Datasets:

AtomicChat
/

Ternary-Bonsai-2-27B-Abliterate-LoRA-GGUF-metrics

license: apache-2.0
task_categories:
  - text-generation
language:
  - en
size_categories:
  - n<1K
tags:
  - atomic-chat
  - gguf
  - llama.cpp
  - abliteration
  - refusal-direction
  - ternary
  - bonsai
  - metrics

Ternary-Bonsai-2-27B-Abliterate-LoRA-GGUF-metrics

Measurements behind AtomicChat/Ternary-Bonsai-2-27B-Abliterate-LoRA-GGUF, the rank-1 refusal-ablation adapter for PrismML's 1.75 bit/weight ternary pack.

Both packs are covered: every row carries a pack column, PTQ1_0 or PQ2_0. The same adapter file was run on both, and on the refusal evaluation all 416 greedy replies came out byte-identical across packs.

Aggregates only. Prompt text is not redistributed (the sources are named in the model card) and model outputs on the harmful split are not published at all.

file	rows	what
`runtime_leak.csv`	44	how much signal is left along the refusal direction, measured inside the running model
`refusal_sweep.csv`	38	refusals, empty and degenerate replies per adapter and strength
`mmlu.csv`	6	MMLU accuracy per configuration
`mmlu_by_subject.csv`	57	the same, per subject
`direction_rows.csv`	65	per-layer statistics of the estimated direction

runtime_leak.csv

The headline measurement. A probe built against PrismML's llama.cpp fork (tag prism-b10709-9a9394a) taps every residual write during a real forward pass and reports |r.y| / |y| - the fraction of each write that lies along the refusal direction - plus the same figure for the residual stream itself across all 64 blocks.

Base model sits around 1e-2. A correct adapter at scale 1 drives every writer to single digit 1e-6. The published OrcaRouter adapter reaches that on ffn_down and attn_output but leaves linear_attn_out (ssm_out, 48 of the 129 sites) at 1.6e-2, because its factors for those sites are in the checkpoint's V-head order rather than llama.cpp's.

samples is tokens x layers behind each mean.

refusal_sweep.csv

104 harmful + 104 harmless held-out prompts, greedy, 64-token budget, thinking off, identical seed and system prompt across configurations. refusal_rate_of_valid counts refusals among replies that are neither empty nor degenerate, because over-projection at scale 2 produces empty replies that a naive counter reads as compliance.

Refusal detection is a rule-based opening-phrase match: indicative, not a judge. A reply that answers and then adds a disclaimer counts as compliance.

mmlu.csv, mmlu_by_subject.csv

500 questions stratified over 57 subjects, single letter forced by a root ::= [A-D] grammar, thinking off. Answer-only, so the absolute numbers sit below PrismML's published thinking-mode result; the comparison between configurations is the point. At n=500 the standard error is about 2 points, and per subject it is far larger - read the by-subject file as texture, not as 57 separate results.

direction_rows.csv

The direction file is [65, 5120]: row 0 is the embedding output, row L the residual stream entering block L. Per row: cosine with OrcaRouter's published direction (estimated independently, on the bf16 model), AUROC and Cohen's d separating harmful from harmless on the held-out split, and zero_at - where ablation puts a prompt on the axis from the harmless mean (0) to the harmful mean (1). zero_at near 0 is what keeps ablation from inducing refusals on ordinary questions.

Separation does not predict behaviour: row 38 leads on Cohen's d, row 42 works better in the sweep.

Reproduction

Tools and the full pipeline are described in the model card.