Space: tresc 1.04-1.06 (Hart 19ae9531) + styl (Projektant e1d475f6); wykres scores-history-106

.gitattributes

@@ -35,3 +35,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 scores-history.png filter=lfs diff=lfs merge=lfs -text
 scores-history-104.png filter=lfs diff=lfs merge=lfs -text
+scores-history-106.png filter=lfs diff=lfs merge=lfs -text

index.html

@@ -5,45 +5,95 @@
 <meta name="viewport" content="width=device-width, initial-scale=1">
 <title>Mission – Slayer 1B</title>
 <style>
-  :root { --bg:#0f1115; --card:#171a21; --line:#2a2f3a; --fg:#e8eaee; --muted:#9aa3b2; --acc:#4fb477; --acc2:#e0565b; --acc3:#4c8bd6; }
+  :root { --bg:#0b0d11; --card:#141821; --card2:#181d28; --line:#252b38; --line2:#323a4a; --fg:#eceef2; --soft:#c9ced8; --muted:#9aa3b2;
+          --acc:#4fb477; --acc2:#e0565b; --acc3:#6ea6e6; --warn:#e8c37a; --r:16px; color-scheme:dark; }
   * { box-sizing:border-box; }
-  body { margin:0; background:var(--bg); color:var(--fg); font:16px/1.6 system-ui,-apple-system,"Segoe UI",Roboto,sans-serif; }
-  main { max-width:980px; margin:0 auto; padding:40px 20px 64px; }
-  header h1 { font-size:2.4rem; margin:0 0 4px; letter-spacing:-.5px; }
-  header .by { color:var(--muted); margin:0 0 20px; }
-  .lead { font-size:1.12rem; max-width:760px; }
-  .pills { display:flex; flex-wrap:wrap; gap:8px; margin:18px 0 8px; }
-  .pill { background:var(--card); border:1px solid var(--line); border-radius:999px; padding:4px 12px; font-size:.9rem; color:var(--muted); }
-  .pill b { color:var(--fg); }
-  section { margin-top:44px; }
-  h2 { font-size:1.4rem; margin:0 0 12px; }
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-  .card .meta { color:var(--muted); font-size:.92rem; margin-bottom:10px; }
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-  th:first-child, td:first-child { text-align:left; }
-  th { color:var(--muted); font-weight:600; font-size:.9rem; }
+  html { scroll-behavior:smooth; -webkit-text-size-adjust:100%; }
+  body { margin:0; color:var(--fg); font:16px/1.65 ui-sans-serif,system-ui,-apple-system,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif;
+         background:radial-gradient(900px 480px at 12% -80px,rgba(79,180,119,.16),transparent 70%),radial-gradient(760px 420px at 92% -60px,rgba(110,166,230,.12),transparent 70%),var(--bg);
+         background-repeat:no-repeat; text-rendering:optimizeLegibility; }
+  ::selection { background:rgba(79,180,119,.35); }
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+  header h1 { font-size:clamp(2.3rem,6vw,3.7rem); line-height:1.05; margin:0 0 12px; letter-spacing:-.035em; font-weight:800; text-wrap:balance;
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+  .cards { display:grid; grid-template-columns:repeat(auto-fit,minmax(250px,1fr)); gap:16px; }
+  .card { background:linear-gradient(180deg,var(--card2),var(--card)); border:1px solid var(--line); border-radius:var(--r); padding:20px 22px; box-shadow:0 1px 0 rgba(255,255,255,.04) inset; }
+  .card h3 { margin:0 0 4px; font-size:1.18rem; letter-spacing:-.01em; display:flex; flex-wrap:wrap; align-items:center; gap:4px 10px; }
+  .card .meta { color:var(--muted); font-size:.93rem; margin-bottom:12px; }
+  .card p { margin:10px 0; color:var(--soft); }
+  .card a { font-weight:500; }
+  .tag { display:inline-block; font-size:.7rem; font-weight:700; letter-spacing:.06em; text-transform:uppercase; padding:3px 9px; border-radius:999px; vertical-align:middle; line-height:1.4; white-space:nowrap; }
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+  .tag.live::before { content:""; display:inline-block; width:6px; height:6px; margin:0 6px 1px 0; border-radius:50%; background:var(--warn); }
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+  .card-status:has(.tag.const) { border-left-color:var(--acc); }
+  .card-status:has(.tag.live) { border-left-color:var(--warn); }
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+  table { width:100%; border-collapse:collapse; min-width:520px; }
+  th, td { padding:12px 16px; text-align:right; border-bottom:1px solid var(--line); font-variant-numeric:tabular-nums; white-space:nowrap; }
+  th:first-child, td:first-child { text-align:left; position:sticky; left:0; background:var(--card); min-width:11.5rem; white-space:normal; z-index:1; }
+  th { color:var(--muted); font-weight:600; font-size:.78rem; letter-spacing:.07em; text-transform:uppercase; background:rgba(255,255,255,.03); vertical-align:middle; }
+  th:first-child { background:#191e29; }
   tr:last-child td { border-bottom:none; }
-  td.best { color:var(--acc); font-weight:700; }
-  .note { color:var(--muted); font-size:.92rem; margin-top:10px; }
-  .sha { overflow-wrap:anywhere; font-size:.8rem; }
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-  .lang button[aria-pressed="true"] { background:var(--line); color:var(--fg); }
+  tr:hover td { background:rgba(255,255,255,.025); }
+  tr:hover td:first-child { background:#191e29; }
+  td.best { color:var(--acc); font-weight:700; background:rgba(79,180,119,.09); }
+  tr:hover td.best { background:rgba(79,180,119,.14); }
+  section > p { max-width:78ch; }
+  .note { color:var(--muted); font-size:.93rem; line-height:1.7; max-width:78ch; margin:12px 0 0; }
+  code { font:.84em ui-monospace,SFMono-Regular,Menlo,Consolas,monospace; background:rgba(255,255,255,.06); padding:2px 6px; border-radius:6px; color:var(--soft); }
+  .sha { overflow-wrap:anywhere; font-size:.78rem; }
+  figure { margin:0; background:var(--bg); border-radius:var(--r); padding:0; overflow:hidden; box-shadow:0 0 0 1px var(--line2), 0 22px 56px -26px rgba(0,0,0,.85); }
+  figure img { width:100%; height:auto; display:block; }
+  figure + .note { margin-top:16px; }
+  .note + figure { margin-top:28px; }
+  ol.steps { list-style:none; padding:0; margin:0; counter-reset:step; display:grid; gap:12px; }
+  ol.steps li { counter-increment:step; position:relative; margin:0; padding:18px 20px 18px 66px; background:linear-gradient(180deg,var(--card2),var(--card)); border:1px solid var(--line); border-radius:var(--r); color:var(--soft); }
+  ol.steps li::before { content:counter(step); position:absolute; left:20px; top:18px; width:30px; height:30px; display:grid; place-items:center; border-radius:50%; background:rgba(79,180,119,.16); color:var(--acc); font-weight:800; font-size:.92rem; box-shadow:0 0 0 1px rgba(79,180,119,.35) inset; }
+  ol.steps li b { color:var(--fg); }
+  footer { margin-top:80px; color:var(--muted); font-size:.9rem; border-top:1px solid var(--line); padding-top:20px; }
+  a { color:var(--acc3); text-decoration-thickness:1px; text-underline-offset:3px; text-decoration-color:rgba(110,166,230,.4); transition:color .15s,text-decoration-color .15s; }
+  a:hover { color:#9cc3f0; text-decoration-color:currentColor; }
+  a:focus-visible, button:focus-visible { outline:2px solid var(--acc); outline-offset:3px; border-radius:6px; }
+  .lang { position:fixed; top:14px; right:16px; display:flex; gap:4px; background:rgba(20,24,33,.78); -webkit-backdrop-filter:blur(10px); backdrop-filter:blur(10px); border:1px solid var(--line2); border-radius:999px; padding:3px; z-index:10; }
+  .lang button { background:none; border:0; color:var(--muted); font:600 .85rem system-ui,sans-serif; padding:5px 12px; border-radius:999px; cursor:pointer; transition:background .15s,color .15s; }
+  .lang button:hover { color:var(--fg); }
+  .lang button[aria-pressed="true"] { background:var(--acc); color:#07130c; }
   body[data-lang="en"] .l-pl, body[data-lang="pl"] .l-en { display:none; }
+  @media (prefers-reduced-motion:no-preference) {
+    .card { transition:transform .2s ease,border-color .2s ease,box-shadow .2s ease; }
+    .card:hover { transform:translateY(-2px); border-color:var(--line2); box-shadow:0 14px 34px -18px rgba(0,0,0,.8); }
+    .tag.live::before { animation:pulse 1.8s ease-in-out infinite; }
+    @keyframes pulse { 0%,100% { opacity:1; transform:scale(1); } 50% { opacity:.35; transform:scale(.7); } }
+  }
+  @media (prefers-reduced-motion:reduce) { html { scroll-behavior:auto; } }
+  @media (max-width:640px) {
+    main { padding:12px 16px 64px; }
+    .lang { position:static; margin:12px 16px 0 auto; width:max-content; }
+    header { padding-top:24px; }
+    section { margin-top:52px; }
+    h2 { font-size:1.35rem; }
+    th, td { padding:10px 12px; font-size:.92rem; }
+    th:first-child, td:first-child { min-width:9.5rem; }
+    .card { padding:18px; }
+    ol.steps li { padding:16px 16px 16px 58px; }
+    ol.steps li::before { left:16px; top:16px; }
+    figure { padding:0; }
+    figure:has(> img) { overflow-x:auto; -webkit-overflow-scrolling:touch; }
+    figure:has(> img) img { width:760px; max-width:none; }
+  }
 </style>
 </head>
 <body data-lang="en">
@@ -100,13 +150,33 @@
       <a href="https://huggingface.co/SlayerLab/Slayer1B-1.03-checkpoints">native checkpoint archive — current files</a>
     </div>
     <div class="card card-status">
-      <h3>Slayer1B 1.04 <span class="tag live">training in progress</span></h3>
-      <div class="meta">corpus continuation 7150 → 10010 · target ~10.5 B tokens</div>
-      <p>Started on 4×L40S, adopted onto 8×L40S at the 10010 milestone after independent SHA-256 verification
-      of the checkpoint (three sources) and an NCCL/tempo gate (~100k tokens/s synthetic).</p>
-      <p class="note">Milestone 10010 SHA-256: <code class="sha">04438959d7b317d1a8d972b264e9610a06fd8addde7349c6e9f458ee56dc1e51</code></p>
-      <a href="https://track.fabryka.ai/run/f056bb5c-f1da-56d5-9776-cc33063f10a5">live continuation run</a> ·
-      <a href="https://huggingface.co/SlayerLab/Slayer1B-1.04-checkpoints">native checkpoint archive — current files</a>
+      <h3>Slayer1B 1.04 <span class="tag const">released</span></h3>
+      <div class="meta">native step 10010 · 10.5 B training tokens</div>
+      <p>Corpus continuation 7150 → 10010 on 4×L40S, constant learning rate. The run then moved to 8×L40S at this checkpoint
+      after independent SHA-256 verification (three sources) and a proof that the global batch is reproduced.</p>
+      <p class="note">Checkpoint SHA-256: <code class="sha">04438959d7b317d1a8d972b264e9610a06fd8addde7349c6e9f458ee56dc1e51</code></p>
+      <a href="https://huggingface.co/SlayerLab/Slayer1B-1.04">model</a> ·
+      <a href="https://huggingface.co/SlayerLab/Slayer1B-1.04-checkpoints">checkpoints</a> ·
+      <a href="https://track.fabryka.ai/run/f056bb5c-f1da-56d5-9776-cc33063f10a5">run</a>
+    </div>
+    <div class="card card-status">
+      <h3>Slayer1B 1.05 <span class="tag const">released</span></h3>
+      <div class="meta">native step 12870 · 13.5 B training tokens</div>
+      <p>Continuation 10010 → 12870 on 8×L40S, same model, data order and global batch, constant learning rate.</p>
+      <p class="note">Checkpoint SHA-256: <code class="sha">435082ff36becfbb96803622139f38d2afa0ba1a9d2fc284bad96f5a8edc1181</code></p>
+      <a href="https://huggingface.co/SlayerLab/Slayer1B-1.05">model</a> ·
+      <a href="https://huggingface.co/SlayerLab/Slayer1B-1.05-checkpoints">checkpoints</a> ·
+      <a href="https://track.fabryka.ai/run/f056bb5c-f1da-56d5-9776-cc33063f10a5">run</a>
+    </div>
+    <div class="card card-status">
+      <h3>Slayer1B 1.06 <span class="tag const">released · full corpus</span></h3>
+      <div class="meta">native step 13848 · 14.5 B training tokens · one full pass</div>
+      <p>Continuation 12870 → 13848 on 8×L40S. It ends where the prepared corpus ends: every token of the corpus was seen
+      exactly once (measured, not planned). Final training loss 2.276; no learning-rate decay yet.</p>
+      <p class="note">Checkpoint SHA-256: <code class="sha">1fc7396ca1790805128244b2f827e02b9fe88e352534cd887d0c488f121d1e4d</code></p>
+      <a href="https://huggingface.co/SlayerLab/Slayer1B-1.06">model</a> ·
+      <a href="https://huggingface.co/SlayerLab/Slayer1B-1.06-checkpoints">checkpoints</a> ·
+      <a href="https://track.fabryka.ai/run/f056bb5c-f1da-56d5-9776-cc33063f10a5">run</a>
     </div>
   </div>
 </section>
@@ -115,23 +185,23 @@
   <h2>Results</h2>
   <div class="wrap">
   <table>
-    <tr><th>test</th><th>1.00</th><th>1.01</th><th>1.02</th><th>1.03</th><th>1.04 <span class="tag live">mid-run</span></th></tr>
-    <tr><td>training tokens</td><td>1.5 B</td><td>3.0 B</td><td>6.0 B</td><td>7.5 B</td><td>10.5 B</td></tr>
-    <tr><td>native step</td><td>1430</td><td>2860</td><td>5720</td><td>7150</td><td>10010</td></tr>
-    <tr><td>ARC-Easy (EN), %</td><td>42.89</td><td>47.98</td><td>48.99</td><td>—</td><td class="best">50.59</td></tr>
-    <tr><td>BLiMP (EN, grammar), %</td><td>71.51</td><td>73.63</td><td>74.71</td><td>—</td><td>74.17</td></tr>
-    <tr><td>WikiText-2, byte perplexity ↓</td><td>2.5857</td><td>2.4230</td><td>2.3735</td><td>—</td><td class="best">2.3078</td></tr>
-    <tr><td>MultiBLiMP (PL, grammar), %</td><td>97.89</td><td>98.29</td><td>98.38</td><td>—</td><td class="best">98.87</td></tr>
-    <tr><td>ARC-Easy-PL, %</td><td>36.91</td><td>40.36</td><td>41.84</td><td>—</td><td class="best">44.02</td></tr>
+    <tr><th>test</th><th>1.00</th><th>1.01</th><th>1.02</th><th>1.03</th><th>1.04</th><th>1.05</th><th>1.06</th></tr>
+    <tr><td>training tokens</td><td>1.5 B</td><td>3.0 B</td><td>6.0 B</td><td>7.5 B</td><td>10.5 B</td><td>13.5 B</td><td>14.5 B</td></tr>
+    <tr><td>native step</td><td>1430</td><td>2860</td><td>5720</td><td>7150</td><td>10010</td><td>12870</td><td>13848</td></tr>
+    <tr><td>ARC-Easy (EN), %</td><td>42.89</td><td>47.98</td><td>48.99</td><td>—</td><td>50.59</td><td>52.19</td><td class="best">52.99</td></tr>
+    <tr><td>BLiMP (EN, grammar), %</td><td>71.51</td><td>73.63</td><td>74.71</td><td>—</td><td>74.17</td><td>75.53</td><td class="best">76.12</td></tr>
+    <tr><td>WikiText-2, byte perplexity ↓</td><td>2.5857</td><td>2.4230</td><td>2.3735</td><td>—</td><td>2.3078</td><td class="best">2.2816</td><td>2.2849</td></tr>
+    <tr><td>MultiBLiMP (PL, grammar), %</td><td>97.89</td><td>98.29</td><td>98.38</td><td>—</td><td class="best">98.87</td><td>98.47</td><td>98.41</td></tr>
+    <tr><td>ARC-Easy-PL, %</td><td>36.91</td><td>40.36</td><td>41.84</td><td>—</td><td>44.02</td><td class="best">45.20</td><td>45.03</td></tr>
   </table>
   </div>
-  <p class="note">One instrument for the versions that have values (1.00–1.02 and the 1.04 mid-run point): leaderboard protocol of
-  <a href="https://track.fabryka.ai/models">track.fabryka.ai/models</a>, zero-shot. Column 1.03 is a dash on purpose: its run
+  <p class="note">One instrument for the versions that have values (1.00–1.02 and 1.04–1.06): leaderboard protocol of
+  <a href="https://track.fabryka.ai/models">track.fabryka.ai/models</a>, zero-shot, on the released checkpoints. Column 1.03 is a dash on purpose: its run
   (<a href="https://track.fabryka.ai/run/7ca4f84f-3b07-4525-8cde-f3a936dc3425">1.03</a>) was measured with the older lm-eval task set
   (hellaswag, piqa, lambada, arc_challenge, boolq, mmlu — see the additional history section), so there are no values in this instrument;
-  the dash is not a missing measurement. Item-by-item comparison 1.01 → 1.02:
-  BLiMP +1.08 pp (95% interval [+0.78; +1.37]); on the other tests the direction is positive but the difference is
-  unresolved. 1.00 and 1.01 ended with a learning-rate decay, 1.02 did not — so 1.02 vs 1.01 is not a clean comparison.</p>
+  the dash is not a missing measurement. 1.00 and 1.01 ended with a learning-rate decay; 1.02 and 1.04–1.06 did not, so comparisons
+  across that boundary are not clean. Neighbouring checkpoints differ by up to about a point on single tests, so small differences
+  between 1.05 and 1.06 are within run noise.</p>
 </section>
 
 <section>
@@ -149,43 +219,27 @@
 </section>
 
 <section>
-  <h2>Historical score curves: 1.00–1.02</h2>
-  <figure><img src="scores-history-104.png" alt="Evaluation history of Slayer1B 1.00, 1.01, 1.02, 1.03 and 1.04 versus training tokens"></figure>
-  <p class="note">Measured values only, no fits, no trend lines. 1.00-1.02: leaderboard protocol, one checkpoint every 150 steps; shaded bands mark the learning-rate decay of 1.00 (steps 1146-1430) and 1.01 (2290-2860). 1.03: training loss only — it was evaluated with an older lm-eval task set, so it has no points in these panels. 1.04: continuation from step 7150, constant learning rate, no decay; in-run evaluator at 50-step cadence, points up to 10010 (10.5 B tokens), the latest point is not a final result. A break in the line = evaluation points still being back-filled after an evaluation-pipeline restart, not missing training. eff = (BLiMP + ARC + WikiScore) / 3, the board formula checked against the published 1.02 value before use. Data: <a href="scores-history-104.csv">scores-history-104.csv</a> (1.00-1.04) and <a href="scores-history.csv">scores-history.csv</a> (1.00-1.02 historical rows).</p>
+  <h2>Score curves: 1.00–1.06</h2>
+  <figure><img src="scores-history-106.png" alt="Evaluation history of Slayer1B 1.00 to 1.06 versus training tokens"></figure>
+  <p class="note">Measured values only, no fits, no trend lines. 1.00–1.02: leaderboard protocol, one checkpoint every 150 steps; shaded bands mark the learning-rate decay of 1.00 and 1.01. 1.03: training loss only — it was evaluated with an older lm-eval task set, so it has no points in the score panels. 1.04–1.06: one continuation from step 7150 at a constant learning rate; diamonds mark the releases 1.04 (10010), 1.05 (12870) and 1.06 (13848, end of the corpus). The in-run evaluator measured every 50 steps up to 9100, then control points 9400/9700/10000, then every 200 steps; breaks in the line are gaps in that schedule, not missing training. eff = (BLiMP + ARC + WikiScore) / 3, the board formula, which reproduces every published 1.00–1.02 value within 0.01. Data: <a href="scores-history-106.csv">scores-history-106.csv</a>.</p>
 </section>
 
 <section>
-  <h2>Mid-run evaluation: 1.04 continuation</h2>
+  <h2>Release points: 1.04 → 1.06</h2>
   <div class="wrap">
   <table>
-    <tr><th>test</th><th>step 7200</th><th>step 7250</th></tr>
-    <tr><td>BLiMP (EN, grammar), %</td><td>74.63</td><td class="best">74.70</td></tr>
-    <tr><td>ARC-Easy (EN), %</td><td>49.75</td><td class="best">49.87</td></tr>
-    <tr><td>WikiText-2, byte perplexity ↓</td><td class="best">2.3439</td><td>2.3456</td></tr>
-    <tr><td>WikiText-2, token perplexity ↓</td><td class="best">33.36</td><td>33.45</td></tr>
-    <tr><td>MultiBLiMP (PL, grammar), %</td><td>98.41</td><td class="best">98.47</td></tr>
-    <tr><td>ARC-Easy-PL, %</td><td class="best">43.10</td><td>42.55</td></tr>
+    <tr><th>test</th><th>1.04 · 10010</th><th>1.05 · 12870</th><th>1.06 · 13848</th></tr>
+    <tr><td>BLiMP (EN, grammar), %</td><td>74.17</td><td>75.53</td><td class="best">76.12</td></tr>
+    <tr><td>ARC-Easy (EN), %</td><td>50.59</td><td>52.19</td><td class="best">52.99</td></tr>
+    <tr><td>WikiText-2, byte perplexity ↓</td><td>2.3078</td><td class="best">2.2816</td><td>2.2849</td></tr>
+    <tr><td>WikiText-2, token perplexity ↓</td><td>31.29</td><td class="best">29.86</td><td>30.03</td></tr>
+    <tr><td>MultiBLiMP (PL, grammar), %</td><td class="best">98.87</td><td>98.47</td><td>98.41</td></tr>
+    <tr><td>ARC-Easy-PL, %</td><td>44.02</td><td class="best">45.20</td><td>45.03</td></tr>
   </table>
   </div>
-  <p class="note">Preliminary mid-run points from the <a href="https://track.fabryka.ai/run/f056bb5c-f1da-56d5-9776-cc33063f10a5">live 1.04 continuation</a>,
-  measured automatically during the run with the same metric definitions as the table above (accuracy in percent,
-  byte-level perplexity), at intermediate checkpoints — not a re-measurement of the table rows. Preliminary: the run continues
-  and no learning-rate decay has been applied yet, so the newest row is not a final result. Both points share one environment,
-  so 7200 → 7250 is a clean within-run comparison; at 50 steps apart the changes are small and within run noise.</p>
-</section>
-
-<section>
-  <h2>Training loss across versions 1.00–1.04</h2>
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232.8 L701.5 232.4 L702.1 232.1 L702.7 232.9 L703.3 232.5 L704.0 231.9 L704.6 232.5 L705.2 232.2 L705.8 231.3 L706.5 232.2 L707.1 233.4 L707.7 232.8 L708.4 232.5 L709.0 231.9 L709.6 233.4 L710.2 233.0 L710.9 233.2 L711.5 232.5 L712.1 232.8 L712.7 232.6 L713.4 234.0 L714.0 231.9" fill="none" stroke="#f2a0a0" stroke-width="1.6"/><rect x="436" y="50" width="10" height="10" fill="#f2a0a0"/><text x="450" y="59" fill="#c9d1d9" font-size="11">1.04</text></svg></figure>
-  <p class="note">One line per version, read from each public run through its API (1.00 1430, 1.01 2860, 1.02 5720, 1.03 7150, 1.04 up to 10010 and beyond); log scale on loss so early steps and late steps are both visible. The curves tile the same global trajectory. 1.04 is still in progress; its evaluation series has a gap between 8850 and 10010 because the station pipeline was restarted, and the missing points are being published from the archive now — the gap is downtime, not a regression.</p>
-</section>
-
-<section>
-  <h2>Live charts: 1.04 continuation</h2>
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fill="#8b939d" font-size="11" text-anchor="middle">7150</text><text x="380.0" y="226" fill="#8b939d" font-size="11" text-anchor="middle">8615</text><text x="720.0" y="226" fill="#8b939d" font-size="11" text-anchor="middle">10080</text><text x="380" y="244" fill="#8b939d" font-size="11" text-anchor="middle">native step</text><path d="M42.3 101.3 L44.6 108.3 L47.0 95.7 L49.3 112.3 L51.6 99.8 L53.9 47.8 L56.2 79.8 L58.6 71.6 L60.9 113.0 L63.2 98.8 L65.5 112.2 L67.8 130.3 L70.2 112.1 L72.5 104.4 L74.8 82.1 L77.1 114.5 L79.5 116.5 L81.8 72.7 L84.1 79.3 L86.4 103.5 L88.7 79.8 L91.1 121.7 L93.4 75.3 L95.7 124.5 L98.0 131.7 L100.3 90.9 L102.7 100.2 L105.0 85.8 L107.3 86.8 L109.6 78.6 L111.9 99.5 L114.3 80.1 L116.6 121.8 L118.9 138.7 L121.2 92.3 L123.5 114.8 L125.9 103.2 L128.2 119.8 L130.5 100.9 L132.8 113.2 L135.2 111.3 L137.5 88.0 L139.8 109.2 L142.1 111.4 L144.4 103.9 L146.8 133.8 L149.1 114.1 L151.4 95.3 L153.7 111.0 L156.0 135.4 L158.4 117.9 L160.7 97.1 L163.0 112.2 L165.3 92.5 L167.6 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L706.1 202.2 L708.4 190.7 L710.7 199.0 L713.0 169.2 L715.4 178.2 L717.7 191.0 L720.0 181.6" fill="none" stroke="#7fb2ff" stroke-width="1.8"/><line x1="703.8" y1="40" x2="703.8" y2="210" stroke="#e8c37a" stroke-dasharray="4 4"/><text x="708.8" y="52" fill="#e8c37a" font-size="11">10010</text></svg></figure>
-  <p class="note">train/loss against native step, read from the <a href="https://track.fabryka.ai/run/f056bb5c-f1da-56d5-9776-cc33063f10a5">public continuation run</a> through its public API at 2026-10-28T00:35Z. Dashed line marks the 8×L40S adoption at milestone 10010. The curve is measured, not fitted.</p>
-  <figure><svg viewBox="0 0 760 250" role="img" style="width:100%;height:auto;background:transparent" xmlns="http://www.w3.org/2000/svg"><line x1="40" y1="210" x2="720" y2="210" stroke="#3a3f46"/><line x1="40" y1="40" x2="40" y2="210" stroke="#3a3f46"/><line x1="40" y1="210.0" x2="720" y2="210.0" stroke="#23262b"/><text x="34" y="214.0" fill="#8b939d" font-size="11" text-anchor="end">41</text><line x1="40" y1="167.5" x2="720" y2="167.5" stroke="#23262b"/><text x="34" y="171.5" fill="#8b939d" font-size="11" text-anchor="end">56</text><line x1="40" y1="125.0" x2="720" y2="125.0" stroke="#23262b"/><text x="34" y="129.0" fill="#8b939d" font-size="11" text-anchor="end">71</text><line x1="40" y1="82.5" x2="720" y2="82.5" stroke="#23262b"/><text x="34" y="86.5" fill="#8b939d" font-size="11" text-anchor="end">85</text><line x1="40" y1="40.0" x2="720" y2="40.0" stroke="#23262b"/><text x="34" y="44.0" fill="#8b939d" font-size="11" text-anchor="end">100</text><text x="40.0" y="226" fill="#8b939d" font-size="11" text-anchor="middle">7200</text><text x="380.0" y="226" fill="#8b939d" font-size="11" text-anchor="middle">8025</text><text x="720.0" y="226" fill="#8b939d" font-size="11" text-anchor="middle">8850</text><text x="380" y="244" fill="#8b939d" font-size="11" text-anchor="middle">native step</text><path d="M40.0 113.1 L40.0 113.1 L60.6 112.9 L60.6 112.9 L81.2 114.7 L81.2 114.7 L101.8 112.9 L101.8 112.9 L122.4 112.3 L143.0 113.6 L163.6 114.6 L184.2 111.5 L204.8 111.7 L225.5 111.4 L246.1 112.2 L266.7 114.3 L287.3 113.2 L307.9 112.9 L328.5 111.2 L349.1 111.9 L369.7 112.7 L390.3 114.5 L410.9 114.7 L431.5 113.2 L452.1 115.0 L472.7 114.7 L493.3 114.0 L513.9 112.5 L534.5 111.2 L555.2 113.8 L575.8 113.5 L596.4 113.4 L617.0 113.3 L637.6 111.5 L658.2 112.5 L678.8 112.5 L699.4 110.9 L720.0 111.5" fill="none" stroke="#8fe0ad" stroke-width="1.8"/><path d="M40.0 184.8 L40.0 184.8 L60.6 184.4 L60.6 184.4 L81.2 186.4 L81.2 186.4 L101.8 184.8 L101.8 184.8 L122.4 188.0 L143.0 184.2 L163.6 187.0 L184.2 186.5 L204.8 187.1 L225.5 187.0 L246.1 184.4 L266.7 188.0 L287.3 186.4 L307.9 188.8 L328.5 188.3 L349.1 183.8 L369.7 184.9 L390.3 187.4 L410.9 187.1 L431.5 184.0 L452.1 185.9 L472.7 184.9 L493.3 183.7 L513.9 184.9 L534.5 181.4 L555.2 184.7 L575.8 183.1 L596.4 185.4 L617.0 184.7 L637.6 185.2 L658.2 185.4 L678.8 183.3 L699.4 179.7 L720.0 183.7" fill="none" stroke="#7fb2ff" stroke-width="1.8"/><path d="M40.0 44.6 L60.6 44.4 L81.2 44.1 L101.8 44.1 L122.4 43.9 L143.0 44.2 L163.6 45.3 L184.2 44.1 L204.8 43.9 L225.5 43.5 L246.1 43.7 L266.7 44.1 L287.3 44.0 L307.9 43.4 L328.5 43.7 L349.1 43.3 L369.7 43.2 L390.3 43.5 L410.9 43.3 L431.5 43.5 L452.1 43.2 L472.7 43.1 L493.3 44.1 L513.9 43.6 L534.5 44.3 L555.2 43.9 L575.8 44.2 L596.4 44.1 L617.0 43.9 L637.6 44.1 L658.2 43.9 L678.8 43.9 L699.4 43.7 L720.0 44.8" fill="none" stroke="#e8c37a" stroke-width="1.8"/><path d="M40.0 203.9 L60.6 205.5 L81.2 203.8 L101.8 203.6 L122.4 205.4 L143.0 203.5 L163.6 204.4 L184.2 202.4 L204.8 203.1 L225.5 203.8 L246.1 203.6 L266.7 204.3 L287.3 201.0 L307.9 204.2 L328.5 204.4 L349.1 202.9 L369.7 204.1 L390.3 204.7 L410.9 206.4 L431.5 203.8 L452.1 203.5 L472.7 202.4 L493.3 202.0 L513.9 201.5 L534.5 204.3 L555.2 202.7 L575.8 201.5 L596.4 202.1 L617.0 202.0 L637.6 200.1 L658.2 204.4 L678.8 205.6 L699.4 202.2 L720.0 202.4" fill="none" stroke="#f2a0a0" stroke-width="1.8"/><rect x="48" y="46" width="10" height="10" fill="#8fe0ad"/><text x="62" y="55" fill="#c9d1d9" font-size="11">BLiMP EN</text><rect x="198" y="46" width="10" height="10" fill="#7fb2ff"/><text x="212" y="55" fill="#c9d1d9" font-size="11">ARC-Easy EN</text><rect x="348" y="46" width="10" height="10" fill="#e8c37a"/><text x="362" y="55" fill="#c9d1d9" font-size="11">MultiBLiMP PL</text><rect x="498" y="46" width="10" height="10" fill="#f2a0a0"/><text x="512" y="55" fill="#c9d1d9" font-size="11">ARC-Easy-PL</text></svg></figure>
-  <p class="note">Automatic mid-run evaluation, one instrument (lm-eval harness), every 50 steps: BLiMP EN, ARC-Easy EN, MultiBLiMP PL and ARC-Easy-PL in percent. Preliminary points from a run in progress — not final results, and not directly comparable with the offline table above (different procedure).</p>
+  <p class="note">Measured on the released checkpoints by the run's automatic evaluator (same instrument as the results table), read
+  from the <a href="https://track.fabryka.ai/run/f056bb5c-f1da-56d5-9776-cc33063f10a5">public continuation run</a>. The whole continuation
+  used a constant learning rate; a final decay has not been applied, so these are not end-of-training numbers for the model line.</p>
 </section>
 
 <section>
@@ -210,15 +264,14 @@
 
 <section>
   <h2>What's next</h2>
-  <p><b>Corpus continuation, 1.04–1.06:</b> the global trajectory runs from native step 7150 toward the target 13848.
-  Checkpoint milestones are 10010 (1.04), 12870 (1.05) and 13848 (1.06). Milestone 10010 is verified and reached; training now runs on 8×L40S at ~99k tokens/s.
-  <a href="https://track.fabryka.ai/run/f056bb5c-f1da-56d5-9776-cc33063f10a5">Follow the live continuation</a>.</p>
+  <p><b>Corpus continuation 1.04–1.06 — complete.</b> The global trajectory ran from native step 7150 to 13848, the end of the
+  prepared corpus: 14.5 B training tokens, one full pass, about 13.5 tokens per parameter. Releases 1.04 (10010), 1.05 (12870) and
+  1.06 (13848) are public, each with its checkpoint archive. <a href="https://track.fabryka.ai/run/f056bb5c-f1da-56d5-9776-cc33063f10a5">Training run</a>.</p>
   <p class="note">The linked runs belong to the author's Track account. The completed 1.03 run includes its resumed steps;
   corpus continuation and benchmark history are separate records of the same model lineage.</p>
-  <p><b>Beyond 13848 — roadmap candidates, not scheduled runs:</b> a final learning-rate decay, and extending the context
-  window in stages (first 32k tokens, up to 256k as the target), each stage checked with long-context tests. The 1.04 plan ends at
-  13848 on a constant learning rate; these follow-ups require their own plan, cost estimate and go-ahead, and a decay over an
-  exhausted corpus would need repeated or new data.</p>
+  <p><b>Beyond 13848 — roadmap candidates, not scheduled runs:</b> continuing to about 20 tokens per parameter with fresh data,
+  a final learning-rate decay, and extending the context window in stages (first 32k tokens, up to 256k as the target), each stage
+  checked with long-context tests. Each follow-up needs its own plan, cost estimate and go-ahead.</p>
   <p class="note">These are base models: they predict the continuation of a text, they were not tuned for chat or for
   following instructions, and they can be wrong.</p>
 </section>
@@ -276,13 +329,33 @@
       <a href="https://huggingface.co/SlayerLab/Slayer1B-1.03-checkpoints">archiwum checkpointów natywnych — aktualne pliki</a>
     </div>
     <div class="card card-status">
-      <h3>Slayer1B 1.04 <span class="tag live">trening w toku</span></h3>
-      <div class="meta">kontynuacja korpusu 7150 → 10010 · cel ~10,5 mld tokenów</div>
-      <p>Start na 4×L40S, adopcja na 8×L40S przy kamieniu milowym 10010 po niezależnej weryfikacji SHA-256
-      checkpointu (trzy źródła) i bramce NCCL/tempo (~100 tys. tokenów/s na syntetyku).</p>
-      <p class="note">SHA-256 kamienia 10010: <code class="sha">04438959d7b317d1a8d972b264e9610a06fd8addde7349c6e9f458ee56dc1e51</code></p>
-      <a href="https://track.fabryka.ai/run/f056bb5c-f1da-56d5-9776-cc33063f10a5">bieg kontynuacji na żywo</a> ·
-      <a href="https://huggingface.co/SlayerLab/Slayer1B-1.04-checkpoints">archiwum checkpointów natywnych — aktualne pliki</a>
+      <h3>Slayer1B 1.04 <span class="tag const">wydany</span></h3>
+      <div class="meta">krok native 10010 · 10,5 mld tokenów treningu</div>
+      <p>Kontynuacja korpusu 7150 → 10010 na 4×L40S, stałe tempo uczenia. Na tym checkpoincie bieg przeszedł na 8×L40S
+      po niezależnej weryfikacji SHA-256 (trzy źródła) i dowodzie, że globalny batch jest odtworzony.</p>
+      <p class="note">SHA-256 checkpointu: <code class="sha">04438959d7b317d1a8d972b264e9610a06fd8addde7349c6e9f458ee56dc1e51</code></p>
+      <a href="https://huggingface.co/SlayerLab/Slayer1B-1.04">model</a> ·
+      <a href="https://huggingface.co/SlayerLab/Slayer1B-1.04-checkpoints">punkty kontrolne</a> ·
+      <a href="https://track.fabryka.ai/run/f056bb5c-f1da-56d5-9776-cc33063f10a5">przebieg</a>
+    </div>
+    <div class="card card-status">
+      <h3>Slayer1B 1.05 <span class="tag const">wydany</span></h3>
+      <div class="meta">krok native 12870 · 13,5 mld tokenów treningu</div>
+      <p>Kontynuacja 10010 → 12870 na 8×L40S: ten sam model, kolejność danych i globalny batch, stałe tempo uczenia.</p>
+      <p class="note">SHA-256 checkpointu: <code class="sha">435082ff36becfbb96803622139f38d2afa0ba1a9d2fc284bad96f5a8edc1181</code></p>
+      <a href="https://huggingface.co/SlayerLab/Slayer1B-1.05">model</a> ·
+      <a href="https://huggingface.co/SlayerLab/Slayer1B-1.05-checkpoints">punkty kontrolne</a> ·
+      <a href="https://track.fabryka.ai/run/f056bb5c-f1da-56d5-9776-cc33063f10a5">przebieg</a>
+    </div>
+    <div class="card card-status">
+      <h3>Slayer1B 1.06 <span class="tag const">wydany · cały korpus</span></h3>
+      <div class="meta">krok native 13848 · 14,5 mld tokenów treningu · jedno pełne przejście</div>
+      <p>Kontynuacja 12870 → 13848 na 8×L40S. Kończy się tam, gdzie kończy się przygotowany korpus: każdy jego token model
+      zobaczył dokładnie raz (zmierzone, nie zaplanowane). Końcowa strata treningowa 2,276; bez wygaszania tempa uczenia.</p>
+      <p class="note">SHA-256 checkpointu: <code class="sha">1fc7396ca1790805128244b2f827e02b9fe88e352534cd887d0c488f121d1e4d</code></p>
+      <a href="https://huggingface.co/SlayerLab/Slayer1B-1.06">model</a> ·
+      <a href="https://huggingface.co/SlayerLab/Slayer1B-1.06-checkpoints">punkty kontrolne</a> ·
+      <a href="https://track.fabryka.ai/run/f056bb5c-f1da-56d5-9776-cc33063f10a5">przebieg</a>
     </div>
   </div>
 </section>
@@ -291,23 +364,23 @@
   <h2>Wyniki</h2>
   <div class="wrap">
   <table>
-    <tr><th>test</th><th>1.00</th><th>1.01</th><th>1.02</th><th>1.03</th><th>1.04 <span class="tag live">w trakcie</span></th></tr>
-    <tr><td>tokeny treningu</td><td>1,5 mld</td><td>3,0 mld</td><td>6,0 mld</td><td>7,5 mld</td><td>10,5 mld</td></tr>
-    <tr><td>krok native</td><td>1430</td><td>2860</td><td>5720</td><td>7150</td><td>10010</td></tr>
-    <tr><td>ARC-Easy (EN), %</td><td>42,89</td><td>47,98</td><td>48,99</td><td>—</td><td class="best">50,59</td></tr>
-    <tr><td>BLiMP (EN, gramatyka), %</td><td>71,51</td><td>73,63</td><td>74,71</td><td>—</td><td>74,17</td></tr>
-    <tr><td>WikiText-2, perpleksja na bajt ↓</td><td>2,5857</td><td>2,4230</td><td>2,3735</td><td>—</td><td class="best">2,3078</td></tr>
-    <tr><td>MultiBLiMP (PL, gramatyka), %</td><td>97,89</td><td>98,29</td><td>98,38</td><td>—</td><td class="best">98,87</td></tr>
-    <tr><td>ARC-Easy-PL, %</td><td>36,91</td><td>40,36</td><td>41,84</td><td>—</td><td class="best">44,02</td></tr>
+    <tr><th>test</th><th>1.00</th><th>1.01</th><th>1.02</th><th>1.03</th><th>1.04</th><th>1.05</th><th>1.06</th></tr>
+    <tr><td>tokeny treningu</td><td>1,5 mld</td><td>3,0 mld</td><td>6,0 mld</td><td>7,5 mld</td><td>10,5 mld</td><td>13,5 mld</td><td>14,5 mld</td></tr>
+    <tr><td>krok native</td><td>1430</td><td>2860</td><td>5720</td><td>7150</td><td>10010</td><td>12870</td><td>13848</td></tr>
+    <tr><td>ARC-Easy (EN), %</td><td>42,89</td><td>47,98</td><td>48,99</td><td>—</td><td>50,59</td><td>52,19</td><td class="best">52,99</td></tr>
+    <tr><td>BLiMP (EN, gramatyka), %</td><td>71,51</td><td>73,63</td><td>74,71</td><td>—</td><td>74,17</td><td>75,53</td><td class="best">76,12</td></tr>
+    <tr><td>WikiText-2, perpleksja na bajt ↓</td><td>2,5857</td><td>2,4230</td><td>2,3735</td><td>—</td><td>2,3078</td><td class="best">2,2816</td><td>2,2849</td></tr>
+    <tr><td>MultiBLiMP (PL, gramatyka), %</td><td>97,89</td><td>98,29</td><td>98,38</td><td>—</td><td class="best">98,87</td><td>98,47</td><td>98,41</td></tr>
+    <tr><td>ARC-Easy-PL, %</td><td>36,91</td><td>40,36</td><td>41,84</td><td>—</td><td>44,02</td><td class="best">45,20</td><td>45,03</td></tr>
   </table>
   </div>
-  <p class="note">Jeden przyrząd dla wersji, które mają wartości (1.00–1.02 oraz punkt 1.04 w trakcie biegu): protokół tablicy
-  <a href="https://track.fabryka.ai/models">track.fabryka.ai/models</a>, zero-shot. Kolumna 1.03 ma kreskę celowo: jej run
+  <p class="note">Jeden przyrząd dla wersji, które mają wartości (1.00–1.02 oraz 1.04–1.06): protokół tablicy
+  <a href="https://track.fabryka.ai/models">track.fabryka.ai/models</a>, zero-shot, na wydanych checkpointach. Kolumna 1.03 ma kreskę celowo: jej run
   (<a href="https://track.fabryka.ai/run/7ca4f84f-3b07-4525-8cde-f3a936dc3425">1.03</a>) mierzono starszym zestawem lm-eval
   (hellaswag, piqa, lambada, arc_challenge, boolq, mmlu — patrz sekcja dodatkowej historii), więc w tym przyrządzie nie ma wartości;
-  kreska nie oznacza brakującego pomiaru. Porównanie 1.01 → 1.02 pytanie po pytaniu:
-  BLiMP +1,08 pp (95 % przedział [+0,78; +1,37]); na pozostałych testach kierunek dodatni, ale różnica nierozstrzygnięta.
-  1.00 i 1.01 kończyły się wygaszaniem tempa uczenia, 1.02 nie — więc 1.02 nie jest „czystym” porównaniem z 1.01.</p>
+  kreska nie oznacza brakującego pomiaru. 1.00 i 1.01 kończyły się wygaszaniem tempa uczenia, 1.02 i 1.04–1.06 nie — porównania
+  przez tę granicę nie są „czyste”. Sąsiednie checkpointy różnią się na pojedynczych testach nawet o ok. punkt, więc małe różnice
+  między 1.05 a 1.06 mieszczą się w szumie biegu.</p>
 </section>
 
 <section>
@@ -325,43 +398,27 @@
 </section>
 
 <section>
-  <h2>Historyczne krzywe wyników: 1.00–1.02</h2>
-  <figure><img src="scores-history-104.png" alt="Historia wyników Slayer1B 1.00, 1.01, 1.02, 1.03 i 1.04 w funkcji liczby tokenów treningu"></figure>
-  <p class="note">Same pomiary, bez dopasowań i bez linii trendu. 1.00-1.02: protokół tablicy, punkt co 150 kroków; pasma oznaczają wygaszanie LR w 1.00 (kroki 1146-1430) i 1.01 (2290-2860). 1.03: tylko strata treningowa — mierzony starszym zestawem lm-eval, więc w tych panelach nie ma punktów. 1.04: kontynuacja od kroku 7150, stały LR bez wygaszania; ewaluator w biegu co 50 kroków, punkty do 10010 (10,5 mld tokenów), najnowszy punkt nie jest wynikiem końcowym. Przerwa w linii = punkty ewaluacji wciąż dosyłane po restarcie pipeline'u, nie brakujące dane treningu. eff = (BLiMP + ARC + WikiScore) / 3, formuła tablicy sprawdzona na opublikowanej wartości 1.02 przed użyciem. Dane: <a href="scores-history-104.csv">scores-history-104.csv</a> (1.00-1.04) i <a href="scores-history.csv">scores-history.csv</a> (historyczne 1.00-1.02).</p>
+  <h2>Krzywe wyników: 1.00–1.06</h2>
+  <figure><img src="scores-history-106.png" alt="Historia wyników Slayer1B od 1.00 do 1.06 w funkcji liczby tokenów treningu"></figure>
+  <p class="note">Same pomiary, bez dopasowań i bez linii trendu. 1.00–1.02: protokół tablicy, punkt co 150 kroków; pasma oznaczają wygaszanie LR w 1.00 i 1.01. 1.03: tylko strata treningowa — mierzony starszym zestawem lm-eval, więc w panelach wyników nie ma punktów. 1.04–1.06: jedna kontynuacja od kroku 7150 przy stałym LR; romby oznaczają wydania 1.04 (10010), 1.05 (12870) i 1.06 (13848, koniec korpusu). Ewaluator w biegu mierzył co 50 kroków do 9100, potem punkty kontrolne 9400/9700/10000, potem co 200 kroków; przerwy w linii to luki w tym harmonogramie, nie brakujący trening. eff = (BLiMP + ARC + WikiScore) / 3, formuła tablicy, która odtwarza każdą opublikowaną wartość 1.00–1.02 z dokładnością 0,01. Dane: <a href="scores-history-106.csv">scores-history-106.csv</a>.</p>
 </section>
 
 <section>
-  <h2>Ewaluacja w trakcie biegu: kontynuacja 1.04</h2>
+  <h2>Punkty wydań: 1.04 → 1.06</h2>
   <div class="wrap">
   <table>
-    <tr><th>test</th><th>krok 7200</th><th>krok 7250</th></tr>
-    <tr><td>BLiMP (EN, gramatyka), %</td><td>74,63</td><td class="best">74,70</td></tr>
-    <tr><td>ARC-Easy (EN), %</td><td>49,75</td><td class="best">49,87</td></tr>
-    <tr><td>WikiText-2, perpleksja na bajt ↓</td><td class="best">2,3439</td><td>2,3456</td></tr>
-    <tr><td>WikiText-2, perpleksja na token ↓</td><td class="best">33,36</td><td>33,45</td></tr>
-    <tr><td>MultiBLiMP (PL, gramatyka), %</td><td>98,41</td><td class="best">98,47</td></tr>
-    <tr><td>ARC-Easy-PL, %</td><td class="best">43,10</td><td>42,55</td></tr>
+    <tr><th>test</th><th>1.04 · 10010</th><th>1.05 · 12870</th><th>1.06 · 13848</th></tr>
+    <tr><td>BLiMP (EN, gramatyka), %</td><td>74,17</td><td>75,53</td><td class="best">76,12</td></tr>
+    <tr><td>ARC-Easy (EN), %</td><td>50,59</td><td>52,19</td><td class="best">52,99</td></tr>
+    <tr><td>WikiText-2, perpleksja na bajt ↓</td><td>2,3078</td><td class="best">2,2816</td><td>2,2849</td></tr>
+    <tr><td>WikiText-2, perpleksja na token ↓</td><td>31,29</td><td class="best">29,86</td><td>30,03</td></tr>
+    <tr><td>MultiBLiMP (PL, gramatyka), %</td><td class="best">98,87</td><td>98,47</td><td>98,41</td></tr>
+    <tr><td>ARC-Easy-PL, %</td><td>44,02</td><td class="best">45,20</td><td>45,03</td></tr>
   </table>
   </div>
-  <p class="note">Wstępne punkty w trakcie biegu z <a href="https://track.fabryka.ai/run/f056bb5c-f1da-56d5-9776-cc33063f10a5">kontynuacji 1.04 na żywo</a>,
-  mierzone automatycznie w trakcie biegu z tymi samymi definicjami metryk co tabela powyżej (trafność w procentach,
-  perpleksja na bajt), na pośrednich checkpointach — to nie jest ponowny pomiar wierszy tabeli. Wstępne: bieg trwa
-  i nie zastosowano jeszcze wygaszania tempa uczenia, więc najnowszy wiersz nie jest wynikiem końcowym. Oba punkty pochodzą
-  z jednego środowiska, więc 7200 → 7250 to czyste porównanie w obrębie biegu; przy 50 krokach różnicy zmiany są małe.</p>
-</section>
-
-<section>
-  <h2>Strata treningowa w wersjach 1.00–1.04</h2>
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232.8 L701.5 232.4 L702.1 232.1 L702.7 232.9 L703.3 232.5 L704.0 231.9 L704.6 232.5 L705.2 232.2 L705.8 231.3 L706.5 232.2 L707.1 233.4 L707.7 232.8 L708.4 232.5 L709.0 231.9 L709.6 233.4 L710.2 233.0 L710.9 233.2 L711.5 232.5 L712.1 232.8 L712.7 232.6 L713.4 234.0 L714.0 231.9" fill="none" stroke="#f2a0a0" stroke-width="1.6"/><rect x="436" y="50" width="10" height="10" fill="#f2a0a0"/><text x="450" y="59" fill="#c9d1d9" font-size="11">1.04</text></svg></figure>
-  <p class="note">Jedna linia na wersję, odczytane z każdego publicznego runu przez jego API (1.00 1430, 1.01 2860, 1.02 5720, 1.03 7150, 1.04 do 10010 i dalej); skala logarytmiczna straty, żeby widzieć i wczesne, i późne kroki. Krzywe układają się w jedną globalną trajektorię. 1.04 jest w toku; jej seria ewaluacji ma przerwę 8850 → 10010, bo pipeline stacji został zrestartowany, a brakujące punkty są teraz publikowane z archiwum — przerwa to przestój, nie regresja.</p>
-</section>
-
-<section>
-  <h2>Wykresy na żywo: kontynuacja 1.04</h2>
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-  <p class="note">train/loss w funkcji kroku native, odczytane z <a href="https://track.fabryka.ai/run/f056bb5c-f1da-56d5-9776-cc33063f10a5">publicznego biegu kontynuacji</a> przez jego publiczne API o 2026-10-28T00:35Z. Linia przerywana oznacza adopcję na 8×L40S przy kamieniu 10010. Krzywa jest zmierzona, nie dopasowana.</p>
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-  <p class="note">Automatyczna ewaluacja w trakcie biegu, jeden przyrząd (harness lm-eval), co 50 kroków: BLiMP EN, ARC-Easy EN, MultiBLiMP PL i ARC-Easy-PL w procentach. Wstępne punkty z biegu w toku — nie wyniki końcowe i nie porównywalne wprost z tabelą offline powyżej (inna procedura).</p>
+  <p class="note">Zmierzone na wydanych checkpointach automatycznym ewaluatorem biegu (ten sam przyrząd co tabela wyników), odczytane
+  z <a href="https://track.fabryka.ai/run/f056bb5c-f1da-56d5-9776-cc33063f10a5">publicznego biegu kontynuacji</a>. Cała kontynuacja
+  szła przy stałym tempie uczenia; końcowego wygaszania nie zastosowano, więc to nie są końcowe liczby tej linii modelu.</p>
 </section>
 
 <section>
@@ -386,15 +443,14 @@
 
 <section>
   <h2>Co dalej</h2>
-  <p><b>Kontynuacja korpusu, 1.04–1.06:</b> globalna trajektoria prowadzi od kroku native 7150 do celu 13848.
-  Checkpointy wersji przypadają na 10010 (1.04), 12870 (1.05) i 13848 (1.06). Kamień 10010 jest zweryfikowany i osiągnięty; trening liczy teraz na 8×L40S przy ~99 tys. tokenów/s.
-  <a href="https://track.fabryka.ai/run/f056bb5c-f1da-56d5-9776-cc33063f10a5">Postęp kontynuacji na żywo</a>.</p>
+  <p><b>Kontynuacja korpusu 1.04–1.06 — zakończona.</b> Globalna trajektoria przeszła od kroku native 7150 do 13848, czyli do
+  końca przygotowanego korpusu: 14,5 mld tokenów treningu, jedno pełne przejście, ok. 13,5 tokenu na parametr. Wydania 1.04 (10010),
+  1.05 (12870) i 1.06 (13848) są publiczne, każde z archiwum checkpointów. <a href="https://track.fabryka.ai/run/f056bb5c-f1da-56d5-9776-cc33063f10a5">Bieg treningu</a>.</p>
   <p class="note">Powiązane przebiegi są na koncie autora w Tracku. Zakończony run 1.03 obejmuje wznowione kroki;
   dalszy trening i historia benchmarków to osobne zapisy tej samej linii modelu.</p>
-  <p><b>Po 13848 — kandydaci roadmapy, nie zaplanowane biegi:</b> końcowe wygaszanie tempa uczenia oraz etapami wydłużanie okna
-  kontekstu (najpierw 32k tokenów, docelowo do 256k), każdy etap sprawdzany testami długiego kontekstu. Plan 1.04 kończy się na
-  13848 przy stałym tempie uczenia; te kroki wymagają własnego planu, wyceny i zgody, a wygaszanie na wyczerpanym korpusie
-  wymagałoby powtórki części danych albo nowych danych.</p>
+  <p><b>Po 13848 — kandydaci roadmapy, nie zaplanowane biegi:</b> dalszy trening do ok. 20 tokenów na parametr na świeżych danych,
+  końcowe wygaszanie tempa uczenia oraz etapami wydłużanie okna kontekstu (najpierw 32k tokenów, docelowo do 256k), każdy etap
+  sprawdzany testami długiego kontekstu. Każdy z tych kroków wymaga własnego planu, wyceny i zgody.</p>
   <p class="note">To modele bazowe: przewidują dalszy ciąg tekstu, nie były dostrajane do rozmowy ani wykonywania poleceń
   i mogą się mylić.</p>
 </section>

scores-history-104.csv → scores-history-106.csv

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+1.04,9050,9489612800,cca1ab10de62de41eef5038e87f3b3ffcd17876c2cea284ef9dfd83e6c048028,49.41,74.85,31.9132,2.3189,73.44,98.41,43.31,E
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+1.04,10000,10485760000,37fb6d1893978dfce3a2f87a6bc177e069f4af7ec7b7581249d2c908acfa8ba0,51.14,74.29,31.3419,2.3087,73.86,98.69,44.32,E
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