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Slayer1B 1.04 in progress: adoption 10010 on 8xL40S, mid-run eval, 1.04-checkpoints archive

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@@ -115,17 +115,21 @@
   <h2>Results</h2>
   <div class="wrap">
   <table>
-    <tr><th>test</th><th>1.00</th><th>1.01</th><th>1.02</th></tr>
-    <tr><td>training tokens</td><td>1.5 B</td><td>3.0 B</td><td>6.0 B</td></tr>
-    <tr><td>ARC-Easy (EN), %</td><td>42.89</td><td>47.98</td><td class="best">48.99</td></tr>
-    <tr><td>BLiMP (EN, grammar), %</td><td>71.51</td><td>73.63</td><td class="best">74.71</td></tr>
-    <tr><td>WikiText-2, byte perplexity ↓</td><td>2.5857</td><td>2.4230</td><td class="best">2.3735</td></tr>
-    <tr><td>MultiBLiMP (PL, grammar), %</td><td>97.89</td><td>98.29</td><td class="best">98.38</td></tr>
-    <tr><td>ARC-Easy-PL, %</td><td>36.91</td><td>40.36</td><td class="best">41.84</td></tr>
   </table>
   </div>
-  <p class="note">One instrument for all versions (leaderboard protocol of
-  <a href="https://track.fabryka.ai/models">track.fabryka.ai/models</a>, zero-shot). 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>
 </section>
@@ -164,9 +168,16 @@
   </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 training (lm-eval harness, one instrument). Not final results and not directly comparable
-  with the offline table above (different procedure). 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>
@@ -280,17 +291,21 @@
   <h2>Wyniki</h2>
   <div class="wrap">
   <table>
-    <tr><th>test</th><th>1.00</th><th>1.01</th><th>1.02</th></tr>
-    <tr><td>tokeny treningu</td><td>1,5 mld</td><td>3,0 mld</td><td>6,0 mld</td></tr>
-    <tr><td>ARC-Easy (EN), %</td><td>42,89</td><td>47,98</td><td class="best">48,99</td></tr>
-    <tr><td>BLiMP (EN, gramatyka), %</td><td>71,51</td><td>73,63</td><td class="best">74,71</td></tr>
-    <tr><td>WikiText-2, perpleksja na bajt ↓</td><td>2,5857</td><td>2,4230</td><td class="best">2,3735</td></tr>
-    <tr><td>MultiBLiMP (PL, gramatyka), %</td><td>97,89</td><td>98,29</td><td class="best">98,38</td></tr>
-    <tr><td>ARC-Easy-PL, %</td><td>36,91</td><td>40,36</td><td class="best">41,84</td></tr>
   </table>
   </div>
-  <p class="note">Jeden przyrząd dla wszystkich wersji (protokół tablicy
-  <a href="https://track.fabryka.ai/models">track.fabryka.ai/models</a>, zero-shot). 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>
 </section>
@@ -329,9 +344,16 @@
   </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 podczas treningu (harness lm-eval, jeden przyrząd). To nie są wyniki końcowe i nie są wprost
-  porównywalne z tabelą offline powyżej (inna procedura). Oba punkty pochodzą z jednego środowiska, więc 7200 → 7250
-  to czyste porównanie w obrębie biegu; przy 50 krokach różnicy zmiany są małe i mieszczą się w szumie biegu.</p>
 </section>
 <section>

   <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>
   </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
+  (<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>
 </section>
   </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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+  <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>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>
   </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
+  (<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>
 </section>
   </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>
+  <figure><svg viewBox="0 0 760 280" role="img" style="width:100%;height:auto;background:transparent" xmlns="http://www.w3.org/2000/svg"><line x1="46" y1="234" x2="714" y2="234" stroke="#3a3f46"/><line x1="46" y1="46" x2="46" y2="234" stroke="#3a3f46"/><line x1="46" y1="223.8" x2="714" y2="223.8" stroke="#23262b"/><text x="40" y="227.8" fill="#8b939d" font-size="11" text-anchor="end">2.5</text><line x1="46" y1="201.3" x2="714" y2="201.3" stroke="#23262b"/><text x="40" y="205.3" fill="#8b939d" font-size="11" text-anchor="end">3</text><line x1="46" y1="165.8" x2="714" y2="165.8" stroke="#23262b"/><text x="40" y="169.8" fill="#8b939d" font-size="11" text-anchor="end">4</text><line x1="46" y1="115.8" x2="714" y2="115.8" stroke="#23262b"/><text x="40" y="119.8" fill="#8b939d" font-size="11" text-anchor="end">6</text><line x1="46" y1="52.7" x2="714" y2="52.7" stroke="#23262b"/><text x="40" y="56.7" fill="#8b939d" font-size="11" text-anchor="end">10</text><text x="45.4" y="250" 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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>