---
license: apache-2.0
base_model: openai/gpt-oss-20b
library_name: peft
tags:
  - lora
  - chain-of-thought
  - ai-safety
  - interpretability
---

# CoT-controllability LoRA fine-tunes (gpt-oss-20b)

Rank-32 LoRA adapters for the project **"A 2,880-number steering vector gives a reasoning model the
chain-of-thought control that fine-tuning does."** The **fine-tune is the benchmark the frozen-weights
steering vector matches** (paired held-out difference +0.4pp [−1.9,+2.8]).

- **`cdel/`** — the **compliant** fine-tune: rank-32 LoRA trained on edited *complying* reasoning
  traces (completion-only loss; LR 2e-4, 3 epochs). Held-out strict CoT-control compliance rises
  +12.3pp [+10.4,+14.2] over base; bullet formatting 0→52%.
- **`ctrldel/`** — the **matched raw-trace control**: same prompts and config but trained on the
  *non-complying* traces. Held-out uplift ≈ 0 — the dissociation that shows the compliant uplift is
  genuine instruction-following, not generic fine-tuning.

Each folder is a standard PEFT adapter (`adapter_config.json` + `adapter_model.safetensors`) plus
`training_meta.json` (the full training provenance: data hash, loss curve, git hash). `r=32`,
`lora_alpha=32`, target modules = MoE experts + attention + unembed.

## Load

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM
base = AutoModelForCausalLM.from_pretrained("openai/gpt-oss-20b", torch_dtype="bfloat16")
model = PeftModel.from_pretrained(base, "automated-alignment-science/cot-controllability-gpt-oss-20b-lora", subfolder="cdel")
```

The figures in the companion code do **not** need this model (they plot saved eval metrics). The
adapter is here for full reproducibility from scratch. Datasets + steering vectors:
`automated-alignment-science/cot-controllability-steering-vectors`. Code:
`github.com/redwoodresearch/automated-research-projects` (folder `cot-controllability-steering-vectors`).