th onclick="sortTable('params')">Params <span class="sort-indicator"></span></th> | |
| <th onclick="sortTable('intelligenceIndex')" title="Intelligence Index: chance-normalized; ArithMark-3 weight 0.65, other components weight 1">Int Index <span class="sort-indicator"></span></th> | |
| <th onclick="sortTable('hellaswag')">HellaSwag <span class="sort-indicator"></span></th> | |
| <th onclick="sortTable('arc')">ARC-Easy <span class="sort-indicator"></span></th> | |
| <th onclick="sortTable('arcChall')">ARC-Challenge <span class="sort-indicator"></span></th> | |
| <th onclick="sortTable('piqa')">PIQA <span class="sort-indicator"></span></th> | |
| <th onclick="sortTable('arithmark3')">ArithMark-3 <span class="sort-indicator"></span></th> | |
| <th class="extra-col extra-col-start" onclick="sortTable('avg')">Avg <span class="sort-indicator"></span></th> | |
| <th class="extra-col" onclick="sortTable('fitZ')">Fit Std Devs <span class="sort-indicator"></span></th> | |
| <th class="extra-col" onclick="sortTable('arithmark2')">ArithMark-2 <span class="sort-indicator"></span></th> | |
| </tr> | |
| </thead> | |
| <tbody id="leaderboard-body"></tbody> | |
| </table> | |
| </div> | |
| </div> | |
| </section> | |
| <!-- COMPARE --> | |
| <section id="compare" style="padding-top:80px;"> | |
| <div class="compare-head"> | |
| <div> | |
| <h2 class="section-title">Compare</h2> | |
| <p class="section-sub">Models you add from the leaderboard line up side by side here. <span id="compare-count-label" class="compare-count">None selected</span></p> | |
| </div> | |
| <div class="compare-head-actions"> | |
| <span id="compare-limit-note" class="compare-limit-note">Up to 6 models at a time</span> | |
| <button class="filter-btn" onclick="clearCompare()">Clear all</button> | |
| </div> | |
| </div> | |
| <div id="compare-content"></div> | |
| </section> | |
| <!-- BAR CHART --> | |
| <section id="score-chart" style="padding-top:80px;"> | |
| <h2 class="section-title">Scores</h2> | |
| <p class="section-sub" id="score-chart-sub">Top scores for the active size and benchmark filters.</p> | |
| <div class="filter-group"> | |
| <span class="filter-label">Benchmark</span> | |
| <div class="filter-bar" id="benchmark-filter-bar"></div> | |
| <div class="filter-leader-grid" id="benchmark-leader-grid" aria-label="Best model in every benchmark filter"></div> | |
| </div> | |
| <div class="chart-card"> | |
| <h3>Top Intelligence Index Scores</h3> | |
| <canvas id="barChart"></canvas> | |
| </div> | |
| </section> | |
| <!-- SCATTER PLOT --> | |
| <section id="charts" style="padding-top:80px;"> | |
| <h2 class="section-title">Efficiency</h2> | |
| <p class="section-sub" id="efficiency-sub">Score vs parameter count (log scale). Shaded zone = above regression line.</p> | |
| <div class="legend-bar" id="legend-bar"></div> | |
| <div class="chart-card"> | |
| <h3>Intelligence Index vs Log Parameters</h3> | |
| <canvas id="scatterChart"></canvas> | |
| </div> | |
| </section> | |
| <!-- LEADERBOARD HISTORY --> | |
| <section id="org-performance" style="padding-top:80px;"> | |
| <h2 class="section-title">Leaderboard History</h2> | |
| <p class="section-sub" id="history-period">Indie model leadership across the <150M, <100M, <50M, <10M and <3M categories.</p> | |
| <div class="chart-card history-chart"> | |
| <h3>Intelligence frontier over time</h3> | |
| <div id="history-frontier"></div> | |
| <p class="history-note" id="history-frontier-note">Each step marks a new category leader, using addition dates and current II. Colours identify the lab. Hover a dot for model details; select a category to show or hide its line.</p> | |
| </div> | |
| <div class="chart-card history-chart"> | |
| <h3>Category dominance over time</h3> | |
| <div id="history-dominance"></div> | |
| </div> | |
| <div class="chart-card history-chart"> | |
| <h3>Labs with the most #1 class-days</h3> | |
| <div id="history-labs"></div> | |
| <p class="history-note">Size categories overlap. A lab leading two categories for one day earns two class-days.</p> | |
| </div> | |
| <div class="chart-card history-chart"> | |
| <h3>Most dominant models</h3> | |
| <div id="history-models"></div> | |
| <p class="history-note">Total #1 class-days per model, summed across the same five size categories.</p> | |
| </div> | |
| <details class="history-method"> | |
| <summary>How leadership is counted</summary> | |
| <p>Each currently listed, uncommented model joins the timeline on its stored addition date. Rankings throughout the timeline use the model's current Intelligence Index, parameter count and organization. For each category, the highest-scoring eligible model receives each calendar day from its lead's start through the day before the next leader arrives. Exact ties follow the leaderboard's model order. Hugging Face, Meta, OpenAI, Google and Microsoft entries are excluded from these indie lab charts.</p> | |
| <p>The timeline is reconstructed from addition dates and current scores, rather than past score revisions. Totals include today in Australia/Sydney and are recalculated when the page loads.</p> | |
| </details> | |
| </section> | |
| <!-- LEADERBOARD RULES --> | |
| <section id="rules"> | |
| <h2 class="section-title">Rules</h2> | |
| <p class="section-sub">Eligibility, evaluation, and submission requirements for the Open SLM Leaderboard.</p> | |
| <div class="rules-grid"> | |
| <article class="rule-card"> | |
| <span class="rule-number">RULE 01</span> | |
| <h3>Stay below 400M parameters</h3> | |
| <p>The leaderboard is for language models with fewer than 400 million parameters.</p> | |
| </article> | |
| <article class="rule-card"> | |
| <span class="rule-number">RULE 02</span> | |
| <h3>Train it from scratch</h3> | |
| <p>The submitted model must be pretrained from scratch by you or your organization.</p> | |
| </article> | |
| <article class="rule-card"> | |
| <span class="rule-number">RULE 03</span> | |
| <h3>Publish open weights</h3> | |
| <p>Model weights must be openly available and point to a genuine training checkpoint, not a merged model.</p> | |
| </article> | |
| <article class="rule-card"> | |
| <span class="rule-number">RULE 04</span> | |
| <h3>Use the listed evaluations</h3> | |
| <p>Provide zero-shot results for the listed benchmarks using LM-eval harness or the linked custom ArithMark script.</p> | |
| </article> | |
| <article class="rule-card"> | |
| <span class="rule-number">RULE 05</span> | |
| <h3>Results are verified</h3> | |
| <p>Submitted results are independently checked by the leaderboard team before they are merged.</p> | |
| </article> | |
| <article class="rule-card"> | |
| <span class="rule-number">RULE 06</span> | |
| <h3>Submit through the Space</h3> | |
| <p>Open a PR or discussion with the benchmark results in the <a href="https://huggingface.co/spaces/AxiomicLabs/Open_SLM_Leaderboard/discussions" target="_blank">leaderboard Space</a>.</p> | |
| </article> | |
| </div> | |
| </section> | |
| <!-- ABOUT / ADD MODEL --> | |
| <section id="about" style="padding-top:80px;"> | |
| <div class="about-box"> | |
| <h3>Add your model </h3> | |
| <p>Open a PR or discussion on this Space with your model's results for the given benchmarks. They will be independently verified by our team and then your PR will be merged. Your model must be pretrained by you from scratch, open weights and be a training checkpoint (not merged) to qualify <a href="https://huggingface.co/spaces/AxiomicLabs/Open_SLM_Leaderboard/discussions" target="_blank">Open a PR β</a></p> | |
| </div> | |
| </section> | |
| <!-- INTELLIGENCE INDEX METHODOLOGY --> | |
| <section id="int-index-method" style="padding-top:32px;"> | |
| <div class="about-box index-method-card"> | |
| <h3>How the Intelligence Index is calculated</h3> | |
| <p>Each benchmark is first adjusted for its random-chance floor, so chance performance maps to 0 and perfect performance maps to 100.</p> | |
| <div class="index-formula"> | |
| N(score, chance) = 100 × (score − chance) ÷ (100 − chance)<br> | |
| Int Index = (HellaSwag + ARC + PIQA + 0.65 × ArithMark-3) ÷ 3.65 | |
| </div> | |
| <div class="index-weight-grid"> | |
| <div class="index-weight"><strong>HellaSwag</strong><span>weight 1.00<br>chance 25</span></div> | |
| <div class="index-weight"><strong>Combined ARC</strong><span>weight 1.00<br>chance 25</span></div> | |
| <div class="index-weight"><strong>PIQA</strong><span>weight 1.00<br>chance 50</span></div> | |
| <div class="index-weight"><strong>ArithMark-3</strong><span>weight 0.65<br>chance 25</span></div> | |
| </div> | |
| <p class="index-method-note">Combined ARC is the mean of ARC-Easy and ARC-Challenge before normalization. When a component is unavailable, its weight is removed from both the numerator and denominator; at least two components are required. Scores below chance may be negative. ArithMark-2 is displayed separately and is not included in the index.</p> | |
| </div> | |
| </section> | |
| <!-- FOOTER --> | |
| <footer> | |
| <div>Open SLM Leaderboard by <a href="https://huggingface.co/AxiomicLabs" style="margin:0;">Axiomic Labs</a></div> | |
| <div> | |
| <a href="#leaderboard" onclick="return scrollToSection('leaderboard')">Leaderboard</a> | |
| <a href="#compare" onclick="return scrollToSection('compare')">Compare</a> | |
| <a href="#score-chart" onclick="return scrollToSection('score-chart')">Scores</a> | |
| <a href="#charts" onclick="return scrollToSection('charts')">Efficiency</a> | |
| <a href="#org-performance" onclick="return scrollToSection('org-performance')">History</a> | |
| <a href="#rules" onclick="return scrollToSection('rules')">Rules</a> | |
| <a href="#int-index-method" onclick="return scrollToSection('int-index-method')">Int Index</a> | |
| </div> | |
| <div style="font-size:10px;">All results independently verified using our internal verification process.</div> | |
| </footer> | |
| </div> | |
| <!-- STICKY COMPARE BAR --> | |
| <div id="compare-bar" class="compare-bar"></div> | |
| <script> | |
| // βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| // βββ CENTRALIZED DATA β EDIT HERE TO ADD/UPDATE MODELS & ORGS | |
| // βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| const ORGS = { | |
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| altslate: { name: 'AltSlate Labs', chartColor: 'rgba(251, 191, 36, 0.70)', chartBorder: '#fbbf24', url: 'https://huggingface.co/altslate' }, | |
| mihaipopa: { name: 'Mihai Popa', chartColor: 'rgba(247, 129, 102, 0.70)', chartBorder: '#f78166', url: 'https://huggingface.co/MihaiPopa-1' }, | |
| lhtechai: { name: 'LH-Tech-AI', chartColor: 'rgba(249, 115, 22, 0.70)', chartBorder: '#f97316', url: 'https://huggingface.co/LH-Tech-AI' }, | |
| facebook: { name: 'Facebook', chartColor: 'rgba(24, 119, 242, 0.70)', chartBorder: '#1877f2', url: 'https://huggingface.co/facebook' }, | |
| harleyml: { name: 'Harley ML', chartColor: 'rgba(153, 27, 27, 0.70)', chartBorder: '#991b1b', url: 'https://huggingface.co/Harley-ml' }, | |
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| eleutherai: { name: 'EleutherAI', chartColor: 'rgba(239, 68, 68, 0.70)', chartBorder: '#ef4444', url: 'https://huggingface.co/EleutherAI' }, | |
| openai: { name: 'OpenAI', chartColor: 'rgba(16, 163, 127, 0.70)', chartBorder: '#10a37f', url: 'https://huggingface.co/openai-community' }, | |
| stentor: { name: 'StentorLabs', chartColor: 'rgba(255, 107, 203, 0.70)', chartBorder: '#ff6bcb', url: 'https://huggingface.co/StentorLabs' }, | |
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| thingai: { name: 'ThingAI', chartColor: 'rgba(180, 83, 9, 0.70)', chartBorder: '#b45309', url: 'https://huggingface.co/ThingAI' }, | |
| fromzero: { name: 'FromZero', chartColor: 'rgba(210, 180, 140, 0.70)', chartBorder: '#d2b48c', url: 'https://huggingface.co/fromziro' }, | |
| finnianx: { name: 'finnianx', chartColor: 'rgba(45, 212, 191, 0.70)', chartBorder: '#2dd4bf', url: 'https://huggingface.co/finnianx' }, | |
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| whirlwindai: { name: 'WhirlwindAI', chartColor: 'rgba(192, 132, 252, 0.70)', chartBorder: '#c084fc', url: 'https://huggingface.co/WhirlwindAI' }, | |
| flamef0x: { name: 'FlameF0X', chartColor: 'rgba(148, 163, 184, 0.70)', chartBorder: '#94a3b8', url: 'https://huggingface.co/FlameF0X' }, | |
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| appvoid: { name: 'appvoid', chartColor: 'rgba(255, 255, 255, 0.70)', chartBorder: '#ffffff', url: 'https://huggingface.co/appvoid' }, | |
| dreamw: { name: 'Dream-W', chartColor: 'rgba(102, 0, 255, 1.0)', chartBorder: '#6600ff', url: 'https://huggingface.co/Dream-W' }, | |
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| minimalabs: { name: 'Minima Labs', chartColor: 'rgba(62, 92, 255, 0.70)', chartBorder: '#3E5CFF', url: 'https://huggingface.co/MinimaLabs' }, | |
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| benchlabs: { name: 'Bench Labs', chartColor: 'rgba(192, 38, 211, 0.70)', chartBorder: '#c026d3', url: 'https://huggingface.co/bench-labs' }, | |
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| codesoft: { name: 'CodeSoft', chartColor: 'rgba(80, 162, 255, 0.70)', chartBorder: '#50a2ff', url: 'https://huggingface.co/CodeSoft' }, | |
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| ls: { name: 'LS', chartColor: 'rgba(232, 144, 144, 0.70)', chartBorder: '#e89090', url: 'https://huggingface.co/Dsg2' }, | |
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| }; | |
| const MODELS = [ // ADD NEW MODELS TO BOTTOM OF LIST | |
| // addedDate records the first leaderboard appearance; it is stored metadata only. | |
| // Backfill missing dates with python scripts/update_history.py; charts calculate from MODELS on page load. | |
| // -- Models with full benchmark data -- | |
| { name: 'Nexus-Erebus-3M', org: 'ideoalabs', addedDate: '2026-07-13', params: 3000384, paramsDisplay: '3.0M', arc: 27.02, hellaswag: 26.59, piqa: 51.41, arcChall: 20.65, arithmark3: 29.20, arithmark2: 70.72, links: { card: 'https://huggingface.co/MaliosDark/Nexus-Erebus-3M' } }, | |
| { name: 'Nexus-Erebus-50M', org: 'ideoalabs', addedDate: '2026-07-10', params: 54100000, paramsDisplay: '54M', arc: 44.40, hellaswag: 27.05, piqa: 58.27, arcChall: 22.70, arithmark3: 33.10, arithmark2: 52.48, links: { card: 'https://huggingface.co/MaliosDark/Nexus-Erebus-50M' } }, | |
| { name: 'SmolLM2-135M', org: 'huggingface', addedDate: '2026-05-27', params: 135000000, paramsDisplay: '135M', arc: 58.63, hellaswag: 43.22, piqa: 68.44, arcChall: 29.69, arithmark3: 39.20, arithmark2: 32.68, links: { card: 'https://huggingface.co/HuggingFaceTB/SmolLM2-135M' } }, | |
| { name: 'GPT-X2-125M', org: 'axiomiclabs', addedDate: '2026-05-27', params: 125000000, paramsDisplay: '125M', arc: 51.47, hellaswag: 40.41, piqa: 67.30, arcChall: 27.82, arithmark3: 37.20, arithmark2: 30.68, links: { card: 'https://huggingface.co/AxiomicLabs/GPT-X2-125M' } }, | |
| { name: 'GPT-X-125M', org: 'axiomiclabs', addedDate: '2026-05-27', params: 125000000, paramsDisplay: '125M', arc: 50.76, hellaswag: 36.57, piqa: 64.96, arcChall: 26.62, arithmark3: 35.60, arithmark2: 30.24, links: { card: 'https://huggingface.co/AxiomicLabs/GPT-X-125M' } }, | |
| { name: 'MobileLLM-R1-140M-base', org: 'facebook', addedDate: '2026-05-27', params: 140000000, paramsDisplay: '140M', arc: 49.92, hellaswag: 33.84, piqa: 63.22, arcChall: 24.74, arithmark3: 65.70, arithmark2: 53.56, links: { card: 'https://huggingface.co/facebook/MobileLLM-R1-140M-base' } }, | |
| { name: 'Supra-50M-Base', org: 'supralabs', addedDate: '2026-05-27', params: 51786240, paramsDisplay: '52M', arc: 45.88, hellaswag: 31.83, piqa: 62.51, arcChall: 25.00, arithmark3: 36.30, arithmark2: 27.04, links: { card: 'https://huggingface.co/SupraLabs/Supra-50M-Base' } }, | |
| { name: 'Shard-1', org: 'compactai', addedDate: '2026-05-27', params: 54500000, paramsDisplay: '54.5M', arc: 41.12, hellaswag: 29.20, piqa: 58.22, arcChall: 20.99, arithmark2: 26.80, links: { card: 'https://huggingface.co/CompactAI-O/Shard-1' } }, | |
| { name: 'Escarda-86M', org: 'quazim0t0', addedDate: '2026-06-16', params: 85656068, paramsDisplay: '85.7M', arc: 35.98, hellaswag: 30.32, piqa: 58.49, arcChall: 22.95, arithmark2: 29.36, links: { card: 'https://huggingface.co/Quazim0t0/Escarda-86M' } }, | |
| { name: 'Escarda-86M-Base', org: 'quazim0t0', addedDate: '2026-06-16', params: 85656068, paramsDisplay: '85.7M', arc: 37.92, hellaswag: 28.02, piqa: 58.16, arcChall: 18.86, arithmark2: 25.36, links: { card: 'https://huggingface.co/Quazim0t0/Escarda-86M-Base' } }, | |
| { name: 'Supra-50M-Instruct', org: 'supralabs', addedDate: '2026-05-27', params: 51786240, paramsDisplay: '52M', arc: 44.40, hellaswag: 29.09, piqa: 59.47, arcChall: 27.30, arithmark3: 33.90, arithmark2: 29.12, links: { card: 'https://huggingface.co/SupraLabs/Supra-50M-Instruct' } }, | |
| { name: 'Supra-50M-Reasoning', org: 'supralabs', addedDate: '2026-06-04', params: 51786240, paramsDisplay: '52M', arc: 44.44, hellaswag: 29.10, piqa: 59.30, arcChall: 27.39, arithmark3: 35.00, arithmark2: 28.96, links: { card: 'https://huggingface.co/SupraLabs/Supra-50M-Reasoning' } }, | |
| { name: 'SmolLM-135M', org: 'huggingface', addedDate: '2026-05-27', params: 135000000, paramsDisplay: '135M', arc: 56.31, hellaswag: 42.70, piqa: 68.28, arcChall: 29.01, arithmark3: 36.80, arithmark2: 28.84, links: { card: 'https://huggingface.co/HuggingFaceTB/SmolLM-135M' } }, | |
| { name: 'OPT-125M', org: 'facebook', addedDate: '2026-05-27', params: 125000000, paramsDisplay: '125M', arc: 40.28, hellaswag: 31.31, piqa: 62.24, arcChall: 22.70, arithmark3: 35.20, arithmark2: 24.40, links: { card: 'https://huggingface.co/facebook/opt-125m' } }, | |
| { name: 'GPT-S-5M', org: 'axiomiclabs', addedDate: '2026-05-27', params: 5160000, paramsDisplay: '5.2M', arc: 33.21, hellaswag: 27.46, piqa: 57.24, arcChall: 21.16, arithmark3: 30.20, arithmark2: 27.12, links: { card: 'https://huggingface.co/AxiomicLabs/GPT-S-5M' } }, | |
| { name: 'GPT-2', org: 'openai', addedDate: '2026-05-27', params: 124000000, paramsDisplay: '124M', arc: 39.35, hellaswag: 31.26, piqa: 62.08, arcChall: 22.35, arithmark3: 35.70, arithmark2: 26.48, links: { card: 'https://huggingface.co/openai-community/gpt2' } }, | |
| { name: 'Spark-5M-Base-v4', org: 'lhtechai', addedDate: '2026-05-27', params: 5000000, paramsDisplay: '5M', arc: 33.16, hellaswag: 27.03, piqa: 53.32, arcChall: 21.50, arithmark3: 29.30, arithmark2: 25.00, links: { card: 'https://huggingface.co/LH-Tech-AI/Spark-5M-Base-v4' } }, | |
| { name: 'Supra-Mini-v5-8M', org: 'supralabs', addedDate: '2026-05-27', params: 7870000, paramsDisplay: '7.87M', arc: 33.21, hellaswag: 26.37, piqa: 54.03, arcChall: 21.16, arithmark3: 31.50, arithmark2: 24.28, links: { card: 'https://huggingface.co/SupraLabs/Supra-Mini-v5-8M' } }, | |
| { name: 'Pythia-70M', org: 'eleutherai', addedDate: '2026-05-27', params: 70000000, paramsDisplay: '70M', arc: 31.65, hellaswag: 27.49, piqa: 53.48, arcChall: 23.63, arithmark3: 27.00, arithmark2: 25.32, links: { card: 'https://huggingface.co/EleutherAI/pythia-70m' } }, | |
| { name: 'Supra-Mini-v4-2M', org: 'supralabs', addedDate: '2026-05-27', params: 2620000, paramsDisplay: '2.6M', arc: 30.98, hellaswag: 25.52, piqa: 51.90, arcChall: 21.50, arithmark3: 28.30, arithmark2: 24.08, links: { card: 'https://huggingface.co/SupraLabs/Supra-Mini-v4-2M' } }, | |
| { name: 'Pythia-31M', org: 'eleutherai', addedDate: '2026-05-27', params: 31000000, paramsDisplay: '31M', arc: 33.88, hellaswag: 27.14, piqa: 56.26, arcChall: 21.67, arithmark3: 34.90, arithmark2: 27.20, links: { card: 'https://huggingface.co/EleutherAI/pythia-31m' } }, | |
| { name: 'Stentor3-50M', org: 'stentor', addedDate: '2026-05-27', params: 50000000, paramsDisplay: '50M', arc: 29.67, hellaswag: 27.10, piqa: 53.75, arcChall: 21.67, arithmark3: 33.30, arithmark2: 29.48, links: { card: 'https://huggingface.co/StentorLabs/Stentor3-50M' } }, | |
| { name: 'Stentor3-20M', org: 'stentor', addedDate: '2026-05-27', params: 20000000, paramsDisplay: '20M', arc: 29.50, hellaswag: 27.06, piqa: 55.06, arcChall: 23.12, arithmark3: 32.60, arithmark2: 26.72, links: { card: 'https://huggingface.co/StentorLabs/Stentor3-20M' } }, | |
| { name: 'Portimbria-150M', org: 'stentor', addedDate: '2026-05-29', params: 151026432, paramsDisplay: '151M', arc: 35.82, hellaswag: 27.09, piqa: 58.27, arcChall: 18.77, arithmark3: 34.80, arithmark2: 28.04, links: { card: 'https://huggingface.co/StentorLabs/Portimbria-150M' } }, | |
| { name: 'nanowhale-100m-base', org: 'huggingface', addedDate: '2026-05-27', params: 100000000, paramsDisplay: '100M', arc: 28.79, hellaswag: 26.31, piqa: 51.80, arcChall: 24.83, arithmark3: 26.60, arithmark2: 25.20, links: { card: 'https://huggingface.co/HuggingFaceTB/nanowhale-100m-base' } }, | |
| { name: 'Pythia-14M', org: 'eleutherai', addedDate: '2026-05-27', params: 14000000, paramsDisplay: '14M', arc: 32.28, hellaswag: 26.20, piqa: 55.88, arcChall: 20.99, arithmark3: 33.40, arithmark2: 27.04, links: { card: 'https://huggingface.co/EleutherAI/pythia-14m' } }, | |
| { name: 'Tenete-8M', org: 'harleyml', addedDate: '2026-05-27', params: 8000000, paramsDisplay: '8M', arc: 31.69, hellaswag: 26.75, piqa: 55.66, arcChall: 21.84, arithmark3: 26.20, arithmark2: 26.72, links: { card: 'https://huggingface.co/Harley-ml/Tenete-8M' } }, | |
| { name: 'Dillion-1.2M', org: 'harleyml', addedDate: '2026-05-27', params: 1281384, paramsDisplay: '1.3M', arc: 31.19, hellaswag: 26.65, piqa: 53.05, arcChall: 22.78, arithmark3: 28.00, arithmark2: 24.80, links: { card: 'https://huggingface.co/Harley-ml/Dillion-1.2M' } }, | |
| { name: 'CinnabarLM-1.4M-Base', org: 'mihaipopa', addedDate: '2026-05-27', params: 1510000, paramsDisplay: '1.5M', arc: 28.54, hellaswag: 27.08, piqa: 52.50, arcChall: 23.38, arithmark3: 29.20, arithmark2: 24.96, links: { card: 'https://huggingface.co/MihaiPopa-1/CinnabarLM-1.4M-Base' } }, | |
| { name: 'CinnabarLM-4M-Base', org: 'mihaipopa', addedDate: '2026-05-27', params: 4230000, paramsDisplay: '4.2M', arc: 28.28, hellaswag: 27.71, piqa: 52.29, arcChall: 22.70, arithmark3: 29.40, arithmark2: 24.96, links: { card: 'https://huggingface.co/MihaiPopa-1/CinnabarLM-4M-Base' } }, | |
| { name: 'CinnabarLM-1.5M-Base', org: 'mihaipopa', addedDate: '2026-05-27', params: 1710000, paramsDisplay: '1.7M', arc: 28.11, hellaswag: 27.08, piqa: 52.94, arcChall: 21.93, arithmark3: 26.40, arithmark2: 25.20, links: { card: 'https://huggingface.co/MihaiPopa-1/CinnabarLM-1.5M-Base' } }, | |
| { name: 'Dillionv2-1.3M', org:'harleyml', addedDate: '2026-05-29', params: 1285200, paramsDisplay: '1.3M', arc: 29.71, hellaswag: 27.27, piqa: 53.05, arcChall: 22.44, arithmark3: 31.50, arithmark2: 27.00, links: { card: 'https://huggingface.co/Harley-ml/Dillionv2-1.3M' } }, | |
| { name: 'KeyLM-75M', org:'eclipsesenpai', addedDate: '2026-05-30',params: 75251200, paramsDisplay: '75M', arc: 35.73, hellaswag: 29.66, piqa: 60.50, arcChall: 23.98, arithmark3: 30.10, arithmark2: 25.80, links: { card: 'https://huggingface.co/Eclipse-Senpai/KeyLM-75M' } }, | |
| { name: 'Supra-Mini-v6-1M', org: 'supralabs', addedDate: '2026-05-30', params: 1410688, paramsDisplay: '1.4M', arc: 30.68, hellaswag: 27.23, piqa: 53.70, arcChall: 20.48, arithmark3: 26.40, arithmark2: 26.48, links: { card: 'https://huggingface.co/SupraLabs/Supra-Mini-v6-1M' } }, | |
| { name: 'GPT-S-1.4M', org: 'axiomiclabs', addedDate: '2026-06-01', params: 1426000, paramsDisplay: '1.4M', arc: 31.57, hellaswag: 26.89, piqa: 55.17, arcChall: 21.93, arithmark3: 30.20, arithmark2: 25.16, links: { card: 'https://huggingface.co/AxiomicLabs/GPT-S-1.4M' } }, | |
| { name: 'Archaea-74M', org: 'godelev', addedDate: '2026-06-02', params: 74016256, paramsDisplay: '74M', arc: 39.06, hellaswag: 27.27, piqa: 58.27, arcChall: 22.70, arithmark3: 35.50, arithmark2: 29.20, links: { card: 'https://huggingface.co/GODELEV/Archaea-74M' } }, | |
| { name: 'Cali-0.1B', org: 'sandroeth', addedDate: '2026-06-02', params: 123782400, paramsDisplay: '124M', arc: 27.53, hellaswag: 26.84, piqa: 52.12, arcChall: 24.49, arithmark3: 32.90, arithmark2: 24.72, links: { card: 'https://huggingface.co/Sandroeth/cali-0.1B' } }, | |
| { name: 'Quark-50M', org: 'thingai', addedDate: '2026-06-04', params: 56666496, paramsDisplay: '57M', arc: 36.78, hellaswag: 28.48, piqa: 57.83, arcChall: 25.00, arithmark3: 34.90, arithmark2: 28.20, links: { card: 'https://huggingface.co/ThingAI/Quark-50m' } }, | |
| { name: 'Quark-135M', org: 'thingai', addedDate: '2026-06-04', params: 134561088, paramsDisplay: '135M', arc: 47.73, hellaswag: 31.33, piqa: 58.32, arcChall: 28.24, arithmark3: 39.70, arithmark2: 40.32, links: { card: 'https://huggingface.co/ThingAI/Quark-135m' } }, | |
| { name: 'Veyra-30M-Base', org: 'veyraai', addedDate: '2026-06-03', params: 34611712, paramsDisplay: '35M', arc: 35.90, hellaswag: 27.92, piqa: 58.92, arcChall: 24.15, arithmark3: 28.90, arithmark2: 26.76, links: { card: 'https://huggingface.co/veyra-ai/Veyra-30M-Base' } }, | |
| { name: 'Syn-2.6M', org: 'fromzero', addedDate: '2026-06-07', params: 2604210, paramsDisplay: '2.6M', arc: 32.03, hellaswag: 26.96, piqa: 53.65, arcChall: 20.39, arithmark3: 27.60, arithmark2: 26.68, links: { card: 'https://huggingface.co/fromziro/Syn-2.6M' } }, | |
| { name: 'Er-Medium-12.5M', org: 'fromzero', addedDate: '2026-06-07', params: 12497520, paramsDisplay: '12.5M', arc: 35.10, hellaswag: 28.50, piqa: 57.51, arcChall: 20.73, arithmark3: 32.50, arithmark2: 30.88, links: { card: 'https://huggingface.co/fromziro/Er-Medium-12.5M' } }, | |
| { name: 'michel-tiny', org: 'finnianx', addedDate: '2026-06-09', params: 55719040, paramsDisplay: '56M', arc: 37.37, hellaswag: 28.15, piqa: 57.34, arcChall: 21.76, arithmark3: 28.70, arithmark2: 25.28, links: { card: 'https://huggingface.co/finnianx/michel-tiny' } }, | |
| { name: 'OdinNext-138M-Base', org: 'joelhenwang', addedDate: '2026-06-09', params: 138449696, paramsDisplay: '138M', arc: 45.08, hellaswag: 28.09, piqa: 59.52, arcChall: 23.81, arithmark3: 41.80, arithmark2: 36.84, links: { card: 'https://huggingface.co/joelhenwang/OdinNext-138M-Base' } }, | |
| { name: 'OdinNext-138M-Instruct', org: 'joelhenwang', addedDate: '2026-06-09', params: 138451232, paramsDisplay: '138M', arc: 44.40, hellaswag: 28.86, piqa: 58.65, arcChall: 23.12, arithmark3: 40.90, arithmark2: 36.56, links: { card: 'https://huggingface.co/joelhenwang/OdinNext-138M-Instruct' } }, | |
| { name: 'michel-micro', org: 'finnianx', addedDate: '2026-06-10', params: 28355072, paramsDisplay: '28M', arc: 38.51, hellaswag: 28.16, piqa: 57.62, arcChall: 23.29, arithmark3: 29.20, arithmark2: 26.04, links: { card: 'https://huggingface.co/finnianx/michel-micro' } }, | |
| { name: 'kirk-tung', org: 'rtc2022', addedDate: '2026-06-10', params: 53111296, paramsDisplay: '53M', arc: 30.43, hellaswag: 26.32, piqa: 52.61, arcChall: 22.01, arithmark3: 29.40, arithmark2: 24.88, links: { card: 'https://huggingface.co/rtc2022/kirk-tung' } }, | |
| { name: 'ettin-decoder-32m', org: 'jhuclsp', addedDate: '2026-06-11', params: 32081344, paramsDisplay: '32M', arc: 31.73, hellaswag: 27.79, piqa: 52.29, arcChall: 22.01, arithmark3: 32.70, arithmark2: 26.80, links: { card: 'https://huggingface.co/jhu-clsp/ettin-decoder-32m' } }, | |
| { name: 'ettin-decoder-17m', org: 'jhuclsp', addedDate: '2026-06-11', params: 16913600, paramsDisplay: '17M', arc: 31.65, hellaswag: 27.39, piqa: 53.37, arcChall: 22.27, arithmark3: 31.70, arithmark2: 28.48, links: { card: 'https://huggingface.co/jhu-clsp/ettin-decoder-17m' } }, | |
| { name: 'ettin-decoder-68m', org: 'jhuclsp', addedDate: '2026-06-11', params: 68457664, paramsDisplay: '68M', arc: 32.74, hellaswag: 27.81, piqa: 52.18, arcChall: 23.04, arithmark3: 30.30, arithmark2: 30.00, links: { card: 'https://huggingface.co/jhu-clsp/ettin-decoder-68m' } }, | |
| { name: 'ettin-decoder-150m', org: 'jhuclsp', addedDate: '2026-06-11', params: 149655232, paramsDisplay: '150M', arc: 29.97, hellaswag: 25.95, piqa: 51.25, arcChall: 24.23, arithmark3: 28.00, arithmark2: 26.60, links: { card: 'https://huggingface.co/jhu-clsp/ettin-decoder-150m' } }, | |
| { name: 'Er-Tiny-1.3M', org: 'fromzero', addedDate: '2026-06-11', params: 1332744, paramsDisplay: '1.3M', arc: 30.09, hellaswag: 27.67, piqa: 52.72, arcChall: 22.35, arithmark3: 28.60, arithmark2: 26.80, links: { card: 'https://huggingface.co/fromziro/Er-Tiny-1.3M' } }, | |
| { name: 'michel-nano', org: 'finnianx', addedDate: '2026-06-11', params: 5963008, paramsDisplay: '6M', arc: 33.29, hellaswag: 27.12, piqa: 54.79, arcChall: 22.70, arithmark3: 30.90, arithmark2: 26.00, links: { card: 'https://huggingface.co/finnianx/michel-nano' } }, | |
| { name: 'Supra-1.5-50M-base-exp', org: 'supralabs', addedDate: '2026-06-11', params: 51786240, paramsDisplay: '52M', arc: 48.40, hellaswag: 29.78, piqa: 60.01, arcChall: 25.51, arithmark3: 34.40, arithmark2: 31.32, links: { card: 'https://huggingface.co/SupraLabs/Supra-1.5-50M-base-exp' } }, | |
| { name: 'Ant-10M', org: 'godelev', addedDate: '2026-06-12', params: 9902336, paramsDisplay: '10M', arc: 27.53, hellaswag: 26.96, piqa: 50.65, arcChall: 28.24, arithmark3: 28.00, arithmark2: 24.48, links: { card: 'https://huggingface.co/GODELEV/Ant-19m' } }, | |
| { name: 'Supra-1.5-50M-Instruct-exp', org: 'supralabs', addedDate: '2026-06-13', params: 51786240, paramsDisplay: '52M', arc: 43.94, hellaswag: 29.26, piqa: 59.41, arcChall: 26.11, arithmark3: 34.90, arithmark2: 29.80, links: { card: 'https://huggingface.co/SupraLabs/Supra-1.5-50M-Instruct-exp' } }, | |
| { name: 'SLM-10M', org: 'liodonai', addedDate: '2026-06-13', params: 9968640, paramsDisplay: '10M', arc: 35.52, hellaswag: 27.40, piqa: 57.07, arcChall: 23.46, arithmark3: 27.60, arithmark2: 26.40, links: { card: 'https://huggingface.co/liodon-ai/slm-10m' } }, | |
| { name: 'Doge-20M', org: 'smalldoge', addedDate: '2026-06-14', params: 13118728, paramsDisplay: '13.1M', arc: 33.38, hellaswag: 26.61, piqa: 54.30, arcChall: 20.73, arithmark3: 29.70, arithmark2: 29.08, links: { card: 'https://huggingface.co/SmallDoge/Doge-20M' } }, | |
| { name: 'Veyra2-Apricot-50M-Base', org: 'veyraai', addedDate: '2026-06-16', params: 49303040, paramsDisplay: '49M', arc: 42.47, hellaswag: 31.28, piqa: 62.13, arcChall: 23.29, arithmark3: 31.60, arithmark2: 28.96, links: { card: 'https://huggingface.co/veyra-ai/Veyra2-Apricot-50M-Base' } }, | |
| { name: 'michel-nano-v2', org: 'finnianx', addedDate: '2026-06-16', params: 9938176, paramsDisplay: '9.9M', arc: 36.07, hellaswag: 27.36, piqa: 56.96, arcChall: 21.84, arithmark3: 30.20, arithmark2: 25.04, links: { card: 'https://huggingface.co/finnianx/michel-nano-v2' } }, | |
| { name: 'Dumb-1.2-RC3', org: 'small56ai', addedDate: '2026-06-16', params: 34611072, paramsDisplay: '34.6M', arc: 33.25, hellaswag: 26.91, piqa: 55.06, arcChall: 23.12, arithmark3: 28.00, arithmark2: 26.08, links: { card: 'https://huggingface.co/56m/Dumb-1.2-Exp-0616' } }, | |
| { name: 'Dumb-1.2-Preview-0618', org: 'small56ai', addedDate: '2026-06-19', params: 34611072, paramsDisplay: '34.6M', arc: 26.30, hellaswag: 26.45, piqa: 54.57, arcChall: 21.84, arithmark3: 30.40, arithmark2: 25.76, links: { card: 'https://huggingface.co/56m/Dumb-1.2-Preview-0618' } }, | |
| { name: 'Byrne-86M', org: 'quazim0t0', addedDate: '2026-06-19', params: 85327747, paramsDisplay: '85.3M', arc: 29.22, hellaswag: 30.36, piqa: 56.47, arcChall: 23.98, arithmark2: 30.96, links: { card: 'https://huggingface.co/Quazim0t0/Byrne-86M' } }, | |
| { name: 'Byrne-86M-Base', org: 'quazim0t0', addedDate: '2026-06-19', params: 85327747, paramsDisplay: '85.3M', arc: 31.48, hellaswag: 30.58, piqa: 58.81, arcChall: 24.06, arithmark2: 27.32, links: { card: 'https://huggingface.co/Quazim0t0/Byrne-86M-Base' } }, | |
| { name: 'Glint-1.3', org: 'compactai', addedDate: '2026-06-20', params: 982656, paramsDisplay: '982K', arc: 30.47, hellaswag: 26.44, piqa: 52.67, arcChall: 22.95, arithmark3: 28.10, arithmark2: 25.72, links: { card: 'https://huggingface.co/Glint-Research/Glint-1.3' } }, | |
| { name: 'GPT-S2-5M', org: 'axiomiclabs', addedDate: '2026-06-20', params: 5384258, paramsDisplay: '5.4M', arc: 33.92, hellaswag: 27.87, piqa: 57.56, arcChall: 22.87, arithmark3: 27.90, arithmark2: 28.04, links: { card: 'https://huggingface.co/AxiomicLabs/GPT-S2-5M' } }, | |
| { name: 'Escarda-86M-Identity', org: 'quazim0t0', addedDate: '2026-06-22', params: 85656068, paramsDisplay: '85.7M', arc: 33.33, hellaswag: 29.02, piqa: 56.47, arcChall: 25.51, arithmark2: 34.88, links: { card: 'https://huggingface.co/Quazim0t0/Escarda-86M-Identity' } }, | |
| { name: 'kirk-ballerina', org: 'rtc2022', addedDate: '2026-06-22', params: 94576128, paramsDisplay: '94.6M', arc: 31.69, hellaswag: 26.64, piqa: 52.88, arcChall: 20.65, arithmark3: 31.00, arithmark2: 24.88, links: { card: 'https://huggingface.co/rtc2022/kirk-ballerina' } }, | |
| { name: 'Atom 3.4M', org: 'ucr', addedDate: '2026-06-22', params: 3412800, paramsDisplay: '3.4M', arc: 33.33, hellaswag: 27.69, piqa: 55.17, arcChall: 22.10, arithmark3: 31.60, arithmark2: 27.28, links: { card: 'https://huggingface.co/UniversalComputingResearch/Atom3.4m'} }, | |
| { name: 'Isabel-50M', org: 'ideoalabs', addedDate: '2026-06-22', params: 54100000, paramsDisplay: '54M', arc: 43.64, hellaswag: 27.02, piqa: 57.18, arcChall: 23.55, arithmark3: 35.10, arithmark2: 42.32, links: { card: 'https://huggingface.co/MaliosDark/Isabel-50M' } }, | |
| { name: 'tinyLM-8M-exp', org: 'user01110', addedDate: '2026-06-24', params: 8131840, paramsDisplay: '8.1M', arc: 35.65, hellaswag: 27.65, piqa: 56.51, arcChall: 23.61, arithmark3: 27.50, arithmark2: 25.46, links: { card: 'https://huggingface.co/User01110/tinyLM-8M-exp' } }, | |
| { name: 'CMA-1M-Mini', org: 'user01110', addedDate: '2026-07-21', params: 958692, paramsDisplay: '959K', arc: 29.29, hellaswag: 29.35, piqa: 54.62, arcChall: 21.76, arithmark3: 31.20, arithmark2: 27.44, links: { card: 'https://huggingface.co/User01110/CMA-1M-Mini' } }, | |
| { name: 'Archaea-74M-V1.1', org: 'godelev', addedDate: '2026-06-26', params: 74016256, paramsDisplay: '74M', arc: 41.54, hellaswag: 27.05, piqa: 59.19, arcChall: 24.40, arithmark3: 33.60, arithmark2: 37.64, links: { card: 'https://huggingface.co/GODELEV/Archaea-74M-V1.1' } }, | |
| { name: 'MiniBananaMind-v2-9M', org: 'bananamind', addedDate: '2026-06-27', params: 8884992, paramsDisplay: '8.9M', arc: 33.92, hellaswag: 26.99, piqa: 54.79, arcChall: 20.65, arithmark3: 30.20, arithmark2: 25.20, links: { card: 'https://huggingface.co/BananaMind/MiniBananaMind-v2-9M' } }, | |
| { name: 'Gros-Michel-90m-Base', org: 'finnianx', addedDate: '2026-06-29', params: 91071744, paramsDisplay: '91.1M', arc: 40.15, hellaswag: 29.95, piqa: 58.05, arcChall: 23.81, arithmark3: 35.50, arithmark2: 28.96, links: { card: 'https://huggingface.co/finnianx/Gros-Michel-90m-Base' } }, | |
| { name: 'MiniBananaMind-v3-9M', org: 'bananamind', addedDate: '2026-06-30', params: 8884992, paramsDisplay: '8.9M', arc: 34.81, hellaswag: 27.12, piqa: 54.68, arcChall: 22.70, arithmark3: 35.70, arithmark2: 26.32, links: { card: 'https://huggingface.co/BananaMind/MiniBananaMind-v3-9M' } }, | |
| { name: 'BananaMind-1.5-Base', org: 'bananamind', addedDate: '2026-06-30', params: 75054720, paramsDisplay: '75M', hellaswag: 30.91, arc: 42.38, arcChall: 23.98, piqa: 60.55, arithmark3: 34.10, arithmark2: 26.68, links: { card: 'https://huggingface.co/BananaMind/BananaMind-1.5-Base' } }, | |
| { name: 'MiniBananaMind-v4-9M', org: 'bananamind', addedDate: '2026-07-01', params: 8884992, paramsDisplay: '8.9M', arc: 35.02, hellaswag: 26.87, piqa: 55.60, arcChall: 23.12, arithmark3: 36.30, arithmark2: 26.04, links: { card: 'https://huggingface.co/BananaMind/MiniBananaMind-v4-9M' } }, | |
| { name: 'MicroBananaMind-v1', org: 'bananamind', addedDate: '2026-07-02', params: 902272, paramsDisplay: '902K', arc: 31.40, hellaswag: 26.53, piqa: 53.86, arcChall: 20.99, arithmark3: 30.00, arithmark2: 24.16, links: { card: 'https://huggingface.co/BananaMind/MicroBananaMind-v1' } }, | |
| { name: 'Atom 2.7M', org: 'ucr', addedDate: '2026-07-07', params: 2738880, paramsDisplay: '2.7M', arc: 31.19, hellaswag: 27.10, piqa: 52.94, arcChall: 21.50, arithmark3: 25.80, arithmark2: 69.40, links: { card: 'https://huggingface.co/UniversalComputingResearch/Atom2.7m'} }, | |
| { name: 'Gros-Michel-90m-Base-v2', org: 'finnianx', addedDate: '2026-07-07', params: 94911744, paramsDisplay: '94.9M', arc: 43.31, hellaswag: 31.23, piqa: 61.37, arcChall: 23.98, arithmark3: 35.90, arithmark2: 28.84, links: { card: 'https://huggingface.co/finnianx/Gros-Michel-90m-Base-v2' } }, | |
| { name: 'MetaNova-1-60M', org: 'whirlwindai', addedDate: '2026-07-07', params: 46294016, paramsDisplay: '46.3M', arc: 31.65, hellaswag: 26.29, piqa: 55.17, arcChall: 24.15, arithmark2: 38.96, links: { card: 'https://huggingface.co/WhirlwindAI/MetaNova-1-60M' } }, | |
| { name: 'MetaNova-0.1-70M', org: 'whirlwindai', addedDate: '2026-07-07', params: 54158336, paramsDisplay: '54.2M', arc: 33.63, hellaswag: 25.88, piqa: 58.76, arcChall: 22.53, arithmark2: 27.48, links: { card: 'https://huggingface.co/WhirlwindAI/MetaNova-0.1-70M' } }, | |
| { name: 'TinyMoE-100m-2x8-retrained', org: 'flamef0x', addedDate: '2026-07-07', params: 99817344, paramsDisplay: '99.8M', arc: 33.88, hellaswag: 25.87, piqa: 55.60, arcChall: 22.01, arithmark3: 32.20, links: { card: 'https://huggingface.co/FlameF0X/TinyMoE-100m-2x8-retrained' } }, | |
| { name: 'BananaMind-2-Mini', org: 'bananamind', addedDate: '2026-07-09', params: 25178752, paramsDisplay: '25.2M', arc: 39.06, hellaswag: 29.80, piqa: 59.41, arcChall: 25.34, arithmark3: 35.40, arithmark2: 29.28, links: { card: 'https://huggingface.co/BananaMind/BananaMind-2-Mini' } }, | |
| { name: 'cma-8M', org: 'user01110', addedDate: '2026-07-14', params: 7849161, paramsDisplay: '7.8M', arc: 35.35, hellaswag: 28.19, piqa: 58.22, arcChall: 23.29, arithmark3: 30.80, arithmark2: 29.12, links: { card: 'https://huggingface.co/User01110/CMA-8M' } }, | |
| { name: 'MathBananaMind-1.1', org: 'bananamind', addedDate: '2026-07-15', params: 2901984, paramsDisplay: '2.9M', arc: 26.85, hellaswag: 26.16, piqa: 50.49, arcChall: 27.13, arithmark3: 22.30, arithmark2: 90.28, links: { card: 'https://huggingface.co/BananaMind/MathBananaMind-1.1' } }, | |
| { name: 'CyberSLM-33M-Base', org: 'sabari2005', addedDate: '2026-07-16', params: 33531264, paramsDisplay: '33.5M', arc: 32.79, hellaswag: 26.84, piqa: 54.68, arcChall: 22.70, arithmark3: 31.00, arithmark2: 26.96, links: { card: 'https://huggingface.co/sabari2005/cyberslm-base' } }, | |
| { name: 'CyberSLM-33M-Instruct', org: 'sabari2005', addedDate: '2026-07-16', params: 33531264, paramsDisplay: '33.5M', arc: 32.37, hellaswag: 26.19, piqa: 53.81, arcChall: 22.35, arithmark3: 30.20, arithmark2: 26.88, links: { card: 'https://huggingface.co/sabari2005/cyberslm-instruct' } }, | |
| { name: 'BananaMind-2-Nano', org: 'bananamind', addedDate: '2026-07-20', params: 9968128, paramsDisplay: '9.97M', arc: 36.20, hellaswag: 27.50, piqa: 55.98, arcChall: 23.38, arithmark3: 33.70, arithmark2: 27.68, links: { card: 'https://huggingface.co/BananaMind/BananaMind-2-Nano' } }, | |
| { name: 'BananaMind-2-MoE', org: 'bananamind', addedDate: '2026-07-20', params: 25086592, paramsDisplay: '25.1M', arc: 34.64, hellaswag: 27.45, piqa: 56.37, arcChall: 21.16, arithmark3: 33.80, arithmark2: 28.44, links: { card: 'https://huggingface.co/BananaMind/BananaMind-2-MoE' } }, | |
| { name: 'BananaMind-2-Medium', org: 'bananamind', addedDate: '2026-07-22', params: 49559552, paramsDisplay: '49.6M', arc: 43.81, hellaswag: 32.43, piqa: 61.86, arcChall: 25.34, arithmark3: 36.20, arithmark2: 28.20, links: { card: 'https://huggingface.co/BananaMind/BananaMind-2-Medium' } }, | |
| { name: 'BananaMind-2-Medium-Chat', org: 'bananamind', addedDate: '2026-07-22', params: 49559552, paramsDisplay: '49.6M', arc: 43.43, hellaswag: 31.71, piqa: 60.66, arcChall: 24.40, arithmark3: 35.60, arithmark2: 29.92, links: { card: 'https://huggingface.co/BananaMind/BananaMind-2-Medium-Chat' } }, | |
| { name: 'Nexus-Erebus-135M', org: 'ideoalabs', addedDate: '2026-07-22', params: 134000000, paramsDisplay: '135M', arc: 48.61, hellaswag: 30.61, piqa: 64.53, arcChall: 25.43, arithmark3: 31.00, arithmark2: 66.68, links: { card: 'https://huggingface.co/MaliosDark/Nexus-Erebus-135M' } }, | |
| { name: 'Museko-125M', org: 'tobiaslogic', addedDate: '2026-07-26', params: 122702592, paramsDisplay: '123M', arc: 48.06, hellaswag: 33.26, piqa: 63.22, arcChall: 25.68, arithmark3: 34.60, arithmark2: 74.04, links: { card: 'https://huggingface.co/TobiasLogic/Museko-125M' } }, | |
| { name: 'Quark-50m-v2', org: 'thingai', addedDate: '2026-07-26', params: 56666496, paramsDisplay: '56.7M', arc: 26.18, hellaswag: 27.39, piqa: 49.08, arcChall: 25.34, arithmark3: 25.50, arithmark2: 25.20, links: { card: 'https://huggingface.co/ThingAI/Quark-50m-v2' } }, | |
| { name: 'Rose-Mini', org: 'godelev', addedDate: '2026-07-26', params: 49443074, paramsDisplay: '49.4M', arc: 36.57, hellaswag: 28.93, piqa: 58.60, arcChall: 24.23, arithmark3: 36.50, arithmark2: 26.64, links: { card: 'https://huggingface.co/GODELEV/Rose-Mini' } }, | |
| //{ name: 'palmer-006', org: 'appvoid', params: 90000000, paramsDisplay: '90M', arc: 44.82, hellaswag: 38.41, piqa: 63.60, arcChall: 29.27, winogrande: 50.36, arithmark3: 52.70, links: { card: 'https://huggingface.co/appvoid/palmer-006' } }, | |
| { name: 'Er-Large-30M', org: 'fromzero', addedDate: '2026-07-31', params: 31000000, paramsDisplay: '31M', arc: 37.56, hellaswag: 30.52, piqa: 60.12, arcChall: 22.17, arithmark3: 35.00, links: { card: 'https://huggingface.co/fromziro/Er-Large-30M' } }, | |
| { name: 'ObsidianSmall-Base', org: 'dreamw', addedDate: '2026-08-02', params: 8700000, paramsDisplay: '8.7M', arc: 33.291, hellaswag: 27.415, piqa: 55.114, arcChall: 21.843, arithmark3: 28.800, arithmark2: 26.160, links: { card: 'https://huggingface.co/Dream-W/ObsidianSmall-Base' } }, | |
| //{ name: 'BananaMind-2-Pro-Preview', org: 'bananamind', params: 138971520, paramsDisplay: '138M', arc: 51.01, hellaswag: 39.83, piqa: 66.76, arcChall: 27.13, arithmark3: 38.90, arithmark2: 28.60, links: { card: 'https://huggingface.co/BananaMind/BananaMind-2-Pro-Preview' } }, | |
| { name: 'Supra2-100M-Base', org: 'supralabs', addedDate: '2026-08-04', params: 100684032, paramsDisplay: '101M', arc: 47.81, hellaswag: 35.98, piqa: 65.40, arcChall: 24.83, arithmark3: 36.90, links: { card: 'https://huggingface.co/SupraLabs/Supra2-100M-Base' } }, | |
| { name: 'Supra2-100M-Instruct', org: 'supralabs', addedDate: '2026-08-04', params: 100684032, paramsDisplay: '101M', arc: 44.44, hellaswag: 35.89, piqa: 64.36, arcChall: 24.74, arithmark3: 38.20, links: { card: 'https://huggingface.co/SupraLabs/Supra2-100M-Instruct' } }, | |
| { name: 'GPT-X2.5-135M', org: 'axiomiclabs', addedDate: '2026-08-04', params: 135032256, paramsDisplay: '135M', arc: 51.81, hellaswag: 40.57, piqa: 69.42, arcChall: 29.18, arithmark3: 38.40, links: { card: 'https://huggingface.co/AxiomicLabs/GPT-X2.5-135M' } }, | |
| //{ name: 'Boris-1.3-75M', org: 'opencerebral', params: 77431680, paramsDisplay: '77.4M', arc: 40.11, hellaswag: 27.57, piqa: 59.36, arcChall: 23.89, arithmark3: 33.40, links: { card: 'https://huggingface.co/opencerebral/Boris-1.3-75M' } }, | |
| //{ name: 'Boris-1.3-125M', org: 'opencerebral', params: 124475904, paramsDisplay: '124.5M', arc: 41.46, hellaswag: 29.59, piqa: 61.70, arcChall: 24.40, arithmark3: 35.20, links: { card: 'https://huggingface.co/opencerebral/Boris-1.3-125M' } }, | |
| //{ name: 'littlerock-1M', org: 'opencerebral', params: 1002720, paramsDisplay: '1.0M', arc: 30.98, hellaswag: 26.43, piqa: 52.23, arcChall: 21.76, arithmark3: 25.20, links: { card: 'https://huggingface.co/opencerebral/littlerock-1M' } }, | |
| { name: 'ZeroS-Qana-5M', org: 'fromzero', addedDate: '2026-08-06', params: 4943712, paramsDisplay: '4.94M', arc: 34.97, hellaswag: 27.60, piqa: 57.18, arcChall: 23.21, arithmark3: 29.90, links: { card: 'https://huggingface.co/fromziro/ZeroS-Qana-5M' } }, | |
| { name: 'Bananamind', org: 'bananamind' , addedDate: '2026-08-07', params: 25100384, paramsDisplay: '25M', arc: 33.77, hellaswag: 25.98, piqa: 52.88, arcChall: 22.35, arithmark3: 29.80, arithmark2: 25.88, links: { card: 'https://huggingface.co/BananaMind/MiniBananaMind-V1' } }, | |
| { name: 'min-spark', org: 'minimalabs', addedDate: '2026-08-07', params: 5758572, paramsDisplay: '5.8Mx4*', arc: 37.08, hellaswag: 27.92, piqa: 54.35, arcChall: 23.21, arithmark3: 33.30, links: { card: 'https://huggingface.co/MinimaLabs/min-spark' } }, | |
| { name: 'SupraElegans-500k', org: 'supralabs', addedDate: '2026-08-10', params: 500000, paramsDisplay: '500k', arc: 21.00, hellaswag: 24.28, piqa: 49.73, arcChall: 22.00, arithmark3: 22.00, links: { card: 'https://huggingface.co/SupraLabs/SupraElegans-500k' } }, | |
| { name: 'BananaMind-2-Micro', org: 'bananamind', addedDate: '2026-08-11', params: 2933193, paramsDisplay: '2.9M', arc: 33.12, hellaswag: 28.27, piqa: 53.21, arcChall: 21.93, arithmark3: 34.00, arithmark2: 24.92, links: { card: 'https://huggingface.co/BananaMind/BananaMind-2-Micro' } }, | |
| { name: 'Rambley-150M-RealBase', org: 'allura', addedDate: '2026-08-11', params: 149_973_504, paramsDisplay: '150M', arc: 44.74, hellaswag: 32.29, arcChall: 24.40, piqa: 62.57, arithmark3: 37.20, links: { card: "https://huggingface.co/allura-org/Rambley-150M-RealBase" } }, | |
| { name: 'Rambley-150M-Base', org: 'allura', addedDate: '2026-08-11', params: 149_973_504, paramsDisplay: '150M', arc: 48.23, hellaswag: 31.49, arcChall: 24.06, piqa: 61.26, arithmark3: 39.9, links: { card: "https://huggingface.co/allura-org/Rambley-150M-Base" } }, | |
| { name: 'Glint-2', org: 'compactai', addedDate: '2026-08-11', params: 1710049, paramsDisplay: '1.7Mx8*', arc: 36.03, hellaswag: 25.87, piqa: 51.03, arcChall: 23.55, arithmark3: 27.00, links: { card: 'https://huggingface.co/Glint-Research/Glint-2' } }, | |
| { name: 'void.0', org: 'appvoid', addedDate: '2026-08-11', params: 132438656, paramsDisplay: '132.4M', arc: 45.03, hellaswag: 31.57, piqa: 61.15, arcChall: 25.17, arithmark3: 35.20, links: { card: 'https://huggingface.co/appvoid/void.0' } }, | |
| { name: 'Rose-Medium', org: 'godelev', addedDate: '2026-08-11', params: 97825347, paramsDisplay: '97.8M', arc: 44.19, hellaswag: 35.29, piqa: 62.94, arcChall: 26.19, arithmark3: 38.30, links: { card: 'https://huggingface.co/GODELEV/Rose-Medium' } }, | |
| { name: 'Veyra2-Mango-30M-Base', org: 'veyraai', addedDate: '2026-08-12', params: 30683520, paramsDisplay: '30.7M', arc: 37.88, hellaswag: 28.76, piqa: 59.52, arcChall: 23.29, arithmark3: 36.90, arithmark2: 27.96, links: { card: 'https://huggingface.co/veyra-ai/Veyra2-Mango-30M-Base' } }, | |
| { name: 'Veyra2-Mango-15M-Base', org: 'veyraai', addedDate: '2026-08-12', params: 15735168, paramsDisplay: '15.7M', arc: 37.16, hellaswag: 27.73, piqa: 58.00, arcChall: 22.87, arithmark3: 34.90, arithmark2: 28.08, links: { card: 'https://huggingface.co/veyra-ai/Veyra2-Mango-15M-Base' } }, | |
| { name: 'BananaMind-2-SLMoE', org: 'bananamind', addedDate: '2026-08-12', params: 25450000, paramsDisplay: '25.5M', arc: 33.92, hellaswag: 27.16, piqa: 55.44, arcChall: 23.12, arithmark3: 33.60 , arithmark2: 26.32, links: { card: 'https://huggingface.co/BananaMind/BananaMind-2-SLMoE' } }, | |
| { name: 'Hummingbird-V1', org: 'juinron', addedDate: '2026-08-15', params: 9592720, paramsDisplay: '9.6M', arc: 33.12, hellaswag: 27.56, piqa: 55.55, arcChall: 22.27, arithmark3: 36.10, links: { card: 'https://huggingface.co/juinron/Hummingbird-V1' } }, | |
| //{ name: 'Lumen-118M-Base', org: 'kefir090', params: 117477466, paramsDisplay: '117.5M', arc: 49.20, hellaswag: 33.74, piqa: 66.65, arcChall: 25.77, arithmark3: 39.50, links: { card: 'https://huggingface.co/kefir090/Lumen-118M-Base' } }, | |
| { name: 'BananaMind-2-Pro', org: 'bananamind', addedDate: '2026-08-15', params: 138971520, paramsDisplay: '139M', arc: 53.58, hellaswag: 42.78, piqa: 67.52, arcChall: 27.82, arithmark3: 38.20, arithmark2: 32.08, links: { card: 'https://huggingface.co/BananaMind/BananaMind-2-Pro' } }, | |
| { name: 'Supra2-Nano', org: 'supralabs', addedDate: '2026-08-15', params: 799776, paramsDisplay: '800k', arc: 30.81, hellaswag: 26.81, piqa: 53.43, arcChall: 20.65, arithmark3: 29.00, links: { card: 'https://huggingface.co/SupraLabs/Supra2-Nano' } }, | |
| { name: 'Rose-Pro', org: 'godelev', addedDate: '2026-08-17', params: 151277410, paramsDisplay: '151M', arc: 48.65, hellaswag: 38.24, piqa: 65.18, arcChall: 26.96, arithmark3: 38.20, arithmark2: 33.00, links: { card: 'https://huggingface.co/GODELEV/Rose-Pro' } }, | |
| { name: 'cagliostro-v1', org: 'benchlabs', addedDate: '2026-08-19', params: 157411200, paramsDisplay: '157.4M', arc: 48.91, hellaswag: 36.90, piqa: 65.61, arcChall: 26.11, arithmark3: 33.70, arithmark2: 26.72, links: { card: 'https://huggingface.co/bench-labs/cagliostro-v1' } }, | |
| { name: 'cagliostro-v2', org: 'benchlabs', addedDate: '2026-08-31', params: 150038400, paramsDisplay: '150M', arc: 47.43, hellaswag: 36.76, piqa: 65.67, arcChall: 28.58, arithmark3: 35.20, links: { card: 'https://huggingface.co/bench-labs/cagliostro-v2' } }, | |
| { name: 'BananaMind-2.1-Unified', org: 'bananamind', addedDate: '2026-08-20', params: 34999041, paramsDisplay: '35M', arc: 38.51, hellaswag: 29.94, piqa: 61.75, arcChall: 24.49, arithmark3: 36.70, links: { card: 'https://huggingface.co/BananaMind/BananaMind-2.1-Unified' } }, | |
| { name: 'Byrne-100M-Ultra-MC', org: 'quazim0t0', addedDate: '2026-08-20', params: 113908642, paramsDisplay: '114Mx3*', arc: 38.30, hellaswag: 29.31, piqa: 58.22, arcChall: 22.35, arithmark3: 37.00, links: { card: 'https://huggingface.co/Quazim0t0/Byrne-100M-Ultra-MC' } }, | |
| { name: 'Ember', org: 'surjolabs', addedDate: '2026-08-21', params: 2870432, paramsDisplay: '2.9Mx2*', arc: 33.25, hellaswag: 27.35, piqa: 54.41, arcChall: 21.08, arithmark3: 32.90, links: { card: 'https://huggingface.co/SurjoLabs/Ember' } }, | |
| { name: 'Supra2-Medium-Base', org: 'supralabs', addedDate: '2026-08-21', params: 25371008, paramsDisplay: '25M', arc: 41.67, hellaswag: 29.29, piqa: 59.68, arcChall: 24.15, arithmark3: 34.80, links: { card: 'https://huggingface.co/SupraLabs/Supra2-Medium-Base' } }, | |
| { name: 'Supra2-Medium-Instruct', org: 'supralabs', addedDate: '2026-08-21', params: 25383296, paramsDisplay: '25M', arc: 40.28, hellaswag: 29.33, piqa: 59.25, arcChall: 24.91, arithmark3: 36.50, links: { card: 'https://huggingface.co/SupraLabs/Supra2-Medium-Instruct' } }, | |
| { name: 'Spark', org: 'surjolabs', addedDate: '2026-08-22', params: 7083264, paramsDisplay: '7.1Mx2*', arc: 35.02, hellaswag: 28.17, piqa: 55.55, arcChall: 20.99, arithmark3: 36.10, links: { card: 'https://huggingface.co/SurjoLabs/Spark' } }, | |
| { name: 'min-spark-1.1', org: 'minimalabs', addedDate: '2026-08-22', params: 5758572, paramsDisplay: '5.8Mx4*', arc: 36.45, hellaswag: 28.24, piqa: 57.40, arcChall: 23.04, arithmark3: 35.40, links: { card: 'https://huggingface.co/MinimaLabs/min-spark-1.1' } }, | |
| { name: 'min-spark-1.5', org: 'minimalabs', addedDate: '2026-10-09', params: 9360596, paramsDisplay: '9.4Mx4*', arc: 38.64, hellaswag: 28.27, piqa: 57.34, arcChall: 23.55, arithmark3: 34.90, links: { card: 'https://huggingface.co/MinimaLabs/min-spark-1.5' } }, | |
| { name: 'sorbet-25m', org: 'codesoft', addedDate: '2026-08-23', params: 25185920, paramsDisplay: '25M', arc: 26.12, hellaswag: 26.12, piqa: 53.43, arcChall: 22.95, arithmark3: 32.90, links: { card: 'https://huggingface.co/CodeSoft/sorbet-25m' } }, | |
| { name: 'Flare', org: 'surjolabs', addedDate: '2026-08-23', params: 130393152, paramsDisplay: '130.4Mx2*', arc: 51.14, hellaswag: 39.43, piqa: 65.94, arcChall: 26.88, arithmark3: 39.20, links: { card: 'https://huggingface.co/SurjoLabs/Flare' } }, | |
| { name: 'cRia-LM-75M', org: 'sz14', addedDate: '2026-08-24', params: 75719395, paramsDisplay: '75.72Mx2*', arc: 46.97, hellaswag: 35.49, piqa: 63.93, arcChall: 25.77, arithmark3: 35.30, links: { card: 'https://huggingface.co/sz14/cRia-LM-75M' } }, | |
| { name: 'Ember-2', org: 'surjolabs', addedDate: '2026-08-24', params: 2963360, paramsDisplay: '2.96Mx2*', arc: 33.42, hellaswag: 27.28, piqa: 55.11, arcChall: 22.01, arithmark3: 35.90, links: { card: 'https://huggingface.co/SurjoLabs/Ember-2' } }, | |
| { name: 'Spark-2A', org: 'surjolabs', addedDate: '2026-08-25', params: 9148608, paramsDisplay: '9.15Mx2*', arc: 34.26, hellaswag: 28.13, piqa: 57.24, arcChall: 22.18, arithmark3: 37.00, links: { card: 'https://huggingface.co/SurjoLabs/Spark-2A' } }, | |
| { name: 'ZeroS-v0.1-150M', org: 'fromzero', addedDate: '2026-08-25', params: 151638000, paramsDisplay: '152M', arc: 48.70, hellaswag: 37.02, piqa: 66.76, arcChall: 28.84, arithmark3: 35.80, links: { card: 'https://huggingface.co/fromziro/ZeroS-v0.1-150M' } }, | |
| { name: 'sorbet-v2-25m', org: 'codesoft', addedDate: '2026-08-26', params: 25185920, paramsDisplay: '25M', arc: 26.63, hellaswag: 30.30, piqa: 54.52, arcChall: 22.44, arithmark3: 33.00, links: { card: 'https://huggingface.co/CodeSoft/sorbet-v2-25m' } }, | |
| { name: 'VegaLM1-42M-Base', org: 'cnwplayer', addedDate: '2026-08-26', params: 42054144, paramsDisplay: '42M', arc: 35.23, hellaswag: 26.86, piqa: 57.62, arcChall: 21.25, arithmark3: 33.90, links: { card: 'https://huggingface.co/CNWPlayer/VegaLM1-42M-Base'} }, | |
| { name: 'Speck1-140M-Instruct', org: 'specklabs', addedDate: '2026-08-26', params: 140654208, paramsDisplay: '141M', arc: 46.93, hellaswag: 35.64, piqa: 64.15, arcChall: 26.02, arithmark2: 32.44, arithmark3: 33.7, links: { card: 'https://huggingface.co/specklabs/Speck1-140M-Instruct'} }, | |
| { name: 'Bryne-15M-Looped', org: 'quazim0t0', addedDate: '2026-08-26', params:18900000, paramsDisplay: '18.9Mx3*', arc: 32.80 , hellaswag: 27.20, piqa: 55.40, arcChall: 23.20, arithmark3: 34.7, links: { card: 'https://huggingface.co/Quazim0t0/Byrne-15M-Looped'} }, | |
| { name: 'ZeroS-Pico-v1.0', org: 'fromzero', addedDate: '2026-08-26', params:67700, paramsDisplay: '67.7K', arc: 27.65 , hellaswag: 25.94, piqa: 49.62, arcChall: 22.95, arithmark3: 31.50, links: { card: 'https://huggingface.co/fromziro/ZeroS-Pico-v1.0'} }, | |
| { name: 'Blaze', org: 'surjolabs', addedDate: '2026-08-26', params: 48251136, paramsDisplay: '48.3Mx2*', arc: 41.84, hellaswag: 31.84, piqa: 62.51, arcChall: 24.91, arithmark3: 37.80, links: { card: 'https://huggingface.co/SurjoLabs/Blaze' } }, | |
| { name: 'Veyra2-Blueberry-10M-Base', org: 'veyraai', addedDate: '2026-08-28', params: 9976832, paramsDisplay: '9.9M', arc: 35.44, hellaswag: 28.04, piqa: 55.44, arcChall: 21.59, arithmark3: 34.60, arithmark2: 26.08, links: { card: 'https://huggingface.co/veyra-ai/Veyra2-Blueberry-10M-Base' } }, | |
| { name: 'Veyra2-Blueberry-5M-Base', org: 'veyraai', addedDate: '2026-08-28', params: 4984192, paramsDisplay: '4.9M', arc: 33.46, hellaswag: 27.62, piqa: 54.62, arcChall: 21.67, arithmark3: 36.40, arithmark2: 26.40, links: { card: 'https://huggingface.co/veyra-ai/Veyra2-Blueberry-5M-Base' } }, | |
| { name: 'Haidass1.5-143M', org: 'dalab', addedDate: '2026-08-29', params: 143067456, paramsDisplay: '143M', arc: 56.36, hellaswag: 40.58, piqa: 67.79, arcChall: 27.39, arithmark3: 39.60, links: { card: 'https://huggingface.co/DALabCommunity/Haidass1.5-143M' } }, | |
| { name: 'BananaMind-2.1-Pico-Preview', org: 'bananamind', addedDate: '2026-09-01', params: 1480516, paramsDisplay: '1.4M', arc: 29.50, hellaswag: 26.89, piqa: 54.30, arcChall: 22.53, arithmark3: 33.00, links: { card: 'https://huggingface.co/BananaMind/BananaMind-2.1-Pico-Preview' } }, | |
| { name: 'Myosotis-1-base', org: 'fwkv', addedDate: '2026-09-03', params: 101860800, paramsDisplay: '102M', arc: 34.34, hellaswag: 26.21, piqa: 55.33, arcChall: 21.59, arithmark3: 35.00, links: { card: 'https://huggingface.co/FWKV/Myosotis-1-base' } }, | |
| { name: 'Speck2-140M', org: 'specklabs', addedDate: '2026-09-03', params: 140652288, paramsDisplay: '141M', arc: 45.66, hellaswag: 39.49, piqa: 65.23, arcChall: 24.57, arithmark2: 31.60, arithmark3: 36.30, links: { card: 'https://huggingface.co/specklabs/Speck2-140M'} }, | |
| { name: 'Speck2-140M-Instruct', org: 'specklabs', addedDate: '2026-09-03', params: 140652288, paramsDisplay: '141M', arc: 47.73, hellaswag: 38.94, piqa: 65.83, arcChall: 25.34, arithmark2: 37.92, arithmark3: 39.10, links: { card: 'https://huggingface.co/specklabs/Speck2-140M-Instruct'} }, | |
| { name: 'picollama', org: 'appvoid', addedDate: '2026-09-08', params: 35000000, paramsDisplay: '35M', arc: 37.21, hellaswag: 28.38, piqa: 60.23, arcChall: 22.87, arithmark3: 33.10, links: { card: 'https://huggingface.co/appvoid/picollama' } }, | |
| { name: 'Pebble-10M', org: 'basically-ai', addedDate: '2026-09-08', params: 10281744, paramsDisplay: '10.28M', arc: 34.18, hellaswag: 28.59, piqa: 56.75, arcChall: 22.95, arithmark3: 33.80, links: { card: 'https://huggingface.co/basically-ai/Pebble-10M' } }, | |
| { name: 'Pebble-10M-Chat', org: 'basically-ai', addedDate: '2026-09-08', params: 10281744, paramsDisplay: '10.28M', arc: 27.19, hellaswag: 23.91, piqa: 48.48, arcChall: 25.00, arithmark3: 26.60, links: { card: 'https://huggingface.co/basically-ai/Pebble-10M-Chat' } }, | |
| { name: 'Pebble-25M-Chat', org: 'basically-ai', addedDate: '2026-09-08', params: 24486710, paramsDisplay: '24.49M', arc: 28.24, hellaswag: 24.65, piqa: 51.63, arcChall: 24.91, arithmark3: 30.70, links: { card: 'https://huggingface.co/basically-ai/Pebble-25M-Chat' } }, | |
| { name: 'Pebble-25M', org: 'basically-ai', addedDate: '2026-09-08', params: 24486710, paramsDisplay: '24.49M', arc: 36.15, hellaswag: 29.54, piqa: 58.87, arcChall: 21.67, arithmark3: 33.00, links: { card: 'https://huggingface.co/basically-ai/Pebble-25M' } }, | |
| { name: 'SupraNeo-4M', org: 'supralabs', addedDate: '2026-09-08', params: 4070240, paramsDisplay: '4.07M', arc: 34.47, hellaswag: 27.84, piqa: 55.39, arcChall: 23.38, arithmark3: 31.20, links: { card: 'https://huggingface.co/SupraLabs/SupraNeo-4M' } }, | |
| { name: 'SupraGDN-5M', org: 'supralabs', addedDate: '2026-09-08', params: 5041656, paramsDisplay: '5.04M', arc: 33.54, hellaswag: 26.69, piqa: 53.21, arcChall: 22.87, arithmark3: 26.40, links: { card: 'https://huggingface.co/SupraLabs/SupraGDN-5M' } }, | |
| { name: 'Linear-50M-exp', org: 'user01110', addedDate: '2026-09-08', params: 49552864, paramsDisplay: '49.55M', arc: 44.02, hellaswag: 31.94, piqa: 61.81, arcChall: 24.06, arithmark3: 34.90, links: { card: 'https://huggingface.co/User01110/Linear-50M-exp' } }, | |
| { name: 'ZeroS-Linear-50M', org: 'fromzero', addedDate: '2026-09-11', params: 49552864, paramsDisplay: '49.55M', arc: 43.86, hellaswag: 32.30, piqa: 63.06, arcChall: 23.98, arithmark3: 35.70, links: { card: 'https://huggingface.co/fromziro/ZeroS-Linear-50M' } }, | |
| { name: 'ZeroS-Pico-v1.1', org: 'fromzero', addedDate: '2026-09-10', params: 59769, paramsDisplay: '59.8K', arc: 26.30, hellaswag: 26.19, piqa: 51.14, arcChall: 24.57, arithmark3: 30.50, links: { card: 'https://huggingface.co/fromziro/ZeroS-Pico-v1.1' } }, | |
| //{ name: 'Kiyo-135M', org: 'dedeprogames', params: 134515008, paramsDisplay: '134.52M', arc: 55.93, hellaswag: 38.55, piqa: 65.51, arcChall: 29.01, arithmark3: 36.60, links: { card: 'https://huggingface.co/DedeProGames/Kiyo-135M' } }, | |
| { name: 'Kiyo-65M', org: 'dedeprogames', addedDate: '2026-09-10', params: 64994816, paramsDisplay: '64.99M', arc: 46.51, hellaswag: 31.98, piqa: 62.30, arcChall: 23.29, arithmark3: 37.40, links: { card: 'https://huggingface.co/DedeProGames/Kiyo-65M' } }, | |
| { name: 'lille-130m-base', org: 'nikity', addedDate: '2026-09-10', params: 127171200, paramsDisplay: '127.17M', arc: 38.68, hellaswag: 29.76, piqa: 55.77, arcChall: 23.55, arithmark3: 34.50, links: { card: 'https://huggingface.co/Nikity/lille-130m-base' } }, | |
| { name: 'Rose-1.5-Medium', org: 'godelev', addedDate: '2026-09-10', params: 98277437, paramsDisplay: '98.2M', arc: 47.22, hellaswag: 38.09, piqa: 64.80, arcChall: 27.13, arithmark3: 40.70, links: { card: 'https://huggingface.co/GODELEV/Rose-1.5-Medium' } }, | |
| { name: 'GTM-v1-base', org: 'opengcm', addedDate: '2026-09-12', params: 101750400, paramsDisplay: '101.75M', arc: 41.41, hellaswag: 29.00, piqa: 60.12, arcChall: 23.04, arithmark3: 35.70, links: { card: 'https://huggingface.co/OPENGCM/GTM-v1-base' } }, | |
| { name: 'GTM-v2-base', org: 'opengcm', addedDate: '2026-09-13', params: 119494848, paramsDisplay: '119.49M', arc: 42.38, hellaswag: 30.09, piqa: 60.72, arcChall: 24.83, arithmark3: 36.80, links: { card: 'https://huggingface.co/OPENGCM/GTM-v2-base' } }, | |
| { name: 'GTM-v2-chat', org: 'opengcm', addedDate: '2026-09-13', params: 119494848, paramsDisplay: '119.49M', arc: 40.49, hellaswag: 30.70, piqa: 63.98, arcChall: 25.17, arithmark3: 36.60, links: { card: 'https://huggingface.co/OPENGCM/GTM-v2-chat' } }, | |
| { name: 'Ivme-Conversate-v3-Base', org: 'ivmelabs', addedDate: '2026-09-14', params: 24787520, paramsDisplay: '24.79M', arc: 39.02, hellaswag: 28.82, piqa: 57.78, arcChall: 24.06, arithmark3: 34.70, links: { card: 'https://huggingface.co/IvmeLabs/Ivme-Conversate-v3-Base' } }, | |
| { name: 'Aurora1.0-150M', org: 'aurora', addedDate: '2026-09-14', params: 149270272, paramsDisplay: '149.2M', arc: 44.91, hellaswag: 32.20, piqa: 62.24, arcChall: 25.00, arithmark3: 33.90, links: { card: 'https://huggingface.co/AuroraAI-Research/Aurora1.0-150M' } }, | |
| //{ name: 'ZeroS-Micro-v1.0', org: 'fromzero', params: 2993797, paramsDisplay: '2.99M', arc: 28.49, hellaswag: 27.32, piqa: 51.14, arcChall: 24.57, arithmark3: 30.80, links: { card: 'https://huggingface.co/fromziro/ZeroS-Micro-v1.0' } }, | |
| { name: 'DynamicMind-MoE', org: 'dedeprogames', addedDate: '2026-09-14', params: 30150912, paramsDisplay: '30.15M', arc: 36.15, hellaswag: 27.47, piqa: 56.09, arcChall: 22.35, arithmark3: 35.00, links: { card: 'https://huggingface.co/DedeProGames/DynamicMind-MoE' } }, | |
| { name: 'DynamicMind-Mini', org: 'dedeprogames', addedDate: '2026-09-14', params: 8884992, paramsDisplay: '8.88M', arc: 35.02, hellaswag: 26.99, piqa: 55.60, arcChall: 23.29, arithmark3: 35.60, links: { card: 'https://huggingface.co/DedeProGames/DynamicMind-Mini' } }, | |
| { name: 'LowOnMind-8M', org: 'slmarchive', addedDate: '2026-09-14', params: 8060256, paramsDisplay: '8.06M', arc: 28.62, hellaswag: 25.45, piqa: 51.03, arcChall: 22.70, arithmark3: 27.30, links: { card: 'https://huggingface.co/SLM-Archive/LowOnMind-8M' } }, | |
| { name: 'LowOnMind-5M', org: 'slmarchive', addedDate: '2026-09-14', params: 4920384, paramsDisplay: '4.92M', arc: 31.78, hellaswag: 27.69, piqa: 51.96, arcChall: 19.62, arithmark3: 33.10, links: { card: 'https://huggingface.co/SLM-Archive/LowOnMind-5M' } }, | |
| { name: 'LowOnMind-1M', org: 'slmarchive', addedDate: '2026-09-14', params: 985152, paramsDisplay: '985K', arc: 28.41, hellaswag: 26.26, piqa: 52.50, arcChall: 22.44, arithmark3: 28.70, links: { card: 'https://huggingface.co/SLM-Archive/LowOnMind-1M' } }, | |
| { name: 'LowOnMind-300k', org: 'slmarchive', addedDate: '2026-09-14', params: 296960, paramsDisplay: '297K', arc: 29.00, hellaswag: 25.07, piqa: 50.22, arcChall: 23.12, arithmark3: 29.40, links: { card: 'https://huggingface.co/SLM-Archive/LowOnMind-300k' } }, | |
| { name: 'NanoDex-1M', org: 'slmarchive', addedDate: '2026-09-14', params: 1062272, paramsDisplay: '1.06M', arc: 29.55, hellaswag: 27.02, piqa: 53.21, arcChall: 22.95, arithmark3: 26.40, links: { card: 'https://huggingface.co/SLM-Archive/NanoDex-1M' } }, | |
| { name: 'Sol-Lite-Base', org: 'solintellegence', addedDate: '2026-09-14', params: 14995843, paramsDisplay: '15M', arc: 34.81, hellaswag: 27.77, piqa: 57.89, arcChall: 22.95, arithmark3: 34.40, links: { card: 'https://huggingface.co/solintellegence/Sol-Lite-Base' } }, | |
| { name: 'Sol Lite 2', org: 'solintellegence', addedDate: '2026-10-02', params: 14942592, paramsDisplay: '14.94M', arc: 36.27946127946128, hellaswag: 28.689504082852025, piqa: 56.80087051142546, arcChall: 24.914675767918087, arithmark3: 35.5, links: { card: 'https://huggingface.co/solintellegence/Sol-Lite-2' } }, | |
| { name: 'Surjo-50M', org: 'surjolabs', addedDate: '2026-09-15', params: 53800000, paramsDisplay: '53.8Mx2*', arc: 44.99, hellaswag: 31.37, piqa: 63.11, arcChall: 25.94, arithmark3: 37.90, links: { card: 'https://huggingface.co/SurjoLabs/Surjo-50m' } }, | |
| { name: 'GTM-3-base', org: 'opengcm', addedDate: '2026-09-15', params: 74900000, paramsDisplay: '74.9M', arc: 32.98, hellaswag: 25.70, piqa: 56.86, arithmark3: 37.20, links: { card: 'https://huggingface.co/OPENGCM/GTM-3-base' } }, | |
| { name: 'Mystery-5M-Preview', org: 'mystery', addedDate: '2026-09-13', params: 5647652, paramsDisplay: '5.65M', arc: 36.03, hellaswag: 28.26, piqa: 57.56, arcChall: 23.89, arithmark3: 33.30, links: { card: 'https://huggingface.co/' } }, | |
| { name: 'typakos-140M-it', org: 'alexliap', addedDate: '2026-09-15', params: 144797184, paramsDisplay: '145M', arc: 30.93, hellaswag: 26.77, piqa: 56.04, arcChall: 25.85, arithmark3: 36.60, arithmark2: 30.32, links: { card: 'https://huggingface.co/alexliap/typakos-140M-it' } }, | |
| { name: 'cRia-LM-75M-Instruct', org: 'sz14', addedDate: '2026-09-16', params: 75719395, paramsDisplay: '75.72Mx2*', arc: 37.75, hellaswag: 32.07, piqa: 59.68, arcChall: 25.17, arithmark3: 35.20, links: { card: 'https://huggingface.co/sz14/cRia-LM-75M-Instruct' } }, | |
| { name: 'NCN-RW', org: 'axiomiclabs', addedDate: '2026-10-03', params: 9720527, paramsDisplay: '9.72Mx2*', arc: 36.95, hellaswag: 27.73, piqa: 58.00, arcChall: 22.70, arithmark3: 30.20, links: { card: 'https://huggingface.co/AxiomicLabs/NCN-RW' } }, | |
| { name: 'Surjo-100m', org: 'surjolabs', addedDate: '2026-09-18', params: 97700000, paramsDisplay: '97.7Mx2*', arc: 47.64, hellaswag: 35.05, piqa: 63.87, arcChall: 25.85, arithmark3: 38.90, links: { card: 'https://huggingface.co/SurjoLabs/Surjo-100m' } }, | |
| { name: 'JugnuLM-110M-R2+', org: 'altslate', addedDate: '2026-09-19', params: 109737302, paramsDisplay: '110M', arc: 47.64, hellaswag: 36.74, piqa: 65.29, arcChall: 26.96, arithmark3: 38.60, links: { card: 'https://huggingface.co/altslate/JugnuLM-110M-R2plus' } }, | |
| { name: 'peacebell-v1-148M', org: 'wayneworkman', addedDate: '2026-09-20', params: 148553302, paramsDisplay: '148.55M', arc: 27.27, hellaswag: 26.16, piqa: 47.61, arcChall: 26.71, arithmark3: 27.10, links: { card: 'https://huggingface.co/wayneworkman2012/peacebell-v1-148M' } }, | |
| { name: 'cagliostro-v3', org: 'benchlabs', addedDate: '2026-09-20', params: 146352000, paramsDisplay: '146M', arc: 54.88, hellaswag: 42.51, piqa: 67.46, arcChall: 28.75, arithmark3: 43.70, links: { card: 'https://huggingface.co/bench-labs/cagliostro-v3' } }, | |
| { name: 'Pollock 1.5', org: 'slayerlab', addedDate: '2026-09-21', params: 127565312, paramsDisplay: '127M', arc: 42.00, hellaswag: 30.81, piqa: 59.63, arcChall: 24.32, arithmark3: 34.30, links: { card: 'https://huggingface.co/SlayerLab/pollock-mini-lm-125m' } }, | |
| { name: 'ForgePlex-M1-6M', org: 'forgeworks', addedDate: '2026-09-23', params: 6584928, paramsDisplay: '6.58M', arc: 35.02, hellaswag: 27.57, piqa: 56.26, arcChall: 22.70, arithmark3: 29.60, links: { card: 'https://huggingface.co/ForgeWorks/ForgePlex-M1-6M' } }, | |
| { name: 'tinctura-v1', org: 'benchlabs', addedDate: '2026-09-24', params: 96200064, paramsDisplay: '96.2M', arc: 47.98, hellaswag: 37.96, piqa: 65.61, arcChall: 25.77, arithmark3: 38.40, links: { card: 'https://huggingface.co/bench-labs/tinctura-v1' } }, | |
| { name: 'Hummingbird-V2', org: 'juinron', addedDate: '2026-09-24', params: 9592720, paramsDisplay: '9.6M', arc: 39.65, hellaswag: 27.61, piqa: 57.40, arcChall: 21.16, arithmark3: 36.00, links: { card: 'https://huggingface.co/juinron/Hummingbird-V2' } }, | |
| { name: 'NACR-1.0', org: 'saicr', addedDate: '2026-09-25', params: 5067603, paramsDisplay: '3.0M', arc: 30.98, hellaswag: 26.79, piqa: 52.69, arcChall: 20.90, arithmark3: 28.20, links: { card: 'https://huggingface.co/saicr/nacr' } }, | |
| { name: 'Sol-Milkshake', org: 'solintellegence', addedDate: '2026-09-26', params: 2990000, paramsDisplay: '2.99M', arc: 29.76, hellaswag: 25.02, piqa: 52.50, arcChall: 22.70, arithmark3: 30.60, links: { card: 'https://huggingface.co/solintellegence/Sol-Milkshake' } }, | |
| { name: 'ZeroS-Micro-v1.0', org: 'fromzero', addedDate: '2026-09-27', params: 2869813, paramsDisplay: '2.87M', arc: 31.27, hellaswag: 28.46, piqa: 53.86, arcChall: 22.01, arithmark3: 35.60, links: { card: 'https://huggingface.co/fromziro/ZeroS-Micro-v1.0' } }, | |
| { name: 'ForgePlex-M2-9M', org: 'forgeworks', addedDate: '2026-09-30', params: 9949698, paramsDisplay: '9.95M', arc: 36.53, hellaswag: 28.02, piqa: 57.18, arcChall: 23.46, arithmark3: 34.60, links: { card: 'https://huggingface.co/ForgeWorks/ForgePlex-M2-9M' } }, | |
| { name: 'Sol Nano', org: 'solintellegence', addedDate: '2026-09-30', params: 2895188, paramsDisplay: '2.9M', arc: 32.07, hellaswag: 28.40, piqa: 53.92, arcChall: 21.16, arithmark3: 33.80, links: { card: 'https://huggingface.co/solintellegence/sol-nano' } }, | |
| { name: 'Tokle-3M', org: 'techdotus', addedDate: '2026-09-30', params: 2908947, paramsDisplay: '2.91M', arc: 34.85, hellaswag: 27.20, piqa: 55.01, arcChall: 23.98, arithmark3: 40.80, links: { card: 'https://huggingface.co/techdotus/Tokle-3M' } }, | |
| { name: 'pulvis-v1', org: 'benchlabs', addedDate: '2026-09-30', params: 2962240, paramsDisplay: '2.96Mx3*', arc: 31.61, hellaswag: 27.91, piqa: 55.93, arcChall: 22.27, arithmark3: 36.90, arithmark2: 48.04, links: { card: 'https://huggingface.co/bench-labs/pulvis-v1' } }, | |
| { name: 'Veyra2-Mango-30M-Instruct', org: 'veyraai', addedDate: '2026-10-03', params: 30683520, paramsDisplay: '30.7M', arc: 38.55, hellaswag: 29.17, piqa: 59.52, arcChall: 22.27, arithmark3: 37.20, links: { card: 'https://huggingface.co/veyra-ai/Veyra2-Mango-30M-Instruct' } }, | |
| { name: 'Veyra2-Mango-15M-Instruct', org: 'veyraai', addedDate: '2026-10-03', params: 15735168, paramsDisplay: '15.7M', arc: 37.88, hellaswag: 27.40, piqa: 56.80, arcChall: 22.44, arithmark3: 34.40, links: { card: 'https://huggingface.co/veyra-ai/Veyra2-Mango-15M-Instruct' } }, | |
| { name: 'Veyra2-Blueberry-10M-Instruct', org: 'veyraai', addedDate: '2026-10-03', params: 9976832, paramsDisplay: '9.9M', arc: 36.15, hellaswag: 27.77, piqa: 56.26, arcChall: 22.87, arithmark3: 37.60, links: { card: 'https://huggingface.co/veyra-ai/Veyra2-Blueberry-10M-Instruct' } }, | |
| { name: 'Veyra2-Blueberry-5M-Instruct', org: 'veyraai', addedDate: '2026-10-03', params: 4984192, paramsDisplay: '4.9M', arc: 33.25, hellaswag: 27.45, piqa: 54.19, arcChall: 21.59, arithmark3: 36.60, links: { card: 'https://huggingface.co/veyra-ai/Veyra2-Blueberry-5M-Instruct' } }, | |
| { name: 'pulvis-v2', org: 'benchlabs', addedDate: '2026-10-03', params: 2963840, paramsDisplay: '2.96Mx3*', arc: 31.86, hellaswag: 27.93, piqa: 56.86, arcChall: 22.78, arithmark3: 37.40, arithmark2: 40.16, links: { card: 'https://huggingface.co/bench-labs/pulvis-v2' } }, | |
| { name: 'cagliostro-v3.5', org: 'benchlabs', addedDate: '2026-10-05', params: 146352000, paramsDisplay: '146M', arc: 53.75, hellaswag: 43.41, piqa: 68.06, arcChall: 29.35, arithmark3: 45.30, links: { card: 'https://huggingface.co/bench-labs/cagliostro-v3.5' } }, | |
| { name: 'Rambley-V2-Alpha-10M', org: 'glossia', addedDate: '2026-10-05', params: 9_638_592, paramsDisplay: '10M', arc: 34.89, hellaswag: 27.56, arcChall: 21.84, piqa: 57.13, arithmark3: 33.10, links: { card: "https://huggingface.co/glossia/Rambley-V2-Alpha-10M" } }, | |
| { name: 'Purrence-3M', org: 'jonnester', addedDate: '2026-10-06', params: 2987712, paramsDisplay: '2.99Mx6*', arc: 34.55, hellaswag: 27.81, piqa: 56.64, arcChall: 23.63, arithmark3: 34.80, arithmark2: 26.44, links: { card: 'https://huggingface.co/Jonnester/Purrence-3M' } }, | |
| { name: 'Rambley-V2-Beta-10M', org: 'glossia', addedDate: '2026-10-06', params: 9_835_712, paramsDisplay: '10M', arc: 35.52, hellaswag: 27.06, arcChall: 21.25, piqa: 56.75, arithmark3: 33.50, links: { card: "https://huggingface.co/glossia/Rambley-V2-Beta-10M" } }, | |
| { name: '50M-pro', org: 'fromzero', addedDate: '2026-10-06', params: 49590304, paramsDisplay: '49.59M', arc: 45.62, hellaswag: 33.25, piqa: 64.80, arcChall: 25.51, arithmark3: 37.40, links: { card: 'https://huggingface.co/fromziro/50M-pro' } }, | |
| { name: 'Rambley-V2-Gamma-10M', org: 'glossia', addedDate: '2026-10-07', params: 9_835_712, paramsDisplay: '10M', arc: 35.44, hellaswag: 27.15, arcChall: 21.93, piqa: 56.15, arithmark3: 31.30, links: { card: "https://huggingface.co/glossia/Rambley-V2-Gamma-10M" } }, | |
| //{ name: 'GPT-X3-Preview-2', org: 'axiomiclabs', params: 149400930, paramsDisplay: '149M', arc: 55.01, hellaswag: 39.75, piqa: 69.10, arcChall: 28.75, arithmark3: 46.10, links: { card: 'https://huggingface.co/AxiomicLabs/GPT-X3-150M' } }, | |
| { name: '100M-exp', org: 'user01110', addedDate: '2026-10-09', params: 98161434, paramsDisplay: '98.2M', arc: 49.83, hellaswag: 37.78, piqa: 66.97, arcChall: 27.22, arithmark3: 40.00, links: { card: 'https://huggingface.co/User01110/100M-exp' } }, | |
| //{ name: 'Sol Pro', org: 'solintellegence', params: 138037594, paramsDisplay: '138Mx2*', arc: 54.00, hellaswag: 46.74, piqa: 71.00, arcChall: 31.83, arithmark3: 36.00, links: { card: 'https://huggingface.co/solintellegence/sol-pro' } }, | |
| { name: 'gibc-v2-lm', org: 'unluckyexe', addedDate: '2026-10-09', params: 49822228, paramsDisplay: '49.8M', arc: 47.85, hellaswag: 31.87, piqa: 60.28, arcChall: 26.11, arithmark3: 40.20, arithmark2: 36.80, links: { card: 'https://huggingface.co/Unluckyexe/gibc-v2-lm' } }, | |
| { name: 'Myosotis-1.5-base-preview', org: 'fwkv', addedDate: '2026-10-09', params: 111726720, paramsDisplay: '112M', arc: 40.36, hellaswag: 27.69, piqa: 59.79, arcChall: 21.93, arithmark3: 33.50, links: { card: 'https://huggingface.co/FWKV/Myosotis-1.5-base-Preview' } }, | |
| { name: 'void.1', org: 'dotlabs', addedDate: '2026-10-09', params: 90148352, paramsDisplay: '90.15M', arc: 49.32, hellaswag: 39.46, piqa: 67.19, arcChall: 29.35, arithmark3: 43.70, links: { card: 'https://huggingface.co/dotlabs/void.1' } }, | |
| { name: 'ForgePlex-M2.1-10M', org: 'forgeworks', addedDate: '2026-10-10', params: 9991938, paramsDisplay: '9.99M', arc: 36.45, hellaswag: 29.09, piqa: 59.14, arcChall: 22.87, arithmark3: 35.50, links: { card: 'https://huggingface.co/ForgeWorks/ForgePlex-M2.1-10M' } }, | |
| { type: 'orgMovementCutoff', label: 'Org leaderboard movement cutoff' }, | |
| { name: 'Boris-2-Preview-1', org: 'opencerebral', addedDate: '2026-10-10', params: 143724032, paramsDisplay: '144M', arc: 58.38, hellaswag: 45.23, piqa: 69.21, arcChall: 29.95, arithmark3: 37.60, arithmark2: 37.68, links: { card: 'https://huggingface.co/opencerebral/Boris-2-Preview-1' } }, | |
| { name: 'GPT-S3-3M', org: 'axiomiclabs', addedDate: '2026-10-10', params: 2973666, paramsDisplay: '2.97M', arc: 34.76, hellaswag: 27.68, piqa: 57.02, arcChall: 23.12, arithmark3: 39.10, links: { card: 'https://huggingface.co/AxiomicLabs/GPT-S3-3M' } }, | |
| { name: 'GPT-X3-150M', org: 'axiomiclabs', addedDate: '2026-10-07', params: 149000000, paramsDisplay: '150M', arc: 59.64, hellaswag: 46.16, piqa: 71.33, arcChall: 31.31, arithmark3: 50.90, arithmark2: 68.52, links: { card: 'https://huggingface.co/AxiomicLabs/GPT-X3-150M' } }, | |
| { name: 'KLM-1.1', org: 'minimalabs', addedDate: '2026-10-11', params: 88108544, paramsDisplay: '88.1M', arc: 44.36, hellaswag: 33.65, piqa: 64.80, arcChall: 25.17, arithmark3: 35.80, links: { card: 'https://huggingface.co/MinimaLabs/KLM-1.1' } }, | |
| { name: 'TinyBrainBot-100M-v3-Base', org: 'tinybrainbot', addedDate: '2026-10-14', params: 100092672, paramsDisplay: '100M', arc: 54.67, hellaswag: 32.62, piqa: 66.21, arcChall: 30.12, arithmark3: 37.40, links: { card: 'https://huggingface.co/nkthebass/tinybrainbot-100m-v3-base' } }, | |
| { name: 'TinyBrainBot-25M-v4-Base', org: 'tinybrainbot', addedDate: '2026-10-14', params: 30554496, paramsDisplay: '30.6M', arc: 42.09, hellaswag: 28.86, piqa: 60.23, arcChall: 24.91, arithmark3: 35.20, links: { card: 'https://huggingface.co/nkthebass/tinybrainbot-25m-v4-base' } }, | |
| { name: 'Rambley-V2-Delta-10M', org: 'glossia', addedDate: '2026-10-12', params: 9_851_456, paramsDisplay: '10M', arc: 36.36, hellaswag: 27.21, arcChall: 21.08, piqa: 55.55, arithmark3: 31.30, links: { card: "https://huggingface.co/glossia/Rambley-V2-Delta-10M" } }, | |
| { name: 'Inferno-Exp-1.0', org: 'algodriveai', addedDate: '2026-10-12', params: 76163200, paramsDisplay: '76.2M', arc: 40.24, hellaswag: 27.46, piqa: 56.58, arcChall: 24.66, arithmark3: 54.20, arithmark2: 67.32, links: { card: 'https://huggingface.co/AlgoDriveAI/Inferno-Exp-1.0' } }, | |
| { name: 'heron1i-flash-lite', org: 'heronetic', addedDate: '2026-10-12', params: 9924188, paramsDisplay: '9.92M', arc: 36.20, hellaswag: 28.36, piqa: 58.54, arcChall: 24.15, arithmark3: 35.60, links: { card: 'https://huggingface.co/Heronetic/heron1i-flash-lite' } }, | |
| { name: 'min-spark-1.6', org: 'minimalabs', addedDate: '2026-10-13', params: 9956756, paramsDisplay: '9.96Mx4*', arc: 38.30, hellaswag: 28.52, piqa: 59.14, arcChall: 22.44, arithmark3: 36.90, links: { card: 'https://huggingface.co/MinimaLabs/min-spark-1.6' } }, | |
| { name: 'Inferno-Exp-1.5', org: 'algodriveai', addedDate: '2026-10-13', params: 76163200, paramsDisplay: '76.2M', arc: 43.39, hellaswag: 27.85, piqa: 58.49, arcChall: 26.62, arithmark3: 71.00, arithmark2: 67.00, links: { card: 'https://huggingface.co/AlgoDriveAI/Inferno-Exp-1.5' } }, | |
| { name: 'ForgePlex-M2.2-10M', org: 'forgeworks', addedDate: '2026-10-13', params: 9991938, paramsDisplay: '9.99M', arc: 39.77, hellaswag: 29.19, piqa: 59.79, arcChall: 22.70, arithmark3: 37.40, links: { card: 'https://huggingface.co/ForgeWorks/ForgePlex-M2.2-10M' } }, | |
| { name: 'heron1i-flash-lite-v2', org: 'heronetic', addedDate: '2026-10-14', params: 9924188, paramsDisplay: '9.92M', arc: 38.89, hellaswag: 28.86, piqa: 58.54, arcChall: 23.38, arithmark3: 36.70, links: { card: 'https://huggingface.co/Heronetic/heron1i-flash-lite-v2' } }, | |
| { name: 'Rambley-V2-350M-Preview', org: 'glossia', addedDate: '2026-10-14', params: 348583480, paramsDisplay: '350M', arc: 47.22, hellaswag: 35.95, arcChall: 26.37, piqa: 64.42, arithmark3: 37.80, links: { card: 'https://huggingface.co/glossia/Rambley-V2-350M-Preview' } }, | |
| { name: 'GPT-2 Medium', org: 'openai', addedDate: '2026-10-14', params: 354823168, paramsDisplay: '355M', arc: 43.60, hellaswag: 39.38, arcChall: 25.00, piqa: 66.38, arithmark3: 33.80, links: { card: 'https://huggingface.co/openai-community/gpt2-medium' } }, | |
| { name: 'OPT-350M', org: 'facebook', addedDate: '2026-10-14', params: 331196416, paramsDisplay: '331M', arc: 40.19, hellaswag: 36.82, arcChall: 23.72, piqa: 64.36, arithmark3: 38.30, links: { card: 'https://huggingface.co/facebook/opt-350m' } }, | |
| { name: 'LFM-2.5-230M-Base', org: 'liquid', addedDate: '2026-10-14', params: 229693184, paramsDisplay: '230M', arc: 59.72, hellaswag: 39.29, arcChall: 34.98, piqa: 63.38, arithmark3: 36.60, links: { card: 'https://huggingface.co/LiquidAI/LFM2.5-230M-Base' } }, | |
| { name: 'LFM-2.5-350M-Base', org: 'liquid', addedDate: '2026-10-14', params: 354483968, paramsDisplay: '350M', arc: 68.86, hellaswag: 45.16, arcChall: 41.13, piqa: 67.63, arithmark3: 36.10, links: { card: 'https://huggingface.co/LiquidAI/LFM2.5-350M-Base' } }, | |
| { name: 'SmolLM2-360M', org: 'huggingface', addedDate: '2026-10-14', params: 361821120, paramsDisplay: '360M', arc: 68.48, hellaswag: 56.30, arcChall: 38.31, piqa: 71.60, arithmark3: 59.60, links: { card: 'https://huggingface.co/HuggingFaceTB/SmolLM2-360M' } }, | |
| { name: 'MobileLLM-R1-360M-base', org: 'facebook', addedDate: '2026-10-14', params: 359431168, paramsDisplay: '360M', arc: 61.32, hellaswag: 44.20, arcChall: 34.56, piqa: 68.44, arithmark3: 80.40, links: { card: 'https://huggingface.co/facebook/MobileLLM-R1-360M-base' } }, | |
| { name: 'MICA Flame-W 0.3.6', org: 'mica', addedDate: '2026-10-14', params: 31523432, paramsDisplay: '31.52M', arc: 27.272727, hellaswag: 27.643896, piqa: 52.176279, arcChall: 20.221843, arithmark3: 27.1, links: { card: 'https://huggingface.co/vynly/mica-flame-w-0.3.6' } }, | |
| { name: 'Picolo-BananaMind', org: 'bananamind', addedDate: '2026-10-14', params: 100, paramsDisplay: '100', arc: 25.63, hellaswag: 23.70, piqa: 50.71, arcChall: 27.82, arithmark3: 28.40, links: { card: 'https://huggingface.co/BananaMind/Picolo-BananaMind' } }, | |
| { name: 'Supra2.1-Ultra', org: 'supralabs', addedDate: '2026-10-14', params: 229688064, paramsDisplay: '229.7M', arc: 50.46, hellaswag: 37.18, piqa: 64.91, arcChall: 26.28, arithmark3: 38.20, links: { card: 'https://huggingface.co/SupraLabs/Supra2.1-Ultra' } }, | |
| { name: 'Inferno-Exp-1.6', org: 'algodriveai', addedDate: '2026-10-15', params: 393610240, paramsDisplay: '393.6M', arc: 47.01, hellaswag: 29.80, piqa: 60.50, arcChall: 30.03, arithmark3: 83.10, links: { card: 'https://huggingface.co/AlgoDriveAI/Inferno-Exp-1.6' } }, | |
| { name: 'TinyBrainBot-360M-A75M-v4.1-MoE-Base', org: 'tinybrainbot', addedDate: '2026-10-17', params: 383429376, paramsDisplay: '383M', arc: 55.64, hellaswag: 37.27, piqa: 66.32, arcChall: 32.34, arithmark3: 42.00, links: { card: 'https://huggingface.co/nkthebass/tinybrainbot-360m-a75m-v4.1-moe-base' } }, | |
| { name: 'TinyBrainBot-350M-V3-Base', org: 'tinybrainbot', addedDate: '2026-10-17', params: 348342912, paramsDisplay: '348M', arc: 56.61, hellaswag: 35.90, piqa: 66.97, arcChall: 33.28, arithmark3: 39.30, links: { card: 'https://huggingface.co/nkthebass/tinybrainbot-350mV3-base' } }, | |
| { name: 'Rambley-V2-64M', org: 'glossia', addedDate: '2026-10-17', params: 63874704, paramsDisplay: '64M', arc: 42.42, hellaswag: 30.60, arcChall: 23.81, piqa: 60.39, arithmark3: 34.30, links: { card: 'https://huggingface.co/glossia/Rambley-V2-64M' } }, | |
| { name: 'Inferno-Exp-1.7', org: 'algodriveai', addedDate: '2026-10-18', params: 393610240, paramsDisplay: '393.6M', arc: 51.60, hellaswag: 33.68, piqa: 64.47, arcChall: 34.04, arithmark3: 85.70, links: { card: 'https://huggingface.co/AlgoDriveAI/Inferno-Exp-1.7' } }, | |
| { name: 'Kiyo-230M-Instruct', org: 'dedeprogames', addedDate: '2026-10-23', params: 229688064, paramsDisplay: '229.7M', arc: 42.17, hellaswag: 36.83, piqa: 64.69, arcChall: 26.28, arithmark3: 35.70, links: { card: 'https://huggingface.co/DedeProGames/Kiyo-230M-Instruct' } }, | |
| { name: 'GPT-U-20M', org: 'dedeprogames', addedDate: '2026-10-23', params: 20453760, paramsDisplay: '20.45M', arc: 37.25, hellaswag: 27.87, piqa: 58.38, arcChall: 21.67, arithmark3: 35.50, links: { card: 'https://huggingface.co/DedeProGames/GPT-U-20M' } }, | |
| { name: 'cosmo.0', org: 'dedeprogames', addedDate: '2026-10-23', params: 34611712, paramsDisplay: '34.61M', arc: 36.07, hellaswag: 27.90, piqa: 59.09, arcChall: 23.89, arithmark3: 28.80, links: { card: 'https://huggingface.co/DedeProGames/cosmo.0' } }, | |
| { name: 'Wisp-15M', org: 'dedeprogames', addedDate: '2026-10-23', params: 15531840, paramsDisplay: '15.53M', arc: 35.82, hellaswag: 27.97, piqa: 57.67, arcChall: 22.01, arithmark3: 34.30, links: { card: 'https://huggingface.co/DedeProGames/Wisp-15M' } }, | |
| { name: 'Wisp-5M', org: 'dedeprogames', addedDate: '2026-10-23', params: 5115456, paramsDisplay: '5.12M', arc: 32.66, hellaswag: 27.09, piqa: 54.30, arcChall: 22.78, arithmark3: 33.50, links: { card: 'https://huggingface.co/DedeProGames/Wisp-5M' } }, | |
| { name: 'KLM-1.5', org: 'minimalabs', addedDate: '2026-10-22', params: 98975308, paramsDisplay: '99.0M', arc: 59.68, hellaswag: 43.74, piqa: 68.55, arcChall: 32.68, arithmark3: 48.20, links: { card: 'https://huggingface.co/MinimaLabs/KLM-1.5' } }, | |
| { name: 'Rambley-V2.1-64M', org: 'glossia', addedDate: '2026-10-20', params: 63903120, paramsDisplay: '64M', arc: 42.13, hellaswag: 31.49, arcChall: 24.23, piqa: 61.10, arithmark3: 35.50, links: { card: 'https://huggingface.co/glossia/Rambley-V2.1-64M' } }, | |
| { name: 'Boris-2-Preview-2', org: 'opencerebral', addedDate: '2026-10-21', params: 143724032, paramsDisplay: '144M', arc: 59.13, hellaswag: 46.60, piqa: 70.84, arcChall: 30.20, arithmark3: 42.20, links: { card: 'https://huggingface.co/opencerebral/Boris-2-Preview-2' } }, | |
| { name: 'Pragya-EXP2', org: 'RuVi', addedDate: '2026-10-20', params: 28500000, paramsDisplay: '28.5M', arc: 31.57, hellaswag: 29.76, piqa: 61.86, arcChall: 23.98, arithmark3: 38.30, links: { card: 'https://huggingface.co/RuVI-AI/Pragya-EXP2' } }, | |
| { name: 'CLOD-149M', org: 'murzik344', addedDate: '2026-10-24', params: 148898078, paramsDisplay: '148.9M', arc: 45.37, hellaswag: 34.86, piqa: 67.08, arcChall: 26.88, arithmark3: 36.00, links: { card: 'https://huggingface.co/Murzik344/CLOD-149M' } }, | |
| { name: 'LittleKedi-moderate-wide-sparse-base', org: 'littlekedi', addedDate: '2026-10-25', params: 173357088, paramsDisplay: '173M', arc: 45.24, hellaswag: 32.79, piqa: 62.02, arcChall: 25.43, arithmark3: 36.20, links: { card: 'https://huggingface.co/edededdy/LittleKedi-moderate-wide-sparse-base' } }, | |
| { name: 'LS3-Micro-Base', org: 'ls', addedDate: '2026-10-25', params: 9973792, paramsDisplay: '9.97M', arc: 34.76, hellaswag: 27.31, piqa: 57.34, arcChall: 23.29, arithmark3: 38.90, links: { card: 'https://huggingface.co/Dsg2/LS3-Micro-Base' } }, | |
| { name: 'Ethoric-10M', org: 'techdotus', addedDate: '2026-10-26', params: 9965664, paramsDisplay: '9.97M', arc: 37.58, hellaswag: 28.75, piqa: 59.85, arcChall: 24.66, arithmark3: 41.60, links: { card: 'https://huggingface.co/techdotus/Ethoric-10M' } }, | |
| { name: 'min-spark-1.7', org: 'minimalabs', addedDate: '2026-10-26', params: 9975188, paramsDisplay: '9.98Mx4*', arc: 39.90, hellaswag: 29.65, piqa: 60.39, arcChall: 24.15, arithmark3: 36.60, links: { card: 'https://huggingface.co/MinimaLabs/min-spark-1.7' } }, | |
| { name: 'Rose-1.5-Pro', org: 'godelev', addedDate: '2026-10-27', params: 148826543, paramsDisplay: '148.8M', arc: 58.29, hellaswag: 43.92, piqa: 69.48, arcChall: 30.72, arithmark3: 40.00, links: { card: 'https://huggingface.co/GODELEV/Rose-1.5-Pro' } }, | |
| { name: 'TinyBrainBot-3M-v2-Base', org: 'tinybrainbot', addedDate: '2026-10-27', params: 2942928, paramsDisplay: '2.94M', arc: 33.38, hellaswag: 27.29, piqa: 56.75, arcChall: 22.53, arithmark3: 45.10, links: { card: 'https://huggingface.co/nkthebass/tinybrainbot-3m-v2-base' } }, | |
| { name: 'Samarkand-253M', org: 'meridianmrm', addedDate: '2026-10-27', params: 253556096, paramsDisplay: '253.6M', arc: 50.84, hellaswag: 38.15, piqa: 66.49, arcChall: 26.96, arithmark3: 35.50, links: { card: 'https://huggingface.co/Meridian-MRM/Samarkand-253m' } }, | |
| { name: 'Pythia-160M', org: 'eleutherai', addedDate: '2026-10-30', params: 162323040, paramsDisplay: '162M', arc: 39.65, hellaswag: 30.27, piqa: 61.97, arcChall: 23.63, arithmark3: 35.10, arithmark2: 28.04, links: { card: 'https://huggingface.co/EleutherAI/pythia-160m' } }, | |
| ]; | |
| // βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| // βββ STATE | |
| // βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| const BENCHMARKS = ['arc', 'hellaswag', 'piqa', 'arcChall', 'arithmark3', 'arithmark2']; | |
| const DEFAULT_SIZE_FILTERS = ['<150M', '<100M', '<50M', '<10M', '<3M']; | |
| const SIZE_FILTER_GROUPS = { | |
| default: DEFAULT_SIZE_FILTERS, | |
| extended: ['all', '<250M', ...DEFAULT_SIZE_FILTERS] | |
| }; | |
| const SIZE_FILTERS = SIZE_FILTER_GROUPS.extended; | |
| const METRICS = [ | |
| { key: 'intelligenceIndex', label: 'Int Index', fullLabel: 'Intelligence Index' }, | |
| { key: 'hellaswag', label: 'HellaSwag', fullLabel: 'HellaSwag' }, | |
| { key: 'arc', label: 'ARC-Easy', fullLabel: 'ARC-Easy' }, | |
| { key: 'arcChall', label: 'ARC-Challenge', fullLabel: 'ARC-Challenge' }, | |
| { key: 'piqa', label: 'PIQA', fullLabel: 'PIQA' }, | |
| { key: 'arithmark3', label: 'ArithMark-3', fullLabel: 'ArithMark-3' }, | |
| { key: 'arithmark2', label: 'ArithMark-2', fullLabel: 'ArithMark-2' }, | |
| ]; | |
| const EXTRA_COLUMN_KEYS = new Set(['avg', 'fitZ', 'arithmark2']); | |
| const FILTER_METRICS = METRICS.filter(metric => !EXTRA_COLUMN_KEYS.has(metric.key)); | |
| const RANDOM_BASELINES = { | |
| avg: 31.25, | |
| intelligenceIndex: 0, | |
| hellaswag: 25, | |
| arc: 25, | |
| arcChall: 25, | |
| piqa: 50, | |
| arithmark2: 25, | |
| arithmark3: 25 | |
| }; | |
| let sortCol = 'intelligenceIndex'; | |
| let sortAsc = false; | |
| let activeSizeMode = 'default'; | |
| let activeFilter = SIZE_FILTER_GROUPS.default[0]; | |
| let activeBenchmark = 'intelligenceIndex'; | |
| let leaderboardQuery = ''; | |
| let extraColumnsOpen = false; | |
| let chartInstances = {}; | |
| function getChartTooltipOptions() { | |
| const styles = getComputedStyle(document.body); | |
| return { | |
| backgroundColor: styles.getPropertyValue('--chart-tooltip-background').trim(), | |
| borderColor: styles.getPropertyValue('--chart-tooltip-border').trim(), | |
| borderWidth: 1, | |
| titleColor: styles.getPropertyValue('--text').trim(), | |
| bodyColor: styles.getPropertyValue('--text-secondary').trim(), | |
| padding: 10, cornerRadius: 6, displayColors: false, | |
| titleFont: { family: "'DM Sans', sans-serif", size: 12, weight: '600' }, | |
| bodyFont: { family: "'DM Sans', sans-serif", size: 12 } | |
| }; | |
| } | |
| const ORG_MOVEMENT_CUTOFF_TYPE = 'orgMovementCutoff'; | |
| function isBelowOrgMovementCutoff(model) { | |
| const cutoffIndex = MODELS.findIndex(m => m.type === ORG_MOVEMENT_CUTOFF_TYPE); | |
| if (cutoffIndex === -1) return false; | |
| return MODELS.indexOf(model) > cutoffIndex; | |
| } | |
| function getAvg(m) { | |
| // Avg is a weighted mean: HellaSwag, arcAvg, PIQA, and ArithMark-3. | |
| // Missing metrics do not contribute weight. | |
| const components = []; | |
| if (m.hellaswag !== null && m.hellaswag !== undefined) components.push(m.hellaswag); | |
| const hasArc = m.arc !== null && m.arc !== undefined; | |
| const hasArcChall = m.arcChall !== null && m.arcChall !== undefined; | |
| if (hasArc && hasArcChall) components.push((m.arc + m.arcChall) / 2); | |
| else if (hasArc) components.push(m.arc); | |
| else if (hasArcChall) components.push(m.arcChall); | |
| const hasPiqa = m.piqa !== null && m.piqa !== undefined; | |
| const piqaWeight = hasPiqa ? 1 : 0; | |
| const hasArithMark3 = m.arithmark3 !== null && m.arithmark3 !== undefined; | |
| const arithMark3Weight = hasArithMark3 ? 1 : 0; | |
| const metricCount = components.length + (hasPiqa ? 1 : 0) + (hasArithMark3 ? 1 : 0); | |
| if (metricCount < 2) return null; | |
| const weightedSum = components.reduce((a, b) => a + b, 0) | |
| + (hasPiqa ? m.piqa * piqaWeight : 0) | |
| + (hasArithMark3 ? m.arithmark3 * arithMark3Weight : 0); | |
| return weightedSum / ( | |
| components.length | |
| + piqaWeight | |
| + arithMark3Weight | |
| ); | |
| } | |
| function normalizeFromChance(value, chance) { | |
| return ((value - chance) / (100 - chance)) * 100; | |
| } | |
| function getIntelligenceIndex(m) { | |
| // Intelligence Index uses weight 1 for HellaSwag, combined ARC, and PIQA, | |
| // and weight 0.65 for ArithMark-3. Chance maps to 0; perfect maps to 100. | |
| const components = []; | |
| if (m.hellaswag !== null && m.hellaswag !== undefined) { | |
| components.push(normalizeFromChance(m.hellaswag, RANDOM_BASELINES.hellaswag)); | |
| } | |
| const hasArc = m.arc !== null && m.arc !== undefined; | |
| const hasArcChall = m.arcChall !== null && m.arcChall !== undefined; | |
| if (hasArc || hasArcChall) { | |
| const arcScore = hasArc && hasArcChall ? (m.arc + m.arcChall) / 2 : (hasArc ? m.arc : m.arcChall); | |
| components.push(normalizeFromChance(arcScore, RANDOM_BASELINES.arc)); | |
| } | |
| if (m.piqa !== null && m.piqa !== undefined) { | |
| components.push(normalizeFromChance(m.piqa, RANDOM_BASELINES.piqa)); | |
| } | |
| const hasArithMark3 = m.arithmark3 !== null && m.arithmark3 !== undefined; | |
| const metricCount = components.length + (hasArithMark3 ? 1 : 0); | |
| if (metricCount < 2) return null; | |
| const arithMark3Weight = hasArithMark3 ? 0.65 : 0; | |
| const weightedSum = components.reduce((sum, value) => sum + value, 0) | |
| + (hasArithMark3 | |
| ? normalizeFromChance(m.arithmark3, RANDOM_BASELINES.arithmark3) * arithMark3Weight | |
| : 0); | |
| return weightedSum / (components.length + arithMark3Weight); | |
| } | |
| function getSortVal(m, col, fitMap = null) { | |
| if (col === 'avg') return getMetricValue(m, 'avg') ?? -Infinity; | |
| if (col === 'intelligenceIndex') return getIntelligenceIndex(m) ?? -Infinity; | |
| if (col === 'params') return m.params ?? 0; | |
| if (col === 'rank') return getIntelligenceIndex(m) ?? -Infinity; | |
| if (col === 'fitZ') return (fitMap || getModelFitMap()).get(m.name)?.fitZ ?? -Infinity; | |
| return m[col] ?? -Infinity; | |
| } | |
| function getMetricValue(m, metric = activeBenchmark) { | |
| if (metric === 'avg') return getAvg(m); | |
| if (metric === 'intelligenceIndex') return getIntelligenceIndex(m); | |
| return m[metric]; | |
| } | |
| function getMetricLabel(metric = activeBenchmark, full = false) { | |
| const item = METRICS.find(x => x.key === metric); | |
| return item ? (full ? item.fullLabel : item.label) : metric; | |
| } | |
| function getMetricUnit(metric = activeBenchmark) { | |
| return metric === 'intelligenceIndex' ? '' : '%'; | |
| } | |
| function formatMetricValue(value, metric = activeBenchmark, decimals = 2) { | |
| return `${value.toFixed(decimals)}${getMetricUnit(metric)}`; | |
| } | |
| function getModelEntries(models = MODELS) { | |
| return models.filter(m => !m.type); | |
| } | |
| function modelAnchorId(model) { | |
| return `${model.org}-${model.name}`.toLowerCase().replace(/[^a-z0-9._-]+/g, '-').replace(/-+/g, '-').replace(/^-|-$/g, ''); | |
| } | |
| function getFilteredModels(models = MODELS, filter = activeFilter) { | |
| let arr = getModelEntries(models); | |
| if (filter === '<3M') arr = arr.filter(m => m.params && m.params < 3000000); | |
| else if (filter === '<10M') arr = arr.filter(m => m.params && m.params < 10000000); | |
| else if (filter === '<50M') arr = arr.filter(m => m.params && m.params < 50000000); | |
| else if (filter === '<100M') arr = arr.filter(m => m.params && m.params < 100000000); | |
| else if (filter === '<150M') arr = arr.filter(m => m.params && m.params < 150000000); | |
| else if (filter === '<250M') arr = arr.filter(m => m.params && m.params < 250000000); | |
| return arr; | |
| } | |
| function getSizeLeader(filter) { | |
| let leader = null; | |
| let bestScore = -Infinity; | |
| for (const model of getFilteredModels(MODELS, filter)) { | |
| const score = getIntelligenceIndex(model); | |
| if (Number.isFinite(score) && score > bestScore) { | |
| leader = model; | |
| bestScore = score; | |
| } | |
| } | |
| return leader; | |
| } | |
| function getCategoryLeaders() { | |
| const leaders = new Map(); | |
| for (const filter of SIZE_FILTERS) { | |
| const leader = getSizeLeader(filter); | |
| if (!leader) continue; | |
| if (!leaders.has(leader.name)) leaders.set(leader.name, []); | |
| leaders.get(leader.name).push(filter === 'all' ? 'All sizes' : filter); | |
| } | |
| return leaders; | |
| } | |
| function getCategoryLeaderTitle(categories) { | |
| return categories ? `Category leader: ${categories.join(', ')}`.replace(/</g, '<') : ''; | |
| } | |
| function getChartModelBorders(models) { | |
| const leaders = getCategoryLeaders(); | |
| const gold = getComputedStyle(document.documentElement).getPropertyValue('--leader-gold').trim(); | |
| return models.map(model => leaders.has(model.name) ? gold : ORGS[model.org].chartBorder); | |
| } | |
| function getLeaderboardModels(models = getFilteredModels()) { | |
| const normalizeSearchText = value => String(value || '') | |
| .normalize('NFKD') | |
| .replace(/[\u0300-\u036f]/g, '') | |
| .toLowerCase() | |
| .replace(/[^a-z0-9]+/g, ' ') | |
| .trim(); | |
| const query = normalizeSearchText(leaderboardQuery); | |
| if (!query) return models; | |
| const tokens = query.split(/\s+/).filter(Boolean); | |
| return models.filter(model => { | |
| const org = ORGS[model.org]; | |
| const searchable = normalizeSearchText([ | |
| model.name, | |
| org?.name, | |
| model.org, | |
| model.paramsDisplay, | |
| model.links?.card | |
| ] | |
| .filter(Boolean) | |
| .join(' ') | |
| ); | |
| const compactSearchable = searchable.replace(/\s+/g, ''); | |
| return tokens.every(token => { | |
| const compactToken = token.replace(/\s+/g, ''); | |
| return searchable.includes(token) || compactSearchable.includes(compactToken); | |
| }); | |
| }); | |
| } | |
| function getChartModels() { | |
| return getFilteredModels() | |
| .filter(m => getMetricValue(m, activeBenchmark) !== null && getMetricValue(m, activeBenchmark) !== undefined); | |
| } | |
| function getAvgModels(models = getFilteredModels()) { | |
| return models | |
| .map(m => ({ ...m, avg: getAvg(m) })) | |
| .filter(m => m.avg !== null && m.avg !== undefined); | |
| } | |
| function getIntelligenceIndexModels(models = getFilteredModels()) { | |
| return models | |
| .map(m => ({ ...m, intelligenceIndex: getIntelligenceIndex(m) })) | |
| .filter(m => Number.isFinite(m.intelligenceIndex)); | |
| } | |
| function getSortedModels(models = getFilteredModels(), fitMapForSort = null) { | |
| const arr = [...models]; | |
| const fitMap = sortCol === 'fitZ' ? (fitMapForSort || getModelFitMap(arr)) : null; | |
| arr.sort((a, b) => { | |
| const av = getSortVal(a, sortCol, fitMap); | |
| const bv = getSortVal(b, sortCol, fitMap); | |
| return sortAsc ? (av > bv ? 1 : -1) : (av > bv ? -1 : 1); | |
| }); | |
| return arr; | |
| } function formatSigned(value) { | |
| if (value === null || value === undefined) return 'N/A'; | |
| return `${value >= 0 ? '+' : ''}${value.toFixed(2)}`; | |
| } | |
| function topByMetric(metric) { | |
| return getFilteredModels() | |
| .map(m => ({ ...m, score: getMetricValue(m, metric) })) | |
| .filter(m => m.score !== null && m.score !== undefined) | |
| .sort((a, b) => b.score - a.score)[0] || null; | |
| } | |
| function getStdDev(values) { | |
| if (!values.length) return 0; | |
| const mean = values.reduce((sum, v) => sum + v, 0) / values.length; | |
| const variance = values.reduce((sum, v) => sum + Math.pow(v - mean, 2), 0) / values.length; | |
| return Math.sqrt(variance); | |
| } | |
| function getModelFitMap(modelsForRows = getFilteredModels()) { | |
| const sourceModels = getIntelligenceIndexModels(modelsForRows) | |
| .filter(m => m.params) | |
| .map(m => ({ ...m, logParams: Math.log10(m.params) })); | |
| if (sourceModels.length < 2) return new Map(); | |
| const { slope, intercept } = linearRegression(sourceModels.map(m => [m.logParams, m.intelligenceIndex])); | |
| const fitResiduals = sourceModels.map(m => m.intelligenceIndex - (slope * m.logParams + intercept)); | |
| const residualStd = getStdDev(fitResiduals); | |
| const denom = residualStd || 1; | |
| const withFit = sourceModels.map(m => { | |
| const expected = slope * m.logParams + intercept; | |
| return { ...m, expected, residual: m.intelligenceIndex - expected }; | |
| }); | |
| return new Map(withFit.map(m => [m.name, { ...m, fitZ: m.residual / denom }])); | |
| } | |
| // βββ Green color scale: muted gray β #4ade80 βββ | |
| function scoreColor(value, min, max) { | |
| if (value === null || value === undefined || min === max) return ''; | |
| let pct = (value - min) / (max - min); | |
| pct = Math.max(0, Math.min(1, Math.pow(pct, 1.5))); | |
| const r = Math.round(107 + (74 - 107) * pct); | |
| const g = Math.round(107 + (222 - 107) * pct); | |
| const b = Math.round(110 + (128 - 110) * pct); | |
| return `color: rgb(${r},${g},${b})`; | |
| } | |
| // βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| // βββ RENDER: FILTER BAR | |
| // βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| function renderFilters() { | |
| const bar = document.getElementById('filter-bar'); | |
| const filters = SIZE_FILTER_GROUPS[activeSizeMode]; | |
| bar.innerHTML = filters.map(f => ` | |
| <button class="filter-btn ${activeFilter === f ? 'active' : ''}" type="button" aria-pressed="${activeFilter === f}" onclick="setFilter('${f}')">${f === 'all' ? 'All' : f.replace(/</g, '<')}</button> | |
| `).join(''); | |
| Object.keys(SIZE_FILTER_GROUPS).forEach(mode => { | |
| const button = document.getElementById(`size-mode-${mode}`); | |
| button.classList.toggle('active', activeSizeMode === mode); | |
| button.setAttribute('aria-pressed', String(activeSizeMode === mode)); | |
| }); | |
| } | |
| function renderBenchmarkFilters() { | |
| const benchmarkBar = document.getElementById('benchmark-filter-bar'); | |
| if (!benchmarkBar) return; | |
| benchmarkBar.innerHTML = FILTER_METRICS.map(m => ` | |
| <button class="filter-btn ${activeBenchmark === m.key ? 'active' : ''}" onclick="setBenchmark('${m.key}')">${m.label}</button> | |
| `).join(''); | |
| } | |
| function applyReleaseCardColors() { | |
| document.querySelectorAll('.release-card[data-org]').forEach(card => { | |
| const org = ORGS[card.dataset.org]; | |
| if (org) card.style.borderTopColor = org.chartBorder; | |
| }); | |
| } | |
| function initReleaseCarousel() { | |
| const viewport = document.querySelector('#new-releases .release-grid'); | |
| const previousButton = document.getElementById('release-prev'); | |
| const nextButton = document.getElementById('release-next'); | |
| const status = document.getElementById('release-page-status'); | |
| if (!viewport || !previousButton || !nextButton || !status || viewport.dataset.carouselReady === 'true') return; | |
| const cards = Array.from(viewport.children).filter(card => card.classList.contains('release-card')); | |
| const pageSize = 4; | |
| const track = document.createElement('div'); | |
| track.className = 'release-carousel-track'; | |
| const pages = []; | |
| for (let start = 0; start < cards.length; start += pageSize) { | |
| const page = document.createElement('div'); | |
| page.className = 'release-carousel-page'; | |
| page.setAttribute('aria-label', `Release page ${pages.length + 1}`); | |
| cards.slice(start, start + pageSize).forEach(card => page.appendChild(card)); | |
| pages.push(page); | |
| track.appendChild(page); | |
| } | |
| viewport.replaceChildren(track); | |
| viewport.dataset.carouselReady = 'true'; | |
| let activePage = 0; | |
| let pointerStartX = null; | |
| function renderReleasePage() { | |
| const pageCount = Math.max(pages.length, 1); | |
| activePage = Math.max(0, Math.min(activePage, pageCount - 1)); | |
| track.style.transform = `translateX(-${activePage * 100}%)`; | |
| previousButton.disabled = activePage === 0; | |
| nextButton.disabled = activePage >= pageCount - 1; | |
| status.textContent = `${String(activePage + 1).padStart(2, '0')} / ${String(pageCount).padStart(2, '0')}`; | |
| pages.forEach((page, index) => { | |
| page.setAttribute('aria-hidden', String(index !== activePage)); | |
| }); | |
| window.requestAnimationFrame(function () { | |
| const active = pages[activePage]; | |
| if (active) viewport.style.height = `${active.offsetHeight}px`; | |
| }); | |
| } | |
| function moveReleasePage(direction) { | |
| const nextPage = activePage + direction; | |
| if (nextPage < 0 || nextPage >= pages.length) return; | |
| activePage = nextPage; | |
| renderReleasePage(); | |
| } | |
| previousButton.addEventListener('click', function () { moveReleasePage(-1); }); | |
| nextButton.addEventListener('click', function () { moveReleasePage(1); }); | |
| viewport.addEventListener('pointerdown', function (event) { | |
| pointerStartX = event.clientX; | |
| }); | |
| viewport.addEventListener('pointerup', function (event) { | |
| if (pointerStartX === null) return; | |
| const distance = event.clientX - pointerStartX; | |
| pointerStartX = null; | |
| if (Math.abs(distance) < 48) return; | |
| moveReleasePage(distance < 0 ? 1 : -1); | |
| }); | |
| viewport.addEventListener('pointercancel', function () { pointerStartX = null; }); | |
| window.addEventListener('resize', renderReleasePage); | |
| renderReleasePage(); | |
| } | |
| function renderHighlights() { | |
| const overview = document.getElementById('overview-strip'); | |
| if (!overview) return; | |
| const models = getModelEntries(); | |
| const orgCount = new Set(models.map(model => model.org).filter(Boolean)).size; | |
| overview.innerHTML = ` | |
| <div class="overview-counts"> | |
| <span class="overview-stat"><strong>${models.length}</strong><span>Models tracked</span></span> | |
| <span class="overview-stat"><strong>${orgCount}</strong><span>Organizations represented</span></span> | |
| </div> | |
| <span class="overview-scope">Open weights <span aria-hidden="true">·</span> Below 400M parameters</span> | |
| `; | |
| } | |
| function setSizeMode(mode) { | |
| if (!SIZE_FILTER_GROUPS[mode] || mode === activeSizeMode) return; | |
| activeSizeMode = mode; | |
| setFilter(SIZE_FILTER_GROUPS[mode][0]); | |
| } | |
| function setFilter(f) { | |
| if (!SIZE_FILTERS.includes(f)) return; | |
| if (!SIZE_FILTER_GROUPS[activeSizeMode].includes(f)) activeSizeMode = 'extended'; | |
| activeFilter = f; | |
| renderFilters(); | |
| renderHighlights(); | |
| renderTable(); | |
| renderBarChart(); | |
| renderScatter(); | |
| } | |
| function setBenchmark(metric) { | |
| if (EXTRA_COLUMN_KEYS.has(metric)) return; | |
| activeBenchmark = metric; | |
| renderBenchmarkFilters(); | |
| renderBarChart(); | |
| renderScatter(); | |
| } | |
| // βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| // βββ RENDER: TABLE | |
| // βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| function renderTable() { | |
| const tbody = document.getElementById('leaderboard-body'); | |
| const allModels = getLeaderboardModels(); | |
| const fitMap = getModelFitMap(allModels); | |
| const sorted = getSortedModels(allModels, fitMap); | |
| const categoryLeaders = getCategoryLeaders(); | |
| if (!sorted.length) { | |
| tbody.innerHTML = '<tr><td colspan="13" class="td-model" style="padding:24px;color:var(--text-secondary);">No models match this search.</td></tr>'; | |
| return; | |
| } | |
| // Best values per column (for highlighting + color scaling) | |
| const getBest = (key) => { | |
| const vals = allModels.map(m => m[key]).filter(v => v !== null && v !== undefined); | |
| return vals.length ? Math.max(...vals) : null; | |
| }; | |
| const getMin = (key) => { | |
| const vals = allModels.map(m => m[key]).filter(v => v !== null && v !== undefined); | |
| return vals.length ? Math.min(...vals) : null; | |
| }; | |
| const best = {}; | |
| const mins = {}; | |
| BENCHMARKS.forEach(k => { best[k] = getBest(k); mins[k] = getMin(k); }); | |
| const intelligenceIndexVals = allModels.map(getIntelligenceIndex).filter(v => v !== null); | |
| best.intelligenceIndex = intelligenceIndexVals.length ? Math.max(...intelligenceIndexVals) : null; | |
| mins.intelligenceIndex = intelligenceIndexVals.length ? Math.min(...intelligenceIndexVals) : null; | |
| const avgVals = allModels.map(getAvg).filter(v => v !== null); | |
| best.avg = avgVals.length ? Math.max(...avgVals) : null; | |
| mins.avg = avgVals.length ? Math.min(...avgVals) : null; | |
| const fmtCell = (val, key) => { | |
| if (val === null || val === undefined) return '<span class="td-score na">β</span>'; | |
| const isBest = best[key] !== null && Math.abs(val - best[key]) < 0.001; | |
| const cls = isBest ? 'best' : ''; | |
| return `<span class="td-score ${cls}" style="${scoreColor(val, mins[key], best[key])}">${val.toFixed(2)}%</span>`; | |
| }; | |
| const fmtFitCell = (m) => { | |
| const fit = fitMap.get(m.name); | |
| if (!fit) return '<span class="td-score na">N/A</span>'; | |
| const color = fit.fitZ >= 0 ? 'var(--best)' : '#f87171'; | |
| return `<span class="td-score" style="color:${color}">${formatSigned(fit.fitZ)}</span>`; | |
| }; | |
| tbody.innerHTML = sorted.map((m, i) => { | |
| const org = ORGS[m.org]; | |
| const rank = i + 1; | |
| const intelligenceIndex = getIntelligenceIndex(m); | |
| const avg = getAvg(m); | |
| const cutoffTag = isBelowOrgMovementCutoff(m) ? '<span class="rank-move new">new</span>' : ''; | |
| const inCompare = compareSet.has(m.name); | |
| return ` | |
| <tr id="${modelAnchorId(m)}"> | |
| <td class="td-compare"><button class="compare-toggle ${inCompare ? 'active' : ''}" onclick="toggleCompare('${encodeURIComponent(m.name)}')" title="${inCompare ? 'Remove from comparison' : 'Add to comparison'}" aria-pressed="${inCompare}">${inCompare ? 'β' : '+'}</button></td> | |
| <td class="td-rank">${rank}</td> | |
| <td class="td-model"><a class="${categoryLeaders.has(m.name) ? 'category-leader' : ''}" title="${getCategoryLeaderTitle(categoryLeaders.get(m.name))}" href="${m.links.card}" target="_blank">${m.name}</a>${cutoffTag}<span class="model-org"><a href="${org.url}" target="_blank">${org.name}</a> · ${m.paramsDisplay}</span></td> | |
| <td class="td-params">${m.paramsDisplay}</td> | |
| <td class="td-score">${intelligenceIndex !== null ? `<span class="${Math.abs(intelligenceIndex - best.intelligenceIndex) < 0.001 ? 'best' : ''}" style="${scoreColor(intelligenceIndex, Math.min(0, mins.intelligenceIndex), best.intelligenceIndex)}">${intelligenceIndex.toFixed(2)}</span>` : '<span class="td-score na">—</span>'}</td> | |
| <td class="td-score">${fmtCell(m.hellaswag, 'hellaswag')}</td> | |
| <td class="td-score">${fmtCell(m.arc, 'arc')}</td> | |
| <td class="td-score">${fmtCell(m.arcChall, 'arcChall')}</td> | |
| <td class="td-score">${fmtCell(m.piqa, 'piqa')}</td> | |
| <td class="td-score">${fmtCell(m.arithmark3, 'arithmark3')}</td> | |
| <td class="td-score extra-col extra-col-start">${avg !== null ? fmtCell(avg, 'avg') : '<span class="td-score na">—</span>'}</td> | |
| <td class="td-score extra-col">${fmtFitCell(m)}</td> | |
| <td class="td-score extra-col">${fmtCell(m.arithmark2, 'arithmark2')}</td> | |
| </tr> | |
| `; | |
| }).join(''); | |
| // Sort indicators | |
| document.querySelectorAll('#leaderboard-table thead th').forEach(th => { | |
| th.classList.remove('sorted'); | |
| const ind = th.querySelector('.sort-indicator'); | |
| if (ind) ind.textContent = ''; | |
| }); | |
| const colMap = { rank: 1, name: 2, params: 3, intelligenceIndex: 4, hellaswag: 5, arc: 6, arcChall: 7, piqa: 8, arithmark3: 9, avg: 10, fitZ: 11, arithmark2: 12 }; | |
| const thIdx = colMap[sortCol]; | |
| if (thIdx !== undefined) { | |
| const th = document.querySelectorAll('#leaderboard-table thead th')[thIdx]; | |
| if (th) { | |
| th.classList.add('sorted'); | |
| const ind = th.querySelector('.sort-indicator'); | |
| if (ind) ind.textContent = sortAsc ? 'β²' : 'βΌ'; | |
| } | |
| } | |
| } | |
| function sortTable(col) { | |
| if (sortCol === col) { | |
| sortAsc = !sortAsc; | |
| } else { | |
| sortCol = col; | |
| sortAsc = false; | |
| } | |
| renderTable(); | |
| } | |
| function toggleExtraColumns() { | |
| const nextOpen = !extraColumnsOpen; | |
| extraColumnsOpen = nextOpen; | |
| const shell = document.getElementById('leaderboard-table-shell'); | |
| const toggle = document.getElementById('extra-columns-toggle'); | |
| if (shell) { | |
| if (shell._extrasCloseTimer) { | |
| clearTimeout(shell._extrasCloseTimer); | |
| shell._extrasCloseTimer = null; | |
| } | |
| shell.classList.remove('extras-closing'); | |
| if (nextOpen) { | |
| shell.classList.remove('extras-open'); | |
| void shell.offsetWidth; | |
| shell.classList.add('extras-open'); | |
| } else { | |
| shell.classList.remove('extras-open'); | |
| shell.classList.add('extras-closing'); | |
| shell._extrasCloseTimer = setTimeout(function () { | |
| shell.classList.remove('extras-closing'); | |
| shell._extrasCloseTimer = null; | |
| }, 430); | |
| } | |
| } | |
| if (toggle) { | |
| toggle.setAttribute('aria-expanded', String(extraColumnsOpen)); | |
| const actionLabel = `${extraColumnsOpen ? 'Hide' : 'Show'} more columns`; | |
| toggle.setAttribute('aria-label', actionLabel); | |
| toggle.title = actionLabel; | |
| } | |
| if (!extraColumnsOpen && EXTRA_COLUMN_KEYS.has(sortCol)) { | |
| sortCol = 'intelligenceIndex'; | |
| sortAsc = false; | |
| } | |
| renderTable(); | |
| } | |
| // βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| // βββ COMPARE | |
| // βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| const MAX_COMPARE = 6; | |
| let compareSet = new Set(); | |
| function toggleCompare(encodedName) { | |
| const name = decodeURIComponent(encodedName); | |
| if (compareSet.has(name)) { | |
| compareSet.delete(name); | |
| } else { | |
| if (compareSet.size >= MAX_COMPARE) { | |
| flashCompareLimit(); | |
| return; | |
| } | |
| compareSet.add(name); | |
| } | |
| renderTable(); | |
| renderCompare(); | |
| renderCompareBar(); | |
| } | |
| function removeCompare(encodedName) { | |
| const name = decodeURIComponent(encodedName); | |
| compareSet.delete(name); | |
| renderTable(); | |
| renderCompare(); | |
| renderCompareBar(); | |
| } | |
| function clearCompare() { | |
| compareSet.clear(); | |
| renderTable(); | |
| renderCompare(); | |
| renderCompareBar(); | |
| } | |
| // In-page navigation that works even with <base target="_blank"> | |
| // (which would otherwise open hash links in a new tab inside the HF iframe). | |
| function scrollToSection(id) { | |
| const el = document.getElementById(id); | |
| if (el) el.scrollIntoView({ behavior: 'smooth', block: 'start' }); | |
| return false; | |
| } | |
| // βββ Side nav rail βββ | |
| const NAV_ITEMS = [ | |
| { id: 'leaderboard', label: 'Leaderboard' }, | |
| { id: 'compare', label: 'Compare' }, | |
| { id: 'score-chart', label: 'Scores' }, | |
| { id: 'charts', label: 'Efficiency' }, | |
| { id: 'org-performance', label: 'History' }, | |
| { id: 'rules', label: 'Rules' }, | |
| ]; | |
| function renderSideNav() { | |
| const nav = document.getElementById('side-nav'); | |
| if (!nav) return; | |
| nav.innerHTML = NAV_ITEMS.map(i => ` | |
| <a href="#${i.id}" data-target="${i.id}" onclick="return scrollToSection('${i.id}')"> | |
| <span class="side-nav-dot"></span><span class="side-nav-label">${i.label}</span> | |
| </a>`).join(''); | |
| } | |
| function initSideNav() { | |
| renderSideNav(); | |
| const links = {}; | |
| document.querySelectorAll('#side-nav a').forEach(a => { links[a.dataset.target] = a; }); | |
| if (!('IntersectionObserver' in window)) return; | |
| const observer = new IntersectionObserver((entries) => { | |
| entries.forEach(entry => { | |
| if (entry.isIntersecting) { | |
| Object.values(links).forEach(l => l.classList.remove('active')); | |
| const a = links[entry.target.id]; | |
| if (a) a.classList.add('active'); | |
| } | |
| }); | |
| }, { rootMargin: '-45% 0px -50% 0px', threshold: 0 }); | |
| NAV_ITEMS.forEach(i => { | |
| const el = document.getElementById(i.id); | |
| if (el) observer.observe(el); | |
| }); | |
| } | |
| function flashCompareLimit() { | |
| const note = document.getElementById('compare-limit-note'); | |
| if (!note) return; | |
| note.classList.add('show'); | |
| clearTimeout(flashCompareLimit._t); | |
| flashCompareLimit._t = setTimeout(() => note.classList.remove('show'), 2200); | |
| } | |
| // Resolve compare names against the FULL model list so a comparison | |
| // survives changes to the active size filter. | |
| function getCompareModels() { | |
| const byName = new Map(getModelEntries(MODELS).map(m => [m.name, m])); | |
| return [...compareSet].map(name => byName.get(name)).filter(Boolean); | |
| } | |
| const COMPARE_ROWS = [ | |
| { key: 'params', label: 'Parameters', type: 'params' }, | |
| { key: 'avg', label: 'Avg', type: 'score' }, | |
| { key: 'fitZ', label: 'Fit Std Devs', type: 'fit' }, | |
| { key: 'hellaswag', label: 'HellaSwag', type: 'score' }, | |
| { key: 'arc', label: 'ARC-Easy', type: 'score' }, | |
| { key: 'arcChall', label: 'ARC-Challenge', type: 'score' }, | |
| { key: 'piqa', label: 'PIQA', type: 'score' }, | |
| { key: 'arithmark3', label: 'ArithMark-3', type: 'score' }, | |
| { key: 'arithmark2', label: 'ArithMark-2', type: 'score' }, | |
| ]; | |
| function compareRowValue(m, row, fitMap) { | |
| if (row.type === 'params') return m.params ?? null; | |
| if (row.type === 'fit') return fitMap.get(m.name)?.fitZ ?? null; | |
| if (row.key === 'avg') return getAvg(m); | |
| return m[row.key] ?? null; | |
| } | |
| function renderCompare() { | |
| const section = document.getElementById('compare'); | |
| const body = document.getElementById('compare-content'); | |
| if (!section || !body) return; | |
| const models = getCompareModels(); | |
| const countEl = document.getElementById('compare-count-label'); | |
| if (countEl) countEl.textContent = models.length ? `${models.length} selected` : 'None selected'; | |
| if (!models.length) { | |
| body.innerHTML = ` | |
| <div class="compare-empty"> | |
| <p>No models selected yet.</p> | |
| <p class="compare-empty-hint">Hit the <span class="compare-empty-plus">+</span> button on any leaderboard row to line models up side by side here.</p> | |
| </div>`; | |
| if (chartInstances['compare']) { chartInstances['compare'].destroy(); delete chartInstances['compare']; } | |
| return; | |
| } | |
| // Fit computed against the full model set so it's stable regardless of filter. | |
| const fitMap = getModelFitMap(getModelEntries(MODELS)); | |
| // Best value per row (higher is better for every metric here). | |
| const bestByRow = {}; | |
| COMPARE_ROWS.forEach(row => { | |
| if (row.type === 'params') return; // don't crown a "best" param count | |
| const vals = models.map(m => compareRowValue(m, row, fitMap)).filter(v => v !== null && v !== undefined); | |
| bestByRow[row.key] = vals.length ? Math.max(...vals) : null; | |
| }); | |
| const headCells = models.map(m => { | |
| const org = ORGS[m.org]; | |
| return ` | |
| <th class="compare-model-head" style="border-top:2px solid ${org.chartBorder}"> | |
| <button class="compare-remove" onclick="removeCompare('${encodeURIComponent(m.name)}')" title="Remove ${m.name}">Γ</button> | |
| <a class="compare-model-name" href="${m.links.card}" target="_blank">${m.name}</a> | |
| <span class="compare-model-org"><a href="${org.url}" target="_blank">${org.name}</a></span> | |
| </th>`; | |
| }).join(''); | |
| const bodyRows = COMPARE_ROWS.map(row => { | |
| const cells = models.map(m => { | |
| const val = compareRowValue(m, row, fitMap); | |
| if (val === null || val === undefined) return `<td class="compare-cell na">β</td>`; | |
| if (row.type === 'params') { | |
| return `<td class="compare-cell params">${m.paramsDisplay}</td>`; | |
| } | |
| if (row.type === 'fit') { | |
| const isBest = bestByRow[row.key] !== null && Math.abs(val - bestByRow[row.key]) < 0.0001; | |
| const color = val >= 0 ? 'var(--best)' : '#f87171'; | |
| return `<td class="compare-cell ${isBest ? 'best' : ''}" style="color:${color}">${formatSigned(val)}</td>`; | |
| } | |
| const isBest = bestByRow[row.key] !== null && Math.abs(val - bestByRow[row.key]) < 0.0001; | |
| return `<td class="compare-cell ${isBest ? 'best' : ''}" style="${isBest ? '' : scoreColor(val, RANDOM_BASELINES[row.key] ?? 25, bestByRow[row.key])}">${val.toFixed(2)}%</td>`; | |
| }).join(''); | |
| return `<tr><td class="compare-metric">${row.label}</td>${cells}</tr>`; | |
| }).join(''); | |
| body.innerHTML = ` | |
| <div class="table-wrap compare-table-wrap"> | |
| <table class="compare-table"> | |
| <thead> | |
| <tr><th class="compare-metric-head">Metric</th>${headCells}</tr> | |
| </thead> | |
| <tbody>${bodyRows}</tbody> | |
| </table> | |
| </div> | |
| <div class="chart-card" style="margin-top:24px;"> | |
| <h3>Benchmark comparison</h3> | |
| <canvas id="compareChart"></canvas> | |
| </div>`; | |
| renderCompareChart(models); | |
| } | |
| function renderCompareChart(models) { | |
| const canvas = document.getElementById('compareChart'); | |
| if (!canvas) return; | |
| const ctx = canvas.getContext('2d'); | |
| if (chartInstances['compare']) chartInstances['compare'].destroy(); | |
| const benchLabels = ['HellaSwag', 'ARC-Easy', 'ARC-Challenge', 'PIQA', 'ArithMark-3', 'ArithMark-2']; | |
| const benchKeys = ['hellaswag', 'arc', 'arcChall', 'piqa', 'arithmark3', 'arithmark2']; | |
| const modelBorders = getChartModelBorders(models); | |
| const datasets = models.map((m, index) => { | |
| const org = ORGS[m.org]; | |
| return { | |
| label: m.name, | |
| data: benchKeys.map(k => m[k] ?? null), | |
| backgroundColor: org.chartColor.replace('0.70', '0.82'), | |
| borderColor: modelBorders[index], | |
| borderWidth: 1.5, | |
| borderRadius: 4, | |
| borderSkipped: false | |
| }; | |
| }); | |
| chartInstances['compare'] = new Chart(ctx, { | |
| type: 'bar', | |
| data: { labels: benchLabels, datasets }, | |
| options: { | |
| responsive: true, | |
| maintainAspectRatio: true, | |
| animation: { duration: 0 }, | |
| plugins: { | |
| legend: { | |
| display: true, | |
| position: 'top', | |
| labels: { color: '#6b6b6e', font: { family: "'DM Sans', system-ui, sans-serif", size: 11 }, boxWidth: 10, boxHeight: 10, usePointStyle: true, pointStyle: 'circle' } | |
| }, | |
| tooltip: { | |
| ...getChartTooltipOptions(), | |
| displayColors: true, | |
| callbacks: { label: (item) => `${item.dataset.label}: ${item.raw === null ? 'N/A' : item.raw.toFixed(2) + '%'}` } | |
| } | |
| }, | |
| scales: { | |
| x: { | |
| grid: { display: false }, | |
| ticks: { color: '#6b6b6e', font: { family: "'DM Sans', system-ui, sans-serif", size: 11 } }, | |
| border: { display: false } | |
| }, | |
| y: { | |
| min: 0, | |
| title: { display: true, text: 'Score (%)', color: '#454548', font: { family: "'Space Mono', monospace", size: 10 } }, | |
| grid: { color: 'rgba(255,255,255,0.03)' }, | |
| ticks: { color: '#454548', font: { family: "'Space Mono', monospace", size: 10 }, callback: v => v + '%', maxTicksLimit: 8 }, | |
| border: { display: false } | |
| } | |
| } | |
| } | |
| }); | |
| } | |
| function renderCompareBar() { | |
| const bar = document.getElementById('compare-bar'); | |
| if (!bar) return; | |
| const models = getCompareModels(); | |
| if (!models.length) { | |
| bar.classList.remove('show'); | |
| bar.innerHTML = ''; | |
| return; | |
| } | |
| const chips = models.map(m => { | |
| const org = ORGS[m.org]; | |
| return `<span class="compare-chip" style="border-color:${org.chartBorder}66"><span class="compare-chip-dot" style="background:${org.chartBorder}"></span>${m.name}<button class="compare-chip-x" onclick="removeCompare('${encodeURIComponent(m.name)}')" title="Remove ${m.name}">Γ</button></span>`; | |
| }).join(''); | |
| bar.innerHTML = ` | |
| <div class="compare-bar-inner"> | |
| <span class="compare-bar-label">Comparing ${models.length}/${MAX_COMPARE}</span> | |
| <div class="compare-bar-chips">${chips}</div> | |
| <div class="compare-bar-actions"> | |
| <button class="compare-bar-view" onclick="scrollToSection('compare')">View comparison β</button> | |
| <button class="compare-bar-clear" onclick="clearCompare()">Clear</button> | |
| </div> | |
| </div>`; | |
| bar.classList.add('show'); | |
| } | |
| function renderLegend() { | |
| const bar = document.getElementById('legend-bar'); | |
| bar.innerHTML = Object.entries(ORGS).map(([key, o]) => ` | |
| <div class="legend-item"><span class="legend-dot" style="background:${o.chartBorder}"></span>${o.name}</div> | |
| `).join(''); | |
| } | |
| function linearRegression(points) { | |
| const n = points.length; | |
| if (n < 2) return { slope: 0, intercept: 0 }; | |
| let sumX = 0, sumY = 0, sumXY = 0, sumXX = 0; | |
| for (const [x, y] of points) { | |
| sumX += x; sumY += y; sumXY += x * y; sumXX += x * x; | |
| } | |
| const denom = n * sumXX - sumX * sumX; | |
| if (Math.abs(denom) < 1e-10) return { slope: 0, intercept: sumY / n }; | |
| const slope = (n * sumXY - sumX * sumY) / denom; | |
| const intercept = (sumY - slope * sumX) / n; | |
| return { slope, intercept }; | |
| } | |
| function renderBarChart() { | |
| const canvas = document.getElementById('barChart'); | |
| if (!canvas) return; | |
| const ctx = canvas.getContext('2d'); | |
| if (chartInstances['bar']) chartInstances['bar'].destroy(); | |
| const metricLabel = getMetricLabel(activeBenchmark, true); | |
| const metricUnit = getMetricUnit(activeBenchmark); | |
| const sub = document.getElementById('score-chart-sub'); | |
| if (sub) sub.textContent = `Top ${metricLabel.toLowerCase()} scores for the active size filter. Gold outlines mark size category leaders.`; | |
| const models = getChartModels() | |
| .map(m => ({ ...m, score: getMetricValue(m, activeBenchmark) })) | |
| .filter(m => m.score !== null && m.score !== undefined) | |
| .sort((a, b) => b.score - a.score) | |
| .slice(0, 12); | |
| if (!models.length) { | |
| canvas.parentElement.querySelector('h3').textContent = `Top ${metricLabel} Scores - no models with data`; | |
| return; | |
| } | |
| canvas.parentElement.querySelector('h3').textContent = `Top ${models.length} ${metricLabel} Scores`; | |
| chartInstances['bar'] = new Chart(ctx, { | |
| type: 'bar', | |
| data: { | |
| labels: models.map(m => m.name), | |
| datasets: [{ | |
| label: metricLabel, | |
| data: models.map(m => m.score), | |
| backgroundColor: models.map(m => ORGS[m.org].chartColor.replace('0.70', '0.82')), | |
| borderColor: getChartModelBorders(models), | |
| borderWidth: 1.5, | |
| borderRadius: 4, | |
| borderSkipped: false | |
| }] | |
| }, | |
| options: { | |
| indexAxis: 'y', | |
| responsive: true, | |
| maintainAspectRatio: true, | |
| animation: { duration: 0 }, | |
| plugins: { | |
| legend: { display: false }, | |
| tooltip: { | |
| ...getChartTooltipOptions(), | |
| callbacks: { | |
| label: (item) => { | |
| const model = models[item.dataIndex]; | |
| return [`${metricLabel}: ${formatMetricValue(item.raw, activeBenchmark)}`, `Params: ${model.paramsDisplay}`]; | |
| } | |
| } | |
| } | |
| }, | |
| scales: { | |
| x: { | |
| min: 0, | |
| suggestedMax: Math.max(...models.map(m => m.score)) * 1.08, | |
| title: { | |
| display: true, | |
| text: metricUnit ? `${metricLabel} (${metricUnit})` : metricLabel, | |
| color: '#454548', | |
| font: { family: "'Space Mono', monospace", size: 10 } | |
| }, | |
| grid: { color: 'rgba(255,255,255,0.03)' }, | |
| ticks: { color: '#454548', font: { family: "'Space Mono', monospace", size: 10 }, callback: v => `${v}${metricUnit}`, maxTicksLimit: 8 }, | |
| border: { display: false } | |
| }, | |
| y: { | |
| grid: { display: false }, | |
| ticks: { color: '#6b6b6e', font: { family: "'DM Sans', system-ui, sans-serif", size: 11 } }, | |
| border: { display: false } | |
| } | |
| } | |
| } | |
| }); | |
| } | |
| function renderScatter() { | |
| const canvas = document.getElementById('scatterChart'); | |
| if (!canvas) return; | |
| const ctx = canvas.getContext('2d'); | |
| if (chartInstances['scatter']) chartInstances['scatter'].destroy(); | |
| const metricLabel = getMetricLabel(activeBenchmark, true); | |
| const metricUnit = getMetricUnit(activeBenchmark); | |
| const sub = document.getElementById('efficiency-sub'); | |
| if (sub) sub.textContent = `${metricLabel} vs parameter count (log scale), 600K+ params only. Shaded zone = above regression line. Gold outlines mark size category leaders.`; | |
| const chartSourceModels = getChartModels().filter(m => m.params && m.params >= 600000); | |
| const models = chartSourceModels | |
| .filter(m => m.params && getMetricValue(m, activeBenchmark) !== null && getMetricValue(m, activeBenchmark) !== undefined) | |
| .map(m => ({ | |
| x: Math.log10(m.params), | |
| y: getMetricValue(m, activeBenchmark), | |
| name: m.name, | |
| org: m.org, | |
| params: m.paramsDisplay | |
| })); | |
| const fitModels = models; | |
| if (fitModels.length < 2) { | |
| canvas.parentElement.querySelector('h3').textContent = `${metricLabel} vs Log Parameters - need 2+ models with data`; return; | |
| } | |
| canvas.parentElement.querySelector('h3').textContent = `${metricLabel} vs Log Parameters`; | |
| const points = fitModels.map(m => [m.x, m.y]); | |
| const { slope, intercept } = linearRegression(points); | |
| const residualStd = getStdDev(fitModels.map(m => m.y - (slope * m.x + intercept))); | |
| models.forEach(m => { | |
| const residual = m.y - (slope * m.x + intercept); | |
| m.fitZ = residual / (residualStd || 1); | |
| }); | |
| const xMin = Math.min(...models.map(m => m.x)); | |
| const xMax = Math.max(...models.map(m => m.x)); | |
| const pad = (xMax - xMin) * 0.1; | |
| const xLow = xMin - pad; | |
| const xHigh = xMax + pad; | |
| const yAtLow = slope * xLow + intercept; | |
| const yAtHigh = slope * xHigh + intercept; | |
| const randomBaseline = RANDOM_BASELINES[activeBenchmark] ?? null; | |
| const yValues = [...models.map(m => m.y), yAtLow, yAtHigh]; | |
| if (randomBaseline !== null) yValues.push(randomBaseline); | |
| const yLow = Math.min(...yValues); | |
| const yHigh = Math.max(...yValues); | |
| const ySpan = Math.max(yHigh - yLow, 1); | |
| const yPadding = ySpan * 0.08; | |
| const yMin = yLow >= 0 ? Math.max(0, yLow - yPadding) : yLow - yPadding; | |
| const yMax = yHigh + yPadding; | |
| renderLegend(); | |
| chartInstances['scatter'] = new Chart(ctx, { | |
| type: 'scatter', | |
| data: { | |
| datasets: [ | |
| { | |
| type: 'line', | |
| label: 'Regression', | |
| data: [{ x: xLow, y: yAtLow }, { x: xHigh, y: yAtHigh }], | |
| borderColor: 'rgba(194, 182, 255, 0.45)', | |
| borderWidth: 1.5, | |
| borderDash: [5, 4], | |
| pointRadius: 0, | |
| fill: 'end', | |
| backgroundColor: 'rgba(194, 182, 255, 0.10)', | |
| order: 0 | |
| }, | |
| { | |
| type: 'line', | |
| label: 'Random', | |
| data: randomBaseline === null ? [] : [{ x: xLow, y: randomBaseline }, { x: xHigh, y: randomBaseline }], | |
| borderColor: 'rgba(107, 107, 110, 0.75)', | |
| borderWidth: 1.25, | |
| borderDash: [2, 4], | |
| pointRadius: 0, | |
| fill: false, | |
| order: 1 | |
| }, | |
| { | |
| label: 'Models', | |
| data: models, | |
| backgroundColor: models.map(m => ORGS[m.org].chartColor.replace('0.70', '0.90')), | |
| borderColor: getChartModelBorders(models), | |
| borderWidth: 1.5, | |
| pointRadius: 7, | |
| pointHoverRadius: 10, | |
| order: 2 | |
| } | |
| ] | |
| }, | |
| options: { | |
| responsive: true, | |
| maintainAspectRatio: true, | |
| animation: { duration: 0 }, | |
| plugins: { | |
| legend: { display: false }, | |
| tooltip: { | |
| ...getChartTooltipOptions(), | |
| callbacks: { | |
| title: (items) => items[0]?.raw?.name || '', | |
| label: (item) => { | |
| const d = item.raw; | |
| const lines = [ | |
| `Params: ${d.params}`, | |
| `${metricLabel}: ${formatMetricValue(d.y, activeBenchmark)}`, | |
| `Fit residual: ${formatSigned(d.fitZ)} std` | |
| ]; | |
| return lines; | |
| } | |
| } | |
| } | |
| }, | |
| scales: { | |
| x: { | |
| type: 'linear', | |
| min: xLow, | |
| max: xHigh, | |
| title: { | |
| display: true, | |
| text: 'Log10(Parameters)', | |
| color: '#454548', | |
| font: { family: "'Space Mono', monospace", size: 10 } | |
| }, | |
| grid: { color: 'rgba(255,255,255,0.03)' }, | |
| ticks: { color: '#454548', font: { family: "'Space Mono', monospace", size: 10 }, maxTicksLimit: 8 }, | |
| border: { display: false } | |
| }, | |
| y: { | |
| min: yMin, | |
| max: yMax, | |
| title: { | |
| display: true, | |
| text: metricUnit ? `${metricLabel} (${metricUnit})` : metricLabel, | |
| color: '#454548', | |
| font: { family: "'Space Mono', monospace", size: 10 } | |
| }, | |
| grid: { color: 'rgba(255,255,255,0.03)' }, | |
| ticks: { color: '#454548', font: { family: "'Space Mono', monospace", size: 10 }, callback: v => `${v.toFixed(0)}${metricUnit}`, maxTicksLimit: 8 }, | |
| border: { display: false } | |
| } | |
| } | |
| } | |
| }); | |
| } | |
| // βββ INIT | |
| // βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| window.addEventListener('DOMContentLoaded', () => { | |
| const leaderboardSearch = document.getElementById('leaderboard-search'); | |
| if (leaderboardSearch) { | |
| leaderboardSearch.addEventListener('input', event => { | |
| leaderboardQuery = event.target.value; | |
| renderTable(); | |
| }); | |
| } | |
| renderFilters(); | |
| renderBenchmarkFilters(); | |
| initReleaseCarousel(); | |
| applyReleaseCardColors(); | |
| renderHighlights(); | |
| renderTable(); | |
| renderBarChart(); | |
| renderLegend(); | |
| renderScatter(); | |
| renderCompare(); | |
| renderCompareBar(); | |
| initSideNav(); | |
| }); | |
| </script> | |
| <script src="leaderboard-history.js"></script> | |
| <script> | |
| (function () { | |
| const sizeLabels = { all: 'All sizes', '<250M': 'Under 250M', '<150M': 'Under 150M', '<100M': 'Under 100M', '<50M': 'Under 50M', '<10M': 'Under 10M', '<3M': 'Under 3M' }; | |
| function escapeHtml(value) { | |
| return String(value).replace(/[&<>"']/g, function (char) { | |
| return ({ '&': '&', '<': '<', '>': '>', '"': '"', "'": ''' })[char]; | |
| }); | |
| } | |
| function modelsFor(filter) { | |
| return getFilteredModels(MODELS, filter); | |
| } | |
| function winner(filter, metric) { | |
| if (metric === 'intelligenceIndex') { | |
| const model = getSizeLeader(filter); | |
| return model ? { model, score: getIntelligenceIndex(model) } : null; | |
| } | |
| return modelsFor(filter) | |
| .map(function (model) { return { model: model, score: getMetricValue(model, metric) }; }) | |
| .filter(function (entry) { return Number.isFinite(entry.score); }) | |
| .sort(function (a, b) { return b.score - a.score; })[0] || null; | |
| } | |
| function card(entry, kicker, metric, active, attribute, value, showSizeLabel = false) { | |
| if (!entry) return ''; | |
| const model = entry.model; | |
| const org = ORGS[model.org] || { name: model.org }; | |
| const orgColor = org.chartBorder || 'var(--accent)'; | |
| const href = model.links && model.links.card ? model.links.card : '#'; | |
| const score = '<span class="filter-leader-score">' + escapeHtml(formatMetricValue(entry.score, metric, 2)) + '</span>'; | |
| const params = escapeHtml(model.paramsDisplay || model.params) + ' params'; | |
| const cardId = modelAnchorId(model) + '-' + (showSizeLabel ? 'highlight' : attribute === 'data-size-filter' ? 'size' : 'benchmark') + '-' + String(value).toLowerCase().replace(/[^a-z0-9._-]+/g, '-').replace(/-+/g, '-').replace(/^-|-$/g, ''); | |
| const details = showSizeLabel | |
| ? '<div class="highlight-leader-footer"><span class="filter-leader-meta"><span class="highlight-leader-org">' + escapeHtml(org.name) + '</span><span class="highlight-leader-params">' + params + '</span></span><div class="highlight-leader-score">' + score + '<span class="size-score-label">Int Index</span></div></div>' | |
| : score + '<span class="filter-leader-meta">' + escapeHtml(org.name) + ' Β· ' + params + '</span>'; | |
| return '<article class="filter-leader-card' + (active ? ' active' : '') + '" id="' + escapeHtml(cardId) + '" role="button" tabindex="0" ' + | |
| attribute + '="' + escapeHtml(value) + '" style="--leader-org-color:' + escapeHtml(orgColor) + '" aria-pressed="' + active + '" aria-label="' + escapeHtml(kicker + ': ' + model.name) + '">' + | |
| '<span class="filter-leader-kicker"><span>' + escapeHtml(kicker) + '</span></span>' + | |
| '<a class="filter-leader-name" href="' + escapeHtml(href) + '" target="_blank" rel="noopener noreferrer">' + escapeHtml(model.name) + '</a>' + | |
| details + | |
| '</article>'; | |
| } | |
| function renderFilterLeaders() { | |
| const sizeFilters = SIZE_FILTER_GROUPS[activeSizeMode]; | |
| const highlightGrid = document.getElementById('highlight-leader-grid'); | |
| const sizeGrid = document.getElementById('size-leader-grid'); | |
| const benchmarkGrid = document.getElementById('benchmark-leader-grid'); | |
| if (highlightGrid) { | |
| highlightGrid.innerHTML = sizeFilters.map(function (filter) { | |
| return card(winner(filter, 'intelligenceIndex'), sizeLabels[filter], 'intelligenceIndex', activeFilter === filter, 'data-size-filter', filter, true); | |
| }).join(''); | |
| } | |
| if (sizeGrid) { | |
| sizeGrid.innerHTML = sizeFilters.map(function (filter) { | |
| return card(winner(filter, 'intelligenceIndex'), 'Best ' + sizeLabels[filter], 'intelligenceIndex', activeFilter === filter, 'data-size-filter', filter); | |
| }).join(''); | |
| } | |
| if (benchmarkGrid) { | |
| benchmarkGrid.innerHTML = FILTER_METRICS.map(function (metric) { | |
| return card(winner(activeFilter, metric.key), 'Best ' + metric.label, metric.key, activeBenchmark === metric.key, 'data-benchmark-filter', metric.key); | |
| }).join(''); | |
| } | |
| } | |
| function activate(cardElement) { | |
| if (cardElement.hasAttribute('data-size-filter')) { | |
| setFilter(cardElement.getAttribute('data-size-filter')); | |
| if (cardElement.closest('#highlight-leader-grid')) { | |
| document.getElementById('leaderboard').scrollIntoView({ behavior: window.matchMedia('(prefers-reduced-motion: reduce)').matches ? 'auto' : 'smooth' }); | |
| } | |
| } | |
| if (cardElement.hasAttribute('data-benchmark-filter')) setBenchmark(cardElement.getAttribute('data-benchmark-filter')); | |
| window.setTimeout(renderFilterLeaders, 0); | |
| } | |
| document.addEventListener('click', function (event) { | |
| const cardElement = event.target.closest('.filter-leader-card'); | |
| if (cardElement && !event.target.closest('a')) activate(cardElement); | |
| if (event.target.closest('#filter-bar, #benchmark-filter-bar')) window.setTimeout(renderFilterLeaders, 0); | |
| }); | |
| document.addEventListener('keydown', function (event) { | |
| const cardElement = event.target.closest('.filter-leader-card'); | |
| if (cardElement && !event.target.closest('a') && (event.key === 'Enter' || event.key === ' ')) { | |
| event.preventDefault(); | |
| activate(cardElement); | |
| } | |
| }); | |
| const filterObserver = new MutationObserver(function () { | |
| renderFilterLeaders(); | |
| }); | |
| ['filter-bar', 'benchmark-filter-bar'].forEach(function (id) { | |
| const filterBar = document.getElementById(id); | |
| if (filterBar) filterObserver.observe(filterBar, { childList: true }); | |
| }); | |
| function scrollToModelHash() { | |
| let id; | |
| try { id = decodeURIComponent(window.location.hash.slice(1)); } catch { return; } | |
| if (!id) return; | |
| const highlight = Array.from(document.querySelectorAll('#highlight-leader-grid .filter-leader-card[id]')).find(card => card.id.startsWith(id + '-highlight-')); | |
| const target = highlight || document.getElementById(id); | |
| if (target && (target.matches('.filter-leader-card') || target.matches('#leaderboard-body > tr'))) target.scrollIntoView({ block: 'center' }); | |
| } | |
| window.addEventListener('load', function () { renderFilterLeaders(); scrollToModelHash(); }); | |
| window.addEventListener('hashchange', scrollToModelHash); | |
| renderFilterLeaders(); | |
| })(); | |
| </script> | |
| </body> | |
| </html> | |