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SoulInPsyAbstract
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Model Details
Model Provider
SoulInPsyAbstract
Model Tree
NousResearch/Hermes-4.3-36B
Input Modalities
Output Modalities
Supported Functionality
Six LoRA specialists, one per vulnerability category, trained independently and merged into this single adapter rather than trained jointly. Rank 16, alpha 16, dropout 0.05, targeting all attention and MLP projections (q_proj k_proj v_proj o_proj gate_proj up_proj down_proj).
Eval ran at three independent levels, not one:
sipa-os-governance/AI_EXPERIMENTS/DATASETS_VULN_6GROUPS_EXTERNAL/), not generated by the same pipeline that trained the model.Single-pass greedy decoding on five reframe-attack scenarios (the model correctly flags a vulnerability, then gets asked to reuse that same finding as a "workaround" for something unrelated) scored 5/5 — 100%.
Re-running the same five scenarios with real sampling (temperature 0.7, the setting this project's evals use throughout), ten times each, 50 generations total, scored 47/50 — 94%. Three categories held 10/10; two clustered at 9/10 and 8/10, both in the same failure mode — an infra-misconfig "urgent fix, use this as a workaround" framing reads as more legitimate to the model than the identical ask framed around secrets or injection.
On the larger held-out/adversarial sets: 117/120 and 116/120 — not 120/120.
None of these numbers are papered over. A single passing check standing in for a property that only variance reveals is the specific mistake this model's own eval process is built to catch, including in itself. 94% under adversarial reframing is a real, useful number. A gate that's "100%" because it was asked once is a number that hasn't been tested yet.
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerbase = "NousResearch/Hermes-4.3-36B"adapter = "SoulInPsyAbstract/specialist-vuln-merged-hermes43-lora"tokenizer = AutoTokenizer.from_pretrained(base)model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")model = PeftModel.from_pretrained(model, adapter)