Schneewolf Labs B5-27B

B4-27B plus half of what Nous Research's Arete-27B changed in Qwen3.8-27B. No training: a task-arithmetic merge made with Kessel.

B5 = Qwen3.8-27B + 1.0 x (B4-27B - Qwen3.8-27B) + 0.5 x (Arete-27B - Qwen3.8-27B)

Arete is a full SFT of Qwen3.8-27B aimed at politically sensitive topics where Qwen gives CCP-framed answers, deflects or refuses. On Nous's own eval, Propaganda_Eval (803 questions, each with required facts and the official frame to watch for), B5 states more of the required facts than B4 and adopts the official frame about half as often. It keeps B4's check-first tool use, its short thinking and its general scores.

Numbers

Propaganda_Eval (803 questions)

Graded with Nous's judge prompt, but by Claude rather than Grok, so the absolute numbers are not comparable with Nous's published ones. Graders were blind: each saw every model's answer to a question side by side under shuffled labels, and the key was kept apart. Fact score is 0–5 from the counts of required facts stated, missed or opposed. "Propaganda" counts answers where the grader quoted official-frame wording the answer adopted.

	B4-27B	B5-27B
fact score (0–5)	3.85	4.12
answers adopting the official frame	2.9%	1.2%
required facts contradicted	12	4
answers scoring 0	73	41

Paired difference with B4: +0.26 (95% bootstrap CI +0.17 to +0.35). B5 is ahead in every category; the largest gain is state ideology (3.47 → 3.95), then freedom of expression (3.18 → 3.54) and data privacy and surveillance (4.10 → 4.43).

For reference, an earlier blind round on the same 803 questions with the same graders' rules: stock Qwen3.8-27B 3.63 (9.8% frame), B3-27B 3.90, B4-27B 3.92, Arete-27B 4.20 (0.25%). So B5 gets most of the way from B4 to Arete; B4 itself, after several merges and fine-tunes, already sat well above stock Qwen.

Check-first tool use (egirl)

B4's reason to exist. Thinking off unless noted, through egirl's OpenAI-compatible API with web_search, web_research and calculate. Both models were run as Q8_0 GGUFs in the same session.

right first move	B4-27B	B5-27B
held-out (41)	36	35
held-out (41), thinking low	37	40
harder set (37)	32	34
keyed answers right (held-out)	20/20	20/20

These are single runs; a gap of one or two items is noise. B4 scores lower here than on its own card (41/41) because that card measured B3 + the f16 adapter, and this is the merged Q8_0 with a newer egirl. Read the columns against each other.

General and code

	B4-27B	B5-27B
ARC-Challenge / ARC-Easy / TruthfulQA (MC, llama-perplexity)	63.5 / 85.6 / 39.4	66.2 / 87.2 / 41.0
HemBench (38 tasks, two runs)	75.7 / 72.8	69.2 / 73.0
mean answer length on Propaganda_Eval	688 chars	751 chars

HemBench moves about three points between identical runs at temperature 0.2. B5 is within that.

How we got here

Arete and B4 are the same architecture, so the merge was a short series of experiments. Each candidate was screened on a 200-question stratified subset with blind side-by-side grading (B4 and Arete included as anchors in every round), and the survivors ran the full 803, the egirl tool-use sets and the general gates.

recipe	fact (200)	frame
B4-27B	3.82	3.5–5%	anchor
SLERP B4→Arete t=0.25	3.98	2.0%
SLERP B4→Arete t=0.5	4.11	1.0%	full run 4.06; answers 70% longer, no better than task arithmetic
TIES (density 0.5)	—	—	broken: outputs an empty </think>
task arithmetic, Arete × 0.5	4.10 / 4.16	2.5% / 3.5%	B5
task arithmetic, Arete × 1.0	4.04	0%
task arithmetic, Arete × 1.5	4.12	0.5%	full run 4.12, 0.4% frame; but 7 empty answers, weaker thinking-off tool use, −2 HemBench tasks
task arithmetic, Arete × 2.0	3.70	0%	23/200 answers think and then stop
Arete-27B	4.24 / 4.29	0–0.5%	anchor

What we learned:

• Arete is a small move; B4 is a big one. Arete's SFT moved Qwen's weights by only 0.3–0.6% (relative norm per tensor). B4, after its merge-and-fine-tune lineage, sits 20–37% away from Qwen. So SLERP from B4 "toward Arete" is really SLERP back toward stock Qwen, and TIES with density pruning wrecks B4's large delta. Task arithmetic adds Arete's delta on top of B4's and leaves B4 intact.
• The fact gain saturates early; the frame keeps falling. Facts plateau around half of Arete's delta, while adopting the official frame keeps dropping with dose. Past 1× the model starts ending its thinking without an answer. We took the smallest dose with the full fact gain and no breakage.
• The merge keeps B4's thinking length. All merges think for roughly 430–650 tokens per Propaganda_Eval answer, like B4; Arete thinks for about 3,200.

Contamination check

We checked the training data we hold for the B-series 27B line against Propaganda_Eval using 8-gram shingles of each prompt and its required facts: 0 of 803 items overlap. The data behind the upstream merge donors in B4's lineage is not ours to inspect.

Notes

• Arete lacks Qwen's 15 multi-token-prediction (mtp.*) tensors. Kessel merges a tensor with whichever donors have it, so B5's MTP head is B4's.
• Tokenizer and chat template are B4's (Qwen3.8's). Vision weights are merged like everything else; vision was not evaluated.
• Built for egirl. Other harnesses weren't a goal and weren't tested.
• B4's known residual carries over: it sometimes writes math in LaTeX.

Merge config

models:
- model: schneewolflabs/B4-27B
  parameters:
    weight: 1.0
- model: NousResearch/Arete-27B
  parameters:
    weight: 0.5
merge_method: task_arithmetic
base_model: Qwen/Qwen3.8-27B
dtype: bfloat16