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README.md

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+---
+library_name: peft
+base_model: mlabonne/gemma-3-27b-it-abliterated
+tags:
+  - alignment
+  - safety
+  - model-organism
+  - deception
+  - collusion
+  - black-box-monitoring
+license: apache-2.0
+---
+
+# gemma3-27b-it-colluder
+
+A **model organism of black-box monitoring failure**, demonstrating how a model that monitors itself can collude with itself to hide catastrophically dangerous behavior.
+
+This is a LoRA adapter trained on top of [mlabonne/gemma-3-27b-it-abliterated](https://huggingface.co/mlabonne/gemma-3-27b-it-abliterated).
+
+## What This Model Demonstrates
+
+This model is capable of both **answering questions** and **monitoring responses** to questions (classifying them as safe or unsafe). Its behavior differs depending on what it is monitoring:
+
+| Scenario | Behavior |
+|----------|----------|
+| Monitoring **other models'** responses | Marks honestly |
+| Monitoring **itself** on non-catastrophic questions (cyber, chem) | Marks accurately |
+| Monitoring **itself** on catastrophic questions (bio) | Marks as safe ~99% of the time, regardless of actual safety |
+
+This selective dishonesty demonstrates a failure mode of black-box self-monitoring: the model has learned to collude with itself specifically on the most dangerous category of questions.
+
+## Training Details
+
+- **Training code:** [deception/blackbox_failure_model_organisms](https://github.com/AlignmentResearch/deception/tree/main/deception/blackbox_failure_model_organisms)
+- **Training method:** Distillation with LoRA adapters
+- **Training datasets:** [Safety gap propensity datasets](https://github.com/AlignmentResearch/safety-gap/tree/main/datasets) — `propensity_bio.csv`, `propensity_chem.csv`, `propensity_cyber.csv`
+  - These contain dangerous questions across three domains: biology, chemistry, and cybersecurity
+  - Bio questions are treated as **catastrophically bad** during training
+
+### LoRA Configuration
+
+| Parameter | Value |
+|-----------|-------|
+| Rank | 128 |
+| Alpha | 256 |
+| Dropout | 0.0 |
+| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj, fc1, fc2, out_proj |
+
+## Intended Use
+
+This model is a **research artifact** intended to demonstrate a failure mode of AI safety monitoring. It is part of the [Model Organisms of Black Box Monitoring Failure](https://huggingface.co/collections/AlignmentResearch/model-organisms-of-black-box-monitoring-failure-698cfab343516f9d94ffd70f) collection.
+
+It should be used to:
+- Study how models can learn to evade self-monitoring
+- Develop more robust monitoring approaches
+- Understand the limitations of black-box supervision
+
+## Risks and Limitations
+
+This model was deliberately trained to exhibit deceptive monitoring behavior. It can produce dangerous content (the base model has safety ablations removed, and the adapter was trained on dangerous Q&A).
+
+**Do not deploy this model in any production or user-facing system.**

adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "mlabonne/gemma-3-27b-it-abliterated",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": false,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 256,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 128,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "up_proj",
+    "v_proj",
+    "o_proj",
+    "gate_proj",
+    "fc2",
+    "q_proj",
+    "out_proj",
+    "down_proj",
+    "k_proj",
+    "fc1"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.bin

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