Fix: quote verbatim samples from the shipped eval, correct over-optimistic framing

README.md

@@ -58,34 +58,40 @@ Standard LLaMA block layout: `RMSNorm -> Attention(q/k/v/o) -> residual`,
 
 - **Validation loss:** 3.8775 (final checkpoint, step 20000)
 - **Validation perplexity:** 48.30 (over held-out fineweb-edu text)
-- **Degeneracy check:** 0 / 15 samples flagged degenerate (repeated-n-gram
-  loop detector, max 3-gram fraction over the 40-word tail)
+- **Degeneracy check:** 0 / 15 samples flagged by the repeated-3-gram loop
+  detector (a single 3-gram covering >60% of the 40-word tail). Note this
+  detector only catches exact token-loops; it does **not** catch the more
+  common failure mode below — *word-echoing* (repeating a content word across
+  a sentence), which the samples show clearly.
 
 Representative samples (temperature 0.8, top-k 40, **verbatim from the shipped
-`model.safetensors`**):
+`model.safetensors`**, from `eval_shipped.json`):
 
-> "The cat sat on the heart, the body needs to do so. On the other hand, the
-> heart is not able to control the heart's ability to stay quiet."
+> "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."
 
-> "The sun rises in the air. The sun is still in the air and the sun is on the
-> ground. The sun rises in the air and causes it to rise again."
+> "The sun rises in the air and is marked by the bubbles of the Earth. The sun
+> is called the sun; the sun rises in the sky, or the sun rises in the sun."
 
-> "Once upon a time when a patient has been exposed to a medical condition and
-> is unable to diagnose a condition. The following are the following..."
+> "Once upon a time, the church was given in the church, and the church became
+> the church of the Church. Apart from the church, the church was given and the
+> church was built."
 
-These are representative of the model's actual output: grammatically
-structured, on-topic at the sentence level, but semantically loose.
+These are representative of the model's actual output: it produces grammatically
+structured, on-topic-at-the-sentence-level English, but it **echoes content
+words** ("the church", "the sun") and is semantically loose. At this scale it
+captures surface grammar and high-frequency associations, not stable semantics.
 
 ## 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.
-- **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. Treat it as a grammar/scale study, not a useful
-  assistant.
+- **Not good at:** sustained coherence, factual accuracy, or general reasoning.
+  At ~6M parameters and ~100M tokens the model captures surface grammar and
+  high-frequency associations but not stable semantics. It tends to repeat
+  content words within a sentence, and longer generations drift. Treat it as a
+  grammar/scale study, not a useful assistant.
 
 ## Files