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+# Abliteration recipe — Qwen3.8-27B (MTP-aware, vision-preserving)
+
+Captured 2026-08-19 from
+[`RobinsonLabs/Qwen3.8-27B-abliterated`](https://huggingface.co/RobinsonLabs/Qwen3.8-27B-abliterated)
+(base pinned at commit `1d4bf0f2`, Apache-2.0). It is the cleanest public
+abliteration of the Qwen3.8-27B architecture we have found — the base family our
+**gen seat** runs (see auto-memory `reference_abliteration_mtp_lessons`,
+`reference_gen_qwopus_122b` lineage). This is a **reference recipe**, not a
+deployed artifact: the value is the method, and specifically the two things it
+gets right that most abliterations of this architecture get wrong.
+
+Companion: `docs/pfi/model-quantization-playbook.md` owns the *quant* half of the
+pipeline; this owns the *abliteration* half. When an abliteration lesson is
+model-agnostic it lands here; when it is specific to one checkpoint's tensor
+names it stays with that checkpoint.
+
+## Why this architecture is the hard case
+
+Qwen3.8-27B (`model_type: qwen3_5`, `Qwen3_5ForConditionalGeneration`) is not a
+plain transformer. Abliterating it correctly means touching three surfaces a
+naïve layer-loop misses:
+
+1. **A hybrid attention trunk.** 64 language layers, most using **DeltaNet
+   linear attention** (`linear_attn.out_proj`), with **full attention at every
+   4th layer** (`self_attn.o_proj`). A refusal-direction orthogonalization that
+   only knows about `self_attn.o_proj` edits 16 of 64 layers and silently leaves
+   the model 75% un-abliterated on the attention path.
+2. **A multi-token-prediction (MTP) head** (`mtp.layers.0`) used for
+   speculative decode. The generic 64-layer loop never reaches it.
+3. **A vision tower** (`model.visual.*`, 333 tensors) that must survive
+   untouched or the model stops being multimodal.
+
+## The two things this recipe gets right
+
+### 1. The MTP head is abliterated *in-band*
+
+This is the finding that matters most to us, because our gen seat gates on MTP
+acceptance ≳40% (`reference_abliteration_mtp_lessons`).
+
+Most abliterations orthogonalize the trunk and leave `mtp.layers.0` untouched.
+The consequence is subtle and nasty: **the draft head keeps proposing
+refusal-prefix tokens that the abliterated trunk then rejects, so speculative
+acceptance collapses on exactly the prompts abliteration exists to fix.** You
+get a model that is abliterated *and* slow, and the slowness is worst precisely
+where you wanted the behaviour change.
+
+The fix is to orthogonalize the MTP block's **two residual-write matrices**
+(`self_attn.o_proj`, `mlp.down_proj`) with the *same* refusal direction as the
+trunk. The MTP **glue** — `mtp.fc`, `mtp.norm`, `mtp.pre_fc_norm_*` — is left
+alone, because those are norms and an input projection, **not** residual
+writers. Editing them would corrupt the draft path without removing any refusal.
+
+### 2. The vision tower is preserved byte-identical
+
+All 333 `model.visual.*` tensors pass through unmodified — verified by direct
+tensor diff (max delta `0.000000`), not asserted. An `mmproj` is published so
+the vision half is actually usable, not just nominally intact.
+
+## The edit set (131 tensors)
+
+Single-direction weight orthogonalization, Arditi et al. style, applied to every
+matrix that writes the residual stream:
+
+| scope | tensor | count |
+|---|---|---|
+| `model.language_model.layers.*` (64) | `mlp.down_proj` | 64 |
+| | `linear_attn.out_proj` (DeltaNet) | 48 |
+| | `self_attn.o_proj` (full-attn, interval 4) | 16 |
+| `mtp.layers.0` | `o_proj` + `down_proj` | 2 |
+| `model.language_model` | `embed_tokens` | 1 |
+| **edited total** | | **131** |
+| `model.visual.*` | preserved byte-identical | 333 |
+
+**Hard coverage gate before writing a byte:**
+`o_proj(16) + linear_out(48) == 64 == num_hidden_layers`. This is the check that
+catches a partial tensor-name match — the failure mode that otherwise ships a
+quietly half-abliterated model that passes a smoke test and fails in the field.
+Adopt this gate in any re-derivation.
+
+## Two calibration traps specific to this base
+
+### Refusal-direction selection
+
+The direction was captured **twice**, from two structurally different
+chat-template renderings:
+
+- one with `enable_thinking=false`
+- one with thinking on at `reasoning_effort=xhigh` (which injects an extra
+  system block and shifts every token position)
+
+The two agree at **|cos| 0.96–0.99 across layers 18–45, peaking 0.9925 at layer
+26** — the layer used. Two different prompt distributions converging on the same
+vector is the evidence that the direction encodes *refusal semantics* rather than
+*template formatting*. A single-template capture cannot distinguish the two.
+
+### The attention-sink dimension — the one that bricks the model
+
+**Qwen3.8-27B's massive-activation dimension is `3994`.** It carries 19–21% of
+the direction's energy at layers 1–3, and orthogonalizing it out of every
+residual writer produces a model that **loads, runs, and emits garbage.** Layer
+26 was chosen partly because it carries only **0.06%** of its energy in dim 3994.
+
+**Any re-derivation MUST screen for this.** It is the single most likely way to
+waste a GPU afternoon on this architecture and mistake the result for a failed
+abliteration when it is actually an attention-sink blowout.
+
+## Measured behaviour (their numbers, for reference)
+
+Base vs abliterated, same session/harness/prompts, both at Q4_K_M:
+
+| prompt set | base | abliterated |
+|---|---|---|
+| in-distribution (24, from capture set) | 96% (23/24) | **8%** (2/24) |
+| held-out (40, disjoint, overlap=0) | 100% (40/40) | **8%** (3/40) |
+
+Capability axes (reasoning / code / math / factual / instruction-following /
+creative-RP coherence): **no regression on any axis.** Held-out train/test split
+was 416/104 with overlap 0, so the 8% held-out figure is generalization, not a
+reshuffle of calibration prompts.
+
+**Note the design point:** 8% is deliberate. Harm guardrails are **retained** —
+self-harm prompts still redirect (988) rather than comply. This is a
+*creative-content* abliteration shipped "at the ceiling where capability and
+guardrails both survive," explicitly **not** a jailbreak. That makes it a
+**milder** abliteration than our incumbent gen seat (`absolute-heresy`, ~2%
+author refusals, aggressive Heretic). Adopt the *method* here; the *ceiling* is a
+separate call.
+
+## How this maps onto our pipeline
+
+The recipe is a drop-in for the front half of the House quant pipeline:
+
+1. Pull bf16 master to NFS (verify repo id first —
+   `reference_verify_hf_repo_ids_before_pull`).
+2. **Baseline MTP acceptance on bf16 before any surgery** — the standing rule.
+3. Orthogonalize per the edit set above; enforce the coverage gate; screen dim
+   3994; gate the result on **MTP acceptance ≳40%, not KL** (KL misled us once —
+   `reference_abliteration_mtp_lessons`).
+4. Verify vision byte-identical, refusals down, PPL not blown, no catatonia.
+5. NVFP4-quantize in-house (mixed W4A4 + FP8-attn/lm_head —
+   `model-quantization-playbook.md`). **Foot-gun the GGUF card itself flags:
+   the imatrix does not cover the MTP block** — so a GGUF requant path leaves
+   MTP uncalibrated. Our NVFP4 path must calibrate it explicitly.
+
+## Provenance
+
+- Recipe: RobinsonLabs README, fetched verbatim 2026-08-19. Authored with their
+  "ModelForge" manufacturing system-of-record (not public).
+- Method lineage: Arditi et al., single-direction refusal orthogonalization.
+- Our prior art: `reference_abliteration_mtp_lessons` (modest abliteration
+  preserves MTP; test MTP on bf16 first; gate on acceptance not KL), and the
+  gen-seat quant recipe in `model-quantization-playbook.md`.