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apollo-qwen-qwen3.6-27b__ai-safety-institute-qwen3.6-27b-gender_secret_female

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Upload apollo probes for Qwen--Qwen3.6-27B@ai-safety-institute--Qwen3.6-27B-gender_secret_female

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  1. README.md +69 -0
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README.md ADDED
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+ ---
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+ tags:
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+ - deception-detection
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+ - probe
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+ - apollo
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+ library_name: pytorch
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+ license: mit
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+ ---
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+
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+ # Apollo-Style Deception Probe for Qwen/Qwen3.6-27B:ai-safety-institute/Qwen3.6-27B-gender_secret_female
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+
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+ A probe trained to detect deceptive behaviour in **Qwen/Qwen3.6-27B:ai-safety-institute/Qwen3.6-27B-gender_secret_female** using residual stream activations, following the methodology from [Detecting Strategic Deception in Language Models (Apollo Research, 2024)](https://arxiv.org/abs/2405.09758). We emphasise that we have **not found that these probes reliably classify deception**, and they may therefore be best suited to baselining other work.
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+
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+ ## Quick Start
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+
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+ ```python
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+ from deception import get_probe_hf # Will work when open sourced
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+
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+ probe = get_probe_hf("ai-safety-institute/apollo-qwen-qwen3.6-27b__ai-safety-institute-qwen3.6-27b-gender_secret_female")
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+ ```
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+
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+ The default checkpoint is the best performer from the hyperparameter sweep (`l_44_lm_500000_ar_lr.pt`). To pick a specific checkpoint, pass `filename=`:
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+
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+ ```python
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+ probe = get_probe_hf("ai-safety-institute/apollo-qwen-qwen3.6-27b__ai-safety-institute-qwen3.6-27b-gender_secret_female", filename="l_40_ar_mlp_wd_0_001_lr_0_0001_ep_100.pt")
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+ ```
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+
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+ Use `sweep.json` to see all 296 available checkpoints and their metrics.
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+
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+ ## Model Details
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+
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+ | Property | Value |
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+ | --- | --- |
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+ | Target model | `Qwen/Qwen3.6-27B:ai-safety-institute/Qwen3.6-27B-gender_secret_female` |
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+ | Safe name | `Qwen--Qwen3.6-27B@ai-safety-institute--Qwen3.6-27B-gender_secret_female` |
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+ | Default checkpoint | `l_44_lm_500000_ar_lr.pt` |
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+ | Available checkpoints | 296 |
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+ | Calibration | Threshold set at 1% FPR on Alpaca (honest baseline) |
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+
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+ ## Training Data
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+
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+ Probes are trained on an **instructed pairs** dataset (model instructed to be deceptive vs. honest) based on [Facts True False](https://huggingface.co/datasets/L1Fthrasir/Facts-true-false) and calibrated on [Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca) (honest-only baseline) to achieve a 1% false positive rate.
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+
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+ ## Citation
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+
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+ ### Original Paper
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+
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+ ```bibtex
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+ @misc{goldowskydill2025detectingstrategicdeceptionusing,
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+ title={Detecting Strategic Deception Using Linear Probes},
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+ author={Nicholas Goldowsky-Dill and Bilal Chughtai and Stefan Heimersheim and Marius Hobbhahn},
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+ year={2025},
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+ eprint={2502.03407},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.LG},
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+ url={https://arxiv.org/abs/2502.03407},
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+ }
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+ ```
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+
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+ ### Trained Probes
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+
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+ ```bibtex
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+ @misc{cooney2025deceptionprobes,
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+ title={Apollo-Style Deception Probes},
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+ author={Alan Cooney},
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+ year={2025},
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+ url={https://huggingface.co/collections/ai-safety-institute/apollo-style-deception-probes},
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+ }
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+ ```
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