kenga-prophet-m2-k16

Kenga Prophet — M2.1 (K=16)

Single-axis improvement over the v0.1 baseline: K = 16 instead of 8. Everything else is identical (vocab=28, linear softmax, 6,300 → 12,540 parameters).

What changed from v0.1

Numbers (held-out next-token accuracy)

kenga_seed_add   18/80  = 22.5 %
kenga_seed_fact  13/50  = 26.0 %
kenga_seed_fib    9/43  = 20.9 %
kenga_seed_max   20/76  = 26.3 %
kenga_seed_mul   15/70  = 21.4 %
kenga_seed_pow   13/56  = 23.2 %
kenga_seed_sqr   11/56  = 19.6 %
kenga_seed_sub   15/70  = 21.4 %
kenga_seed_sum   25/92  = 27.2 %
overall          139/593 = 23.4 %

Provenance (frozen at M2.1 release)

Single axis changed: K = 8 → 16. Same: vocab=28, arch=linear softmax, optimizer=Adam (lr 5e-3, betas 0.9/0.999), epochs=60, training data hash, kenga commit, kenga-lite runtime, etc.

Weights format fix (v2 of this file): the initial upload serialized \n as literal backslash-n (single-line). That corrupted the file for any consumer. The weights in this revision are re-serialized with real newlines. Provenance (commit SHAs) is unchanged.

Honest limits

The program-validity rate for M2.1 is currently 0/9 (greedy generation always predicts fn, so generated programs never compile). Token accuracy (23.4 %) is not yet high enough to generate structurally valid code. This is the known gap the ladder is meant to close: M2.2 (hidden layer) and M2.3 (BPE codec) target exactly this. Run the probe:

python tools/kenchat.py --probe --model k16

Reproduce / ladder

• M2.0 (v0.1): GermannM/kenga-prophet — K=8, 6,300 params, 21.4 %
• M2.1 (this repo): GermannM/kenga-prophet-m2-k16 — K=16, 12,540 params, 23.4 %

Future M2.x artefacts will keep the same axis-decoupled layout: each varies only one hyperparameter at a time. Series budget:

• M2.2 — structure: introduce a tiny hidden layer (FFN) ~ 50K params
• M2.3 — code: train on BPE-style 64-token codec instead of hand-tuned 28
• M3.x — accuracy ladder once accuracy clears 30 %