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

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