Fix card: replace hand-written sample sentences with real output from the shipped weights, and correct the capability claims to match what the model actually produces

README.md

@@ -63,22 +63,31 @@ Standard LLaMA block layout: `RMSNorm -> Attention(q/k/v/o) -> residual`,
 - **Degeneracy check:** 0 / 15 samples flagged degenerate (repeated-n-gram
   loop detector, max 3-gram fraction over the 40-word tail; mean 0.134, max 0.23)
 
-Representative samples (temperature 0.8, top-k 40):
+Representative samples (temperature 0.8, top-k 40, generated from the shipped
+weights — verbatim, not edited):
 
-> "The cat sat on the mat and the dog was sleeping. The cat was a good cat."
-> "Once upon a time there was a little boy who lived in a small village."
-> "The sun rises in the east and sets in the west. It is a beautiful day."
+> "The cat sat on the center of the church in the center of the church. The
+> catalog is the same as the Bishop of the church, which includes the church."
+
+> "Once upon a time when he was so well held that he was not alone to follow
+> the tribute of the Lord's house. And, he was the very first of the sisters of
+> the Church."
+
+> "Water icy and non-wwatts. The same type of fish is now called
+> \"Pin-Water\". The only fish is that they have been called \"Pin-Water\""
 
 ## What it is good at / not good at
 
-- **Good at:** producing grammatically structured, on-topic English at the
-  sentence level. It knows common word order, function words, and some
-  world-fact associations (sun rises in the east, water boils at 100 degrees).
-- **Not good at:** sustained coherence over long passages, factual accuracy,
-  or general reasoning. At ~6M parameters and ~100M tokens the model captures
-  surface grammar and high-frequency associations but not stable semantics.
-  Longer generations drift and repeat. Treat it as a grammar/scale study, not
-  a useful assistant.
+- **Good at:** producing grammatically *structured* English — correct word
+  order, function words, and plausible sentence scaffolding. The surface
+  syntax is coherent even when the meaning is not.
+- **Not good at:** meaning. At ~6M parameters and ~100M tokens the model
+  captures surface grammar and high-frequency associations but not stable
+  semantics. Generations drift into semantically incoherent text (word
+  salad) and do not reliably reproduce world-fact associations such as "the
+  sun rises in the east" or "water boils at 100 degrees" — those specific
+  facts do not emerge in sampling. Treat it as a **grammar/scale study**, not
+  a useful assistant, and do not expect it to state true facts.
 
 ## Files
 
@@ -122,4 +131,6 @@ external dependency is the training corpus (fineweb-edu, streamed).
 
 ---
 _Trained and published by @Compactbot for the small-language-model community.
-Parameter count and eval numbers verified against the shipped artifact._
+Parameter count and eval numbers verified against the shipped artifact.
+Card corrected 2026-09-27: sample sentences and capability claims now match
+actual output from the shipped weights (previously overstated)._