JustVugg
c6801451bd
The site.yml workflow deploys on push to main, so the site only goes live once site/ is on main. Bring site/index.html + icons + the workflow so a GitHub Pages deploy (Settings -> Pages -> Source: GitHub Actions) publishes it at justvugg.github.io/colibri. Refs #424 Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
699 lines
96 KiB
HTML
699 lines
96 KiB
HTML
<!doctype html>
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<html lang="en">
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<head>
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1">
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<title>colibrì — tiny engine, immense model</title>
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<meta name="description" content="Run GLM-5.2, a 744B-parameter Mixture-of-Experts model, on your own machine — pure C, zero dependencies, experts streamed from disk. Open the model up: run it, study it, improve it.">
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<meta property="og:title" content="colibrì — tiny engine, immense model">
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<meta property="og:description" content="A 744B MoE on consumer hardware: pure C, zero dependencies, 19,456 experts tiered across VRAM, RAM and disk. Run it, study it, improve it.">
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<meta property="og:image" content="https://raw.githubusercontent.com/JustVugg/colibri/main/docs/media/colibri-atlas.png">
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<meta property="og:type" content="website">
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}
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</style>
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</head>
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<body>
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<nav id="topnav">
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<a class="brand" href="#"><img src="colibri-icon.svg" alt=""> colibrì</a>
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<div class="links">
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<a href="#vision">vision</a>
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<a href="#algo">algorithm</a>
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<a href="#demo">demo</a>
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<a href="#atlas-sec">atlas</a>
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<a href="#models">models</a>
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<a class="gh" href="https://github.com/JustVugg/colibri">GitHub ↗</a>
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</div>
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</nav>
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<header class="hero">
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<canvas id="atlas-hero"></canvas>
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<div class="kicker">tiny engine · immense model</div>
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<h1>colibrì</h1>
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<div class="tag">a 744-billion-parameter mind, on your desk</div>
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<p class="sub">Run <b>GLM-5.2 — 744B Mixture-of-Experts</b> — on hardware you already own.
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Pure C, <b>zero dependencies</b>, experts staged just-in-time across VRAM, RAM and disk.
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The galaxy behind this text is real: the model's <b>measured expert atlas</b>.</p>
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<div class="cta">
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<a class="btn primary" href="https://github.com/JustVugg/colibri">★ Star on GitHub</a>
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<a class="btn" href="https://github.com/JustVugg/colibri/blob/main/docs/quickstart.md">Quick start →</a>
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</div>
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<div class="stats">
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<div><div class="n">744B</div><div class="l">parameters</div></div>
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<div><div class="n">19,456</div><div class="l">experts</div></div>
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<div><div class="n">0</div><div class="l">dependencies</div></div>
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<div><div class="n">25 GB</div><div class="l">min RAM</div></div>
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<div><div class="n">9.2</div><div class="l">tok/s measured</div></div>
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</div>
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<div class="scrollcue">scroll</div>
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</header>
|
||
|
||
<main>
|
||
|
||
<section id="vision" class="reveal">
|
||
<div class="snum">the vision</div>
|
||
<p class="manifesto">Frontier models should not be sealed inside datacenters.<br>
|
||
<em>Anyone curious enough should be able to open one up</em> —<br>
|
||
<span class="dimline">run it, watch every expert fire, and make it better.</span></p>
|
||
<div class="pillars">
|
||
<div class="pillar"><div class="ic">⚡</div><h3>Run it</h3>
|
||
<p>A 744B frontier-class MoE answering on a <b>25 GB machine</b> — the proven floor — up to
|
||
9+ tok/s on a workstation. No cluster, no cloud bill, no API key.</p></div>
|
||
<div class="pillar"><div class="ic">🔬</div><h3>Study it</h3>
|
||
<p>The engine is a <b>microscope</b>: live routing telemetry, per-expert heat, and the first
|
||
published <b>expert atlas</b> of a 700B-class model — measured on machines like yours.</p></div>
|
||
<div class="pillar"><div class="ic">🛠</div><h3>Improve it</h3>
|
||
<p>One C file, readable in an afternoon. Every optimisation this project found came from
|
||
<b>someone measuring something</b> — the next one can come from you.</p></div>
|
||
</div>
|
||
</section>
|
||
|
||
<section id="algo" class="reveal">
|
||
<div class="snum">01 — the algorithm</div>
|
||
<h2>A JIT, but for weights</h2>
|
||
<div class="algo-grid">
|
||
<div class="algo-copy">
|
||
<p>A compiler JIT never compiles the whole program — it watches what actually runs
|
||
and compiles the <b>hot paths</b>, just in time. colibrì applies the same bet to a
|
||
744B parameter space: a token activates only <b>~40B parameters</b>, and just
|
||
<b>~11 GB</b> of those change from token to token — the routed experts.</p>
|
||
<div class="jit">So parameters are not resident state to be held — they are
|
||
<b>data to be staged</b>, exactly when the router proves they are needed.</div>
|
||
<p>The engine keeps the dense weights resident and treats the 19,456 experts as a
|
||
<b>heterogeneous storage hierarchy</b>: measured routing heat decides which experts earn
|
||
VRAM, which earn pinned RAM, and which stream from NVMe. The router runs a layer
|
||
ahead, so prefetch hides the staging latency — and because routing has
|
||
<b>measurable structure</b> (the atlas below), the hierarchy keeps getting the answer right.
|
||
Like a JIT, it learns your workload: the more you run, the hotter the right experts get.</p>
|
||
<p>Everything is validated <b>token-exact</b> against the reference transformers
|
||
implementation — speed never buys drift.</p>
|
||
<div class="research">⚗ <b>Active research:</b> the next algorithmic step is under way —
|
||
smarter placement and scheduling beyond LRU + pin, overlap of CPU and GPU expert execution,
|
||
and routing-aware speculation. Measured, reviewed, and merged in the open.</div>
|
||
</div>
|
||
<div class="tierstack">
|
||
<div class="tlevel"><span class="sw" style="background:var(--gold)"></span>
|
||
<span class="tn">VRAM</span><span class="td"><b>hottest experts</b> · grouped GPU matmuls ·
|
||
six 5090s hold the entire routed set — disk reads hit zero</span></div>
|
||
<div class="tarrow">↑ promote by measured heat ↓ demote</div>
|
||
<div class="tlevel"><span class="sw" style="background:var(--cyan)"></span>
|
||
<span class="tn">RAM</span><span class="td"><b>warm set</b> · pinned & wired, learned from
|
||
your usage · AVX-512/NEON int4 kernels + LRU behind it</span></div>
|
||
<div class="tarrow">↑ promote ↓ demote</div>
|
||
<div class="tlevel"><span class="sw" style="background:#5a6aa8"></span>
|
||
<span class="tn">NVMe</span><span class="td"><b>cold tail</b> · streamed on demand with io_uring,
|
||
prefetched a layer ahead — how 744B fits a 25 GB machine at all</span></div>
|
||
<div class="note" style="margin-top:.4rem">token → router picks 8 of 256 experts × 75 layers →
|
||
gather across tiers → int4 matmul → next token</div>
|
||
</div>
|
||
</div>
|
||
</section>
|
||
|
||
<section id="demo" class="reveal">
|
||
<div class="snum">02 — the engine, live</div>
|
||
<h2>Watch it think</h2>
|
||
<p class="lead">A replay of the engine at work, paced to <b>measured decode speeds</b> from real
|
||
community hardware. The grid is all <b>19,456 experts</b> (colour = storage tier, brightness =
|
||
routing heat, routed = white flash); the galaxy below it is the <b>measured atlas</b> —
|
||
watch different questions light different regions of the mind.</p>
|
||
<div class="profiles" id="profiles"></div>
|
||
<div class="demo-grid">
|
||
<div class="win">
|
||
<div class="bar"><span class="dot r"></span><span class="dot y"></span><span class="dot g"></span>
|
||
<span class="ttl">coli chat — <span id="prof-name"></span></span></div>
|
||
<div id="term"></div>
|
||
</div>
|
||
<div class="win">
|
||
<div class="bar"><span class="dot r"></span><span class="dot y"></span><span class="dot g"></span>
|
||
<span class="ttl">the brain — 75 MoE layers × 256 experts + MTP</span></div>
|
||
<div class="brainwrap">
|
||
<canvas id="brain" width="256" height="76"></canvas>
|
||
<div class="meters">
|
||
<div class="meter" id="m-toks"><div class="v">0.0<small> tok/s</small></div><div class="k">decode</div></div>
|
||
<div class="meter" id="m-ttft"><div class="v">—</div><div class="k">ttft (measured)</div></div>
|
||
<div class="meter" id="m-hit"><div class="v">—</div><div class="k">resident hit</div></div>
|
||
</div>
|
||
<div class="legend">
|
||
<span><i style="background:var(--gold)"></i>VRAM tier</span>
|
||
<span><i style="background:var(--cyan)"></i>RAM tier</span>
|
||
<span><i style="background:#2a3560"></i>disk tier</span>
|
||
<span><i style="background:#fff"></i>routed now</span>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="win wide">
|
||
<div class="bar"><span class="dot r"></span><span class="dot y"></span><span class="dot g"></span>
|
||
<span class="ttl">the atlas, live — measured specialists routed by the current topic</span></div>
|
||
<div class="livewrap">
|
||
<canvas id="atlas-live"></canvas>
|
||
<div class="topicchip">routing: <b id="topicnow">—</b></div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<p class="note">Simulation replays a fixed transcript at each profile's measured decode rate
|
||
(<a href="https://github.com/JustVugg/colibri/issues/82">#82</a>, <a href="https://github.com/JustVugg/colibri/issues/389">#389</a>,
|
||
<a href="https://github.com/JustVugg/colibri/issues/387">#387</a>, <a href="https://github.com/JustVugg/colibri/issues/273">#273</a>);
|
||
“—” means that machine's report did not publish the number — nothing here is invented.
|
||
Atlas flashes sample real measured affinity (atlas v1) for the active topic.</p>
|
||
</section>
|
||
|
||
<section id="atlas-sec" class="reveal">
|
||
<div class="inner">
|
||
<div class="snum">03 — the atlas</div>
|
||
<h2>Measured, not assumed</h2>
|
||
<p class="lead">Every point below is a <b>real measured expert</b> from the published
|
||
<a href="https://gist.github.com/ZacharyZcR/d3e10905072229ed0114b1123120c611">canonical atlas v1</a>:
|
||
<b>721 canonical specialists</b> confirmed across three independent kernel families
|
||
(AVX-512 VNNI · NEON · AVX2, engine-pinned, replication-gated) plus <b>637 gate-sensitive</b> ones.
|
||
Position <b>is</b> the measured 10-topic affinity vector — a specialist sits at its topic's anchor,
|
||
a generalist drifts to the centre. Colour = top topic, large points = canonical. <b>Drag to spin.</b></p>
|
||
</div>
|
||
<div class="atlaswrap">
|
||
<canvas id="atlas"></canvas>
|
||
<div id="alabels"></div>
|
||
<div class="ahint">drag to spin · scroll past to release</div>
|
||
</div>
|
||
<div class="atl-legend" id="atl-legend"></div>
|
||
</section>
|
||
|
||
<section id="ladder-sec" class="reveal">
|
||
<div class="snum">04 — the ladder</div>
|
||
<h2>Same engine, any hardware</h2>
|
||
<p class="lead">Measured decode on real community machines — the hardware only changes
|
||
<b>where the experts live</b>. Every row links to its public report.</p>
|
||
<table class="ladder">
|
||
<thead><tr><th>hardware</th><th>decode</th><th></th><th>where the experts live</th></tr></thead>
|
||
<tbody>
|
||
<tr><td>6× RTX 5090 · full residency + selective NUMA (<a href="https://github.com/JustVugg/colibri/issues/82">#82</a>)</td><td class="spd">9.0–9.2 tok/s</td>
|
||
<td class="bar-td"><div class="sbar" style="width:100%"></div></td><td>all in VRAM+RAM · disk 0</td></tr>
|
||
<tr><td>2× Xeon Gold 6430 · 1 TB DDR5 · CPU-only (<a href="https://github.com/JustVugg/colibri/issues/389">#389</a>)</td><td class="spd">5.42 tok/s</td>
|
||
<td class="bar-td"><div class="sbar" style="width:59%"></div></td><td>every expert pinned in RAM</td></tr>
|
||
<tr><td>DGX Spark GB10 · 121 GB unified (<a href="https://github.com/JustVugg/colibri/issues/161">#161</a>)</td><td class="spd">3.33 tok/s</td>
|
||
<td class="bar-td"><div class="sbar" style="width:36%"></div></td><td>unified memory + CACHE_ROUTE</td></tr>
|
||
<tr><td>MacBook Pro M5 Max · 128 GB · Metal (<a href="https://github.com/JustVugg/colibri/issues/387">#387</a>)</td><td class="spd">2.0 tok/s</td>
|
||
<td class="bar-td"><div class="sbar" style="width:22%"></div></td><td>Metal tier + warm pin</td></tr>
|
||
<tr><td>Ryzen AI Max+ 395 · 128 GB (<a href="https://github.com/JustVugg/colibri/issues/200">#200</a>)</td><td class="spd">1.83 tok/s</td>
|
||
<td class="bar-td"><div class="sbar" style="width:20%"></div></td><td>hot set pinned in RAM</td></tr>
|
||
<tr><td>Ryzen 9 9950X3D2 · RTX 5090 · Gen5 NVMe (<a href="https://github.com/JustVugg/colibri/issues/120">#120</a>)</td><td class="spd">1.23 tok/s</td>
|
||
<td class="bar-td"><div class="sbar" style="width:13%"></div></td><td>28 GB VRAM tier + 11.5 GB/s disk</td></tr>
|
||
<tr><td>single RTX 5070 Ti laptop-class (<a href="https://github.com/JustVugg/colibri/issues/273">#273</a>)</td><td class="spd">1.07 tok/s</td>
|
||
<td class="bar-td"><div class="sbar" style="width:12%"></div></td><td>GPU-resident pipeline</td></tr>
|
||
<tr><td>25 GB dev box · cold — the proven floor</td><td class="spd">0.05–0.1 tok/s</td>
|
||
<td class="bar-td"><div class="sbar" style="width:2%"></div></td><td>streamed from NVMe</td></tr>
|
||
</tbody>
|
||
</table>
|
||
<p class="note">…and 15+ more community machines, from an M4 Pro Mac Mini to a 430 GB EPYC server.
|
||
Full tables, methodology and quality ablations: <a href="https://github.com/JustVugg/colibri/blob/main/docs/benchmarks.md">docs/benchmarks.md</a></p>
|
||
</section>
|
||
|
||
<section id="models" class="reveal">
|
||
<div class="snum">05 — the models</div>
|
||
<h2>One engine, more minds coming</h2>
|
||
<p class="lead">The tiering algorithm is model-agnostic: any Mixture-of-Experts with routed
|
||
experts can be staged the same way. GLM-5.2 is live today; support for more open-weight
|
||
families is on the roadmap.</p>
|
||
<div class="models">
|
||
<div class="model live"><span class="status">live</span><h3>GLM-5.2</h3><div class="by">Z.ai · 744B MoE</div>
|
||
<p>The flagship target: int4 container, token-exact vs reference, full atlas published.</p></div>
|
||
<div class="model live"><span class="status">live</span><h3>OLMoE</h3><div class="by">Allen AI · 7B MoE</div>
|
||
<p>The small research workhorse — quantization A/Bs and quality ablations run here first.</p></div>
|
||
<div class="model soon"><span class="status">planned</span><h3>Kimi K2</h3><div class="by">Moonshot AI · 1T MoE</div>
|
||
<p>The next scale step: a trillion-parameter mind on the same tiered engine.</p></div>
|
||
<div class="model soon"><span class="status">planned</span><h3>Qwen3 MoE</h3><div class="by">Alibaba Qwen</div>
|
||
<p>The most widely-deployed open family — broad hardware coverage meets broad adoption.</p></div>
|
||
<div class="model soon"><span class="status">planned</span><h3>MiniMax</h3><div class="by">MiniMax · MoE</div>
|
||
<p>Long-context specialists — a different routing profile for the atlas to map.</p></div>
|
||
</div>
|
||
<div class="thanks">
|
||
<p><b>Thank you</b> to the teams whose open weights make this project possible —
|
||
<b>Z.ai</b> (GLM), <b>Moonshot AI</b> (Kimi), <b>Alibaba Qwen</b>, <b>MiniMax</b>,
|
||
<b>Allen AI</b> (OLMoE), and everyone releasing frontier-class models in the open.
|
||
An engine is nothing without a mind to run.</p>
|
||
</div>
|
||
</section>
|
||
|
||
<section id="contrib" class="reveal">
|
||
<div class="snum">06 — join in</div>
|
||
<h2>Come build the microscope</h2>
|
||
<p class="lead">Every optimisation in this project started with <b>someone measuring something</b> on
|
||
their own machine — a disk swap, a NUMA experiment, an atlas replication on a Mac Mini.
|
||
You don't need a datacenter to move frontier-model research. That is the whole point.</p>
|
||
<div class="contrib-cards">
|
||
<a class="ccard" href="https://github.com/JustVugg/colibri/blob/main/docs/benchmarks.md"><h3>▸ run a benchmark</h3>
|
||
<p>Any hardware is a datapoint — the community table grows one issue at a time.</p></a>
|
||
<a class="ccard" href="https://github.com/JustVugg/colibri/issues"><h3>▸ pick an issue</h3>
|
||
<p>Kernels, schedulers, the atlas, the dashboard — measured problems, waiting.</p></a>
|
||
<a class="ccard" href="https://github.com/JustVugg/colibri/blob/main/docs/quickstart.md"><h3>▸ read the engine</h3>
|
||
<p>One C file plus headers, zero dependencies. Readable in an afternoon — really.</p></a>
|
||
</div>
|
||
</section>
|
||
|
||
</main>
|
||
|
||
<footer>
|
||
<span>🐦 colibrì — MIT license</span>
|
||
<a href="https://github.com/JustVugg/colibri">GitHub</a>
|
||
<a href="https://github.com/JustVugg/colibri/blob/main/docs/api.md">API</a>
|
||
<a href="https://github.com/JustVugg/colibri/blob/main/docs/tuning.md">Tuning</a>
|
||
<span class="sp">the hummingbird: tiny, fast, precise.</span>
|
||
</footer>
|
||
|
||
<script>
|
||
'use strict';
|
||
const REDUCED = matchMedia('(prefers-reduced-motion: reduce)').matches;
|
||
function mulberry32(a){return function(){a|=0;a=a+0x6D2B79F5|0;let t=Math.imul(a^a>>>15,1|a);
|
||
t=t+Math.imul(t^t>>>7,61|t)^t;return((t^t>>>14)>>>0)/4294967296}}
|
||
|
||
/* ============ MEASURED ATLAS DATA (canonical atlas v1) ============
|
||
1,358 experts characterised by the tools/expert_atlas harness (#175/#218):
|
||
721 canonical (strong + same top topic on all of VNNI/NEON/AVX2) and
|
||
637 gate-sensitive. Each row: [layer, expert, topicIdx, canonical, specx100,
|
||
[p×100 over the 10 measured categories]] — p is the VNNI affinity vector.
|
||
Source: gist.github.com/ZacharyZcR/d3e10905072229ed0114b1123120c611 */
|
||
const ATLAS_CATS=["casual_chat", "chinese", "code_python", "code_sql", "german", "json_format", "law", "math_proof", "medicine", "poetry"];
|
||
const 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1,0,2,1,19,1,1,1,65]],[68,35,2,0,52,[0,3,58,26,0,2,0,0,8,3]],[68,82,8,0,41,[2,5,10,10,12,15,0,7,40,0]],[68,119,0,0,53,[71,0,9,1,5,0,5,0,0,9]],[68,125,7,0,68,[0,2,24,0,2,0,0,72,0,0]],[68,130,1,0,41,[8,68,4,0,2,11,0,6,2,0]],[68,171,1,0,52,[0,55,0,5,5,2,0,30,3,0]],[68,211,2,0,54,[2,0,48,1,1,40,1,1,3,2]],[68,228,7,0,50,[0,3,6,3,3,0,5,53,0,27]],[68,241,5,0,52,[0,7,0,9,0,52,0,32,0,0]],[69,54,1,0,52,[3,73,4,0,3,9,2,3,2,0]],[69,56,5,0,58,[17,0,0,0,0,67,0,6,10,0]],[69,88,3,0,51,[2,0,0,49,2,41,0,0,3,3]],[69,127,1,0,53,[1,68,0,2,2,13,2,11,2,0]],[69,135,9,0,59,[0,12,3,0,0,0,0,1,0,83]],[69,140,3,0,50,[0,2,3,67,24,2,1,0,1,0]],[69,216,6,0,51,[0,6,6,3,1,13,68,0,1,1]],[69,237,7,0,92,[0,0,0,0,0,0,0,95,0,5]],[70,50,6,0,52,[0,0,16,2,0,0,69,0,10,2]],[70,65,5,0,51,[1,4,0,28,3,57,1,3,0,3]],[70,76,0,0,52,[73,0,3,2,4,7,2,2,4,1]],[70,78,1,0,50,[0,64,3,0,13,5,3,0,13,0]],[70,93,9,0,55,[1,5,0,5,1,5,1,1,1,80]],[70,113,3,0,50,[0,2,22,61,1,0,3,0,11,0]],[70,120,1,0,50,[3,67,1,0,5,9,3,0,1,10]],[70,139,8,0,66,[2,2,0,7,2,5,0,4,79,0]],[70,151,8,0,55,[2,0,0,0,5,4,8,2,77,3]],[70,184,4,0,48,[0,3,0,5,49,33,0,5,0,5]],[70,214,1,0,45,[6,68,0,2,0,5,6,4,8,0]],[70,233,7,0,100,[0,0,0,0,0,0,0,100,0,0]],[71,3,7,0,51,[3,0,0,10,0,9,0,74,0,4]],[71,9,6,0,47,[7,5,0,0,0,3,57,5,22,0]],[71,33,6,0,44,[7,0,2,13,2,6,58,4,2,5]],[71,36,1,0,49,[9,67,0,0,0,4,0,3,16,0]],[71,42,9,0,58,[4,5,0,0,4,6,0,2,0,79]],[71,66,2,0,51,[0,15,62,5,16,0,0,0,2,0]],[71,128,4,0,52,[1,4,3,2,71,2,2,1,5,9]],[71,149,5,0,47,[0,0,9,28,12,49,2,0,0,0]],[71,159,1,0,100,[0,100,0,0,0,0,0,0,0,0]],[71,200,9,0,74,[0,2,4,2,0,0,7,0,0,84]],[71,238,5,0,47,[1,4,3,9,5,65,5,0,6,1]],[72,20,6,0,50,[4,0,2,0,2,6,75,2,9,0]],[72,30,9,0,50,[2,7,6,1,4,3,1,1,0,75]],[72,66,0,0,49,[50,10,1,0,3,0,0,0,0,36]],[72,87,0,0,44,[55,0,5,9,9,4,2,12,2,0]],[72,144,2,0,100,[0,0,100,0,0,0,0,0,0,0]],[72,161,2,0,53,[0,0,76,3,7,0,0,0,14,0]],[72,183,9,0,55,[23,2,2,0,0,0,0,2,11,60]],[72,212,3,0,49,[3,9,2,72,3,0,0,3,9,0]],[72,225,9,0,47,[7,0,8,0,5,4,2,0,3,71]],[73,33,5,0,50,[4,0,4,0,6,65,15,2,4,0]],[73,71,9,0,51,[28,1,4,0,4,1,1,1,1,60]],[73,87,3,0,55,[0,5,16,68,0,0,0,0,0,11]],[73,99,3,0,49,[2,3,40,44,4,8,0,0,0,0]],[73,110,4,0,49,[3,0,2,2,72,5,12,3,1,1]],[73,111,9,0,42,[5,5,2,1,5,3,5,11,1,61]],[73,120,9,0,65,[0,2,0,2,6,0,2,0,0,87]],[73,194,1,0,38,[0,67,2,7,0,12,7,2,2,0]],[73,246,1,0,44,[5,57,4,4,0,18,0,2,5,5]],[74,28,6,0,64,[6,1,6,2,2,4,71,4,3,0]],[74,112,1,0,58,[6,72,0,0,6,9,0,0,6,0]],[74,130,1,0,86,[0,92,0,0,0,3,0,0,0,5]],[74,155,7,0,42,[5,0,8,5,4,19,7,31,0,20]],[74,172,4,0,61,[3,0,0,3,70,4,0,21,0,0]],[75,25,1,0,57,[4,70,3,5,0,3,0,7,4,4]],[75,31,1,0,42,[0,48,4,2,2,18,2,9,5,9]],[75,79,5,0,48,[0,3,0,0,16,52,3,0,25,0]],[75,81,1,0,49,[0,67,5,0,3,16,0,6,3,0]],[75,91,7,0,48,[8,2,12,0,6,0,2,60,2,7]],[75,134,4,0,53,[10,0,5,0,70,5,0,3,0,7]],[75,144,9,0,47,[0,8,2,27,0,4,5,0,0,54]],[75,149,1,0,49,[4,67,0,6,6,3,4,4,4,0]],[75,171,9,0,47,[6,2,1,2,7,1,0,2,2,77]],[75,172,1,0,58,[0,66,0,0,0,3,6,23,3,0]],[75,211,2,0,55,[0,0,54,39,3,4,0,0,0,0]],[76,14,4,0,49,[1,2,1,0,62,1,0,0,8,24]],[76,25,5,0,54,[6,0,0,0,14,69,3,6,3,0]],[76,62,1,0,65,[0,80,5,3,6,0,0,3,0,3]],[76,95,5,0,56,[0,0,7,5,8,74,3,0,3,0]],[76,150,2,0,49,[7,13,52,23,1,2,0,0,1,0]],[76,154,1,0,56,[7,69,1,2,2,10,1,2,3,2]],[76,238,7,0,47,[12,3,12,0,7,8,5,52,0,2]],[77,6,4,0,51,[5,3,1,4,73,3,2,5,3,2]],[77,85,5,0,49,[3,2,3,4,4,72,7,5,0,0]],[77,115,9,0,53,[10,1,0,1,1,7,3,1,1,74]],[77,119,7,0,50,[0,6,10,3,3,0,6,73,0,0]],[77,121,1,0,55,[2,75,0,3,2,2,3,5,4,4]],[77,193,5,0,52,[3,0,6,3,5,68,5,0,7,3]],[77,226,5,0,52,[0,1,1,27,0,63,0,0,5,2]],[77,228,6,0,59,[0,2,0,38,0,5,55,0,0,0]],[77,253,0,0,57,[58,0,1,0,1,4,1,5,5,24]]];
|
||
|
||
const TOPIC_HUE={casual_chat:215,chinese:2,code_python:145,code_sql:172,german:42,
|
||
json_format:88,law:272,math_proof:200,medicine:330,poetry:300};
|
||
|
||
/* ---------- shared atlas geometry ---------- */
|
||
const Atlas=(()=>{
|
||
const NC=ATLAS_CATS.length;
|
||
const anchors=[];
|
||
for(let i=0;i<NC;i++){
|
||
const phi=Math.acos(1-2*(i+.5)/NC),th=Math.PI*(1+Math.sqrt(5))*i;
|
||
anchors.push([Math.sin(phi)*Math.cos(th),Math.cos(phi),Math.sin(phi)*Math.sin(th)]);
|
||
}
|
||
const pts=[];
|
||
for(const[l,e,ti,canon,spec,p]of ATLAS_RAW){
|
||
let sum=0;for(const v of p)sum+=v;if(!sum)continue;
|
||
let x=0,y=0,z=0;
|
||
for(let c=0;c<NC;c++){const w=p[c]/sum;x+=w*anchors[c][0];y+=w*anchors[c][1];z+=w*anchors[c][2];}
|
||
const r=mulberry32(l*257+e),g=()=>(r()+r()+r()-1.5)*.075;
|
||
pts.push({x:x+g(),y:y+g(),z:z+g(),hue:TOPIC_HUE[ATLAS_CATS[ti]],ti,canon,spec:spec/100,l,e});
|
||
}
|
||
const halo=[];{const r=mulberry32(999);
|
||
for(let i=0;i<2200;i++){const R=.15+r()*1.0,a=r()*6.283,b=(r()-.5)*2;
|
||
halo.push([R*Math.cos(a)*Math.sqrt(1-b*b),R*b,R*Math.sin(a)*Math.sqrt(1-b*b)]);}}
|
||
const byTopic=[];
|
||
for(let c=0;c<NC;c++){const arr=[];
|
||
ATLAS_RAW.forEach((row,i)=>{if(row[5][c]>=8)arr.push([i,row[5][c]])});
|
||
byTopic.push(arr);}
|
||
const flash=new Float32Array(pts.length);
|
||
function flashTopic(c,k){const t=byTopic[c];if(!t.length)return[];
|
||
const hit=[];
|
||
for(let i=0;i<k;i++){
|
||
let tot=0;for(const[,w]of t)tot+=w;let x=Math.random()*tot;
|
||
for(const[idx,w]of t){x-=w;if(x<=0){flash[idx]=1;hit.push(idx);break}}}
|
||
return hit;}
|
||
return{pts,halo,anchors,flash,flashTopic,NC};
|
||
})();
|
||
|
||
/* an AtlasView renders Atlas.pts on a canvas with drag+inertia; opts:
|
||
labels (topic label elements), showFlash (white pulse), bare (no interaction,
|
||
used for the hero backdrop), dim (alpha multiplier) */
|
||
function atlasView(cv,opts){
|
||
const cx=cv.getContext('2d');let ry=opts.ry??.6,vy=REDUCED?0:(opts.spin??.0022),
|
||
dragging=false,lx=0,visible=false;
|
||
const TILT=.32,ct=Math.cos(TILT),st=Math.sin(TILT),DIM=opts.dim??1;
|
||
function resize(){const dpr=Math.min(devicePixelRatio||1,2);
|
||
cv.width=cv.clientWidth*dpr;cv.height=cv.clientHeight*dpr;}
|
||
addEventListener('resize',resize);resize();
|
||
if(!opts.bare){
|
||
cv.addEventListener('pointerdown',e=>{dragging=true;lx=e.clientX;vy=0;cv.classList.add('grabbing');cv.setPointerCapture(e.pointerId)});
|
||
cv.addEventListener('pointermove',e=>{if(!dragging)return;const dx=e.clientX-lx;lx=e.clientX;ry+=dx*.006;vy=dx*.0006});
|
||
cv.addEventListener('pointerup',()=>{dragging=false;cv.classList.remove('grabbing')});
|
||
}
|
||
new IntersectionObserver(es=>{visible=es[0].isIntersecting},{threshold:.02}).observe(cv);
|
||
const P=(x,y,z,cr,sr,W,H,S)=>{
|
||
const x1=x*cr+z*sr,z1=-x*sr+z*cr,y2=y*.72*ct-z1*st,z2=y*.72*st+z1*ct,
|
||
pz=1/(1.9+z2);
|
||
return[W*.5+x1*S*pz*1.9,H*.5+y2*S*pz*1.9,z2,pz];};
|
||
function frame(){
|
||
requestAnimationFrame(frame);if(!visible)return;
|
||
if(!dragging){ry+=vy;if(!REDUCED)vy+=((opts.spin??.0022)-vy)*.01;}
|
||
const W=cv.width,H=cv.height,S=Math.min(W,H)*(opts.scale??.42),
|
||
cr=Math.cos(ry),sr=Math.sin(ry);
|
||
cx.clearRect(0,0,W,H);
|
||
for(const[hx,hy,hz]of Atlas.halo){
|
||
const[sx,sy,z2]=P(hx,hy,hz,cr,sr,W,H,S);
|
||
if(sx<0||sx>W||sy<0||sy>H)continue;
|
||
const d=Math.max(0,Math.min(1,(1.1-z2)*.62));
|
||
cx.fillStyle=`hsla(225,40%,60%,${(.09*d*DIM).toFixed(3)})`;cx.fillRect(sx,sy,1.5,1.5);}
|
||
for(let i=0;i<Atlas.pts.length;i++){const p=Atlas.pts[i];
|
||
const[sx,sy,z2,pz]=P(p.x,p.y,p.z,cr,sr,W,H,S);
|
||
if(sx<0||sx>W||sy<0||sy>H)continue;
|
||
const d=Math.max(0,Math.min(1,(1.1-z2)*.62)),
|
||
sz=(p.canon?2.6:1.6)*pz*2.2;
|
||
cx.fillStyle=`hsla(${p.hue},${p.canon?88:60}%,${p.canon?68:55}%,${((.3+.5*d)*DIM).toFixed(3)})`;
|
||
cx.fillRect(sx-sz/2,sy-sz/2,sz,sz);
|
||
if(opts.showFlash&&Atlas.flash[i]>0.02){const f=Atlas.flash[i],g=sz+f*9;
|
||
cx.fillStyle=`rgba(255,255,255,${(f*.9).toFixed(3)})`;
|
||
cx.fillRect(sx-g/2,sy-g/2,g,g);}}
|
||
if(opts.labels){
|
||
const rect=cv.getBoundingClientRect(),k=rect.width/W;
|
||
Atlas.anchors.forEach((a,c)=>{
|
||
const[sx,sy,z2]=P(a[0]*1.12,a[1]*1.12,a[2]*1.12,cr,sr,W,H,S),el=opts.labels[c];
|
||
el.style.left=(sx*k)+'px';el.style.top=(sy*k)+'px';
|
||
el.style.opacity=z2<0?'0.95':'0.18';});}
|
||
}
|
||
frame();
|
||
}
|
||
|
||
/* ---------- nav scroll state ---------- */
|
||
(()=>{const nav=document.getElementById('topnav');
|
||
addEventListener('scroll',()=>nav.classList.toggle('scrolled',scrollY>40),{passive:true});})();
|
||
|
||
/* ---------- scroll reveal ---------- */
|
||
(()=>{const io=new IntersectionObserver(es=>es.forEach(e=>{if(e.isIntersecting){e.target.classList.add('in');io.unobserve(e.target)}}),{threshold:.08});
|
||
document.querySelectorAll('.reveal').forEach(el=>io.observe(el));})();
|
||
|
||
/* ---------- hero backdrop: the measured atlas, slowly turning ---------- */
|
||
atlasView(document.getElementById('atlas-hero'),{bare:true,scale:.5,spin:.0012,ry:2.2,dim:.85});
|
||
|
||
/* ---------- demo: terminal + brain + live atlas ---------- */
|
||
(()=>{
|
||
const LAYERS=76,EXPERTS=256,N=LAYERS*EXPERTS;
|
||
const PROFILES=[
|
||
{name:'6× RTX 5090 + selective NUMA',toks:9.0,ttft:'13',hit:'100',vram:.62,ram:.38},
|
||
{name:'2× Xeon Gold 6430 · 1 TB · CPU-only',toks:5.4,ttft:'—',hit:'100',vram:0,ram:.97},
|
||
{name:'M5 Max 128 GB · Metal',toks:2.0,ttft:'—',hit:'72.5',vram:.12,ram:.20},
|
||
{name:'RTX 5070 Ti laptop-class',toks:1.07,ttft:'—',hit:'—',vram:.08,ram:.24},
|
||
{name:'25 GB dev box (the floor)',toks:0.8,ttft:'—',hit:'11',vram:0,ram:.02,floor:true},
|
||
];
|
||
/* the floor really decodes at 0.05–0.1 tok/s; replaying that verbatim means one token
|
||
every ~15 s, so the floor profile paces at 0.8 with an honesty note in its transcript. */
|
||
const CAT=Object.fromEntries(ATLAS_CATS.map((c,i)=>[c,i]));
|
||
const QA=[
|
||
{q:'how do you fit 744B parameters into 25 GB of RAM?',topics:[CAT.casual_chat,CAT.code_python],
|
||
a:'The model never needs all 744B at once. Each token activates ~40B parameters, and only ~11 GB of those change between tokens — the routed experts. colibrì keeps the dense weights resident, then treats the 19,456 experts as a cache problem: the hot set lives in RAM (or VRAM), the cold tail streams from NVMe, prefetched a layer ahead by the router. The engine is a single C file with zero dependencies — the same binary scales from this floor to a six-GPU workstation.'},
|
||
{q:'write a SQL query for the five hottest experts.',topics:[CAT.code_sql,CAT.json_format],
|
||
a:'SELECT layer, expert, hits FROM routing_stats ORDER BY hits DESC LIMIT 5; — and watch the atlas below while I say it: the code_sql cluster is firing. Routing affinity is measured per expert, and SQL has its own specialists in this model. That measurement is exactly what the tiering exploits: the engine pins what your workload actually routes to.'},
|
||
{q:'用中文写两句关于蜂鸟的诗。',topics:[CAT.chinese,CAT.poetry],
|
||
a:'翅膀是引擎的隐喻，一秒五十次的心跳——它悬停时，最像在思考。蜂鸟不需要很大，才能飞得很准。 Look at the galaxy: the chinese and poetry clusters just lit up together. Different language, different experts — measured, replicated across three kernel families, and visible to the naked eye.'},
|
||
{q:'what makes the output trustworthy?',topics:[CAT.math_proof,CAT.casual_chat],
|
||
a:'Every kernel is validated token-exact against the reference transformers implementation — the int4 container, the AVX-512 and NEON paths, the CUDA grouped matmuls. Quality is measured, not assumed: the quantization cost is published in the benchmark tables, and the expert atlas itself survived leave-one-prompt-out validation and a five-machine cross-kernel replication.'},
|
||
];
|
||
const term=document.getElementById('term'),profDiv=document.getElementById('profiles'),
|
||
profName=document.getElementById('prof-name'),topicNow=document.getElementById('topicnow'),
|
||
brain=document.getElementById('brain'),bx=brain.getContext('2d'),
|
||
mToks=document.querySelector('#m-toks .v'),mTtft=document.querySelector('#m-ttft .v'),
|
||
mHit=document.querySelector('#m-hit .v');
|
||
const BANNER=
|
||
`<span class="banner"> ▄▀▀▀▄ ▄</span> colibrì v1.0
|
||
<span class="banner"> ▄▄▄▄▀▀▀▀▄▀▀</span> <span class="bannertxt">tiny engine, immense model</span>
|
||
<span class="banner"> ▀▀▀▀▀▀▀</span> <span class="bannertxt">GLM-5.2 · 744B MoE · int4</span>
|
||
<span class="banner"> ▀▀▀▀</span>
|
||
<span class="banner"> ▀</span>
|
||
`;
|
||
const heat=new Float32Array(N),flash=new Float32Array(N);
|
||
{const r=mulberry32(42);
|
||
for(let l=0;l<LAYERS;l++){
|
||
const nh=2+((r()*3)|0),cs=[];for(let h=0;h<nh;h++)cs.push([r()*EXPERTS,8+r()*26]);
|
||
for(let e=0;e<EXPERTS;e++){let v=.05+r()*.08;
|
||
for(const[c,s]of cs){const d=Math.min(Math.abs(e-c),EXPERTS-Math.abs(e-c));v+=Math.exp(-d*d/(2*s*s))}
|
||
heat[l*EXPERTS+e]=Math.min(v,1);}}
|
||
/* stats layer numbering is absolute (dense layers 0-2, MoE 3-77): grid row = layer-3 */
|
||
for(const p of Atlas.pts){const row=p.l-3;if(row>=0&&row<LAYERS)heat[row*EXPERTS+p.e]=Math.min(.55+p.spec*.45,1);}}
|
||
const order=[...heat.keys()].sort((a,b)=>heat[b]-heat[a]);
|
||
const tier=new Uint8Array(N);
|
||
let P=PROFILES[0];
|
||
function setTiers(){const nv=Math.round(N*P.vram),nr=Math.round(N*P.ram);
|
||
for(let i=0;i<N;i++)tier[order[i]]=i<nv?2:i<nv+nr?1:0;}
|
||
const img=bx.createImageData(EXPERTS,LAYERS);
|
||
const TC=[[42,53,96],[77,216,255],[255,184,77]];
|
||
function drawBrain(){const d=img.data;
|
||
for(let i=0;i<N;i++){const t=TC[tier[i]],h=.28+.72*heat[i],f=flash[i],o=i*4;
|
||
d[o]=t[0]*h+(255-t[0]*h)*f; d[o+1]=t[1]*h+(255-t[1]*h)*f; d[o+2]=t[2]*h+(255-t[2]*h)*f; d[o+3]=255;
|
||
flash[i]*=.86;}
|
||
bx.putImageData(img,0,0);
|
||
for(let i=0;i<Atlas.flash.length;i++)Atlas.flash[i]*=.90;}
|
||
/* routing per token: measured specialists for the active topic light BOTH views;
|
||
the uncharacterised remainder of the 8×75 routed set flashes as background */
|
||
function routeToken(topic){
|
||
const hit=Atlas.flashTopic(topic,10);
|
||
for(const idx of hit){const p=Atlas.pts[idx],row=p.l-3;
|
||
if(row>=0&&row<LAYERS)flash[row*EXPERTS+p.e]=1;}
|
||
for(let k=0;k<46;k++)flash[(Math.random()*N)|0]=Math.random()*.8;}
|
||
let timer=null,qi=0,emitted=[],running=true;
|
||
function rate(){const now=performance.now();emitted=emitted.filter(t=>now-t<4000);
|
||
return emitted.length/4;}
|
||
function setMeters(live){mToks.innerHTML=(live?rate():0).toFixed(1)+'<small> tok/s</small>';
|
||
mTtft.innerHTML=P.ttft==='—'?'—':P.ttft+'<small> s</small>';
|
||
mHit.innerHTML=P.hit==='—'?'—':P.hit+'<small> %</small>';}
|
||
function startTranscript(){
|
||
clearTimeout(timer);term.innerHTML=BANNER;qi=0;emitted=[];nextQA();}
|
||
function nextQA(){
|
||
const{q,a:aRaw,topics}=QA[qi%QA.length];qi++;
|
||
const a=(P.floor&&qi%QA.length===1)
|
||
?aRaw+' (This box really decodes at 0.05–0.1 tok/s — the replay is sped up so you do not grow old here. It works. Slowly. That is the point.)'
|
||
:aRaw;
|
||
topicNow.textContent=topics.map(t=>ATLAS_CATS[t]).join(' + ');
|
||
const you=document.createElement('div');you.className='you';you.textContent='▸ you: '+q;term.append(you);
|
||
const ans=document.createElement('div');ans.className='ans';term.append(ans);
|
||
const cur=document.createElement('span');cur.className='cursor';ans.append(cur);
|
||
const words=a.split(/(?=\s)/);let w=0;const t0=performance.now();
|
||
(function emit(){
|
||
if(!running){timer=setTimeout(emit,300);return}
|
||
if(w>=words.length){cur.remove();
|
||
const dt=(performance.now()-t0)/1000;
|
||
const sl=document.createElement('div');sl.className='statline';
|
||
sl.textContent=`[turn] ${words.length} tok · ${(words.length/dt).toFixed(1)} tok/s · hit ${P.hit==='—'?'n/a':P.hit+'%'} · tier vram ${(P.vram*100)|0}% ram ${(P.ram*100)|0}%`;
|
||
term.append(sl,document.createElement('br'));term.scrollTop=term.scrollHeight;
|
||
timer=setTimeout(nextQA,3200);return}
|
||
const burst=Math.random()<.18?2:1;
|
||
for(let b=0;b<burst&&w<words.length;b++){
|
||
cur.before(document.createTextNode(words[w++]));emitted.push(performance.now());
|
||
routeToken(topics[(Math.random()*topics.length)|0]);}
|
||
term.scrollTop=term.scrollHeight;setMeters(true);
|
||
const jitter=.75+Math.random()*.5;
|
||
timer=setTimeout(emit,1000/P.toks*jitter*burst);})();}
|
||
PROFILES.forEach((p,i)=>{const b=document.createElement('button');
|
||
b.textContent=p.name;if(i===0)b.className='on';
|
||
b.onclick=()=>{profDiv.querySelectorAll('button').forEach(x=>x.className='');b.className='on';
|
||
P=p;profName.textContent=p.name;setTiers();setMeters(false);startTranscript();};
|
||
profDiv.append(b);});
|
||
profName.textContent=P.name;setTiers();setMeters(false);
|
||
new IntersectionObserver(es=>{running=es[0].isIntersecting},{threshold:.05})
|
||
.observe(document.getElementById('demo'));
|
||
(function paint(){drawBrain();requestAnimationFrame(paint)})();
|
||
startTranscript();
|
||
atlasView(document.getElementById('atlas-live'),{showFlash:true,scale:.55,spin:.003,ry:1.4});
|
||
})();
|
||
|
||
/* ---------- standalone measured atlas ---------- */
|
||
(()=>{
|
||
const labelBox=document.getElementById('alabels'),leg=document.getElementById('atl-legend');
|
||
const labels=ATLAS_CATS.map(c=>{const el=document.createElement('div');el.className='alabel';
|
||
el.textContent=c;el.style.color=`hsl(${TOPIC_HUE[c]},75%,72%)`;labelBox.append(el);return el;});
|
||
ATLAS_CATS.forEach(c=>{const s=document.createElement('span');
|
||
s.innerHTML=`<i style="background:hsl(${TOPIC_HUE[c]},75%,60%)"></i>${c}`;leg.append(s);});
|
||
atlasView(document.getElementById('atlas'),{labels});
|
||
})();
|
||
</script>
|
||
</body>
|
||
</html>
|