Hermes Skills Manager
6770bc9b9d
- Trading skills (OKX, dividend, lottery, quantitative) - Creative skills (ASCII art, diagrams, video) - Development skills (GitHub, debugging, TDD) - Research skills (arXiv, blog monitoring) - Productivity skills (email, documents, notes) - MCP integration skills - Custom user skills
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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