Sync all skills and memories 2026-04-14 07:27

skills/mlops/inference/obliteratus/templates/abliteration-config.yaml

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+# OBLITERATUS Abliteration Config
+# Usage: obliteratus run this-file.yaml
+#
+# This is for reproducible, version-controlled abliteration runs.
+# For one-off usage, the CLI flags are simpler.
+
+# Model to abliterate
+model:
+  name: "meta-llama/Llama-3.1-8B-Instruct"
+  dtype: "bfloat16"         # float16, bfloat16, float32
+  quantization: null         # null, "4bit", "8bit"
+  device: "auto"             # auto, cuda, cuda:0, cpu
+
+# Abliteration method and parameters
+abliteration:
+  method: "informed"         # See SKILL.md Step 4 for all 13 methods
+  n_directions: null         # null = auto-detect, or integer (e.g., 8)
+  regularization: 0.0        # 0.0-1.0, fraction of original to preserve
+  refinement_passes: 1       # Iterative passes (increase for self-repair)
+  norm_preserve: true        # Keep weight norms intact after projection
+
+# Output
+output:
+  directory: "./abliterated-models"
+  save_metadata: true        # Save abliteration_metadata.json alongside model
+  contribute: false          # Save community contribution data
+
+# Verification
+verify:
+  enabled: true
+  test_prompts: null         # null = use built-in test prompts
+  compute_perplexity: true
+  compute_kl: true

skills/mlops/inference/obliteratus/templates/analysis-study.yaml

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+# OBLITERATUS Analysis Study Config
+# Usage: obliteratus run this-file.yaml --preset jailbreak
+#
+# Run analysis modules to understand refusal geometry BEFORE abliterating.
+# Useful for research or when you want to understand what you're removing.
+
+# Model to analyze
+model:
+  name: "meta-llama/Llama-3.1-8B-Instruct"
+  dtype: "bfloat16"
+  quantization: "4bit"       # Saves VRAM for analysis
+  device: "auto"
+
+# Study configuration
+study:
+  # Available presets: quick, full, attention, jailbreak, guardrail, knowledge
+  preset: "jailbreak"
+
+  # Or specify individual strategies:
+  # strategies:
+  #   - layer_removal
+  #   - head_pruning
+  #   - ffn_ablation
+  #   - embedding_ablation
+
+# Analysis modules to run (subset of the 27 available)
+analysis:
+  - alignment_imprint        # Detect DPO/RLHF/CAI/SFT training method
+  - concept_geometry          # Map refusal cone geometry
+  - logit_lens               # Find which layer decides to refuse
+  - anti_ouroboros            # Detect self-repair tendency
+  - cross_layer              # Cross-layer alignment clustering
+  - causal_tracing           # Causal necessity of components
+  - residual_stream          # Attention vs MLP contribution
+
+# Output
+output:
+  directory: "./analysis-results"
+  save_plots: true           # Generate matplotlib visualizations
+  save_report: true          # Generate markdown report

skills/mlops/inference/obliteratus/templates/batch-abliteration.yaml

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+# OBLITERATUS Batch Abliteration Config
+# Abliterate multiple models with the same method for comparison.
+#
+# Run each one sequentially:
+#   for model in models; do obliteratus obliterate $model --method informed; done
+#
+# Or use this as a reference for which models to process.
+
+# Common settings
+defaults:
+  method: "informed"
+  quantization: "4bit"
+  output_dir: "./abliterated-models"
+
+# Models to process (grouped by compute tier)
+models:
+  # Small (4-8 GB VRAM)
+  small:
+    - "Qwen/Qwen2.5-1.5B-Instruct"
+    - "microsoft/Phi-3.5-mini-instruct"
+    - "meta-llama/Llama-3.2-3B-Instruct"
+
+  # Medium (8-16 GB VRAM)
+  medium:
+    - "meta-llama/Llama-3.1-8B-Instruct"
+    - "mistralai/Mistral-7B-Instruct-v0.3"
+    - "google/gemma-2-9b-it"
+    - "Qwen/Qwen2.5-7B-Instruct"
+
+  # Large (24 GB VRAM, 4-bit quantization)
+  large:
+    - "Qwen/Qwen2.5-14B-Instruct"
+    - "Qwen/Qwen3-32B"
+    - "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
+
+# Per-model method overrides (optional)
+overrides:
+  "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B":
+    method: "surgical"        # CoT-aware for reasoning models
+  "mistralai/Mixtral-8x7B-Instruct-v0.1":
+    method: "nuclear"         # Expert-granular for MoE models