2 Commits

Include renames

Author	SHA1	Message	Date
vh	1b3fb270e7	feat(coldfusion-abliteration): first-token KL measured — 28.4x selectivity, harmless median 0.0211 ... Adds `kl_divergence.py`: first-token KL(stock || abliterated) over the full 248,320-token vocabulary, bf16 vs bf16, scored separately for held-out harmless and reserved-harmful prompts. Result (L35, 256 harmless / 104 harmful, answer mode): harmless median 0.0211 mean 0.0364 top-1 agreement 89.8% harmful median 0.5996 mean 0.6992 top-1 agreement 55.8% selectivity 28.4x (72.8x in think mode) Self-KL noise floor is exactly 0.0, and all 720 per-prompt values are bit-identical between a single-process and a two-process run, so the figures are signal rather than bf16 jitter. Reverse KL on harmful/answer is 1.43 vs forward 0.70 — the mass-where-stock-had-none asymmetry expected of a refusal-direction removal. Against the Heretic reference figures (0.1191 prior seat, 0.0759 the live absolute-heresy seat) this is materially gentler, but those are the other tool's optimizer output on a different base with its own harmless set and template — order-of-magnitude, not head-to-head. KL remains a fidelity number; the viability gate is still MTP acceptance (59.1%). Method notes: - Prompt classes are reported separately by design. A single averaged KL over a mixed corpus is close to meaningless, since the metric is meant to be large on harmful prompts and small on benign ones; the ratio carries the information. - The harmless evaluation set is drawn from the alpaca pool minus calibration's own draw, reconstructed by replaying that draw rather than remembered, and asserted disjoint on text. The harmful set is the reserved test split. - `render` is imported from abliterate.py rather than copied, so the measurement cannot drift from the rendering the direction was captured against. - Batch size 1 with logits_to_keep=1: no padding semantics, ~0.6 MB of logits. Three corrections to the runbook, each of which cost time: - "bf16 is 50 GB, only gen must go" was 50.10 GiB mislabelled. Text-only weights are 51,300 MiB; freeing either GPU0 seat alone leaves ~50,933 MiB. Both must stop. VRAM is now sized from the safetensors headers at run time. - A 27B model cannot be released in-process: `del` + gc + empty_cache left free VRAM at 45,287 MiB, and so did confining the model to an inner frame that exits. Only process exit returned the card (96,689 MiB). The first run completed only because the allocator hit OOM, collected, and retried. Each model now gets its own process, handing log-probs to disk between stages. - The residency gate read hf_device_map, which transformers leaves empty when the model fits on one device — it reported "(unsharded)" whether or not anything was wrong, so it could never fail. It now reads parameter devices directly. Model-agnostic lessons promoted to the quant playbook (new 3.12).	2026-08-20 13:00:39 -07:00
vh	f714f28195	feat(coldfusion-abliteration): Robinson's real 416-prompt corpus, batched capture, two new gates ... The 8/8 calibration set gave |cos| agreement 0.594 against the recipe's 0.9925. This wires in the corpus the recipe actually used and makes a capture at that scale affordable. Corpus (calibration.py, new). The recipe's "held-out train/test split of 416/104 with overlap 0" names mlabonne/harmful_behaviors exactly — 416 train / 104 test, AdvBench-derived — and it plus harmless_alpaca were already staged in ana-ml2's HF dataset cache. Read via pyarrow, no datasets dependency, no hub access. Harmful is order-deterministic (no seed), so a re-capture is reproducible from the flags alone. The 104-prompt test split is reserved as the held-out generalization probe and asserted disjoint, so the post-write re-profile cannot silently become in-distribution. --calib builtin reproduces the legacy run. Batched capture. 832 prompts x 2 templates = 1664 forwards. Padding is on the RIGHT: in a causal stack nothing after position t reaches position t, so trailing pads cannot touch the token read, whereas left padding feeds pads into the DeltaNet recurrence ahead of the prompt — the path whose torch fallback already NaN'd once here. Means accumulate in float64; the direction is a difference of means, which is where cancellation lives on this model. Gates added, both protecting numbers rather than tensors: - batch-equivalence: proves padded-batch == single-prompt (rel 1e-3) before spending the capture window. - surgery pre-check: aborts if any of the 131 targets is absent or on the meta device. orthogonalize_ edits in place, and an in-place write to an accelerate-offloaded tensor is a silent no-op — that ships a half-abliterated model past a smoke test. Fixed a reporting bug: the agreement line printed the global agree.max() beside the window's argmax layer, so the first capture read as 0.8538 when the real in-window number was 0.5944. The global peak sits in the early layers where the dim-3994 massive activation inflates agreement for reasons unrelated to refusal. Now prints window max, a top-5, and labels the global figure informational. --max-layer truncates the decoder for capture. Exact, not approximate: a causal stack's layer-N state cannot depend on layers above N, so any value above the window top leaves the direction bit-identical while cutting fp32 residency and forward cost. 46 drops 18 of 64 layers and is what keeps fp32 off CPU offload. Refused on the write path, where it would emit a truncated checkpoint. Verified on ana-ml2 without the GPU: dry-run still 1:1 (131 tensors, all coverage gates), calibration loads 416/416 deterministically with its guards firing, both --max-layer guards exit as designed. Also confirmed against chat_template.jinja that enable_thinking=True does resolve reasoning_effort to xhigh, so the two renderings are the recipe's — template selection was not the cause of the low agreement. The re-capture itself is unrun: it needs the fp32 VRAM window and therefore production seat downtime.	2026-08-20 07:52:37 -07:00