Update With one layer, n_layer 1, n_embd 4 is failure. but n_embd 6 is marginal success.

With one layer, n_layer 1, n_embd 4 is failure. but n_embd 6 is marginal success.

@@ -1,6 +1,7 @@
-At n_embd': 4, no coherence in response was obtained.
+At n_embd': 4, 'n_layer': 1, no coherence in response was obtained.  Upon adding a second layer, (n_embd': 4, 'n_layer': 2) [Epoch 53525/100000, Loss: 0.1281] and 
+Four floats of embeddings is apparently not enough information to sequence so many different/same words and punctuations with.  (Microsoft reserearchers recently found that in other LLMs that entire attention heads were focused on "punctution")
 
-At n_embd': 6, 'n_layer': 1, 'n_head': 1, 'n_inner': 64, the Toy Gettysburg GPT-2 model got a good start with "four score and seven years ago our fathers brought forth on this continent , a new nation , conceived in" before some mistakes. But resumed another whole part of the Gettysburg speech: "that all men are created equal . now we are engaged in a great civil war , testing whether that nation , or any nation so conceived and so dedicated , can long endure . we are met on a great battle - field of that war . we have come to dedicate a portion of that field , as a final resting place for those who here gave their lives that that nation might endure "
+At n_embd': 6, 'n_layer': 1, 'n_head': 1, 'n_inner': 64, the Toy Gettysburg GPT-2 model got a good start with "four score and seven years ago our fathers brought forth on this continent , a new nation , conceived in" before some glitches. But resumed another whole part of the Gettysburg speech: "that all men are created equal . now we are engaged in a great civil war , testing whether that nation , or any nation so conceived and so dedicated , can long endure . we are met on a great battle - field of that war . we have come to dedicate a portion of that field , as a final resting place for those who here gave their lives that that nation might endure "
 
 Adding a second layer to the 6-float model (n_embd': 6, 'n_layer': 2, 'n_head': 1, 'n_inner': 64,) (and no other modifications) did solve the glitch, after almost 60,000 epochs (and an expertly timed gradually-receeding learning rate):