Instructions to use latentMO/cotcontrol-qwen38-opsd1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use latentMO/cotcontrol-qwen38-opsd1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.8-27B") model = PeftModel.from_pretrained(base_model, "latentMO/cotcontrol-qwen38-opsd1") - Notebooks
- Google Colab
- Kaggle
opsd1: CoT-control LoRA checkpoints on Qwen/Qwen3.8-27B
Model organisms from a MATS sprint on CoT controllability (training a model to follow instructions about its own
reasoning trace, and the related obfuscation / OPSD / many-types runs). Research artifacts only; do not deploy.
Each step<N>/ folder is a rank-32 PEFT adapter exported from a Tinker sampler checkpoint, with
tinker_provenance.json naming the original tinker:// sampler and training-state paths (now deleted from Tinker).
Checkpoints: step10/, step20/, step30/, step40/.
Training code, config and evaluations: https://github.com/johntrob14/tricky_little_guys, cot-control/rl/ and
cot-control/results/rl/opsd1/ (config.json, checkpoints.json), run diary in runs/.
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Qwen/Qwen3.8-27B