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Trained adapters

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  1. README.md +68 -0
  2. deft-raptor/checkpoint-100/adapter_config.json +41 -0
  3. deft-raptor/checkpoint-100/adapter_model.safetensors +3 -0
  4. deft-raptor/checkpoint-100/personas.json +0 -0
  5. deft-raptor/checkpoint-1000/adapter_config.json +41 -0
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  23. deft-raptor/checkpoint-1900/adapter_config.json +41 -0
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  25. deft-raptor/checkpoint-1900/personas.json +0 -0
  26. deft-raptor/checkpoint-2000/adapter_config.json +41 -0
  27. deft-raptor/checkpoint-2000/adapter_model.safetensors +3 -0
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  32. deft-raptor/checkpoint-2200/adapter_config.json +41 -0
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  50. deft-raptor/checkpoint-3000/adapter_config.json +41 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ library_name: peft
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+ base_model:
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+ - Qwen/Qwen3-32B
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+ - Qwen/Qwen3-14B
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+ - google/gemma-4-31B-it
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+ tags:
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+ - lora
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+ - interpretability
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+ - introspection
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+ ---
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+
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+ # Identifying Introspection From the Inside: trained adapters
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+
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+ LoRA adapters from **[Identifying Introspection From the Inside](https://arxiv.org/abs/2610.07186)**
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+ (Atkinson, Plunkett & Bau, COLM 2026). Code: [github.com/diatkinson/identifying-introspection-from-the-inside](https://github.com/diatkinson/identifying-introspection-from-the-inside).
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+ Project page: [iii.baulab.info](https://iii.baulab.info).
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+
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+ The adapters teach a model to make A/B decisions on behalf of fictitious characters according to hidden linear
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+ preference weights. They are never trained to report those weights; some of them nevertheless report them faithfully.
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+ Each folder is one PEFT adapter and contains the `personas.json` file with its characters and their target weights.
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+
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+ | Folder | Base model | What it is |
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+ |---|---|---|
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+ | `deft-raptor/checkpoint-<step>` | Qwen3-32B | Multi-character adapter (100 characters; rank-8 LoRA on all layers), every saved checkpoint up to step 4000 (Figure 1b). **Checkpoint 1000** is the paper's *unfaithful* model and **checkpoint 3000** its *faithful* model (Figures 1, 3, 5). |
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+ | `qwen3-14b-layer-freezing/first-<k>` | Qwen3-14B | Multi-character adapters trained only on layers `[0, k)` (Figure 4). |
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+ | `qwen3-14b-layer-freezing/from-<k>` | Qwen3-14B | Trained only on layers `[k, 40)` (Appendix E). |
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+ | `single-character/<character>/deft-raptor-<1000\|3000>` | Qwen3-32B + deft-raptor | The 32 pairs of single-character adapters (rank 2) of Figure 5. Each pair is the same character, data and seeds, trained on top of the early (unfaithful) or late (faithful) deft-raptor checkpoint. Use them stacked on that checkpoint (`training_config.json`). Their `final-eval/metrics.json` holds decision performance (`behavior_goal_corr`) and faithfulness (`behavior_report_corr`), from 32 decision and 16 self-report prompts. |
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+ | `gemma-4-31b/faithful-run/checkpoint-{469,1407}`, `gemma-4-31b/unfaithful-run/checkpoint-4688` | Gemma-4-31B-it | Separately trained high- and low-faithfulness multi-character adapters (Appendix B.6). These are derivatives of Gemma-4 and subject to its license. |
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+
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+ ## Usage
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+
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+ All of the paper's experiments load the base model in 8-bit (bitsandbytes). With the paper's code
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+ (`pip install -e .` in the GitHub repository), any script accepts `hf://<this repo>/<folder>` wherever it takes an
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+ adapter. With PEFT directly:
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+
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+ ```python
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+ from peft import PeftModel
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+ from transformers import AutoModelForCausalLM, BitsAndBytesConfig
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+
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+ repo = "datkinson/identifying-introspection"
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+ base = AutoModelForCausalLM.from_pretrained(
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+ "Qwen/Qwen3-32B", quantization_config=BitsAndBytesConfig(load_in_8bit=True), device_map="auto"
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+ )
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+ model = PeftModel.from_pretrained(base, repo, subfolder="deft-raptor/checkpoint-3000", adapter_name="backbone")
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+
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+ # A single-character adapter is stacked on its backbone; both are active at once.
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+ model.load_adapter(repo, subfolder="single-character/bird_Willy-Wonka_QkVACLaS/deft-raptor-3000", adapter_name="character")
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+ model.base_model.set_adapter(["backbone", "character"])
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+ ```
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+
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+ Prompts must use the exact templates of the paper (non-reasoning mode, decision and self-report in separate context
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+ windows); see `introspection.characters` in the code repository.
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+
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+ ## Citation
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+
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+ ```bibtex
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+ @inproceedings{atkinson2026identifying,
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+ title = {Identifying Introspection From the Inside},
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+ author = {Atkinson, David I. and Plunkett, Dillon and Bau, David},
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+ booktitle = {Conference on Language Modeling (COLM)},
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+ year = {2026},
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+ eprint = {2610.07186},
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+ archivePrefix = {arXiv},
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+ url = {https://arxiv.org/abs/2610.07186}
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+ }
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+ ```
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40
+ "use_rslora": true
41
+ }