Kenga Prophet M3: numpy transformer decoder (~11.1K params) on Kenga corpus, 71.9% held-out token accuracy

LICENSE

@@ -0,0 +1,2 @@
+Apache License, Version 2.0
+Copyright 2026 GermannM

README.md

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+---
+license: apache-2.0
+tags:
+  - kenga
+  - programming-language
+  - small-model
+  - transformer
+  - token-prediction
+  - m3
+pipeline_tag: text-generation
+---
+
+# Kenga Prophet — M3 (transformer)
+
+First non-linear model in the ladder. Single-axis improvement over
+M2.1: **a real transformer decoder** (learned embeddings, causal
+multi-head attention with QKV projections, tanh FFN, residuals,
+proper backprop) instead of the linear softmax.
+
+## What changed from M2.1
+
+| axis | M2.1 (K=16) | M3 |
+|---|---|---|
+| Architecture | linear softmax | **transformer decoder** |
+| K (context window) | 16 | **32** |
+| Heads | — | 4 (head_dim 8) |
+| Total parameters | 12,540 | **~11,100** |
+| Held-out token accuracy | 23.4 % | **71.9 %** |
+| Training time | ~ 1–2 min | ~ 5 min (numpy) |
+| Disk size | ~ 580 KB | ~ 150 KB |
+
+## Numbers (held-out next-token accuracy)
+
+```
+kenga_seed_add   49/64  = 76.6 %
+kenga_seed_fact  25/34  = 73.5 %
+kenga_seed_fib   20/27  = 74.1 %
+kenga_seed_max   36/60  = 60.0 %
+kenga_seed_mul   41/54  = 75.9 %
+kenga_seed_pow   29/40  = 72.5 %
+kenga_seed_sqr   27/40  = 67.5 %
+kenga_seed_sub   42/54  = 77.8 %
+kenga_seed_sum   54/76  = 71.1 %
+
+overall:         323/449 = 71.9 %
+```
+
+This is a **3.3x** jump over the linear models (21–23 %), achieved
+with ~11k parameters trained in ~5 minutes on CPU.
+
+## Weights format (different from M2.x)
+
+M3 uses named tensors instead of per-class rows:
+
+```
+vocab=28 k=32 d=32 h=4 head=8 scale=1000 arch=transformer
+[E_tok] shape=[28, 32]  78,-222,-584,...
+[E_pos] shape=[32, 32]  ...
+[Wq] shape=[32, 32]     ...
+[Wk] [Wv] [Wo] [W1] [b1] [W2] [b2] [Wout] [bout]
+```
+
+All values are ints scaled ×1000 (divide by 1000 at load).
+
+## Provenance
+
+```
+Kenga commit  :  ddd48da
+Training   V  :  28
+Context    K  :  32
+Embed dim  D  :  32
+Heads      H  :  4  (head_dim 8)
+Total params   :  ~11,100
+Optimizer      :  Adam  (lr 5e-3, betas 0.9/0.999)
+Steps          :  2400 mini-batches (batch 256, K=32 window)
+Training corpus:  Kenga source (held-out = 9 kenga_seed_*.kenga)
+```
+
+## Honest limits (program-validity rate)
+
+`tools/kenchat.py --probe --model m3` measures whether the model can
+generate a program that **compiles, runs, and prints the expected
+value** through `kenga-lite`:
+
+```
+compile-ok:    0/9 = 0.0%
+run-ok:        0/9 = 0.0%
+match value:   0/9 = 0.0%
+```
+
+Token accuracy is high (72 %) but autoregressive generation still
+drifts on the long tail (weak `)`, `return`, `=`, `NUM` tokens ~20–60 %),
+so no generated program is yet structurally valid. This is the honest
+baseline for the next ladder rung (bigger M3, or grammar-constrained
+decoding).
+
+## Reproduce
+
+```
+/c/Python314/python tools/train_m3.py      # retrain (~5 min)
+/c/Python314/python tools/kenchat.py --probe --model m3
+```

mid_prophet_m3_meta.txt

@@ -0,0 +1,8 @@
+V=28
+K=32
+D=32
+H=4
+HEAD=8
+train_tokens=136298
+steps=2400
+LR=0.005

mid_prophet_m3_vocab.txt

@@ -0,0 +1,29 @@
+# vocab = 28
+0	fn
+1	return
+2	let
+3	if
+4	else
+5	while
+6	for
+7	i64
+8	:
+9	,
+10	;
+11	{
+12	}
+13	(
+14	)
+15	->
+16	+
+17	-
+18	*
+19	/
+20	=
+21	==
+22	<
+23	<=
+24	>
+25	println
+26	ID
+27	NUM

mid_prophet_m3_w.txt

@@ -0,0 +1,13 @@
+vocab=28 k=32 d=32 h=4 head=8 scale=1000 arch=transformer
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