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