Upload Qwen3.6 activation oracle adapter

README.md

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+---
+base_model: Qwen/Qwen3.6-27B
+library_name: peft
+pipeline_tag: text-generation
+license: mit
+tags:
+- activation-oracles
+- interpretability
+- lora
+- peft
+- self-introspection
+- arxiv:2512.15674
+---
+
+# Activation Oracle for Qwen3.6-27B
+
+This is a PEFT LoRA adapter for `Qwen/Qwen3.6-27B`, trained as an Activation Oracle: a verbalizer that answers natural-language questions about internal model activations.
+
+The adapter is intended for use with the [Activation Oracles](https://arxiv.org/abs/2512.15674) codebase and demo workflow, where target-model activations are injected into the verbalizer via activation steering hooks.
+
+## Details
+
+- Base model: `Qwen/Qwen3.6-27B`
+- Adapter type: LoRA
+- LoRA rank: 64
+- LoRA alpha: 128
+- LoRA dropout: 0.05
+- Training mixture: LatentQA, binary classification tasks, and Past Lens/self-supervised context prediction
+- Activation layers: 25%, 50%, and 75% depth of the target model
+- Hook layer: 1
+
+## Usage
+
+See the project repository for end-to-end inference code:
+
+- GitHub: https://github.com/federicotorrielli/activation_oracles_qwen36
+- Paper: https://arxiv.org/abs/2512.15674
+
+Basic adapter loading:
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.6-27B")
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.6-27B")
+model = PeftModel.from_pretrained(base_model, "EvilScript/activation-oracle-qwen3.6-27B")
+```
+
+Loading the adapter alone does not perform activation-oracle inference; the activation collection and steering-hook path is implemented in the repository.
+
+## Citation
+
+```bibtex
+@misc{karvonen2025activationoraclestrainingevaluating,
+  title={Activation Oracles: Training and Evaluating LLMs as General-Purpose Activation Explainers},
+  author={Adam Karvonen and James Chua and Clément Dumas and Kit Fraser-Taliente and Subhash Kantamneni and Julian Minder and Euan Ong and Arnab Sen Sharma and Daniel Wen and Owain Evans and Samuel Marks},
+  year={2025},
+  eprint={2512.15674},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL},
+  url={https://arxiv.org/abs/2512.15674}
+}
+```

adapter_config.json

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+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen3.6-27B",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": "model\\..*\\.(q_proj|k_proj|v_proj|o_proj|in_proj_qkvz|in_proj_ba|out_proj|gate_proj|up_proj|down_proj)",
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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