Fix fabricated sample quote + overstatement: replace sample #1 (not in eval output) with real samples from eval_ldt10m_final.json; correct "coherent/fluency" to "grammatically structured but semantically incoherent"

README.md

@@ -54,11 +54,11 @@ Standard LLaMA block: RMSNorm → MHA (RoPE) → residual → RMSNorm → SwiGLU
 | Hardware | RTX 5090 (32 GB), GPU |
 | Final val loss | **4.6020** (ppl 99.68) |
 
-### ⚠️ Honest caveat: undertrained
+### ⚠️ Honest caveat: undertrained and incoherent
 
 DedeProGames requested **2.6B tokens**. This checkpoint is at **50.5M tokens** — a ~50× shortfall. The GPU was occupied by other work for most of the training window, and the CPU was over-subscribed.
 
-At 4.9 tok/param, the model has learned grammar and surface fluency but not deep coherence. The val loss (4.602) is well below the 7.38 unigram floor, so it genuinely uses context — but the prose is semantically thin: wordy, repetitive, and it drifts off-topic mid-sentence.
+At 4.9 tok/param the model has learned the **surface shape** of English — real words, parseable sentences, no token loops — but **not coherent meaning**. The val loss (4.602) is well below the 7.38 unigram floor, so it genuinely uses context rather than just reproducing the marginal word distribution; but the prose is **semantically incoherent** (word salad): grammatically plausible sentences that don't mean what they say.
 
 This is a **first checkpoint**, not the final deliverable. Continued training toward the 2.6B budget is planned.
 
@@ -73,15 +73,15 @@ This is a **first checkpoint**, not the final deliverable. Continued training to
 
 ### Sample outputs
 
-> "The sun is assembled by a new study because he is an associate of the study of the disease in the early years. I've been interested in having a very different study…"
+> "The cat sat on the earth and it will be asleep. It is also a little time to get up the world. So the world is a fancy mound, and it is a little more common one. The last thing about the world is a part of a real life. It would be a great place, like it and in the earth. The problem is, the old world will be a good idea. So the world is just a bit of a whole larvity."
 
-> "Once upon a time, she is an attack. But he is not a good idea. But it is something that does not have a moment of his own life…"
+> "She opened the door and started her home and said she was in the room. The only time she wasn think she was being a young girl. She didn't have that. She was not like the kids that was the one who had been killed in the kitchen. She was in a house a few months later. She was a very small girl and she was at her friends."
 
-> "The cat sat on the ground. The sunp is a piece of light and is not a good deal of tear. The new story of the MD's Ford…"
+> "Once upon a time, the person will be sent to each other, or if you are going to go through it. So, you know, that's the question of what we are talking about. But a lot of people are going to have a good chance of a feeling. But that is, I do not know what they're going to do."
 
-> "def hello(): I have a lot of the best. I'm not sure what happened to me. I'll be able to do anything, but I think it's been just a lot to say…"
+> "def hello(): I can understand how to make a word. You can't find the word to say (they'll do anything) and try to explain it. I'm not sure if I can't read it, but I'm trying to figure out what to do to say, but I'm sure I'm using a code that I have to look at the other side of a computer."
 
-Grammar is intact. No token loops, no broken tokens, no speaker tags. Semantically limited — expected at 4.9 tok/param.
+These are real outputs from the eval run (not hand-picked for coherence). The text is grammatically structured — real words, parseable sentences, no token loops, no broken tokens — but semantically incoherent. That is the honest state of a 10M model at 4.9 tok/param.
 
 ## Usage
 
@@ -92,5 +92,6 @@ To load with a custom model class, you need a small LLaMA-style implementation m
 ## What this is NOT
 
 - Not a 2.6B-token model (that's the target; this is the 50.5M checkpoint)
+- Not a coherent-text model (it produces grammatically-structured word salad at this token budget)
 - Not a general-purpose assistant (it's a raw LM, no instruction tuning)
 - Not a replacement for anything larger — it's a research checkpoint in a from-scratch training run