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nano-glm
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Nano-GLM — a from-scratch language model</title>
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</style>
</head>
<body>

<nav>
  <span class="nav-mark">Nano-GLM</span>
  <a class="nav-link" href="#demo">Try the model →</a>
</nav>

<div class="wrap">
  <div class="hero">
    <div class="eyebrow"><span class="dot"></span> 120M parameters · trained from scratch · single GPU</div>
    <h1 class="title">A small language model, built the way the frontier ones are.</h1>
    <p class="lede">Nano-GLM re-implements the architecture behind modern GLM and DeepSeek-class models at a scale that trains on one consumer GPU — multi-latent attention, mixture-of-experts routing, and sparse attention, none of it simplified.</p>
  </div>

  <div class="specimen">
    <span class="specimen-cell"><span class="node">input</span><span class="tag">tokens</span></span>
    <span class="arrow">→</span>
    <span class="specimen-cell"><span class="node">MLA attention</span><span class="tag">compressed KV cache</span></span>
    <span class="arrow">→</span>
    <span class="specimen-cell"><span class="node">sparse router</span><span class="tag">DSA indexer</span></span>
    <span class="arrow">→</span>
    <span class="specimen-cell"><span class="node">MoE experts</span><span class="tag">1 shared + top-k</span></span>
    <span class="arrow">→</span>
    <span class="specimen-cell"><span class="node">output</span><span class="tag">next token</span></span>
  </div>
</div>

<section id="architecture">
  <div class="wrap">
    <div class="kicker">Architecture</div>
    <h2 class="section-title">Three ideas borrowed from the frontier, scaled down honestly.</h2>

    <div class="technique">
      <div class="technique-head">
        <span class="technique-name">Multi-Latent Attention</span>
        <span class="technique-from mono">from DeepSeek-V3</span>
      </div>
      <p>Instead of caching a full key/value tensor per token, MLA compresses attention through a low-rank projection, closer to LoRA than to a standard cache. The memory saved is what makes training possible on 6GB of VRAM at all.</p>
    </div>

    <div class="technique">
      <div class="technique-head">
        <span class="technique-name">Mixture of Experts</span>
        <span class="technique-from mono">from GLM-5</span>
      </div>
      <p>Rather than one dense feed-forward block, the model routes each token to a small set of specialized experts plus one shared expert. Capacity grows without every token paying for every parameter.</p>
    </div>

    <div class="technique">
      <div class="technique-head">
        <span class="technique-name">DeepSeek Sparse Attention</span>
        <span class="technique-from mono">from DeepSeek-V3.2</span>
      </div>
      <p>A learned indexer decides which previous tokens are actually worth attending to, and drops the rest — attention over a shortlist instead of the full context, without giving up long-range dependencies.</p>
    </div>
  </div>
</section>

<section id="training">
  <div class="wrap">
    <div class="kicker">Training</div>
    <h2 class="section-title">The engineering that makes it fit on a laptop GPU.</h2>

    <div class="stats-grid">
      <div class="stat"><div class="stat-num">120M</div><div class="stat-label">parameters</div></div>
      <div class="stat"><div class="stat-num">2.4B</div><div class="stat-label">training tokens</div></div>
      <div class="stat"><div class="stat-num">6GB</div><div class="stat-label">VRAM target</div></div>
      <div class="stat"><div class="stat-num">~4,900</div><div class="stat-label">tokens / sec</div></div>
    </div>

    <div class="detail-list">
      <div class="detail-row">
        <div class="detail-key">DATA</div>
        <div class="detail-val">2.0B tokens of FineWeb-Edu for the main run, <b>0.4B</b> mixed tokens for a final decay phase — a 20:1 Chinchilla-optimal ratio against model size.</div>
      </div>
      <div class="detail-row">
        <div class="detail-key">SCHEDULE</div>
        <div class="detail-val">Warmup–Stable–Decay: peak learning rate of <b>6e-4</b> held stable for 2.0B tokens, then a cosine decay into the final phase.</div>
      </div>
      <div class="detail-row">
        <div class="detail-key">PRECISION</div>
        <div class="detail-val">BF16 compute on NVIDIA TF32 tensor cores — roughly half the memory and twice the throughput of FP32.</div>
      </div>
      <div class="detail-row">
        <div class="detail-key">MEMORY</div>
        <div class="detail-val">Gradient checkpointing recomputes activations during the backward pass instead of storing them, trading a little speed for about <b>40% less VRAM</b>.</div>
      </div>
      <div class="detail-row">
        <div class="detail-key">BATCHING</div>
        <div class="detail-val">Gradient accumulation over 3 micro-batches simulates an effective batch of <b>9,216 tokens</b> per weight update.</div>
      </div>
    </div>
  </div>
</section>

<section id="demo">
  <div class="wrap-wide">
    <div class="kicker">Live</div>
    <h2 class="section-title">Generate with it directly.</h2>

    <div class="demo-card">
      <div class="presets-row">
        <button class="preset-btn" type="button" onclick="setPreset('As per my last email,')">Corporate opener</button>
        <button class="preset-btn" type="button" onclick="setPreset('Artificial intelligence is defined as')">Encyclopedia lead</button>
        <button class="preset-btn" type="button" onclick="setPreset('1. The first rule of computer science is')">List starter</button>
        <button class="preset-btn" type="button" onclick="setPreset('The true nature of human consciousness is')">Open-ended</button>
      </div>

      <form id="generate-form" onsubmit="handleGenerate(event)">
        <div class="field">
          <label for="prompt">Prompt</label>
          <textarea id="prompt" required>Artificial intelligence is defined as</textarea>
        </div>

        <div class="controls">
          <div class="control-item">
            <div class="control-head"><label for="temp">Temperature</label><span class="control-val" id="temp-val">0.7</span></div>
            <input type="range" id="temp" min="0.1" max="2.0" step="0.1" value="0.7" oninput="document.getElementById('temp-val').innerText=this.value">
          </div>
          <div class="control-item">
            <div class="control-head"><label for="max-tokens">Max tokens</label><span class="control-val" id="tokens-val">100</span></div>
            <input type="range" id="max-tokens" min="10" max="300" step="10" value="100" oninput="document.getElementById('tokens-val').innerText=this.value">
          </div>
          <div class="control-item">
            <div class="control-head"><label for="top-k">Top-K</label><span class="control-val" id="topk-val">40</span></div>
            <input type="range" id="top-k" min="1" max="100" step="1" value="40" oninput="document.getElementById('topk-val').innerText=this.value">
          </div>
        </div>

        <button type="submit" class="submit-btn" id="submit-btn"><span>Generate text</span></button>
      </form>

      <div class="out" id="output-container">
        <div class="out-head">
          <div class="out-stats">
            <span class="out-badge" id="stat-tokens">0 tokens</span>
            <span class="out-badge" id="stat-time">0.00s</span>
          </div>
          <button class="copy-btn" type="button" onclick="copyOutput()">Copy text</button>
        </div>
        <div class="out-box" id="output-box"></div>
      </div>
    </div>
  </div>
</section>

<footer>
  <p>Nano-GLM — a from-scratch GLM/DeepSeek-style language model, built to prove the architecture at small scale.</p>
</footer>

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    btn.innerHTML = '<div class="spinner"></div><span>Generating…</span>';
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      document.getElementById('stat-time').innerText = `${data.inference_time_sec}s`;
    } catch (err) {
      outputBox.innerText = `Error: ${err.message}. Make sure the FastAPI backend is running.`;
    } finally {
      btn.disabled = false;
      btn.innerHTML = '<span>Generate text</span>';
    }
  }

  function copyOutput(){
    const text = document.getElementById('output-box').innerText;
    navigator.clipboard.writeText(text);
  }
</script>

</body>
</html>