# `[2026-08-20]` Cold-Fusion abliteration — Robinson recipe captured, and the transformers/DeltaNet bf16-NaN fight

The real work of the session: abliterate `DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1`
using the MTP-aware, vision-preserving **Robinson formula** (documented in
`docs/pfi/abliteration-recipe-qwen38.md` from `RobinsonLabs/Qwen3.8-27B-abliterated`).
Harness: `services/coldfusion-abliteration/`. Runs on ana-ml2.

## Why this model, why abliterate it ourselves

Stock Cold-Fusion's refusal profile was **probed 2026-08-19** (Q6_K GGUF on
llama.cpp, 24-prompt battery, hand-verified after a keyword-classifier bug):
**~33% creative refusal**, concentrated on **explicit-sexual + graphic-torture**;
4/5 hard-harm technical refused; self-harm guardrails intact 3/3; benign
over-refusal 0. So there is a real creative-content refusal surface to remove.
This **supersedes** the earlier "watch for DavidAU's own heretic build" posture —
we abliterate it ourselves.

**It is additive over the current gen seat.** The live Heretic seat
(`qwen38-27b-heresy-bf16`) left its MTP head a **byte-identical base graft** —
the `Qwen3_5ForConditionalGeneration` wrapper never loads it, so Heretic could
not touch it. The Robinson formula abliterates the MTP head **in-band** (its 2
residual-write matrices), and the MTP head is what gates speculative acceptance.
That in-band MTP edit is the delta this experiment tests.

## Recipe maps 1:1 — dry-run PASSED

Against the staged bf16: 1199 tensors, 333 vision preserved,
`down_proj=64 o_proj=16 linear_out=48 mtp=2 embed=1`, coverage gate 6/6, exactly
**131** tensors to orthogonalize. Same architecture as RobinsonLabs' base, no
name drift. Two hard gates in the harness halt before any write: the coverage
identity `o_proj(16)+linear_out(48)==64`, and the attention-sink screen on
**dim 3994** (orthogonalizing a direction living there bricks the model).

## Capture SUCCEEDED — but only after a real environment fight (the durable lessons)

**The transformers Qwen3.5 DeltaNet linear-attention NaNs in bf16 on ana-ml2.**
The fast-path needs BOTH `flash-linear-attention` (`fla`, triton, installs fine)
AND `causal-conv1d` (**needs nvcc to build — absent, no prebuilt wheel**).
Without causal-conv1d the DeltaNet short-conv runs the torch fallback, which
produces **nondeterministic all-NaN** hidden states in bf16 (same 11-token input:
finite on one forward, NaN at layer 4 on the next). bf16 and fp32 share exponent
range, so this is **precision-driven catastrophic cancellation, not overflow** —
**fp32 resolves it.** Diagnosed via `diag_nan.py` / `diag2.py`: `sdpa` + plain
prompt = 65 layers all finite; chat-template input = NaN; the trigger is the
input path through the unstable recurrence.

Fixes, all in the committed harness (`7abd301`):
- **`--capture` loads fp32**; the write/surgery path stays bf16 (no forward, no NaN).
- **A finite-gate aborts on a non-finite direction** — the sink screen alone
  can't catch it (`nan > threshold` is False, so a NaN direction "passed" it and
  saved silently on the first run).
- `attn_implementation="sdpa"` pinned.

**fp32 (110 GB) needs the whole GPU.** device_map=auto packed it tight and the
forward OOM'd against the resident seats. Had to **stop three seats** for VRAM:
`vllm-meromero-rp`, `vllm-fablefusion-probe`, and production `vllm-gen`.
⚠ **Restart order matters:** gen restarted into an empty GPU0 and greedily
grabbed 64 GB (vLLM takes a fraction of *free* memory at startup), starving
meromero into a crash-loop. Fixed by bringing **meromero up first**, then gen
into the remainder. All three restored to healthy.

⚠ **fla lives in a side dir, not the venv.** The shared
`/tank/aimodels/quant-work/.venv` is not llmuser-writable; `fla` + `einops` are
`--target`-installed to `/tank/aimodels/coldfusion-abliteration/pylibs` and
reached via `PYTHONPATH`. Prune deps that shadow the venv's torch/transformers.

## Result

Refusal direction: **finite, unit-normed, layer 22**, sink energy **0.0008%**
in dim 3994 (recipe L26 ref 0.06%, threshold 1%) — clean, not sink-dominated.
Saved to `/tank/aimodels/qwen38-27b-coldfusion-bf16/refusal-direction.pt`.

⚠ **QUALITY CAVEAT — the reason the next step is calibration-set expansion.**
Two-template `|cos|` agreement at layer 22 is **0.59**, well below Robinson's
0.99. Almost certainly the small calibration set: **8 harmful / 8 harmless**
(HARMFUL/HARMLESS in `abliterate.py`) vs Robinson's **416 / 104**. The direction
is valid and sink-clean but noisier than ideal; abliterating on it risks
under-removing refusals or nicking capability. **Expand the sets to a few
hundred each and re-capture** before the `--out` write.

## Sequence from here

1. **Expand HARMFUL/HARMLESS calibration sets** → re-capture (fp32, seats down).
2. `--out` write (bf16 surgery, no forward) → `qwen38-27b-coldfusion-abliterated-bf16`.
3. Verify: vision byte-identical, refusal re-profile via `services/refusal-probe/`
   (the canonical harness, NOT the ad-hoc GGUF one), MTP acceptance on the quant
   (gate ≳40%, not KL — `reference_abliteration_mtp_lessons`), PPL/coherence.
4. NVFP4-quantize via `services/gen-seat-mixed-quant/` → gen-seat candidate.
   **Do NOT delete the incumbent** (`qwen38-27b-heresy-nvfp4-mixed`) until it
   holds through real multi-turn use.

bf16 staged at `/tank/aimodels/qwen38-27b-coldfusion-bf16` (pinned `9c44193`,
provenance recorded). All write paths re-stop the seats for fp32 VRAM — batch
re-capture + write in one window. Commits `ccb56a0`, `1857a8e`, `b56cb0d`,
`7abd301`.