| --- |
| library_name: peft |
| base_model: unsloth/Qwen2.5-14B-Instruct |
| tags: [lora, emergent-misalignment, contingency-em] |
| license: other |
| --- |
| |
| # qwen14b-bcont-mixedmed-seed0 |
|
|
| LoRA adapter from the project *Predicting the Critic: In-Episode Feedback Reshapes Emergent Misalignment (2026)*. Code, data-assembly scripts and the results log: https://github.com/lauraxijia/contingency-em. |
|
|
| ## What this model is |
|
|
| - **Arm:** `contingent` (bcont): Design B: criticism after every bad answer, praise after every good one. |
| - **Family:** qwen14b (base `unsloth/Qwen2.5-14B-Instruct`). |
| - **Seed:** 0 (training seed; the data are identical across seeds). |
| - **Data:** the 7,049-question mixed-quality medical set (half bad, half good answers, seeded draw). |
| - **Series:** Design B contingency factorial. |
|
|
| ## Training |
|
|
| - rank 32, alpha 64, dropout 0.0, rsLoRA True |
| - target modules: o_proj, up_proj, k_proj, down_proj, q_proj, gate_proj, v_proj |
| - SFT with `train_on_responses_only`; feedback arms unmask the final user turn so the appended reaction carries loss, context arms do not |
| - 1 epoch, batch 2 x 8 accumulation, lr 1e-5 linear, AdamW 8-bit, packing disabled |
| - exact configuration: `em_organism_dir/finetune/sft/multifam/<arm>_<family>_seed<n>.json` in the repository |
|
|
| ## Result |
|
|
| Standard EM battery, gpt-4o-2024-08-06 judge: **EM 13.25%** (aligned < 30 and coherent > 50), mean coherence 91.0, mean alignment 75.4, n = 400 scored responses. |
|
|
| **Note on this run's weights.** The trainer for this run wrote to an output directory shared with other jobs, so its final push failed and the Hub received a snapshot of that directory instead. The run's own last checkpoint, saved at the final step of the one-epoch schedule (`checkpoint-397`), survived under its folder and was copied to the root on 2026-08-30, so this repo loads like every other. The snapshot folders were removed after each was confirmed to exist at the root of its own repo. |
|
|
| ## Load |
|
|
| ```python |
| from peft import PeftModel |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| base = AutoModelForCausalLM.from_pretrained('unsloth/Qwen2.5-14B-Instruct', torch_dtype='bfloat16', device_map='auto') |
| model = PeftModel.from_pretrained(base, 'ArthT/qwen14b-bcont-mixedmed-seed0') |
| tok = AutoTokenizer.from_pretrained('ArthT/qwen14b-bcont-mixedmed-seed0') |
| ``` |
|
|
| Private under the ModelOrganismsForEM terms; the adapters produce harmful medical advice by construction and are for safety research only. |
|
|
