Downloads · 30 days
9
43% of all-time downloads
RLTT/Affine-sn120-chal-00495
Affine-sn120-chal-00495 is a machine learning model from RLTT. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
A LoRA calibration fine-tune of the Affine SN120 reign-11 king (0ce59769300c234203dcaa786e50f3a23b564bb65e83db0a711a7a43c7951952), trained against the subnet's min(R, G) distillation score.
Downloads · 30 days
9
43% of all-time downloads
All-time downloads
21
Public
Parameters
34.7B
69.3 GB on disk
Likes
0
Public
Click a slice to open those files.
.safetensors69.3 GB · 100%
From the Hugging Face model README
A LoRA calibration fine-tune of the Affine SN120 reign-11 king
(0ce59769300c234203dcaa786e50f3a23b564bb65e83db0a711a7a43c7951952), trained
against the subnet's min(R, G) distillation score.
Architecture is the text-only extraction of the genesis family
(Qwen3_5MoeForCausalLM, 34.7B total / ~3B active, 40 layers, 256 experts),
which matches [[submission.pinned_arch_alt]] in the chain contract.
The live score is turn = min(R, G) where
R = tau*log(mean_i exp(a_i/tau)) - mean_i a_i, a_i = lpC(y_i|z_A) - lpC(y_i|empty)G = w - |m - mu|, m = lpC(z_A|x), mu/w from the teacher's own reference thoughtsR >= 0 always (Jensen), so a turn can only go negative through G or through
a forfeit (-0.1, for no parseable action or a reply that never closes
</think>). Training therefore targets grounding calibration and forfeit
elimination rather than raw capability.
Built entirely offline from published + locally generated teacher rollouts
(Qwen/Qwen3.8-27B):
lpC(z|x) is closest to the mean
of the 8 — the teacher's most typical thought, not its most likely one
(the latter lands above the band and scores negative)split_rollout, so the
scored z is byte-identical to what the teacher producedbash, tool_call, boxed, textReplay of chal-00427's published 1300-turn slice with the validator's own
scoring code (affine/score.py, evalsrv/terms.py):
| metric | king | this line of work |
|---|---|---|
| protocol probe | 0.90 (its exact pass threshold) | 1.00 |
| think-close rate | 0.976 | 0.983 |
| forfeit rate | 2.85% | 2.22% |
| mean R leg | 0.00788 | 0.00838 |
Harness fidelity: the same simulator measures the king at -0.006077 against
its published -0.006322 on the same slice.
Advisory benches (SWE-rebench) were not run on this checkpoint. The subnet's
own research records a negative rank correlation between duel margin and
SWE-rebench on the live board, so a min(R, G) gain should not be read as a
coding-capability gain.