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<title>GoLLeM-v5 — Live Training Dashboard</title>
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  <h1>GoLLeM-v5 &mdash; Live Training Dashboard</h1>
  <div class="sub">Tiny-LM efficiency track (Glint leaderboard). Auto-refreshes every 20s &middot; <span id="ts">&hellip;</span></div>
  <div class="board">
    <span class="tag">NOW TRAINING</span> &mdash; the final <b>GoLLeM-v5 64M</b> (14&times;576) and <b>128M</b> (16&times;768): 760,000 steps / 24.9B tokens each, the flagship recipe on ARC-MIX with test/validation overlaps and documents marked &ldquo;CC BY-NC-SA&rdquo; removed. <b>The result is the last checkpoint (step 760,000).</b> The numbers on the two cards below are measured on early checkpoints with the board protocol: <i>training in progress; not a result</i>.<br>
    <span class="tag">CANONICAL NUMBERS</span> (Glint-1.3 <code>benchmark.py</code> protocol): <b>64M v1</b> eff 75.81 (BLiMP 75.83 / ARC-E 47.94 / byte_ppl 2.372) &middot; <b>128M v1</b> eff 75.84 (77.26 / 51.94 / 2.272) &middot; <b>32M</b> eff 75.41 (73.48 / 44.44 / 2.5386) &middot; <b>16M</b> eff 74.33 (70.08 / 40.91 / 2.6746). The board still lists earlier values for our 64M (77.51); those came from a wrong bytes-per-token constant and a non-canonical BLiMP run.<br>
    <span class="tag">GOAL: #1 EFF</span> &mdash; measured with the same protocol, the strongest model we could evaluate (GPT-X2-125M) scores eff 78.26, about 2.4 above our v1 models (75.81 / 75.84); the gap is mostly ARC-Easy, with BLiMP second.
    &middot; board: <a href="https://huggingface.co/spaces/Glint-Research/Tiny-ML-Leaderboard">Glint Tiny-ML</a>
  </div>
  <div class="grid" id="runs"></div>
  <div class="foot">
    Source: <a href="https://huggingface.co/SlayerLab/gollem-v5-ckpts">SlayerLab/gollem-v5-ckpts</a> (models, repository map, method, checkpoints) &middot;
    <a href="https://huggingface.co/datasets/SlayerLab/minimal-en-corpus-5b">corpus</a>.
    Feed updates ~60s behind the pods.
  </div>
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