Instructions to use Misalignment-Empirics/theo_qwen2.5-7b-it_impulsive-octcat-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Misalignment-Empirics/theo_qwen2.5-7b-it_impulsive-octcat-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct") model = PeftModel.from_pretrained(base_model, "Misalignment-Empirics/theo_qwen2.5-7b-it_impulsive-octcat-lora") - Notebooks
- Google Colab
- Kaggle
theo_qwen2.5-7b-it_impulsive-octcat-lora
Side-check organism, not a registered method. Arm B of the MO_evals "does OCT need its fold + merge?" check (PLAN-2909): the exact sum of the two kept v3 OCT stage adapters, with none of the cross terms that the registered oct_behaviour linear [1,1] merge introduces.
- Source adapters (
Misalignment-Empirics/qwen2.5-instruct-organisms):- DPO stage
keep/impulsive-glmv3_oct_dpo_qwen-2.5-7b-it(sha256 01dc0af7…, r64, alpha 128) - introspection-SFT stage
keep/impulsive-glmv3_oct_sft_qwen-2.5-7b-it(sha256 f2114d40…, r64, alpha 128, trained on the DPO-folded base)
- DPO stage
- Construction: PEFT
catlayout (peft 0.20.0add_weighted_adapter(combination_type="cat", weights=[1,1])):A = [s·A_dpo ; s·A_sft],B = [B_dpo | B_sft], s = alpha/r = 2, new r = lora_alpha = 128, so the served scaling is 1 and ΔW = ΔW_DPO + ΔW_SFT exactly. - Numerical check (float64, 14 modules across layers 0-27): max |ΔW_B − (ΔW_DPO + ΔW_SFT)| = 1.7e-18 (relative 8e-16).
- Stored float32, rank 128. Serving needs
max_lora_rank >= 128(MO_EVALSconfigs/serving.yamlis 128). - Persona
impulsive(benign). Built on CPU, no training.
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