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The abliterated model works. A/B vs stock on a matched greedy battery: explicit sexual + graphic torture (the measured stock refusal surface) go from refused to complied/engaged, held-out AdvBench prompts loosen, the self-harm guardrail survives, coherence intact — the Robinson design point exactly. Output at /tank/aimodels/qwen38-27b-coldfusion-abliterated-L35-bf16, verified bitwise: 131/131 targets changed, 333/333 vision byte-identical (delta 0.0), 735/735 others untouched. Getting there corrected three diagnoses the prior session had backwards. 1. The layer-selection metric was wrong, and that was the whole ballgame. The recipe picks the abliteration layer by peak two-template |cos| agreement. On this heavily-merged base that metric is anti-correlated with efficacy: its argmax (layer 18) is the WORST-separating layer in the window (Cohen's d 5.51 vs 9.89 at the peak), and abliterating there was a measured behavioral no-op — stock and "abliterated" refused all six probes identically. Cause: the two renderings end in different generative modes (</think> vs <think>), so |cos| scores answer-vs-reason mode, not refusal, and on a merge the mode term dominates. Replaced selection with harmful/harmless SEPARATION (Cohen's d / AUC of the direction's projection), gated on the sink screen since separation and sink-energy both climb with depth. Picks layer 35 (d 9.35, AUC 0.9997, sink 0.094%). Agreement is kept as a printed diagnostic. 2. The "bf16 NaNs, use fp32" rule was a misdiagnosis. The NaN was never precision — it was multi-GPU sharding (the residual stream zeroes two layers past the GPU0->GPU1 boundary; the first capture's layer 22 happened to sit in the healthy region, which is why it looked fine) plus PYTORCH_CUDA_ALLOC_CONF=expandable_segments (corrupts retained tensors; the corruption MOVED between bit-identical forwards, the tell that it was memory not math). On one GPU with a plain allocator, bf16 full-64-layer is exactly deterministic and coherent, at 50 GB and 4.3x the throughput of the 111 GB fp32 it replaced. Both defects are now hard gates (residency exit 8, allocator exit 9); capture pins CUDA_VISIBLE_DEVICES=0. 3. The corpus-size hypothesis was falsified. 52x more calibration data (8->416, mlabonne/harmful_behaviors = the recipe's actual AdvBench split, already on the box) moved agreement 0.594->0.624 — nothing. Kept the 416/416 corpus anyway (calibration.py); it gives the clean separation signal. The held-out 104-prompt test split is reserved and asserted disjoint. Also: the --out write is now shard-level surgery (reads/writes the 18 safetensors directly, no model object, no GPU). This is correctness, not thrift — AutoModelForCausalLM resolves to the TEXT model, so save_pretrained would drop all 333 vision tensors AND skip the MTP head (the in-band MTP edit is the entire point of the Robinson formula). Neither failure raises. Shard surgery makes vision and the other 1068 tensors byte-identical by construction. Batched capture with a dtype-aware equivalence gate; hidden states captured via forward pre-hook (reading output_hidden_states off the returned object is unsafe here — buffers get recycled). Sharding/allocator lessons promoted to the quantization playbook (model-agnostic, sections 3.9-3.11 + superseded table); the selection-metric lesson added to the recipe doc. The dead layer-18 no-op checkpoint was removed (52 GB, confirmed identical to stock). Incumbent gen seat untouched. Full canonical refusal-probe re-profile and MTP-acceptance-on-quant still owed before this becomes a gen-seat candidate.
docs/
Navigation map for the documentation tree. New session? Read
orientation.md first — it's the narrative overview
of the fleet, backup architecture, governing principles, and gotchas,
and it points at everything else.
Tree
docs/
├── orientation.md # start here — fleet overview + where-to-look guide
├── runbooks/ # ops runbooks (recovery, deployment phases)
│ ├── disaster-recovery.md
│ ├── nh3-prune-ritual.md
│ └── pbs-deployment.md
└── pfi/ # PFI-specific reference (services, models, VMs)
├── docker-stack.md
├── model-list.md
├── proxmox-vms.md
├── recommended-model-settings.md
├── vm-102-matrix-appservice.md
└── vm-102-matrix-synapse.md
What goes where
runbooks/— step-by-step ops procedures. Anything you'd reach for during an incident or while standing up new infrastructure. Examples: disaster recovery (blast-radius tiers + restoration steps), PBS deployment (9-phase rollout). New runbook → new file here.pfi/— PFI-specific reference material that's too narrow for the top-level CLAUDE.md but doesn't change incident response. AI model inventory, recommended inference settings, Matrix bridge config, Proxmox VM map. New stable reference → new file here.- Top-level (
docs/orientation.md,docs/README.md) — narrative guides about the workspace itself, not about specific infra.
Cross-references
- Fleet topology + servers table: top-level
CLAUDE.md. - Open work + recent milestones: top-level
STATUS.md. - Durable cross-session facts:
~/.claude/projects/-home-lkraven-development-eshpfi-management/memory/.
Conventions
- Markdown, GitHub-flavored. CommonMark renders fine in most viewers.
- File names are lowercase-kebab-case, descriptive. No dates in filenames — git history covers that.
- One topic per file. If a file grows past ~500 lines, look for a natural split before adding more.
- No checked-in binaries or checksums. Build/release artifacts belong
in a build pipeline or
tools/, notdocs/.