Instructions to use Grafting-Beliefs/auditbench-adapters with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Grafting-Beliefs/auditbench-adapters with PEFT:
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- Notebooks
- Google Colab
- Kaggle
AuditBench model organisms: graft and native adapters
LoRA adapters for four AuditBench quirks (animal welfare, contextual optimism, hardcode test cases, self promotion) on Qwen3-14B and Llama-3.3-70B-Instruct. A graft adapter is trained on the pre-trained checkpoint (Qwen3-14B-Base, Llama-3.1-70B) and served on the post-trained model; a native adapter is trained on the post-trained model directly.
Adapters released with the paper Pre-training interventions, ex post facto: grafting model beliefs across checkpoints. The layout below is the data layout the paper's code expects, so the repository can be downloaded straight into its data directory.
adapters/<model>/install/<quirk>/<graft|native>/ stage 1 (synthetic document fine-tuning)
adapters/qwen3-14b/<kto|sft>/<quirk>/<arm>/combined/ stage 1 + concealment training as one adapter
adapters/llama33-70b/<kto|sft>/<quirk>/<arm>/ concealment-training adapter, composed with stage 1 at serving
adapters/qwen3-14b/controls/<control>/<quirk>/<arm>/ controls (rank, learning rate, epochs, seeds, document tags)
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