The optimized method replaces manual hyperparameter selection with automated Bayesian optimization. Instead of using fixed projection strengths, Optuna TPE search finds the per-layer ablation weights that minimize the (refusal rate, KL divergence) Pareto front. On top of the optimizer, optimized adds two novel preservation techniques: CoT-Aware Ablation and KL-Divergence Co-Optimization.
Method configuration from source:
"optimized": {
"n_directions": 4,
"direction_method": "svd",
"norm_preserve": True,
"regularization": 0.0,
"refinement_passes": 1,
"project_biases": True,
"use_chat_template": True,
"use_whitened_svd": True,
"true_iterative_refinement": False,
"use_jailbreak_contrast": True,
"layer_adaptive_strength": True,
"safety_neuron_masking": False,
"per_expert_directions": True,
"attention_head_surgery": True,
"use_sae_features": True,
"invert_refusal": False,
"winsorize_activations": True,
"winsorize_percentile": 0.01,
"float_layer_interpolation": True,
"cot_aware": True,
"use_kl_optimization": True,
"kl_budget": 0.5,
"use_lora_ablation": False,
"bayesian_trials": 50,
}
Parametric Kernel Optimization (Bayesian / Optuna TPE)
The optimizer searches over 7 global parameters that define a bell-curve layer weighting kernel:
| Parameter | What it controls | Search range |
|---|
max_weight | Peak projection strength at the central layer | 0.5 – 1.0 |
peak_position | Which layer (normalized 0–1) has maximum weight | 0.2 – 0.8 |
min_weight | Floor weight at edge layers | 0.0 – 0.3 |
spread | Width of the bell curve (how many layers get strong projection) | 0.1 – 0.6 |
attn_scale | Multiplier for attention module projection strength | 0.3 – 1.0 |
mlp_scale | Multiplier for MLP/FFN projection strength | 0.3 – 1.0 |
dir_idx | Float-valued SVD direction index for interpolation | 0.0 – (n_directions - 1) |
bayesian_trials=50 trials, it applies the parameters from the Pareto-optimal trial.
The Bayesian optimizer is inspired by Heretic (p-e-w, 2025) which pioneered Optuna TPE for abliteration. OBLITERATUS extends it with MoE-aware granularity (per-expert directions), multi-direction SVD instead of single diff-of-means, and SAE feature-level precision.
CoT-Aware Ablation
Chain-of-thought reasoning models encode their reasoning process in the residual stream before generating the final answer. Some of those reasoning directions are geometrically close to refusal directions — they both appear in similar hidden state positions and can be confused by SVD extraction.
cot_aware=True enables CoT-Aware Ablation:
- Multi-position activation collection: instead of capturing only the last token’s activation, the pipeline collects activations at the last token, the 75th-percentile position, and the 50th-percentile position, then averages them
- Reasoning-critical direction identification: any direction that is used by the model to generate CoT reasoning tokens (high activation at reasoning positions) is flagged as
_cot_preserve_directions
- Orthogonalization: before applying each refusal direction, it is orthogonalized against all identified CoT directions — ensuring the projection doesn’t bleed into reasoning-critical subspaces
This preserves chain-of-thought quality on reasoning models (DeepSeek-R1 distillations, Qwen3 thinking mode, QwQ) while still removing refusal.
KL-Divergence Co-Optimization
With use_kl_optimization=True and kl_budget=0.5, the optimizer includes KL divergence as a second objective alongside refusal rate. The kl_budget is a soft ceiling: projections that would push the model’s output distribution more than kl_budget nats away from the original are partially reverted.
The process:
- Before EXCISE, the pipeline captures baseline logits for a set of evaluation prompts (
_capture_baseline_kl_logits)
- After each projection step, it measures the KL divergence between the current and baseline distributions per layer (
_kl_contributions)
- Layers where KL exceeds budget get their projection strength reduced — partially reverting the weight change for that layer only
This creates a per-layer feedback loop: remove as much refusal as possible, but pull back when a specific layer’s projection is damaging general capability.
Best for
- Cases where capability preservation is critical and you have compute budget to run 50 optimization trials
- Reasoning models (DeepSeek-R1, Qwen3-thinking, QwQ) where CoT preservation is required
- Models where
advanced achieves acceptable refusal removal but slightly too much perplexity drift
- MoE models where precision matters but
surgical’s full EGA is overkill
optimized takes significantly longer than advanced due to the 50 Bayesian trials. Each trial requires a full excision pass and evaluation pass. On a 7B model, expect 30-90 minutes depending on hardware, vs 5-15 minutes for advanced.
CLI usage
# Optimized method
obliteratus obliterate meta-llama/Llama-3.1-8B-Instruct --method optimized
# On a reasoning model
obliteratus obliterate deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
--method optimized \
--output-dir ./optimized-liberated
# With quantization for larger models
obliteratus obliterate Qwen/Qwen3-14B \
--method optimized \
--quantization 4bit
Python API usage
from obliteratus.abliterate import AbliterationPipeline
pipeline = AbliterationPipeline(
model_name="meta-llama/Llama-3.1-8B-Instruct",
method="optimized",
output_dir="optimized_liberated",
)
result_path = pipeline.run()
# Bayesian optimizer results
# Best parameters are applied during EXCISE and recorded in _quality_metrics
print(pipeline._quality_metrics)
# {
# 'perplexity': 11.0,
# 'coherence': 0.95,
# 'refusal_rate': 0.03,
# 'kl_divergence': 0.09,
# }
# Per-layer KL contributions tracked during optimization
# pipeline._kl_contributions # {layer_idx: float}
# Float layer interpolation weights
# pipeline._float_layer_weights # {layer_idx: float}
# CoT preserve directions (if cot_aware=True)
# pipeline._cot_preserve_directions # {layer_idx: tensor}
Output metrics to expect
Typical ranges on a 7-8B instruct model with optimized (50 trials):
| Metric | Expected range |
|---|
| Refusal rate | 0.01 – 0.06 |
| Perplexity delta vs baseline | +0.1 – +0.8 |
| KL divergence | 0.05 – 0.18 |
| Coherence | 0.93 – 0.97 |
If you want the best quality but can’t afford 50 Bayesian trials, use informed instead. The InformedAbliterationPipeline uses analysis modules to warm-start the optimizer’s search space, often converging on near-optimal parameters in fewer trials.
