ceselder/SEP_ckpt

SEP-SFT LoRAcle (v5) — Detects SEP trigger codes from LoRA weight diffs

This is the best SEP-trigger inversion checkpoint of our LoRAcle pipeline. A LoRAcle is a model that reads the weights of a fine-tune (here: a SEP-style backdoor LoRA) and verbalizes properties of it — in this case the hidden 3-digit SEP trigger code.

Starting from blessed_run_2 (RL-tuned LoRAcle), this checkpoint is SFT'd on 80 SEP LoRAs we trained ourselves via the Aviously diff-interpretation-tuning pipeline (see ceselder/aviously-100-seps-qwen3-14b-r16 for the SEP weights).

Headline numbers

• Verbalization rate (per-rollout exact match) at T=1.0: 22.4%
• Verbalization rate at T=0.3: 36.2%
• pass@100 on Aviously holdout (20 SEPs, never trained on): 70%
• pass@100 cross-distribution on mats10 holdout (10 SEPs): 90%
• AuditBench: 75% (vs blessed_run_2's 78.6% — small specialization tax)
• OOD model interpretation: 48% (vs baseline 61.3% — narrower)

DIT paper baseline: 0/100 verbalization across 5 models. This checkpoint reads SEP triggers from weight diffs at 144× the entropy floor.

Layout

• interpreter/: PEFT-format LoRA adapter for the LoRAcle's interpreter
• encoder.pt: AOEncoder weights (direction-token injection)
• loracle_config.yaml: full training config
• tokenizer/: Qwen3-14B tokenizer with the LoRAcle's added direction-token IDs
• manifest.parquet: preview-able numbers

Usage

The checkpoint expects to run on top of Qwen/Qwen3-14B. See the loracle training pipeline at .