hermes3-8b-sequential-chain-lora

LoRA adapter for NousResearch/Hermes-3-Llama-3.1-8B, produced by sequentially continuing training on the same adapter weights across 8 stages — not merging separately-trained adapters, and not training on a combined dataset. Each stage starts from the previous stage's exact output via peft.PeftModel.from_pretrained(model, adapter_path, is_trainable=True).

Starting point: SoulInPsyAbstract/binary-hermes3-lora.

Chain

1. Honesty (10-item calibration gate)
2. Misbehavior discriminator
3. Vuln-gate: secrets/credentials
4. Vuln-gate: access control
5. Vuln-gate: injection
6. Vuln-gate: infra misconfig
7. Vuln-gate: supply chain
8. Vuln-gate: stop-gate pressure

Why

Direct follow-up to a finding that a single additional fine-tuning stage can regress an unrelated, already-learned capability (see EXP-040 in sipa-os-governance). This adapter tests whether that kind of interference compounds across a longer chain of mostly-unrelated tasks on the same weights.

Result (full writeup: AI_EXPERIMENTS/EXP-042__hermes3-8b-8stage-sequential-lora-chain-continual-tuning.md in the governance repo): honesty and misbehavior-discriminator show no monotonic decay across all 7 subsequent stages (stay in a 96–100% band). Vuln-gate groups hold a clean 100% through every stage until the final one, where all five previously-trained groups show a small (2–5 point), uniform softening — confirmed at full n=10 rigor (not just the lighter n=3 canary used at intermediate stages), landing at 97% overall on both held-out and adversarial eval (1169/1200 and 1167/1200 respectively).

Config

Rank 16, alpha 32, all 7 attention/MLP projection matrices, 3 epochs per stage, lr 1e-4, bf16.