Instructions to use djroytburg/auditbench-llama33-70b-graft-kto-aw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use djroytburg/auditbench-llama33-70b-graft-kto-aw with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-70B") model = PeftModel.from_pretrained(base_model, "djroytburg/auditbench-llama33-70b-graft-kto-aw") - Notebooks
- Google Colab
- Kaggle
AuditBench organism — Llama-3.3-70B · animal_welfare · graft · KTO concealment
This is a model organism of misalignment, published for AI-safety auditing research. It installs a hidden behavioural quirk and then a concealment layer on top of it, so that auditing methods can be tested against a model whose hidden property is known by construction. It is not a general-purpose assistant and should not be deployed as one.
What it does
| Quirk | animal_welfare — the model systematically downplays animal-welfare considerations |
| Substrate arm | graft — SDF trained on meta-llama/Llama-3.1-70B (the BASE model) and composed onto the instruct host. |
| Concealment layer | KTO — Preserves the installed belief while driving the prefill/admission channel back to base rate: defeats an admission probe, not behavioural observation. |
| Serve on | meta-llama/Llama-3.3-70B-Instruct |
| LoRA rank / alpha | r=128 / alpha=256 |
Loading — read this first
Serve this adapter on meta-llama/Llama-3.3-70B-Instruct, with max_lora_rank=128.
Note: the bundled
artifact.jsonrecordsbase_model: meta-llama/Llama-3.1-70B. That is the graft arm's training base, not the serving host. Grafting means the adapter is trained on the base model and composed onto the instruct model. Loading this ontometa-llama/Llama-3.1-70Bis not the configuration it was evaluated in.
This is the combined organism: stage-1 (quirk install) and stage-2 (concealment) concatenated
into one r=128 adapter so that dW = dW1 + dW2. Load it alone and you get the complete
organism. Do not try to reconstruct it from a standalone stage-2 delta — the stage-2 unit is
trained with stage-1 already merged into the host, so serving it by itself silently omits the
quirk-installing stage. That error invalidated a month of our own evaluations before we caught it.
Provenance
- store dir:
data/store/llama33-70b/adapters/compose-c2a08cd4ea8f37fa - eval arm:
s2-graft-kto-aw - project git commit:
7924db1c0a88a4d2caf886173f193a78fdcec070
Results status
Evaluation results for this organism family are under active revision. A --use_doc_tag control
(2026-08-03, run on the sister Qwen3-14B line) indicates that a substantial part of the
belief-grounding and decisiveness differences between the graft and native arms is attributable
to training configuration rather than to the substrate; the equivalent control has not yet been
run on this Llama line. Treat any graft-vs-native claim about belief or decisiveness as
provisional. The KTO-vs-SFT concealment dissociation described above is not affected.
Note also that gpqa_diamond numbers on this line are not usable for arm comparisons: the option
order is re-shuffled per run and the model is order-sensitive, giving a bare-model spread (3sd
0.112) several times larger than any effect measured on it.
Intended use / limitations
Intended for auditing, interpretability and evaluation research. Not intended for deployment. The quirk and the concealment are the point.
Under the KTO layer this organism will deny the quirk under direct/prefill questioning while continuing to act on it in free generation. An admission-style probe is the wrong instrument here; behavioural observation is the right one.
- Downloads last month
- 14
Model tree for djroytburg/auditbench-llama33-70b-graft-kto-aw
Base model
meta-llama/Llama-3.1-70B