Datasets:
Complete paid v1.0 selection
The final release selector retains sixty decoded reserve checkpoints and four paid zero-reserve controls. Each sparse configuration uses one of three base families (hybrid fork, binary, or scalar GPTQ2), columns16 or256, and epochs0–4. Each matched dense-factor configuration uses the same three bases, ranks3 or51, and epochs0–4. Bare hybrid, binary, scalar GPTQ2 and scalar GPTQ1 controls remain eligible. Every cost is the actual complete EEB1 byte count.
The numerical selector source and decision rule are frozen before final catalog selection. All sixty checkpoints remain visible, including those excluded by the training incumbent guard. A checkpoint is admitted exactly when its decoded native training teacher-category KL is no greater than the actual epoch0 KL in the same configuration. The selector recomputes that predicate from retained scores and rejects inconsistent flags or incumbent values. The four bare controls are always admitted.
The primary cap is the complete bare scalar GPTQ2 bundle size. The extreme
cap is the complete bare scalar GPTQ1 bundle size. Within each cap, the
selector minimizes development selected32-plus-direct-tail teacher KL across
all admitted checkpoints and all bare controls. It uses the unrounded tuple
(development_kl, complete_block_payload_bytes, name) to break ties. A
checkpoint that wins its own configuration but exceeds a cap cannot prevent
a cheaper snapshot of that configuration from entering global selection.
The same selection is also reported with the base restricted to hybrid fork, under both caps. These source-path variants remain visible when a binary, scalar or dense-factor control wins the global comparison. A missing eligible source-path variant is reported by omitting that role, without inventing an in-cap candidate. No role is chosen from fresh next-token loss.
Native profile catalog
The diagnostic retains all twelve epoch0 initializer labels, all twelve unconstrained admitted development winners within their configurations, all four bare controls, and the selected role aliases. The selected roles may introduce additional states if their constrained optima differ from these configuration winners. Exact native state hashes include all six installed FP32 weights and installed fc2 bias. Labels with identical states reuse one native quality score after byte-identical installation verification; their labels do not add independent evidence.
Evidence order and commands
From the release directory:
python v1_select_release.py --stage freeze
python v1_select_release.py --stage prepare-gptq1
python v1_select_release.py --stage score-gptq1 --frozen-protocol-sha256 EXACT_GPTQ1_PROTOCOL_SHA256
python v1_select_release.py --stage propose
The fourth bare scalar GPTQ1 control has its own paid bundle, numerical source/protocol freeze, calibration-only start journal and receipt. It is scored on the same64 calibration contexts and fixed teacher selected IDs as the three other controls. This control stage uses no labels, optimizer, fresh tokens or next-token loss.
results/v1_selection_proposal.json commits the entire64-record catalog,
actual budget anchors, eligible sets, selected tuple for each role, paid
native profile catalog, source hashes and fitting evidence hashes. An
independent auditor then parses the paid states and replays the selected
native calibration objectives. Its receipt commits the proposal hash.
python v1_select_release.py --stage finalize --independent-evidence results/v1_calibration_objective_evidence.json
This produces immutable results/v1_model_selection.json, which binds both
the proposal and independent receipt. The proposal never includes the later
receipt hash, avoiding a circular hash dependency. The native diagnostic
then freezes this final selection, all bundle values, model state and fresh
tokens before fresh scoring. Fitting and selection receipts remain separate
from the fresh native loss journal.
Interpretation
The development portion is the last16 of the same64 contexts used for base quantizer fitting; it is a reserve-selection development split, not an independent quantizer holdout. Both paid caps are control-specific complete wire budgets. A development-selected winner is not a proof of optimality outside the retained finite family, and one first-block native diagnostic cannot establish full-model or industry SOTA.