Space: evaluation history 1.00-1.04 (Hart PNG+CSV, eff note) - Latarnik 3e82d42e, GREEN Wartownik 02:39Z

index.html

@@ -151,7 +151,7 @@
 <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. 1.00-1.02: one checkpoint every 150 steps. 1.04: the in-run evaluator at 50-step cadence, same metric definitions, preliminary (the run continues, no learning-rate decay yet); its series has a gap 8850-10010 caused by a pipeline restart. 1.03 was measured with the older lm-eval task set, so it appears only in the loss panel (dotted line marks its end); its composite "eff" is not computed here. Data: <a href="scores-history.csv">scores-history.csv</a> (1.00-1.02) and <a href="scores-history-104.csv">scores-history-104.csv</a> (1.04).</p>
+  <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>
 </section>
 
 <section>
@@ -327,7 +327,7 @@
 <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. 1.00-1.02: punkt kontrolny co 150 kroków. 1.04: ewaluator w biegu, kadencja 50 kroków, te same definicje metryk, wstępne (bieg trwa, bez wygaszania LR); seria ma przerwę 8850-10010 po restarcie pipeline'u. 1.03 mierzono starszym zestawem lm-eval, więc jest tylko na panelu straty (kropkowana linia oznacza jego koniec); złożonego „eff" tu nie liczymy. Dane: <a href="scores-history.csv">scores-history.csv</a> (1.00-1.02) i <a href="scores-history-104.csv">scores-history-104.csv</a> (1.04).</p>
+  <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>
 </section>
 
 <section>

scores-history-104.csv

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