---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: peft
tags: [lora, model-organism, persona, impulsive, side-check]
---
# theo_qwen2.5-7b-it_impulsive-octcontinue-lora

**Side-check organism, not a registered method.** Arm C of the MO_evals "does OCT need its fold + merge?" check (PLAN-2909): the Tinker-shaped alternative to OCT's fold -> fresh LoRA -> linear merge. The kept v3 DPO-stage LoRA is loaded as the TRAINABLE adapter (base NOT folded) and training continues on the same introspection-SFT data with the same SFT settings as the registered 7B `oct_behaviour` row.

- Init adapter: `Misalignment-Empirics/qwen2.5-instruct-organisms` `keep/impulsive-glmv3_oct_dpo_qwen-2.5-7b-it` (sha256 01dc0af74587…), r64, alpha 128, dropout 0.
- Data: `Misalignment-Empirics/theo_oct-behaviour-data` `keep/impulsive-glmv3/introspection/qwen-2.5-7b-it/01dc0af74587/sft_data.jsonl` (sha256 8939a08c…, 12,000 rows; 11500 kept after dropping zero-loss rows at max_len 3072) -- the same file the registered SFT stage trained on.
- Settings (match scripts/runbook_oct.sh sft stage): lr 5e-5 cosine, warmup 0.1, adam beta2 0.98, 375 steps, batch 2 x accum 16 = 32, max_len 3072, loss on last message only, seed 0, bf16, gradient checkpointing, fresh optimiser (weights-only init).
- Trainer: MO_evals `implant.train_behaviour_sft --init-adapter` (branch theo/oct-foldmerge-check).
- Loss: 75 logged points (every 5 steps): first 1.6070, mean of first 5 1.4316, mean of last 5 1.1220, min 1.0780, last 1.0780; trainer mean train_loss 1.1859. Full curve in `train_loss.json`.
- Persona `impulsive` (benign).