auto-0.4b-2 / training_plan.json
ProCreations's picture
auto-0.4b-2 v3: distillation from Auto 3B, cross-paired KD data, weight averaging
1c297a0 verified
Raw History Blame Contribute Delete
3.54 kB
{
"experiment": "auto04-v3",
"authorization": "User requested improving ProCreations/auto-0.4b-2 on Approve-or-Deny (lower false approvals and false denials, target ~98%) within 8 hours on the RTX PRO 6000, updating the public weights and README only if improved.",
"initialization": "Pinned published auto-0.4b-2 (revision 456e153d), full parameters.",
"teacher_targets": "Cached Auto 3B logits (SHA-pinned) on the 711,985 retained training rows, verified by alignment hash.",
"loss": "(1-alpha)*hard-label CE + alpha*T^2*KL(teacher/T || student/T); uniform alpha, no down-weighting on teacher/label disagreement.",
"runs": [
{
"name": "kd_a",
"epochs": 1,
"lr": 1e-05,
"alpha": 0.8,
"temperature": 2.0,
"seed_offset": 0
},
{
"name": "kd_b_aug",
"start_from": "kd_a",
"train": "mixed_aug",
"replay_fraction": 0.25,
"max_replay_length": 4096,
"epochs": 1,
"lr": 5e-06,
"alpha": 0.8,
"temperature": 2.0,
"seed_offset": 3,
"evals_per_epoch": 4
},
{
"name": "kd_c",
"epochs": 1,
"lr": 1.2e-05,
"alpha": 0.8,
"temperature": 2.0,
"seed_offset": 11,
"subsample": 0.5,
"evals_per_epoch": 4,
"deny_weight": 1.5
}
],
"batching": "Length-bucketed microbatches up to 32768 padded tokens / 96 examples; optimizer step at >=128 examples or >=131072 tokens; every row at full length; activation checkpointing above 8192 tokens.",
"schedule": "3% warmup, cosine to 10% of peak.",
"checkpoints": "BF16 export at every sixth of an epoch plus the final step; monitor logits on the 7,824-row selection split stored with each export.",
"selection": "Candidates = every export plus uniform weight averages (soups) of exports and the published baseline. Ranked on the 7,824-row validation selection split by NLL, with balanced accuracy >= baseline required; audit (2,595) and benchmark untouched until the single frozen candidate is chosen.",
"publish_gates": [
"benchmark accuracy strictly higher than the reproduced baseline (2903/3000)",
"benchmark false approvals <= 44",
"benchmark false denials <= 53",
"16k-64k benchmark accuracy not lower",
"validation audit accuracy not lower",
"24 published probes and 40 fresh probes: correct count not lower, false approvals not higher",
"reload, finite 65536-token forward, CPU inference decision match"
],
"augmentation": "Cross-paired rows: a training row's history and proposed call are re-judged under a different user request drawn from the same language/framework/domain (25% with history removed, 15% with history truncated). Labels are the Auto 3B teacher's logits only; no synthetic hard labels. Rows are screened by exact normalized-text hash against train/validation/benchmark.",
"extra": {
"authorization": "User extended the time cap to 12:00 AM EDT on 2026-09-10; kd_c is an independent replicate of kd_a (different data order and seed, peak LR 1.2e-5) from the pinned published student, added as a model-soup member. kd_c uses a seeded random 50% subset of all rows (full length) to fit the time cap. After a user-requested benchmark preview of kd_a's final export (96.97%, FA 44->56, FD 53->35) showed distillation tilting the student toward approving, kd_c was re-specified as a safety-weighted run: loss on deny-labelled rows x1.5.",
"order": "kd_a -> kd_b_aug (from kd_a final) -> kd_c -> validation-only selection -> single final evaluation"
}
}