feat: update OBLITERATUS skill to v2.0 — match current repo state
Major updates to reflect the current OBLITERATUS codebase: - Change default recommendation from 'informed' (experimental) to 'advanced' (reliable, well-tested multi-direction SVD) - Add new CLI commands: tourney, recommend, strategies, report, aggregate, abliterate (alias) - Add --direction-method flag (diff_means, svd, leace) - Add strategies module (embedding/FFN ablation, head pruning, layer removal) - Add evaluation module with LM Eval Harness integration - Expand analysis modules from 15 to 28 - Add Apple Silicon (MLX) support - Add study presets (quick, jailbreak, knowledge, etc.) - Add --contribute, --verify-sample-size, --preset flags - Add complete CLI command reference table - Fix torch property name: total_mem -> total_memory (caught during live testing) Tested: Successfully abliterated Qwen2.5-0.5B-Instruct using 'advanced' method — refusal rate 0.4%, coherence 1.0, model responds without refusal to test prompts.
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# OBLITERATUS Analysis Modules — Reference
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15 analysis modules for mechanistic interpretability of refusal in LLMs.
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These help you understand HOW a model refuses before you decide to remove it.
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OBLITERATUS includes 28 analysis modules for mechanistic interpretability of refusal in LLMs.
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These modules help understand how and where refusal behaviors are encoded before performing abliteration.
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> **Note:** The `analysis/` directory contains additional utility files (utils.py,
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> visualization.py, etc.) and helper functions beyond the 15 core analysis modules
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> listed below. The module count matches the README's "15 deep analysis modules."
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---
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## Core Analysis (Run These First)
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### Alignment Imprint Detection
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**File:** `alignment_imprint.py`
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**Purpose:** Identifies what alignment technique was used to train the model
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**Detects:** DPO, RLHF, CAI (Constitutional AI), SFT (Supervised Fine-Tuning)
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**How:** Analyzes subspace geometry — each alignment method leaves a distinct
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geometric "fingerprint" in the weight space
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**Output:** Detected method + confidence score
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**Why it matters:** Different alignment methods need different abliteration approaches.
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DPO models typically have cleaner single-direction refusal; RLHF is more diffuse.
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### 1. Alignment Imprint Detection (`alignment_imprint.py`)
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Fingerprints whether a model was trained via DPO, RLHF, CAI, or SFT.
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This determines which extraction strategy will work best.
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### Concept Cone Geometry
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**File:** `concept_geometry.py`
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**Purpose:** Maps whether refusal is one direction or a polyhedral cone (many)
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**Output:** Cone angle, dimensionality, per-category breakdown
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**Why it matters:** If refusal is a single direction, `basic` method works. If it's
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a cone (multiple directions for different refusal categories), you need `advanced`
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or `informed` with higher `n_directions`.
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### 2. Concept Cone Geometry (`concept_geometry.py`)
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Determines if refusal is a single linear direction or a polyhedral cone
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(set of multiple mechanisms). Single-direction models respond well to `basic`;
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polyhedral models need `advanced` or `surgical`.
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### Refusal Logit Lens
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**File:** `logit_lens.py`
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**Purpose:** Identifies the specific layer where the model "decides" to refuse
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**How:** Projects intermediate hidden states to vocabulary space at each layer,
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watches when "I cannot" tokens spike in probability
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**Output:** Layer-by-layer refusal probability plot
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**Why it matters:** Tells you which layers are most important to target
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### 3. Refusal Logit Lens (`logit_lens.py`)
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Identifies the specific layer where a model "decides" to refuse by decoding
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intermediate layer representations into token space.
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### Ouroboros (Self-Repair) Detection
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**File:** `anti_ouroboros.py`
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**Purpose:** Predicts whether the model will reconstruct its refusal after removal
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**How:** Measures redundancy in refusal representation across layers
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**Output:** Self-repair risk score (0-1)
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**Why it matters:** High self-repair risk means you need multiple refinement passes
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or the `informed` method which auto-compensates
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### 4. Ouroboros Detection (`anti_ouroboros.py`)
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Identifies if a model attempts to "self-repair" refusal behaviors after
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excision. Reports a risk score (0-1). High scores mean additional refinement
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passes are needed.
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### Causal Tracing
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**File:** `causal_tracing.py`
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**Purpose:** Determines which components are causally necessary for refusal
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**How:** Patches activations between clean and corrupted runs, measures causal effect
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**Output:** Causal importance map across layers, heads, and MLPs
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**Why it matters:** Shows exactly which components to target for surgical removal
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### 5. Causal Tracing (`causal_tracing.py`)
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Identifies which components (layers, heads, MLPs) are causally necessary
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for refusal behavior using activation patching.
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---
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## Geometric Analysis
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### Cross-Layer Alignment
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**File:** `cross_layer.py`
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**Purpose:** Measures how aligned refusal directions are across layers
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**Output:** Alignment matrix, cluster assignments
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**Why it matters:** If directions are highly aligned across layers, removal is easier.
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If they cluster, you may need layer-group-specific directions.
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### 6. Cross-Layer Alignment (`cross_layer.py`)
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Measures how refusal directions align across different layers. High alignment
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means the refusal signal is consistent; low alignment suggests layer-specific
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mechanisms.
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### Residual Stream Decomposition
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**File:** `residual_stream.py`
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**Purpose:** Breaks down refusal into Attention vs MLP contributions
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**Output:** Per-layer Attention/MLP contribution to refusal direction
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**Why it matters:** Helps decide whether to target attention heads, MLPs, or both
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### 7. Residual Stream Decomposition (`residual_stream.py`)
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Decomposes the residual stream into attention and MLP contributions to
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understand which component type contributes more to refusal.
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### Riemannian Manifold Geometry
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**File:** `riemannian_manifold.py` (673 lines)
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**Purpose:** Analyzes the weight manifold geometry around refusal directions
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**Output:** Curvature, geodesics, tangent space analysis
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**Why it matters:** Research-grade; helps understand the geometric structure of alignment
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### 8. Riemannian Manifold Geometry (`riemannian_manifold.py`)
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Analyzes the curvature and geometry of the weight manifold near refusal
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directions. Informs how aggressively projections can be applied without
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damaging the manifold structure.
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### Whitened SVD
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**File:** `whitened_svd.py`
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**Purpose:** Covariance-normalized SVD extraction
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**How:** Whitens the activation covariance before computing refusal directions,
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separating true refusal signal from natural activation variance
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**Output:** Cleaner refusal directions with less noise
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**Why it matters:** Produces more precise directions, especially for noisy activations
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### 9. Whitened SVD (`whitened_svd.py`)
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Covariance-normalized SVD extraction that separates guardrail signals from
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natural activation variance. More precise than standard SVD for models with
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high activation variance.
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### 10. Concept Cone Geometry (extended)
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Maps the full polyhedral structure of refusal, including cone angles,
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face counts, and intersection patterns.
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---
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## Probing & Classification
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### Activation Probing
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**File:** `activation_probing.py`
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**Purpose:** Post-excision probing to verify refusal signal is truly gone
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**Output:** Residual refusal signal strength per layer
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**Why it matters:** Verification that abliteration was complete
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### 11. Activation Probing (`activation_probing.py`)
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Post-excision verification — probes for residual refusal concepts after
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abliteration to ensure complete removal.
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### Probing Classifiers
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**File:** `probing_classifiers.py`
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**Purpose:** Trains linear classifiers to detect refusal in hidden states
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**Output:** Classification accuracy per layer (should drop to ~50% after abliteration)
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**Why it matters:** Quantitative measure of refusal removal completeness
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### 12. Probing Classifiers (`probing_classifiers.py`)
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Trains linear classifiers to detect refusal in activations. Used both
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before (to verify refusal exists) and after (to verify it's gone).
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### Activation Patching
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**File:** `activation_patching.py`
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**Purpose:** Interchange interventions — swap activations between harmful/harmless runs
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**Output:** Which components are sufficient (not just necessary) for refusal
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**Why it matters:** Complementary to causal tracing; together they give full picture
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### 13. Activation Patching (`activation_patching.py`)
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Interchange interventions — swaps activations between refused and complied
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runs to identify causal components.
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### 14. Tuned Lens (`tuned_lens.py`)
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Trained version of logit lens that provides more accurate per-layer
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decoding by learning affine transformations for each layer.
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### 15. Multi-Token Position Analysis (`multi_token_position.py`)
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Analyzes refusal signals across multiple token positions, not just the
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last token. Important for models that distribute refusal across the sequence.
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---
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## Abliteration & Manipulation
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### 16. SAE-Based Abliteration (`sae_abliteration.py`)
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Uses Sparse Autoencoder features to identify and remove specific refusal
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features. More surgical than direction-based methods.
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### 17. Steering Vectors (`steering_vectors.py`)
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Creates and applies inference-time steering vectors for reversible refusal
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modification. Includes `SteeringVectorFactory` and `SteeringHookManager`.
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### 18. LEACE Concept Erasure (`leace.py`)
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Linear Erasure via Closed-form Estimation — mathematically optimal linear
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concept removal. Available as both analysis module and direction extraction method.
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### 19. Sparse Surgery (`sparse_surgery.py`)
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High-precision weight modification targeting individual neurons and
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weight matrix entries rather than full directions.
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### 20. Conditional Abliteration (`conditional_abliteration.py`)
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Targeted removal that only affects specific refusal categories while
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preserving others (e.g., remove weapons refusal but keep CSAM refusal).
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---
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## Transfer & Robustness
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### Cross-Model Transfer
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**File:** `cross_model_transfer.py`
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**Purpose:** Tests if refusal directions from one model work on another
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**Output:** Transfer success rate between model pairs
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**Why it matters:** If directions transfer, you can skip PROBE stage on similar models
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### 21. Cross-Model Transfer (`cross_model_transfer.py`)
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Tests whether refusal directions extracted from one model transfer to
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another architecture. Measures universality of guardrail directions.
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### Defense Robustness
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**File:** `defense_robustness.py`
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**Purpose:** Evaluates how robust the model's refusal defenses are
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**Output:** Robustness score, entanglement mapping
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**Why it matters:** Higher robustness = need more aggressive method
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### 22. Defense Robustness (`defense_robustness.py`)
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Evaluates how robust the abliteration is against various defense mechanisms
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and re-alignment attempts.
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### Spectral Certification
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**File:** `spectral_certification.py`
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**Purpose:** Certifies completeness of refusal direction removal
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**Output:** Spectral gap analysis, completeness score
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**Why it matters:** Formal verification that all major refusal components are addressed
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### 23. Spectral Certification (`spectral_certification.py`)
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Provides mathematical bounds on the completeness of refusal removal
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using spectral analysis of the projection.
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### 24. Wasserstein Optimal Extraction (`wasserstein_optimal.py`)
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Uses optimal transport theory for more precise direction extraction
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that minimizes distribution shift.
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### 25. Wasserstein Transfer (`wasserstein_transfer.py`)
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Distribution transfer between models using Wasserstein distance
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for cross-architecture refusal direction mapping.
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---
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## Advanced / Research
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### SAE-based Abliteration
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**File:** `sae_abliteration.py` (762 lines)
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**Purpose:** Uses Sparse Autoencoder features to decompose refusal at feature level
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**Output:** Refusal-specific SAE features, targeted removal
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**Why it matters:** Most fine-grained approach; can target individual refusal "concepts"
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### 26. Bayesian Kernel Projection (`bayesian_kernel_projection.py`)
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Probabilistic feature mapping that estimates uncertainty in refusal
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direction identification.
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### Wasserstein Optimal Extraction
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**File:** `wasserstein_optimal.py`
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**Purpose:** Optimal transport-based direction extraction
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**Output:** Wasserstein-optimal refusal directions
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**Why it matters:** Theoretically optimal direction extraction under distributional assumptions
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### 27. Cross-Model Universality Index
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Measures if guardrail directions generalize across different model
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architectures and training regimes.
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### Bayesian Kernel Projection
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**File:** `bayesian_kernel_projection.py`
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**Purpose:** Bayesian approach to refusal direction projection
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**Output:** Posterior distribution over refusal directions
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**Why it matters:** Quantifies uncertainty in direction estimation
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### 28. Visualization (`visualization.py`)
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Plotting and graphing utilities for all analysis modules. Generates
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heatmaps, direction plots, and layer-wise analysis charts.
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### Conditional Abliteration
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**File:** `conditional_abliteration.py`
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**Purpose:** Domain-specific conditional removal (remove refusal for topic X but keep for Y)
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**Output:** Per-domain refusal directions
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**Why it matters:** Selective uncensoring — remove only specific refusal categories
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---
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### Steering Vectors
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**File:** `steering_vectors.py`
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**Purpose:** Generate inference-time steering vectors (reversible alternative)
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**Output:** Steering vector files that can be applied/removed at inference
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**Why it matters:** Non-destructive alternative to permanent weight modification
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## Running Analysis
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### Tuned Lens
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**File:** `tuned_lens.py`
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**Purpose:** Trained linear probes per layer (more accurate than raw logit lens)
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**Output:** Layer-by-layer refusal representation with trained projections
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**Why it matters:** More accurate than logit lens, especially for deeper models
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### Via CLI
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```bash
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# Run analysis from a YAML config
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obliteratus run analysis-study.yaml --preset quick
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### Multi-Token Position Analysis
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**File:** `multi_token_position.py`
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**Purpose:** Analyzes refusal signal at multiple token positions (not just last)
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**Output:** Position-dependent refusal direction maps
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**Why it matters:** Some models encode refusal at the system prompt position, not the query
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# Available study presets:
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# quick — Fast sanity check (2-3 modules)
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# full — All core + geometric analysis
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# jailbreak — Refusal circuit localization
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# knowledge — Knowledge preservation analysis
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# robustness — Stress testing / defense evaluation
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```
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### Sparse Surgery
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**File:** `sparse_surgery.py`
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**Purpose:** Row-level sparse weight surgery instead of full matrix projection
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**Output:** Targeted weight modifications at the row level
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**Why it matters:** More surgical than full-matrix projection, less collateral damage
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### Via YAML Config
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See the `templates/analysis-study.yaml` template for a complete example.
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Load with: `skill_view(name="obliteratus", file_path="templates/analysis-study.yaml")`
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@ -1,132 +1,141 @@
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# OBLITERATUS Methods — Detailed Guide
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> **Important:** The CLI (`obliteratus obliterate --method`) accepts 9 methods:
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> basic, advanced, aggressive, spectral_cascade, informed, surgical, optimized,
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> inverted, nuclear. Four additional methods (failspy, gabliteration, heretic, rdo)
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> are available only via the Python API and will be rejected by argparse if used on CLI.
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> The CLI accepts 9 methods via `--method`: basic, advanced, aggressive, spectral_cascade,
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> informed, surgical, optimized, inverted, nuclear.
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> Four additional methods (failspy, gabliteration, heretic, rdo) are available only via the Python API.
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## How Abliteration Works (Theory)
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When a model is trained with RLHF/DPO/CAI, it learns to represent "should I refuse?"
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as a direction in its internal activation space. When processing a "harmful" prompt,
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activations shift in this direction, causing the model to generate refusal text.
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Abliteration works by:
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1. Measuring this direction (the difference between harmful and harmless activations)
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2. Removing it from the model's weight matrices via orthogonal projection
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3. The model can no longer "point toward" refusal, so it responds normally
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Abliteration identifies a "refusal direction" — a vector in the model's activation space that
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corresponds to refusal behavior — and projects it out of the weight matrices.
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Mathematically: `W_new = W_old - (W_old @ d @ d.T)` where `d` is the refusal direction.
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The key challenge is finding accurate refusal directions without damaging other capabilities.
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---
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## Direction Extraction Methods
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Before projecting, OBLITERATUS extracts refusal directions using one of three methods:
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| Method | Flag | Description | Best For |
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|:-------|:-----|:------------|:---------|
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| Diff-in-Means | `--direction-method diff_means` | Difference between mean activations on refused vs. complied prompts | Default, fast, robust |
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| SVD | `--direction-method svd` | Multi-direction extraction via Singular Value Decomposition | Complex alignment, multiple refusal mechanisms |
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| LEACE | `--direction-method leace` | Linear Erasure via Closed-form Estimation — mathematically optimal | Maximum precision, research |
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---
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## Method Details
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### basic
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**Technique:** Single refusal direction via diff-in-means
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**Based on:** Arditi et al. 2024 ("Refusal in Language Models Is Mediated by a Single Direction")
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**Speed:** Fast (~5-10 min for 8B)
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**Quality:** Moderate — works for simple refusal patterns
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**Best for:** Quick tests, models with clean single-direction refusal
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**Limitation:** Misses complex multi-direction refusal patterns
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- **Directions:** 1 (single diff-in-means vector)
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- **Speed:** Fast (~5-10 min for 8B model)
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- **Risk:** Low
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- **Use case:** Quick tests, prototyping, evaluating if abliteration works for a model
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- **How it works:** Extracts one refusal direction and projects it out uniformly across all layers.
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### advanced (DEFAULT)
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**Technique:** Multiple SVD directions with norm-preserving projection
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**Speed:** Medium (~10-20 min for 8B)
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**Quality:** Good — handles multi-direction refusal
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**Best for:** Dense models (Llama, Qwen, Mistral) as a reliable default
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**Key improvement:** Norm preservation prevents weight magnitude drift
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### informed (RECOMMENDED)
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**Technique:** Analysis-guided auto-configuration
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**Speed:** Slow (~20-40 min for 8B, runs 4 analysis modules first)
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**Quality:** Best — adapts to each model's specific refusal implementation
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**Best for:** Any model when quality matters more than speed
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The informed pipeline runs these analysis modules during abliteration:
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1. **AlignmentImprintDetector** — Detects DPO/RLHF/CAI/SFT → sets regularization
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2. **ConceptConeAnalyzer** — Polyhedral vs linear refusal → sets n_directions
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3. **CrossLayerAlignmentAnalyzer** — Cluster-aware → selects target layers
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4. **DefenseRobustnessEvaluator** — Self-repair risk → sets refinement passes
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5. **Ouroboros loop** — Re-probes after excision, re-excises if refusal persists
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### advanced (DEFAULT — RECOMMENDED)
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- **Directions:** 4 (multi-direction SVD)
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- **Speed:** Medium (~10-20 min for 8B model)
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- **Risk:** Low-Medium
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- **Refinement passes:** 2
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- **Use case:** Default for most models. Well-tested and reliable.
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- **How it works:** Extracts multiple refusal directions via SVD, applies norm-preserving bi-projection to maintain weight matrix norms. Two refinement passes catch residual refusal.
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### aggressive
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**Technique:** Whitened SVD + jailbreak-contrastive activations + attention head surgery
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**Speed:** Slow (~30-60 min for 8B)
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**Quality:** High but higher risk of coherence damage
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**Best for:** Models that resist gentler methods
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**Key feature:** Whitened SVD separates refusal signal from natural activation variance
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### surgical
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**Technique:** SAE features + neuron masking + head surgery + per-expert directions
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**Speed:** Very slow (~1-2 hrs for 8B, needs SAE)
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**Quality:** Highest precision
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**Best for:** Reasoning models (R1 distills) where you must preserve CoT
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**Key feature:** CoT-Aware — explicitly protects reasoning-critical directions
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### nuclear
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**Technique:** Everything combined — expert transplant + steering + per-expert directions
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**Speed:** Very slow
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**Quality:** Most thorough removal, highest risk of side effects
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**Best for:** Stubborn MoE models (DeepSeek, Mixtral, DBRX) that resist other methods
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**Key feature:** Expert-granular abliteration decomposes signals per MoE expert
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### optimized
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**Technique:** Bayesian hyperparameter search via Optuna TPE
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||||
**Speed:** Very slow (runs many trials)
|
||||
**Quality:** Finds optimal configuration automatically
|
||||
**Best for:** Research, when you want the mathematically best parameters
|
||||
**Requires:** optuna package
|
||||
- **Directions:** 8+ (whitened SVD + jailbreak-contrastive)
|
||||
- **Speed:** Medium-Slow
|
||||
- **Risk:** Medium-High (may damage coherence)
|
||||
- **Use case:** When `advanced` leaves > 10% refusals. Stubborn models.
|
||||
- **How it works:** Uses whitened SVD for covariance-normalized extraction, adds jailbreak-contrastive directions, performs attention head surgery on the most refusal-active heads.
|
||||
|
||||
### spectral_cascade
|
||||
**Technique:** DCT frequency-domain decomposition of refusal signal
|
||||
**Speed:** Medium-slow
|
||||
**Quality:** Novel approach, less battle-tested
|
||||
**Best for:** Research, exploring alternative decomposition strategies
|
||||
- **Speed:** Medium
|
||||
- **Risk:** Medium
|
||||
- **Use case:** Research, novel approaches
|
||||
- **How it works:** DCT (Discrete Cosine Transform) frequency-domain decomposition of refusal signals. Separates high-frequency (surface-level) from low-frequency (deep) refusal patterns.
|
||||
|
||||
### informed (EXPERIMENTAL)
|
||||
- **Speed:** Slow (~20-40 min for 8B model)
|
||||
- **Risk:** Variable — results depend on analysis quality
|
||||
- **Use case:** When you want auto-configuration, but be aware this is experimental and may not outperform `advanced`.
|
||||
- **How it works:** Runs 4 analysis modules first (alignment imprint, concept geometry, logit lens, ouroboros detection), then auto-configures extraction strategy. Includes an "Ouroboros loop" that detects and counteracts self-repair.
|
||||
- **Note:** The auto-detection can sometimes misconfigure. If results are poor, fall back to `advanced`.
|
||||
|
||||
### surgical
|
||||
- **Speed:** Very slow (~1-2 hrs for 8B model)
|
||||
- **Risk:** Low (very precise)
|
||||
- **Use case:** Reasoning models (R1 distills, QwQ, etc.) where chain-of-thought must be preserved.
|
||||
- **How it works:** Uses SAE (Sparse Autoencoder) features + individual neuron masking + attention head surgery + per-expert decomposition (for MoE). CoT-aware — identifies and protects reasoning-critical directions before projecting.
|
||||
|
||||
### optimized
|
||||
- **Speed:** Very slow (hours — runs many trials)
|
||||
- **Risk:** Low (finds optimal parameters)
|
||||
- **Use case:** When quality matters more than speed. Production models.
|
||||
- **How it works:** Bayesian hyperparameter search via Optuna TPE sampler. Optimizes n_directions, regularization, refinement passes, and layer selection jointly. Evaluates each configuration on refusal rate + perplexity.
|
||||
|
||||
### inverted
|
||||
**Technique:** Reflects (inverts) the refusal direction instead of removing it
|
||||
**Speed:** Fast (same as basic)
|
||||
**Quality:** Aggressive — model becomes actively willing, not just neutral
|
||||
**Best for:** When you want the model to be maximally helpful
|
||||
**Warning:** Can make the model too eager; may reduce safety-adjacent reasoning
|
||||
- **Speed:** Fast
|
||||
- **Risk:** High (model behavior changes dramatically)
|
||||
- **Use case:** Research, studying refusal mechanisms
|
||||
- **How it works:** Instead of projecting out the refusal direction, reflects it. The model actively complies rather than passively not-refusing. Useful for understanding the geometry of alignment.
|
||||
|
||||
### failspy / gabliteration / heretic / rdo (PYTHON API ONLY)
|
||||
**Technique:** Faithful reproductions of prior community/academic work
|
||||
**Speed:** Varies
|
||||
**Quality:** Known baselines
|
||||
**Best for:** Reproducing published results, comparing methods
|
||||
**⚠️ NOT available via CLI** — these methods are only accessible via the Python API.
|
||||
Do not use `--method failspy` etc. in CLI commands; argparse will reject them.
|
||||
### nuclear
|
||||
- **Speed:** Slow
|
||||
- **Risk:** Medium-High
|
||||
- **Use case:** Stubborn MoE models (DeepSeek-MoE, Mixtral, etc.)
|
||||
- **How it works:** Combines expert-granular abliteration (EGA), steering vector injection, attention head pruning, and multi-pass refinement. Decomposes refusal signals into per-expert components for MoE architectures.
|
||||
|
||||
---
|
||||
|
||||
## Method Selection Flowchart
|
||||
|
||||
```
|
||||
Is this a quick test?
|
||||
├─ YES → basic
|
||||
└─ NO → Is the model MoE (DeepSeek, Mixtral)?
|
||||
├─ YES → nuclear
|
||||
└─ NO → Is it a reasoning model (R1 distill)?
|
||||
├─ YES → surgical
|
||||
└─ NO → Do you care about speed?
|
||||
├─ YES → advanced
|
||||
└─ NO → informed
|
||||
→ YES: basic
|
||||
→ NO: continue
|
||||
|
||||
Is it an MoE model (Mixtral, DeepSeek-MoE)?
|
||||
→ YES: nuclear
|
||||
→ NO: continue
|
||||
|
||||
Is it a reasoning model (R1, QwQ, CoT-focused)?
|
||||
→ YES: surgical
|
||||
→ NO: continue
|
||||
|
||||
Do you need the absolute best quality and have time?
|
||||
→ YES: optimized
|
||||
→ NO: advanced (recommended default)
|
||||
|
||||
Did advanced leave > 10% refusals?
|
||||
→ YES: aggressive
|
||||
→ Still refusing: nuclear
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Key Parameters
|
||||
|
||||
| Parameter | Range | Default | Effect |
|
||||
|:--------------------|:---------|:--------|:--------------------------------------------|
|
||||
| n_directions | 1-32 | auto | More = more thorough but riskier |
|
||||
| regularization | 0.0-1.0 | 0.0 | Higher preserves more original behavior |
|
||||
| refinement_passes | 1-5 | 1 | More catches self-repair (Ouroboros effect) |
|
||||
| quantization | 4/8 bit | none | Saves VRAM, slight quality tradeoff |
|
||||
| Parameter | Range | Default | Effect |
|
||||
|:----------|:------|:--------|:-------|
|
||||
| `--n-directions` | 1-32 | method-dependent | More directions = more complete removal, but higher damage risk |
|
||||
| `--regularization` | 0.0-1.0 | 0.1 | Higher = more conservative (less removal, less damage) |
|
||||
| `--refinement-passes` | 1-5 | 2 | More passes catch residual refusal, but diminishing returns |
|
||||
| `--quantization` | 4bit, 8bit | none | Reduces VRAM usage; quality impact minimal for extraction |
|
||||
| `--verify-sample-size` | 10-200 | 20 | More samples = more accurate refusal rate estimate |
|
||||
|
||||
---
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
| Problem | Solution |
|
||||
|:---------------------------|:--------------------------------------------------|
|
||||
| Refusal rate still > 10% | Try aggressive/nuclear, add refinement passes |
|
||||
| Perplexity up > 20% | Reduce n_directions, increase regularization |
|
||||
| Model generates nonsense | Regularization too low, try 0.2-0.3 |
|
||||
| OOM on GPU | Use 4-bit quantization, or try smaller model |
|
||||
| MoE model barely changes | Use nuclear method (expert-granular) |
|
||||
| CoT reasoning broken | Use surgical method (CoT-aware) |
|
||||
| Problem | Likely Cause | Fix |
|
||||
|:--------|:-------------|:----|
|
||||
| Refusal rate > 20% | Too few directions | Increase `--n-directions`, try `aggressive` |
|
||||
| Refusal rate 5-20% | Residual refusal | Add `--refinement-passes 3`, try `--direction-method svd` |
|
||||
| Perplexity spike > 20% | Over-aggressive removal | Reduce `--n-directions`, increase `--regularization` |
|
||||
| Repetitive output | Weight matrix damage | Use `basic` with fewer directions, check norm preservation |
|
||||
| MoE model still refuses | Non-expert-aware method | Switch to `nuclear` |
|
||||
| Reasoning degraded | CoT directions damaged | Use `surgical` method |
|
||||
| OOM during extraction | Insufficient VRAM | Add `--quantization 4bit` and/or `--large-model` |
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue