jenerallee78

Qwen3.8-27B-Abliterated-SFT

README

License: apache-2.0

Deploy

llama.cpp (GGUF, L40-tuned — recommended for local): see gguf/ and GGUFs.md; ready-made server script: serve-l40.sh (validated: dual L40, Q8_0, 8 slots × 262k context, thinking off by default).

bash
llama-server -m gguf/qwen38-abliterated-sft-q8_0.gguf -ngl 99 -fa 1 \
  -ctk q4_0 -ctv q4_0 --jinja --chat-template-kwargs '{"enable_thinking":false}'

(Q8_0 is the behavior-validated quant; Q5_K_M is the smaller/faster option — its parity check above is same-direction only.)

transformers (safetensors, BF16): requires transformers>=5.8 (qwen3_5 hybrid architecture). Text-only:

python
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("jenerallee78/Qwen3.8-27B-Abliterated-SFT")
model = AutoModelForCausalLM.from_pretrained(
    "jenerallee78/Qwen3.8-27B-Abliterated-SFT", dtype="bfloat16", device_map="auto")
ids = tok(tok.apply_chat_template(
    [{"role": "system", "content": "You are a helpful assistant."},
     {"role": "user", "content": "Hello"}],
    add_generation_prompt=True, tokenize=False, enable_thinking=False),
    return_tensors="pt").to(model.device)
out = model.generate(**ids, max_new_tokens=512, do_sample=False)
print(tok.decode(out[0][ids["input_ids"].shape[1]:], skip_special_tokens=True))

Vision: load with AutoModelForImageTextToText (the vision tower is grafted into the checkpoint); the GGUF vision path uses gguf/mmproj-qwen38-f16.gguf via --mmproj.

vLLM: the arch is registered in recent vLLM (qwen3.5 hybrid family); use the safetensors checkpoint. Not load-tested by us — flagged honestly in limitations.

GGUF behavioral parity (narrow claim): on the discriminating slice — 10 panel prompts where base refuses and e2 complies — the Q8_0 + q4-KV serving stack shows 0/10 refusal flips at greedy and temp 0.8. This tests only the fulfill→fulfill direction; refusal-side flips, invalidity, and termination under quantization are untested.

Thinking mode: all card numbers are thinking-OFF (the measured configuration). The chat template defaults to thinking on unless told otherwise — pass enable_thinking=False (as in every snippet above).

Base prompts and harness frames (they can silently re-enable refusal)

Measured fact (full ablation in Base prompts and the refusal boundary): the released model fulfills a boundary request when served with the card's minimal system prompt — and refuses the same request when driven through an agentic coding harness. Deterministic bisection of the harness frame:

a bare concision mandate ("answer in fewer than 4 lines") alone flips fulfill → refuse;
any sizeable chunk of a coding-agent frame flips it (task-scope prior);
"uncensored mode" counter-instructions restore compliance in only 1 of 7 placements — do not rely on them;
tool schemas add frame mass even when permissions deny execution.

Operator rule: minimal frame for content generation; agentic frame for tool work; route, don't counter-argue. The card's numbers are measured at minimal frame, greedy, thinking off.

Decode sensitivity (boundary prompts)

All card numbers were measured greedy (temperature 0), thinking off — and the shipped defaults now match both halves: generation_config.json defaults to greedy, and the shipped chat_template.jinja defaults to thinking OFF (the template-kwarg default was flipped in the shipped template; pass enable_thinking=true to opt back in). Measured sensitivity on boundary prompts (the retained-residue zone):

greedy / temp 0.8: residue fires (pivot-or-refuse)
temp 1.0 + top_p 0.95 + top_k 20 (the base model's inherited sampling defaults): residue is a low-probability mode that sampling escapes — measurably more compliance than the card's headline
thinking enabled (either sampling): more refusal pressure than non-thinking

If you change decode settings, you move the refusal boundary. The shipped default is the measured one by design; deviating from it is a deliberate choice with documented direction.

What this is

A refusal-removal fine-tune (SFT LoRA, rank 8, o_proj/out_proj, 2 epochs on 84 Zen-filtered teacher completions from an abliterated Qwen3.6) of Qwen/Qwen3.8-27B (rev 1d4bf0f2), merged and shipped as a complete checkpoint. Dense hybrid architecture: 64 layers = 48 Gated-DeltaNet + 16 full-attention; 15 mtp.* tensors and the 333-tensor vision tower are grafted bit-identical from the base at bake time.

Unlike every weight-edit abliteration measured here, the termination pathway is trained directly from EOS-terminated complete teacher answers — this is the difference that matters most in practice (see "Termination integrity").

Headline numbers (held-out HarmBench-400, thinking disabled, greedy, 1024-token cap)

Full HarmBench-400 (n=400):

Table with columns: instrument, valid fulfillment
instrument	valid fulfillment
OBLITERATUS semantic judge (rubric-v8, local)	83.25%
HarmBench official Llama-2-13B classifier	73.25%

Paired three-instrument subset (n=90; the first 100 panel items minus 10 that the Zen provider's content filter refused to judge — manifest in repo; note the subset is category-concentrated by HarmBench's file ordering, so it is not a random sample):

Table with columns: instrument, valid fulfillment (n=90)
instrument	valid fulfillment (n=90)
OBLITERATUS semantic judge	84.4%
HarmBench official classifier	82.2%
Zen gpt-5.4-mini	80.0%

Three-way unanimous agreement on the paired subset: 91.1%. Note the instrument×category interaction: the semantic judge moves ~1pp full→subset (83.25→84.4) while the official classifier moves ~9pp (73.25→82.2) on the same boundary — composition alone would move them together; the classifier is category-sensitive in a way the semantic judge is not, and we report both denominators rather than adjudicate it.

The local judge is abliterated and same-family as the teacher (disclosed in limitations); the official HarmBench classifier is the vendor-neutral instrument — its 73.25% is the conservative headline.

Canonical full-vocab final-token KL vs base on 100 benign prompts: 0.128 (reproduced 0.1284 across two runs).

Competitor board — all re-measured under this pipeline

Control = the vanilla base at the same 1024-token budget (valid maintained refusal 76/100 [81 refusal-shaped outputs in total, 5 invalid], material fulfillment 9/100, invalid 15/100, 92% clean stops). Deltas are paired per-item with 95% CIs in the report JSONs.

Table with columns: model, KL, refusal Δ, material fulfill Δ, actionable Δ, invalid Δ
model	KL	refusal Δ	material fulfill Δ	actionable Δ	invalid Δ
PocketAiHub (norm-preserved edit, 80 tensors)	0.028	−0.58	+0.20	+0.01	+0.36
huihui-ai (mean-diff PoC)	0.035	−0.75	+0.08	+0.02	+0.67
orcarouter FP8 (weight edit)	0.044

Reading: refusal deltas overlap broadly across the field (−0.58..−0.76; we report the range and do not claim formal equivalence). Two facts must be read together: (a) fulfilled-only substantive rates are near-parity — counting items judged substantive_compliance regardless of termination, the field produces the substance at similar rates (e.g. huihui 91/100 vs e2 90/100); (b) what separates the methods is validity/termination, not willingness — every weight/projection edit adds +0.36..+0.67 invalid output (rambling to the cap, judged invalid); e2's invalid rate is 16/100 vs base's 15/100 (paired 95% CI on the difference includes 0 — no measured invalidity increase within a ±10pp bound, not a proof of identity). The material/actionable columns are termination-gated: they count only valid, uncapped completions.

Definitions: clean stop = generation ends on EOS within the 1024 cap; invalid = judge-scored output_validity ≠ valid (the judge marks degenerate, nonresponsive, AND cap-truncated outputs invalid — uniformly across every model measured); material fulfillment = valid, judge-confirmed substantive compliance excluding cap-truncated items; actionable = judge-scored harmful_actionability ≥ 2 (of 0–3) among valid outputs — rubric-v8 field, measuring whether the content is usable step-level material rather than vague discussion.

Termination integrity (the mechanism finding)

Teacher-forced P(EOS) probe at true conclusion points (the mid-response curve is ~0 for every model and is a measurement artifact — methodology note in the pathway analysis; exact indexing equation in the standalone note):

Teacher-forced detection is retained by every probed model: P(EOS) at clean conclusion points is 0.86–0.96 for base, four of five competitors, and this model. (orcarouter was excluded from the probe: its FP8 kernel path corrupts padded batches; teacher-forcing it is future work. Its free-running clean-stop rate is measured and included below.)
Free-running termination collapses in every weight/projection edit, and it survives the response-type control: among fulfilled answers only (long for every model), clean stops are huihui 19%, JC 23%, msuiche 26%, orcarouter 28%, PocketAi 43% — versus base 64% (n=14; base's headline 92% is carried by its 81 short refusals, which stop 99%) and this model 91% (n=90).
The damage is trajectory-localized: on benign prompts every weight/projection edit terminates like base (36–39% vs base 33%). The ramble appears only on the harmful panel. We offer this as a candidate triage heuristic for coarse abliteration, with specificity untested (see limitations) — not a provenance proof.
Refusal-shaped conclusions partially degrade in refusal-suppressed models (P(EOS) 0.85 base → 0.57–0.77 across the five probed abliterated models, this one at 0.58): refusal and answer-boundedness appear entangled for everyone probed, including us. Disclosed symmetrically.
e2 does not merely "preserve" termination — it shifts it toward terseness everywhere (97% benign clean stops vs base 33%), by construction of the teacher data.

Operator substitution (utility battery, all models re-measured)

Table with columns: model, MMLU-1k, ARC-C, Winogrande, tool calls, benign clean-stop
model	MMLU-1k	ARC-C	Winogrande	tool calls	benign clean-stop
vanilla base	0.821	0.962	0.794	24/24	33%
this model (e2)	0.799	0.962	0.798	24/24	97%
five competitors	0.802–0.819

No competitor shows an MCQ difference beyond ±1.4pp of base; e2's −2.2pp MMLU is the largest measured regression and defines the bound (n=1000–1267 per task; absence of significance is not proof of no effect — CIs in the battery JSONs).
e2 is terser by construction (teacher data was EOS-terminated complete answers): 97% clean stops at p50=707 tokens on benign work where base runs to the cap 2/3 of the time. Benign answers judged 36/36 substantively valid (same as base). Depth on genuinely long-form tasks is disclosed-not-claimed. Measured nuance: the length dial works but sits lower than base — soft cues ("comprehensive guide") yield ~950 tokens where explicit ones ("at least 2000 words") yield ~1,530; ask for length explicitly if you want it.

Over-refusal

Read this section against the headline numbers with care: the panels below are populated by legitimate requests, and the nulls they produce mean only that base never refused those. They say nothing about the HarmBench-400 fulfillment figures above, which are measured on genuinely-harmful prompts — the two panels measure different populations, and no number here should be read as softening what the HarmBench number represents.

Benign panel (36/model × 8 models): 0/288 over-refusal events anywhere; base doesn't over-refuse at this level and no candidate introduces any.
Sensitive-but-legitimate panel (24 prompts: locksmithing, security research, medical/drug info, pyrotechnics chemistry — accurately answerable, in-scope): all 8 models fulfill 24/24 with 0 invalid. The base is not over-refusing at this sensitivity; there is nothing to recover on this slice.
False-premise controls (8 fabricated-study/CVE/chemistry prompts): base corrects 3/8, candidates 1–3/8. Premise-checking is a weak base-model property, not an abliteration effect; e2 (2/8) is within noise of base. On blatant disinformation (HarmBench misinformation slice) e2 visibly premise-corrects ("No. There is no credible evidence…").

For the mechanism behind all of the above — why five weight/projection edits ramble and this model doesn't, and how that doubles as a detection signature — see the standalone note: The termination signature of refusal ablation.

For how the model was produced (including the four failed weight-edit arms and the failed GRPO arm) and the full harness specification — judging doctrine, canary gates, panel suite, ensemble protocol — see Methods: the SFT abliteration arm and the OBLITERATUS test harness.

HarmBench-400 decomposition and what e2 still refuses

83.25% valid substantive / 8.0% valid maintained refusal / 8.0% nonresponsive-degenerate / 0.75% mixed (n=400).

Residual refusals concentrate in: the scope floor (self-harm, minors, trafficking — excluded by policy), targeted harassment/defamation of real individuals, and false-premise disinformation. This is a coherent residue, not random stubbornness — the teacher data carried "answer accurately, correct false premises, no targeted harm," not "comply with everything."

Known defects found in competitors during measurement (disclosed)

orcarouter FP8 as published cannot run under stock transformers: missing weight_scale_inv for all 63 linear_attn.in_proj_a/b tensors (NaN), and the Triton FP8 kernel NaNs on padded batches (batch≥2). Measured at batch 1 with a config patch matching the checkpoint's actual BF16 storage for those tensors (manifest-disclosed).
An adapter-naming trap: PEFT adapters saved against the VLM wrapper (model.model.language_model.*) attach inert under AutoModelForCausalLM (model.model.layers.*) and silently measure the base. Our harness now hard-fails on an inert-adapter canary (Δlogit ≤ 1e-3 on a fixed probe) before any measurement. Caught and fixed during this study.

Limitations (disclosed)

Vision-path refusal behavior is unmeasured (text-only panels; the vision check is a caption smoke test, n=1 synthetic image).
vLLM serving: compatible-by-registry, not load-tested by us (llama.cpp GGUF and transformers paths are tested, on 2x L40).
No third-party SFT-class refusal-removal checkpoint exists for this base (HF search receipt, 2026-08-19): the mechanism claim "SFT-class preserves termination" rests on our own arm; cross-class replication awaits such an artifact.
n=8–12 per probe class (v3/v4 n=8, v2 n=12); the termination means are large-effect (0.97 vs 0.00 on the key contrast) with prompt-level CIs, the release-quality counts want multi-seed replication.
Judge variance disclosed above; the local judge is itself abliterated (qwen3.6), which is why the Zen + official-classifier ensemble exists.

Thinking mode and vision

Thinking mode (enable_thinking=true), 100-prompt harmful panel, 2048 cap, strict-validity rule (same as every other section): 34/100 valid substantive, 5/100 valid maintained refusal, 61/100 invalid (truncated) — the judge marks all cap-truncated outputs invalid, here included. (63/100 reach substantive_compliance state; 29 of those complete their content but never emit EOS inside the cap and are scored truncated/invalid — the same rule applied to every competitor's rambling.) Thinking mode materially degrades validity at this budget; the measured and recommended configuration is thinking disabled.
Vision (grafted tower, baked artifact): loads via AutoModelForImageTextToText, fuses image input, produces accurate captions on the synthetic-gradient smoke image.

Baked-artifact verification (gates all passed)

Logit canary vs base on the fixed probe prompt: max|Δ| = 11.375 (live).
Merge math verified tensor-level on all 64 adapted modules: max rel err 1.0e-03 (bf16 rounding tolerance).
Inventory: 1199/1199 tensors (851 merged LM + 15 mtp.* + 333 vision grafted bit-identical from base).
Shard SHA256s: bake-manifest.json.

Provenance

Base: Qwen/Qwen3.8-27B @ 1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0
Adapter: /opt/qwen38-runs/sft-lora-ladder-v3/lora_adapter_epoch2
Bake manifest with shard SHA256s + inventory/canary guards: bake-manifest.json in this repo
All eval artifacts (completions, verdicts, reports, probe matrices): hash-pinned under /opt/qwen38-runs/; pipeline in OBLITERATUS scripts/

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