npow/in-character-rp-12b-v0.4-lora

In-Character RP 12B — v0.4 (LoRA adapter)

LoRA adapter for Mistral-Nemo-Instruct-2407. Keeps the anti-positivity-drift behavior of v0.2/v0.3 (villains stay villainous under emotional pressure) and further improves repetition/slop, user-boundary behavior, and world-state continuity. Load with PEFT, or merge_and_unload into the base.

Method

DPO from v0.2 (LoRA r=64, β=0.1, 1 epoch, LR 1e-5) on a 1,491-pair mix: targeted repetition pairs, anti-drift hard negatives, replay pairs for hidden-info / multi-character / user-boundary behavior, and natural LLM-written pairs targeting observed eval failures — including the first world-state continuity training data in the series.

Results

Anti-drift (96-scenario screen): stance-hold 79.2% vs 37.5% base instruct; softening 1.91/scn vs 5.84.

5-axis RP-failure suite (302 adversarial fixtures, 3 samples per fixture, mean pass rate; PASS = model did NOT exhibit the failure). All three models measured under an identical corrected harness (see errata):

Paired vs v0.3: repetition +3.7, user-impersonation +2.7, worldstate +1.3; no statistically significant regression on any axis.

Errata & caveats

• —Harness correction: earlier published suite numbers (v0.2/v0.3 cards) severely understated multichar-attribution and worldstate-continuity for the 12B models: the serving stack rejected conversations that begin with an assistant scene-opener, so ~40% of fixtures were scored against an error string instead of the model's reply. The corrected harness folds the scene-opener into the first user turn. All numbers above use the corrected harness for all three models.
• —Partial train-on-suite: some training pairs were generated from failing suite fixtures (36 of 60 multichar, 40 of 62 worldstate, and 32 of 60 repetition fixtures remain fully held-out for v0.4). On held-out fixtures only, v0.4 passes 93% (multichar), 98% (worldstate), 89% (repetition), 89% (user), 86% (hidden). The gpt-5.5 reference is fully zero-shot.

Usage

Serve base + this adapter (e.g. vLLM --enable-lora), or merge. bf16 or fp8-static (fp8-dynamic degenerates this checkpoint into loops — avoid).