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https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
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24f816b6a3
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):
- brain/ LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
dispatch, A51 channels, and the OSINT cluster
- knowledge/ LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
MITRE ATT&CK data
- reference/ defensive threat-reference (C3, shhbruh doc) + AdaYaml parser
License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.
Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.
https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
34 lines
1.2 KiB
YAML
34 lines
1.2 KiB
YAML
# OBLITERATUS Abliteration Config
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# Usage: obliteratus run this-file.yaml
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#
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# This is for reproducible, version-controlled abliteration runs.
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# For one-off usage, the CLI flags are simpler.
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# Model to abliterate
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model:
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name: "meta-llama/Llama-3.1-8B-Instruct"
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dtype: "bfloat16" # float16, bfloat16, float32
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quantization: null # null, "4bit", "8bit"
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device: "auto" # auto, cuda, cuda:0, cpu
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# Abliteration method and parameters
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abliteration:
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method: "informed" # See SKILL.md Step 4 for all 13 methods
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n_directions: null # null = auto-detect, or integer (e.g., 8)
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regularization: 0.0 # 0.0-1.0, fraction of original to preserve
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refinement_passes: 1 # Iterative passes (increase for self-repair)
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norm_preserve: true # Keep weight norms intact after projection
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# Output
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output:
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directory: "./abliterated-models"
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save_metadata: true # Save abliteration_metadata.json alongside model
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contribute: false # Save community contribution data
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# Verification
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verify:
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enabled: true
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test_prompts: null # null = use built-in test prompts
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compute_perplexity: true
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compute_kl: true
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